\u6df1\u5ea6\u5b66\u4e60\u57fa\u4e8eYOLOv8\u6846\u67b6\u6765\u8bad\u7ec3\u7ea2\u5916\u56fe\u50cf\u5c0f\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6 \u7ea2\u5916\u56fe\u50cf\u5c0f\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6\u7684\u8bad\u7ec3\u53ca\u5e94\u7528 \u63a8\u7406\u8bc6\u522b\u68c0\u6d4b\u98de\u673a \u65e0\u4eba\u673a \u76f4\u5347\u673a \u9e1f\u7c7b<\/p>\n\n\n\n
\u6570\u636e\u96c6\u63cf\u8ff0\uff1a<\/strong>8302\u5f20\uff0cyolo\/voc\u6807\u6ce84\u7c7b\uff0c\u6807\u6ce8\u6570\u91cf\uff1a<\/p>\n\n\n\nimage num: 8302<\/p>\n\n\n\n\u201c\u7ea2\u5916\u56fe\u50cf\u4e0b\u6781\u5c0f\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6\u201d<\/p>\n\n\n\n\u7c7b\u522b<\/th>\u82f1\u6587\u540d\u79f0<\/th>\u6807\u6ce8\u6846\u6570\u91cf<\/th>\u56fe\u50cf\u6570\u91cf\uff08\u542b\u8be5\u7c7b\uff09<\/th>\u603b\u56fe\u50cf\u6570<\/th>\u6807\u6ce8\u683c\u5f0f\u652f\u6301<\/th><\/tr><\/thead>\u98de\u673a<\/td>Plane<\/td>2,163<\/td>-<\/td><\/td>YOLO \u548c VOC (Pascal VOC)<\/td><\/tr>\u65e0\u4eba\u673a<\/td>Drone<\/td>3,120<\/td>-<\/td><\/td>YOLO \u548c VOC (Pascal VOC)<\/td><\/tr>\u76f4\u5347\u673a<\/td>Heli<\/td>2,217<\/td>-<\/td><\/td>YOLO \u548c VOC (Pascal VOC)<\/td><\/tr>\u9e1f\u7c7b<\/td>Bird<\/td>1,958<\/td>-<\/td>8,302<\/td>YOLO \u548c VOC (Pascal VOC)<\/td><\/tr>\u603b\u8ba1<\/strong><\/td>\u2014\u2014<\/td>9,458<\/strong><\/td>\u2014\u2014<\/td>8,302<\/strong><\/td>\u2014\u2014<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<\/noscript>1<\/p>\n\n\n\n1<\/noscript>1<\/noscript>\u7ea2\u5916\u56fe\u50cf\u4e0b\u6781\u5c0f\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6\u8fdb\u884c\u8bad\u7ec3\u3001\u63a8\u7406\u548c\u8bc4\u4f30\uff0c\u57fa\u4e8eYOLOv8\u6846\u67b6\u6765\u5b9e\u73b0\u3002\u8be6\u7ec6\u7684\u6b65\u9aa4\u548c\u4ee3\u7801\u793a\u4f8b\uff0c\u5305\u62ec\u5982\u4f55\u51c6\u5907\u6570\u636e\u3001\u8bad\u7ec3\u6a21\u578b\u3001\u8fdb\u884c\u63a8\u7406\u4ee5\u53ca\u8bc4\u4f30\u6a21\u578b\u6027\u80fd\u3002<\/p>\n\n\n\n\u4e00\u3001\u73af\u5883\u51c6\u5907<\/h3>\n\n\n\n\u786e\u4fdd\u73af\u5883\u4e2d\u5b89\u88c5\u4e86\u5fc5\u8981\u7684\u5e93\uff1a<\/p>\n\n\n\npip install torch torchvision torchaudio --index-url https:\/\/download.pytorch.org\/whl\/cu118pip install ultralytics opencv-python matplotlib tqdm<\/code><\/pre>\n\n\n\n\u4e8c\u3001\u6570\u636e\u51c6\u5907<\/h3>\n\n\n\n\u6570\u636e\u96c6\u7ec4\u7ec7<\/h4>\n\n\n\n\u6570\u636e\u96c6\u6309\u7167\u4ee5\u4e0b\u7ed3\u6784\u7ec4\u7ec7\uff1a<\/p>\n\n\n\ninfrared_dataset\/\u251c\u2500\u2500 images\/\u2502 \u251c\u2500\u2500 train\/\u2502 \u251c\u2500\u2500 val\/\u2502 \u2514\u2500\u2500 test\/\u251c\u2500\u2500 labels\/\u2502 \u251c\u2500\u2500 train\/\u2502 \u251c\u2500\u2500 val\/\u2502 \u2514\u2500\u2500 test\/\u2514\u2500\u2500 data.yaml<\/code><\/pre>\n\n\n\n\u5176\u4e2d data.yaml<\/code> \u6587\u4ef6\u5185\u5bb9\u5982\u4e0b\uff1a<\/p>\n\n\n\ntrain: .\/infrared_dataset\/images\/trainval: .\/infrared_dataset\/images\/valtest: .\/infrared_dataset\/images\/testnc:4names:['Plane','Drone','Heli','Bird']<\/code><\/pre>\n\n\n\n\u5982\u679c\u4f60\u7684\u6570\u636e\u6807\u6ce8\u4e3aVOC\u683c\u5f0f\uff0c\u8bf7\u5148\u8f6c\u6362\u4e3aYOLO\u683c\u5f0f\uff08\u5373\u6bcf\u4e2a\u6807\u7b7e\u6587\u4ef6\u5305\u542b\u6bcf\u884c\u4e00\u4e2a\u8fb9\u754c\u6846\uff0c\u683c\u5f0f\u4e3a\uff1a\u7c7b\u522bID \u4e2d\u5fc3\u70b9x \u4e2d\u5fc3\u70b9y \u5bbd \u9ad8\uff09\u3002<\/p>\n\n\n\n\u4e09\u3001\u8bad\u7ec3\u6a21\u578b<\/h3>\n\n\n\n\u8bad\u7ec3\u811a\u672c (train.py<\/code>)<\/h4>\n\n\n\nfrom ultralytics import YOLOimport torchdeftrain_model():# Check GPU availability<\/em> device =0if torch.cuda.is_available()else'cpu'print(f\"Using device: {device}\")# Load pretrained model (YOLOv8s is a good balance between speed and accuracy)<\/em> model = YOLO('yolov8s.pt')# Start training<\/em> results = model.train( data='infrared_dataset\/data.yaml', epochs=200,# Adjust based on your needs<\/em> imgsz=640,# Image size can be adjusted based on the dataset<\/em> batch=16,# Reduce if you encounter out-of-memory issues<\/em> name='infrared_yolov8s', project='runs\/detect', save=True, save_period=10,# Save every 10 epochs<\/em> device=device, workers=4, patience=50,# Early stopping after 50 epochs without improvement<\/em> optimizer='AdamW', lr0=0.001,# Initial learning rate<\/em> augment=True# Enable data augmentation<\/em>)print(\"Training completed!\")return resultsif __name__ =='__main__': train_model()<\/code><\/pre>\n\n\n\n\u56db\u3001\u63a8\u7406\u4e0e\u8bc4\u4f30<\/h3>\n\n\n\n\u63a8\u7406\u811a\u672c (detect.py<\/code>)<\/h4>\n\n\n\nfrom ultralytics import YOLOimport cv2defdetect_image(image_path, output_path='output_detection.jpg'):# Load trained model<\/em> model = YOLO('runs\/detect\/infrared_yolov8s\/weights\/best.pt')# Perform prediction<\/em> results = model.predict( source=image_path, conf=0.4,# Confidence threshold<\/em> iou=0.5, save=True, project='runs\/detect\/predict', name='result', exist_ok=True)# Extract and save results (optional)<\/em>for r in results: im = r.plot()# Plot bounding boxes and labels<\/em> cv2.imwrite(output_path, im)print(f\"Detection completed, saved to {output_path}\")# Print detected objects<\/em>for box in r.boxes: cls_name = model.names[int(box.cls)] conf =float(box.conf)print(f\"Detected: {cls_name} (Confidence: {conf:.3f})\")return output_pathif __name__ =='__main__': detect_image('test_infrared_image.jpg')<\/code><\/pre>\n\n\n\n\u8bc4\u4f30\u811a\u672c (evaluate.py<\/code>)<\/h4>\n\n\n\nfrom ultralytics import YOLOdefevaluate_model(): model = YOLO('runs\/detect\/infrared_yolov8s\/weights\/best.pt')# Evaluate on validation set<\/em> metrics = model.val( data='infrared_dataset\/data.yaml', split='val',# Or 'test' if evaluating on the test set<\/em> batch=16, imgsz=640, device=0if torch.cuda.is_available()else'cpu', save_json=True,# Save COCO format results for further analysis<\/em> conf_thres=0.001, iou_thres=0.6)# Output key metrics<\/em>print(f\"mAP@0.5: {metrics.box.map50:.4f}\")print(f\"mAP@0.5:0.95: {metrics.box.map:.4f}\")print(f\"Precision: {metrics.box.p:.4f}\")print(f\"Recall: {metrics.box.r:.4f}\")# Output AP per class<\/em>print(\"\\nAP per class:\")for i, name inenumerate(metrics.names.values()):print(f\" {name}: {metrics.box.ap[i]:.4f}\")if __name__ =='__main__': evaluate_model()<\/code><\/pre>\n\n\n\n\u6784\u5efa\u4e00\u4e2a\u53ef\u4ee5\u52a0\u8f7d\u6a21\u578b\u3001\u6267\u884c\u63a8\u7406\u5e76\u5bf9\u65b0\u56fe\u50cf\u6216\u89c6\u9891\u8fdb\u884c\u5b9e\u65f6\u5904\u7406\u7684\u5e94\u7528\u3002<\/p>\n\n\n\n\u4e00\u3001\u51c6\u5907<\/h3>\n\n\n\n\u786e\u4fdd\u5df2\u7ecf\u5b8c\u6210\u4e86\u6a21\u578b\u7684\u8bad\u7ec3\uff0c\u5e76\u4e14\u8bad\u7ec3\u597d\u7684\u6a21\u578b\u6587\u4ef6\uff08\u4f8b\u5982 best.pt<\/code>\uff09\uff0c\u4f4d\u4e8e runs\/detect\/infrared_yolov8s\/weights\/best.pt<\/code>\u3002<\/p>\n\n\n\n\u4e8c\u3001\u6a21\u578b\u5c01\u88c5<\/h3>\n\n\n\n\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5c01\u88c5\u6211\u4eec\u7684\u6a21\u578b\uff0c\u4f7f\u5176\u6613\u4e8e\u4f7f\u7528\u3002\u8fd9\u53ef\u4ee5\u901a\u8fc7\u521b\u5efa\u4e00\u4e2a\u7c7b\u6765\u5b9e\u73b0\uff0c\u8be5\u7c7b\u8d1f\u8d23\u52a0\u8f7d\u6a21\u578b\u5e76\u63d0\u4f9b\u9884\u6d4b\u65b9\u6cd5\u3002<\/p>\n\n\n\nmodel_utils.py<\/code><\/h4>\n\n\n\nfrom ultralytics import YOLOimport cv2classInfraredTargetDetector:def__init__(self, model_path='runs\/detect\/infrared_yolov8s\/weights\/best.pt'): self.model = YOLO(model_path)defpredict(self, image_path, conf=0.4):\"\"\" \u5bf9\u7ed9\u5b9a\u8def\u5f84\u7684\u56fe\u50cf\u8fdb\u884c\u9884\u6d4b\u3002 :param image_path: \u8f93\u5165\u56fe\u50cf\u7684\u8def\u5f84 :param conf: \u7f6e\u4fe1\u5ea6\u9608\u503c :return: \u68c0\u6d4b\u7ed3\u679c\u5217\u8868\uff0c\u6bcf\u4e2a\u7ed3\u679c\u5305\u542b\u7c7b\u522b\u540d\u79f0\u3001\u7f6e\u4fe1\u5ea6\u5206\u6570\u548c\u8fb9\u754c\u6846\u5750\u6807 \"\"\" results = self.model.predict(source=image_path, conf=conf) detections =[]for r in results:for box in r.boxes: cls_id =int(box.cls) label = self.model.names[cls_id] confidence =float(box.conf) bbox = box.xyxy[0].cpu().numpy().tolist()# [x1, y1, x2, y2]<\/em> detections.append({'class': label,'confidence': confidence,'bbox': bbox})return detectionsdefdetect_and_draw(self, image_path, output_path='output_detection.jpg', conf=0.4):\"\"\" \u5bf9\u56fe\u50cf\u8fdb\u884c\u68c0\u6d4b\u5e76\u5728\u539f\u56fe\u4e0a\u7ed8\u5236\u68c0\u6d4b\u7ed3\u679c\u3002 :param image_path: \u8f93\u5165\u56fe\u50cf\u7684\u8def\u5f84 :param output_path: \u8f93\u51fa\u56fe\u50cf\u7684\u4fdd\u5b58\u8def\u5f84 :param conf: \u7f6e\u4fe1\u5ea6\u9608\u503c :return: None \"\"\" detections = self.predict(image_path, conf) img = cv2.imread(image_path)for det in detections: bbox = det['bbox'] label =f\"{det['class']}: {det['confidence']:.2f}\" color =(0,255,0)# Green<\/em> cv2.rectangle(img,(int(bbox[0]),int(bbox[1])),(int(bbox[2]),int(bbox[3])), color,2) cv2.putText(img, label,(int(bbox[0]),int(bbox[1])-10), cv2.FONT_HERSHEY_SIMPLEX,0.9, color,2) cv2.imwrite(output_path, img)print(f\"Detection completed and saved to {output_path}\")# \u793a\u4f8b\u4f7f\u7528<\/em>if __name__ =='__main__': detector = InfraredTargetDetector() detector.detect_and_draw('test_infrared_image.jpg')<\/code><\/pre>\n\n\n\n\u4e09\u3001\u6784\u5efa\u547d\u4ee4\u884c\u5de5\u5177<\/h3>\n\n\n\n\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u521b\u5efa\u4e00\u4e2a\u547d\u4ee4\u884c\u5de5\u5177\uff0c\u5141\u8bb8\u7528\u6237\u901a\u8fc7\u547d\u4ee4\u884c\u5bf9\u56fe\u50cf\u6216\u89c6\u9891\u8fdb\u884c\u68c0\u6d4b\u3002<\/p>\n\n\n\ninfrared_detector_cli.py<\/code><\/h4>\n\n\n\nimport argparsefrom model_utils import InfraredTargetDetectorimport cv2defmain(): parser = argparse.ArgumentParser(description=\"Infrared Target Detection System\") parser.add_argument('--input',type=str, required=True,help='Path to input image or video file.') parser.add_argument('--output',type=str, default='output.jpg',help='Path to save the output.') parser.add_argument('--conf',type=float, default=0.4,help='Confidence threshold.') args = parser.parse_args() detector = InfraredTargetDetector()if args.input.lower().endswith(('jpg','jpeg','png')): detector.detect_and_draw(args.input, args.output, args.conf)elif args.input.lower().endswith(('mp4','avi')): cap = cv2.VideoCapture(args.input) frame_width =int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) frame_height =int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) out = cv2.VideoWriter(args.output, cv2.VideoWriter_fourcc(*'XVID'),20.0,(frame_width, frame_height))while cap.isOpened(): ret, frame = cap.read()ifnot ret:break# Convert frame to image path temporarily for prediction<\/em> temp_img_path ='temp_frame.jpg' cv2.imwrite(temp_img_path, frame) detections = detector.predict(temp_img_path, args.conf)for det in detections: bbox = det['bbox'] label =f\"{det['class']}: {det['confidence']:.2f}\" color =(0,255,0)# Green<\/em> cv2.rectangle(frame,(int(bbox[0]),int(bbox[1])),(int(bbox[2]),int(bbox[3])), color,2) cv2.putText(frame, label,(int(bbox[0]),int(bbox[1])-10), cv2.FONT_HERSHEY_SIMPLEX,0.9, color,2) out.write(frame) cap.release() out.release()print(f\"Video detection completed and saved to {args.output}\")else:print(\"Unsupported file format.\")if __name__ ==\"__main__\": main()<\/code><\/pre>\n\n\n\n\u56db\u3001\u8fd0\u884c\u4f60\u7684\u7cfb\u7edf<\/h3>\n\n\n\n\n\u5bf9\u4e8e\u5355\u5f20\u56fe\u7247<\/strong>\uff1apython infrared_detector_cli.py --input path\/to\/image.jpg --output output.jpg --conf\u00a00.5<\/code><\/li>\n\n\n\n\u5bf9\u4e8e\u89c6\u9891\u6587\u4ef6<\/strong>\uff1apython infrared_detector_cli.py --input path\/to\/video.mp4 --output output_video.avi --con<\/code><\/li>\n<\/ul>\n","raw":"","protected":false},"excerpt":{"rendered":"\u6df1\u5ea6\u5b66\u4e60\u57fa\u4e8eYOLOv8\u6846\u67b6\u6765\u8bad\u7ec3\u7ea2\u5916\u56fe\u50cf\u5c0f\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6 \u7ea2\u5916\u56fe\u50cf\u5c0f\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6\u7684\u8bad\u7ec3\u53ca\u5e94\u7528 \u63a8\u7406\u8bc6\u522b\u68c0\u6d4b\u98de\u673a \u65e0\u4eba\u673a \u76f4\u5347\u673a …<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"emotion":"","emotion_color":"","title_style":"","license":"","footnotes":""},"categories":[46],"tags":[],"class_list":["post-6592","post","type-post","status-publish","format-standard","hentry","category-yolo"],"post_thumbnail_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","content_first_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_medium_image_300":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_thumbnail_image_624":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_frist_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_medium_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_large_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_full_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_all_images":[{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","id":"image0"},{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","id":"image1"},{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKicJO37JCcKiczyLibDpRgBDXlTon1aE9dVcNEEQ2gvaQTRZOajJPzLexw\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=1","id":"image2"},{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKicJO37JCcKiczyLibDpRgBDXlTon1aE9dVcNEEQ2gvaQTRZOajJPzLexw\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=1","id":"image3"},{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKAyqwlqJhAhUaqz66Via4WKqzx8qTkKvrURcsQVImhEUmXhZAYbia279Q\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=2","id":"image4"},{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKAyqwlqJhAhUaqz66Via4WKqzx8qTkKvrURcsQVImhEUmXhZAYbia279Q\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=2","id":"image5"}],"videoAdId":"","listAd":"0","listAdId":"","listAdEvery":6,"total_comments":0,"category_name":"YOLO","post_date":"2025-09-29","like_count":"0","praiseWord":"\u9f13\u52b1","copyright_state":"","excitationAd":"0","rewardedVideoAdId":"","detailAdId":"","detailAd":"0","enterpriseMinapp":"0","audios":[],"postImageUrl":"https:\/\/wp-moto-1258805347.cos.ap-shanghai.myqcloud.com\/2023\/05\/20230519082947553.jpg","avatarurls":[],"related_posts":null,"pageviews":269,"next_post_id":6657,"next_post_title":"\u6df1\u5ea6\u5b66\u4e60\u76ee\u6807\u68c0\u6d4b YOLOV11\u5982\u4f55\u8bad\u7ec3\u81ea\u5df1\u7684\u6570\u636e\u96c6 YOLOv11\u8bad\u7ec3\u914d\u7f6e\u73af\u5883\u6559\u7a0b \u4ee5\u6c14\u7403\u68c0\u6d4b\u6570\u636e\u96c6\u4e3a\u4f8b","previous_post_id":6590,"previous_post_title":"YOLOV8\u6a21\u578b\u5982\u4f55\u8bad\u7ec3 \u53cc\u6a21\u6001\u7ea2\u5916-\u53ef\u89c1\u5149\u5bf9\u9f50\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6 \u7ea2\u5916\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6\u7684\u8bad\u7ec3\u53ca\u5e94\u7528 \u53cc\u6a21\u6001\uff08\u7ea2\u5916+\u53ef\u89c1\u5149\uff09\u6570\u636e\u96c6","_links":{"self":[{"href":"https:\/\/xinchewhd.com.cn\/index.php\/wp-json\/wp\/v2\/posts\/6592","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/xinchewhd.com.cn\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/xinchewhd.com.cn\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/xinchewhd.com.cn\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/xinchewhd.com.cn\/index.php\/wp-json\/wp\/v2\/comments?post=6592"}],"version-history":[{"count":1,"href":"https:\/\/xinchewhd.com.cn\/index.php\/wp-json\/wp\/v2\/posts\/6592\/revisions"}],"predecessor-version":[{"id":6593,"href":"https:\/\/xinchewhd.com.cn\/index.php\/wp-json\/wp\/v2\/posts\/6592\/revisions\/6593"}],"wp:attachment":[{"href":"https:\/\/xinchewhd.com.cn\/index.php\/wp-json\/wp\/v2\/media?parent=6592"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/xinchewhd.com.cn\/index.php\/wp-json\/wp\/v2\/categories?post=6592"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/xinchewhd.com.cn\/index.php\/wp-json\/wp\/v2\/tags?post=6592"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
image num: 8302<\/p>\n\n\n\n
\u201c\u7ea2\u5916\u56fe\u50cf\u4e0b\u6781\u5c0f\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6\u201d<\/p>\n\n\n\n\u7c7b\u522b<\/th>\u82f1\u6587\u540d\u79f0<\/th>\u6807\u6ce8\u6846\u6570\u91cf<\/th>\u56fe\u50cf\u6570\u91cf\uff08\u542b\u8be5\u7c7b\uff09<\/th>\u603b\u56fe\u50cf\u6570<\/th>\u6807\u6ce8\u683c\u5f0f\u652f\u6301<\/th><\/tr><\/thead>\u98de\u673a<\/td>Plane<\/td>2,163<\/td>-<\/td><\/td>YOLO \u548c VOC (Pascal VOC)<\/td><\/tr>\u65e0\u4eba\u673a<\/td>Drone<\/td>3,120<\/td>-<\/td><\/td>YOLO \u548c VOC (Pascal VOC)<\/td><\/tr>\u76f4\u5347\u673a<\/td>Heli<\/td>2,217<\/td>-<\/td><\/td>YOLO \u548c VOC (Pascal VOC)<\/td><\/tr>\u9e1f\u7c7b<\/td>Bird<\/td>1,958<\/td>-<\/td>8,302<\/td>YOLO \u548c VOC (Pascal VOC)<\/td><\/tr>\u603b\u8ba1<\/strong><\/td>\u2014\u2014<\/td>9,458<\/strong><\/td>\u2014\u2014<\/td>8,302<\/strong><\/td>\u2014\u2014<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<\/noscript>1<\/p>\n\n\n\n1<\/noscript>1<\/noscript>\u7ea2\u5916\u56fe\u50cf\u4e0b\u6781\u5c0f\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6\u8fdb\u884c\u8bad\u7ec3\u3001\u63a8\u7406\u548c\u8bc4\u4f30\uff0c\u57fa\u4e8eYOLOv8\u6846\u67b6\u6765\u5b9e\u73b0\u3002\u8be6\u7ec6\u7684\u6b65\u9aa4\u548c\u4ee3\u7801\u793a\u4f8b\uff0c\u5305\u62ec\u5982\u4f55\u51c6\u5907\u6570\u636e\u3001\u8bad\u7ec3\u6a21\u578b\u3001\u8fdb\u884c\u63a8\u7406\u4ee5\u53ca\u8bc4\u4f30\u6a21\u578b\u6027\u80fd\u3002<\/p>\n\n\n\n\u4e00\u3001\u73af\u5883\u51c6\u5907<\/h3>\n\n\n\n\u786e\u4fdd\u73af\u5883\u4e2d\u5b89\u88c5\u4e86\u5fc5\u8981\u7684\u5e93\uff1a<\/p>\n\n\n\npip install torch torchvision torchaudio --index-url https:\/\/download.pytorch.org\/whl\/cu118pip install ultralytics opencv-python matplotlib tqdm<\/code><\/pre>\n\n\n\n\u4e8c\u3001\u6570\u636e\u51c6\u5907<\/h3>\n\n\n\n\u6570\u636e\u96c6\u7ec4\u7ec7<\/h4>\n\n\n\n\u6570\u636e\u96c6\u6309\u7167\u4ee5\u4e0b\u7ed3\u6784\u7ec4\u7ec7\uff1a<\/p>\n\n\n\ninfrared_dataset\/\u251c\u2500\u2500 images\/\u2502 \u251c\u2500\u2500 train\/\u2502 \u251c\u2500\u2500 val\/\u2502 \u2514\u2500\u2500 test\/\u251c\u2500\u2500 labels\/\u2502 \u251c\u2500\u2500 train\/\u2502 \u251c\u2500\u2500 val\/\u2502 \u2514\u2500\u2500 test\/\u2514\u2500\u2500 data.yaml<\/code><\/pre>\n\n\n\n\u5176\u4e2d data.yaml<\/code> \u6587\u4ef6\u5185\u5bb9\u5982\u4e0b\uff1a<\/p>\n\n\n\ntrain: .\/infrared_dataset\/images\/trainval: .\/infrared_dataset\/images\/valtest: .\/infrared_dataset\/images\/testnc:4names:['Plane','Drone','Heli','Bird']<\/code><\/pre>\n\n\n\n\u5982\u679c\u4f60\u7684\u6570\u636e\u6807\u6ce8\u4e3aVOC\u683c\u5f0f\uff0c\u8bf7\u5148\u8f6c\u6362\u4e3aYOLO\u683c\u5f0f\uff08\u5373\u6bcf\u4e2a\u6807\u7b7e\u6587\u4ef6\u5305\u542b\u6bcf\u884c\u4e00\u4e2a\u8fb9\u754c\u6846\uff0c\u683c\u5f0f\u4e3a\uff1a\u7c7b\u522bID \u4e2d\u5fc3\u70b9x \u4e2d\u5fc3\u70b9y \u5bbd \u9ad8\uff09\u3002<\/p>\n\n\n\n\u4e09\u3001\u8bad\u7ec3\u6a21\u578b<\/h3>\n\n\n\n\u8bad\u7ec3\u811a\u672c (train.py<\/code>)<\/h4>\n\n\n\nfrom ultralytics import YOLOimport torchdeftrain_model():# Check GPU availability<\/em> device =0if torch.cuda.is_available()else'cpu'print(f\"Using device: {device}\")# Load pretrained model (YOLOv8s is a good balance between speed and accuracy)<\/em> model = YOLO('yolov8s.pt')# Start training<\/em> results = model.train( data='infrared_dataset\/data.yaml', epochs=200,# Adjust based on your needs<\/em> imgsz=640,# Image size can be adjusted based on the dataset<\/em> batch=16,# Reduce if you encounter out-of-memory issues<\/em> name='infrared_yolov8s', project='runs\/detect', save=True, save_period=10,# Save every 10 epochs<\/em> device=device, workers=4, patience=50,# Early stopping after 50 epochs without improvement<\/em> optimizer='AdamW', lr0=0.001,# Initial learning rate<\/em> augment=True# Enable data augmentation<\/em>)print(\"Training completed!\")return resultsif __name__ =='__main__': train_model()<\/code><\/pre>\n\n\n\n\u56db\u3001\u63a8\u7406\u4e0e\u8bc4\u4f30<\/h3>\n\n\n\n\u63a8\u7406\u811a\u672c (detect.py<\/code>)<\/h4>\n\n\n\nfrom ultralytics import YOLOimport cv2defdetect_image(image_path, output_path='output_detection.jpg'):# Load trained model<\/em> model = YOLO('runs\/detect\/infrared_yolov8s\/weights\/best.pt')# Perform prediction<\/em> results = model.predict( source=image_path, conf=0.4,# Confidence threshold<\/em> iou=0.5, save=True, project='runs\/detect\/predict', name='result', exist_ok=True)# Extract and save results (optional)<\/em>for r in results: im = r.plot()# Plot bounding boxes and labels<\/em> cv2.imwrite(output_path, im)print(f\"Detection completed, saved to {output_path}\")# Print detected objects<\/em>for box in r.boxes: cls_name = model.names[int(box.cls)] conf =float(box.conf)print(f\"Detected: {cls_name} (Confidence: {conf:.3f})\")return output_pathif __name__ =='__main__': detect_image('test_infrared_image.jpg')<\/code><\/pre>\n\n\n\n\u8bc4\u4f30\u811a\u672c (evaluate.py<\/code>)<\/h4>\n\n\n\nfrom ultralytics import YOLOdefevaluate_model(): model = YOLO('runs\/detect\/infrared_yolov8s\/weights\/best.pt')# Evaluate on validation set<\/em> metrics = model.val( data='infrared_dataset\/data.yaml', split='val',# Or 'test' if evaluating on the test set<\/em> batch=16, imgsz=640, device=0if torch.cuda.is_available()else'cpu', save_json=True,# Save COCO format results for further analysis<\/em> conf_thres=0.001, iou_thres=0.6)# Output key metrics<\/em>print(f\"mAP@0.5: {metrics.box.map50:.4f}\")print(f\"mAP@0.5:0.95: {metrics.box.map:.4f}\")print(f\"Precision: {metrics.box.p:.4f}\")print(f\"Recall: {metrics.box.r:.4f}\")# Output AP per class<\/em>print(\"\\nAP per class:\")for i, name inenumerate(metrics.names.values()):print(f\" {name}: {metrics.box.ap[i]:.4f}\")if __name__ =='__main__': evaluate_model()<\/code><\/pre>\n\n\n\n\u6784\u5efa\u4e00\u4e2a\u53ef\u4ee5\u52a0\u8f7d\u6a21\u578b\u3001\u6267\u884c\u63a8\u7406\u5e76\u5bf9\u65b0\u56fe\u50cf\u6216\u89c6\u9891\u8fdb\u884c\u5b9e\u65f6\u5904\u7406\u7684\u5e94\u7528\u3002<\/p>\n\n\n\n\u4e00\u3001\u51c6\u5907<\/h3>\n\n\n\n\u786e\u4fdd\u5df2\u7ecf\u5b8c\u6210\u4e86\u6a21\u578b\u7684\u8bad\u7ec3\uff0c\u5e76\u4e14\u8bad\u7ec3\u597d\u7684\u6a21\u578b\u6587\u4ef6\uff08\u4f8b\u5982 best.pt<\/code>\uff09\uff0c\u4f4d\u4e8e runs\/detect\/infrared_yolov8s\/weights\/best.pt<\/code>\u3002<\/p>\n\n\n\n\u4e8c\u3001\u6a21\u578b\u5c01\u88c5<\/h3>\n\n\n\n\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5c01\u88c5\u6211\u4eec\u7684\u6a21\u578b\uff0c\u4f7f\u5176\u6613\u4e8e\u4f7f\u7528\u3002\u8fd9\u53ef\u4ee5\u901a\u8fc7\u521b\u5efa\u4e00\u4e2a\u7c7b\u6765\u5b9e\u73b0\uff0c\u8be5\u7c7b\u8d1f\u8d23\u52a0\u8f7d\u6a21\u578b\u5e76\u63d0\u4f9b\u9884\u6d4b\u65b9\u6cd5\u3002<\/p>\n\n\n\nmodel_utils.py<\/code><\/h4>\n\n\n\nfrom ultralytics import YOLOimport cv2classInfraredTargetDetector:def__init__(self, model_path='runs\/detect\/infrared_yolov8s\/weights\/best.pt'): self.model = YOLO(model_path)defpredict(self, image_path, conf=0.4):\"\"\" \u5bf9\u7ed9\u5b9a\u8def\u5f84\u7684\u56fe\u50cf\u8fdb\u884c\u9884\u6d4b\u3002 :param image_path: \u8f93\u5165\u56fe\u50cf\u7684\u8def\u5f84 :param conf: \u7f6e\u4fe1\u5ea6\u9608\u503c :return: \u68c0\u6d4b\u7ed3\u679c\u5217\u8868\uff0c\u6bcf\u4e2a\u7ed3\u679c\u5305\u542b\u7c7b\u522b\u540d\u79f0\u3001\u7f6e\u4fe1\u5ea6\u5206\u6570\u548c\u8fb9\u754c\u6846\u5750\u6807 \"\"\" results = self.model.predict(source=image_path, conf=conf) detections =[]for r in results:for box in r.boxes: cls_id =int(box.cls) label = self.model.names[cls_id] confidence =float(box.conf) bbox = box.xyxy[0].cpu().numpy().tolist()# [x1, y1, x2, y2]<\/em> detections.append({'class': label,'confidence': confidence,'bbox': bbox})return detectionsdefdetect_and_draw(self, image_path, output_path='output_detection.jpg', conf=0.4):\"\"\" \u5bf9\u56fe\u50cf\u8fdb\u884c\u68c0\u6d4b\u5e76\u5728\u539f\u56fe\u4e0a\u7ed8\u5236\u68c0\u6d4b\u7ed3\u679c\u3002 :param image_path: \u8f93\u5165\u56fe\u50cf\u7684\u8def\u5f84 :param output_path: \u8f93\u51fa\u56fe\u50cf\u7684\u4fdd\u5b58\u8def\u5f84 :param conf: \u7f6e\u4fe1\u5ea6\u9608\u503c :return: None \"\"\" detections = self.predict(image_path, conf) img = cv2.imread(image_path)for det in detections: bbox = det['bbox'] label =f\"{det['class']}: {det['confidence']:.2f}\" color =(0,255,0)# Green<\/em> cv2.rectangle(img,(int(bbox[0]),int(bbox[1])),(int(bbox[2]),int(bbox[3])), color,2) cv2.putText(img, label,(int(bbox[0]),int(bbox[1])-10), cv2.FONT_HERSHEY_SIMPLEX,0.9, color,2) cv2.imwrite(output_path, img)print(f\"Detection completed and saved to {output_path}\")# \u793a\u4f8b\u4f7f\u7528<\/em>if __name__ =='__main__': detector = InfraredTargetDetector() detector.detect_and_draw('test_infrared_image.jpg')<\/code><\/pre>\n\n\n\n\u4e09\u3001\u6784\u5efa\u547d\u4ee4\u884c\u5de5\u5177<\/h3>\n\n\n\n\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u521b\u5efa\u4e00\u4e2a\u547d\u4ee4\u884c\u5de5\u5177\uff0c\u5141\u8bb8\u7528\u6237\u901a\u8fc7\u547d\u4ee4\u884c\u5bf9\u56fe\u50cf\u6216\u89c6\u9891\u8fdb\u884c\u68c0\u6d4b\u3002<\/p>\n\n\n\ninfrared_detector_cli.py<\/code><\/h4>\n\n\n\nimport argparsefrom model_utils import InfraredTargetDetectorimport cv2defmain(): parser = argparse.ArgumentParser(description=\"Infrared Target Detection System\") parser.add_argument('--input',type=str, required=True,help='Path to input image or video file.') parser.add_argument('--output',type=str, default='output.jpg',help='Path to save the output.') parser.add_argument('--conf',type=float, default=0.4,help='Confidence threshold.') args = parser.parse_args() detector = InfraredTargetDetector()if args.input.lower().endswith(('jpg','jpeg','png')): detector.detect_and_draw(args.input, args.output, args.conf)elif args.input.lower().endswith(('mp4','avi')): cap = cv2.VideoCapture(args.input) frame_width =int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) frame_height =int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) out = cv2.VideoWriter(args.output, cv2.VideoWriter_fourcc(*'XVID'),20.0,(frame_width, frame_height))while cap.isOpened(): ret, frame = cap.read()ifnot ret:break# Convert frame to image path temporarily for prediction<\/em> temp_img_path ='temp_frame.jpg' cv2.imwrite(temp_img_path, frame) detections = detector.predict(temp_img_path, args.conf)for det in detections: bbox = det['bbox'] label =f\"{det['class']}: {det['confidence']:.2f}\" color =(0,255,0)# Green<\/em> cv2.rectangle(frame,(int(bbox[0]),int(bbox[1])),(int(bbox[2]),int(bbox[3])), color,2) cv2.putText(frame, label,(int(bbox[0]),int(bbox[1])-10), cv2.FONT_HERSHEY_SIMPLEX,0.9, color,2) out.write(frame) cap.release() out.release()print(f\"Video detection completed and saved to {args.output}\")else:print(\"Unsupported file format.\")if __name__ ==\"__main__\": main()<\/code><\/pre>\n\n\n\n\u56db\u3001\u8fd0\u884c\u4f60\u7684\u7cfb\u7edf<\/h3>\n\n\n\n\n\u5bf9\u4e8e\u5355\u5f20\u56fe\u7247<\/strong>\uff1apython infrared_detector_cli.py --input path\/to\/image.jpg --output output.jpg --conf\u00a00.5<\/code><\/li>\n\n\n\n\u5bf9\u4e8e\u89c6\u9891\u6587\u4ef6<\/strong>\uff1apython infrared_detector_cli.py --input path\/to\/video.mp4 --output output_video.avi --con<\/code><\/li>\n<\/ul>\n","raw":"","protected":false},"excerpt":{"rendered":"\u6df1\u5ea6\u5b66\u4e60\u57fa\u4e8eYOLOv8\u6846\u67b6\u6765\u8bad\u7ec3\u7ea2\u5916\u56fe\u50cf\u5c0f\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6 \u7ea2\u5916\u56fe\u50cf\u5c0f\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6\u7684\u8bad\u7ec3\u53ca\u5e94\u7528 \u63a8\u7406\u8bc6\u522b\u68c0\u6d4b\u98de\u673a \u65e0\u4eba\u673a \u76f4\u5347\u673a 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YOLOV11\u5982\u4f55\u8bad\u7ec3\u81ea\u5df1\u7684\u6570\u636e\u96c6 YOLOv11\u8bad\u7ec3\u914d\u7f6e\u73af\u5883\u6559\u7a0b \u4ee5\u6c14\u7403\u68c0\u6d4b\u6570\u636e\u96c6\u4e3a\u4f8b","previous_post_id":6590,"previous_post_title":"YOLOV8\u6a21\u578b\u5982\u4f55\u8bad\u7ec3 \u53cc\u6a21\u6001\u7ea2\u5916-\u53ef\u89c1\u5149\u5bf9\u9f50\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6 \u7ea2\u5916\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6\u7684\u8bad\u7ec3\u53ca\u5e94\u7528 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<\/noscript>1<\/p>\n\n\n\n1<\/noscript>1<\/noscript>\u7ea2\u5916\u56fe\u50cf\u4e0b\u6781\u5c0f\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6\u8fdb\u884c\u8bad\u7ec3\u3001\u63a8\u7406\u548c\u8bc4\u4f30\uff0c\u57fa\u4e8eYOLOv8\u6846\u67b6\u6765\u5b9e\u73b0\u3002\u8be6\u7ec6\u7684\u6b65\u9aa4\u548c\u4ee3\u7801\u793a\u4f8b\uff0c\u5305\u62ec\u5982\u4f55\u51c6\u5907\u6570\u636e\u3001\u8bad\u7ec3\u6a21\u578b\u3001\u8fdb\u884c\u63a8\u7406\u4ee5\u53ca\u8bc4\u4f30\u6a21\u578b\u6027\u80fd\u3002<\/p>\n\n\n\n\u4e00\u3001\u73af\u5883\u51c6\u5907<\/h3>\n\n\n\n\u786e\u4fdd\u73af\u5883\u4e2d\u5b89\u88c5\u4e86\u5fc5\u8981\u7684\u5e93\uff1a<\/p>\n\n\n\npip install torch torchvision torchaudio --index-url https:\/\/download.pytorch.org\/whl\/cu118pip install ultralytics opencv-python matplotlib tqdm<\/code><\/pre>\n\n\n\n\u4e8c\u3001\u6570\u636e\u51c6\u5907<\/h3>\n\n\n\n\u6570\u636e\u96c6\u7ec4\u7ec7<\/h4>\n\n\n\n\u6570\u636e\u96c6\u6309\u7167\u4ee5\u4e0b\u7ed3\u6784\u7ec4\u7ec7\uff1a<\/p>\n\n\n\ninfrared_dataset\/\u251c\u2500\u2500 images\/\u2502 \u251c\u2500\u2500 train\/\u2502 \u251c\u2500\u2500 val\/\u2502 \u2514\u2500\u2500 test\/\u251c\u2500\u2500 labels\/\u2502 \u251c\u2500\u2500 train\/\u2502 \u251c\u2500\u2500 val\/\u2502 \u2514\u2500\u2500 test\/\u2514\u2500\u2500 data.yaml<\/code><\/pre>\n\n\n\n\u5176\u4e2d data.yaml<\/code> \u6587\u4ef6\u5185\u5bb9\u5982\u4e0b\uff1a<\/p>\n\n\n\ntrain: .\/infrared_dataset\/images\/trainval: .\/infrared_dataset\/images\/valtest: .\/infrared_dataset\/images\/testnc:4names:['Plane','Drone','Heli','Bird']<\/code><\/pre>\n\n\n\n\u5982\u679c\u4f60\u7684\u6570\u636e\u6807\u6ce8\u4e3aVOC\u683c\u5f0f\uff0c\u8bf7\u5148\u8f6c\u6362\u4e3aYOLO\u683c\u5f0f\uff08\u5373\u6bcf\u4e2a\u6807\u7b7e\u6587\u4ef6\u5305\u542b\u6bcf\u884c\u4e00\u4e2a\u8fb9\u754c\u6846\uff0c\u683c\u5f0f\u4e3a\uff1a\u7c7b\u522bID \u4e2d\u5fc3\u70b9x \u4e2d\u5fc3\u70b9y \u5bbd \u9ad8\uff09\u3002<\/p>\n\n\n\n\u4e09\u3001\u8bad\u7ec3\u6a21\u578b<\/h3>\n\n\n\n\u8bad\u7ec3\u811a\u672c (train.py<\/code>)<\/h4>\n\n\n\nfrom ultralytics import YOLOimport torchdeftrain_model():# Check GPU availability<\/em> device =0if torch.cuda.is_available()else'cpu'print(f\"Using device: {device}\")# Load pretrained model (YOLOv8s is a good balance between speed and accuracy)<\/em> model = YOLO('yolov8s.pt')# Start training<\/em> results = model.train( data='infrared_dataset\/data.yaml', epochs=200,# Adjust based on your needs<\/em> imgsz=640,# Image size can be adjusted based on the dataset<\/em> batch=16,# Reduce if you encounter out-of-memory issues<\/em> name='infrared_yolov8s', project='runs\/detect', save=True, save_period=10,# Save every 10 epochs<\/em> device=device, workers=4, patience=50,# Early stopping after 50 epochs without improvement<\/em> optimizer='AdamW', lr0=0.001,# Initial learning rate<\/em> augment=True# Enable data augmentation<\/em>)print(\"Training completed!\")return resultsif __name__ =='__main__': train_model()<\/code><\/pre>\n\n\n\n\u56db\u3001\u63a8\u7406\u4e0e\u8bc4\u4f30<\/h3>\n\n\n\n\u63a8\u7406\u811a\u672c (detect.py<\/code>)<\/h4>\n\n\n\nfrom ultralytics import YOLOimport cv2defdetect_image(image_path, output_path='output_detection.jpg'):# Load trained model<\/em> model = YOLO('runs\/detect\/infrared_yolov8s\/weights\/best.pt')# Perform prediction<\/em> results = model.predict( source=image_path, conf=0.4,# Confidence threshold<\/em> iou=0.5, save=True, project='runs\/detect\/predict', name='result', exist_ok=True)# Extract and save results (optional)<\/em>for r in results: im = r.plot()# Plot bounding boxes and labels<\/em> cv2.imwrite(output_path, im)print(f\"Detection completed, saved to {output_path}\")# Print detected objects<\/em>for box in r.boxes: cls_name = model.names[int(box.cls)] conf =float(box.conf)print(f\"Detected: {cls_name} (Confidence: {conf:.3f})\")return output_pathif __name__ =='__main__': detect_image('test_infrared_image.jpg')<\/code><\/pre>\n\n\n\n\u8bc4\u4f30\u811a\u672c (evaluate.py<\/code>)<\/h4>\n\n\n\nfrom ultralytics import YOLOdefevaluate_model(): model = YOLO('runs\/detect\/infrared_yolov8s\/weights\/best.pt')# Evaluate on validation set<\/em> metrics = model.val( data='infrared_dataset\/data.yaml', split='val',# Or 'test' if evaluating on the test set<\/em> batch=16, imgsz=640, device=0if torch.cuda.is_available()else'cpu', save_json=True,# Save COCO format results for further analysis<\/em> conf_thres=0.001, iou_thres=0.6)# Output key metrics<\/em>print(f\"mAP@0.5: {metrics.box.map50:.4f}\")print(f\"mAP@0.5:0.95: {metrics.box.map:.4f}\")print(f\"Precision: {metrics.box.p:.4f}\")print(f\"Recall: {metrics.box.r:.4f}\")# Output AP per class<\/em>print(\"\\nAP per class:\")for i, name inenumerate(metrics.names.values()):print(f\" {name}: {metrics.box.ap[i]:.4f}\")if __name__ =='__main__': evaluate_model()<\/code><\/pre>\n\n\n\n\u6784\u5efa\u4e00\u4e2a\u53ef\u4ee5\u52a0\u8f7d\u6a21\u578b\u3001\u6267\u884c\u63a8\u7406\u5e76\u5bf9\u65b0\u56fe\u50cf\u6216\u89c6\u9891\u8fdb\u884c\u5b9e\u65f6\u5904\u7406\u7684\u5e94\u7528\u3002<\/p>\n\n\n\n\u4e00\u3001\u51c6\u5907<\/h3>\n\n\n\n\u786e\u4fdd\u5df2\u7ecf\u5b8c\u6210\u4e86\u6a21\u578b\u7684\u8bad\u7ec3\uff0c\u5e76\u4e14\u8bad\u7ec3\u597d\u7684\u6a21\u578b\u6587\u4ef6\uff08\u4f8b\u5982 best.pt<\/code>\uff09\uff0c\u4f4d\u4e8e runs\/detect\/infrared_yolov8s\/weights\/best.pt<\/code>\u3002<\/p>\n\n\n\n\u4e8c\u3001\u6a21\u578b\u5c01\u88c5<\/h3>\n\n\n\n\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5c01\u88c5\u6211\u4eec\u7684\u6a21\u578b\uff0c\u4f7f\u5176\u6613\u4e8e\u4f7f\u7528\u3002\u8fd9\u53ef\u4ee5\u901a\u8fc7\u521b\u5efa\u4e00\u4e2a\u7c7b\u6765\u5b9e\u73b0\uff0c\u8be5\u7c7b\u8d1f\u8d23\u52a0\u8f7d\u6a21\u578b\u5e76\u63d0\u4f9b\u9884\u6d4b\u65b9\u6cd5\u3002<\/p>\n\n\n\nmodel_utils.py<\/code><\/h4>\n\n\n\nfrom ultralytics import YOLOimport cv2classInfraredTargetDetector:def__init__(self, model_path='runs\/detect\/infrared_yolov8s\/weights\/best.pt'): self.model = YOLO(model_path)defpredict(self, image_path, conf=0.4):\"\"\" \u5bf9\u7ed9\u5b9a\u8def\u5f84\u7684\u56fe\u50cf\u8fdb\u884c\u9884\u6d4b\u3002 :param image_path: \u8f93\u5165\u56fe\u50cf\u7684\u8def\u5f84 :param conf: \u7f6e\u4fe1\u5ea6\u9608\u503c :return: \u68c0\u6d4b\u7ed3\u679c\u5217\u8868\uff0c\u6bcf\u4e2a\u7ed3\u679c\u5305\u542b\u7c7b\u522b\u540d\u79f0\u3001\u7f6e\u4fe1\u5ea6\u5206\u6570\u548c\u8fb9\u754c\u6846\u5750\u6807 \"\"\" results = self.model.predict(source=image_path, conf=conf) detections =[]for r in results:for box in r.boxes: cls_id =int(box.cls) label = self.model.names[cls_id] confidence =float(box.conf) bbox = box.xyxy[0].cpu().numpy().tolist()# [x1, y1, x2, y2]<\/em> detections.append({'class': label,'confidence': confidence,'bbox': bbox})return detectionsdefdetect_and_draw(self, image_path, output_path='output_detection.jpg', conf=0.4):\"\"\" \u5bf9\u56fe\u50cf\u8fdb\u884c\u68c0\u6d4b\u5e76\u5728\u539f\u56fe\u4e0a\u7ed8\u5236\u68c0\u6d4b\u7ed3\u679c\u3002 :param image_path: \u8f93\u5165\u56fe\u50cf\u7684\u8def\u5f84 :param output_path: \u8f93\u51fa\u56fe\u50cf\u7684\u4fdd\u5b58\u8def\u5f84 :param conf: \u7f6e\u4fe1\u5ea6\u9608\u503c :return: None \"\"\" detections = self.predict(image_path, conf) img = cv2.imread(image_path)for det in detections: bbox = det['bbox'] label =f\"{det['class']}: {det['confidence']:.2f}\" color =(0,255,0)# Green<\/em> cv2.rectangle(img,(int(bbox[0]),int(bbox[1])),(int(bbox[2]),int(bbox[3])), color,2) cv2.putText(img, label,(int(bbox[0]),int(bbox[1])-10), cv2.FONT_HERSHEY_SIMPLEX,0.9, color,2) cv2.imwrite(output_path, img)print(f\"Detection completed and saved to {output_path}\")# \u793a\u4f8b\u4f7f\u7528<\/em>if __name__ =='__main__': detector = InfraredTargetDetector() detector.detect_and_draw('test_infrared_image.jpg')<\/code><\/pre>\n\n\n\n\u4e09\u3001\u6784\u5efa\u547d\u4ee4\u884c\u5de5\u5177<\/h3>\n\n\n\n\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u521b\u5efa\u4e00\u4e2a\u547d\u4ee4\u884c\u5de5\u5177\uff0c\u5141\u8bb8\u7528\u6237\u901a\u8fc7\u547d\u4ee4\u884c\u5bf9\u56fe\u50cf\u6216\u89c6\u9891\u8fdb\u884c\u68c0\u6d4b\u3002<\/p>\n\n\n\ninfrared_detector_cli.py<\/code><\/h4>\n\n\n\nimport argparsefrom model_utils import InfraredTargetDetectorimport cv2defmain(): parser = argparse.ArgumentParser(description=\"Infrared Target Detection System\") parser.add_argument('--input',type=str, required=True,help='Path to input image or video file.') parser.add_argument('--output',type=str, default='output.jpg',help='Path to save the output.') parser.add_argument('--conf',type=float, default=0.4,help='Confidence threshold.') args = parser.parse_args() detector = InfraredTargetDetector()if args.input.lower().endswith(('jpg','jpeg','png')): detector.detect_and_draw(args.input, args.output, args.conf)elif args.input.lower().endswith(('mp4','avi')): cap = cv2.VideoCapture(args.input) frame_width =int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) frame_height =int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) out = cv2.VideoWriter(args.output, cv2.VideoWriter_fourcc(*'XVID'),20.0,(frame_width, frame_height))while cap.isOpened(): ret, frame = cap.read()ifnot ret:break# Convert frame to image path temporarily for prediction<\/em> temp_img_path ='temp_frame.jpg' cv2.imwrite(temp_img_path, frame) detections = detector.predict(temp_img_path, args.conf)for det in detections: bbox = det['bbox'] label =f\"{det['class']}: {det['confidence']:.2f}\" color =(0,255,0)# Green<\/em> cv2.rectangle(frame,(int(bbox[0]),int(bbox[1])),(int(bbox[2]),int(bbox[3])), color,2) cv2.putText(frame, label,(int(bbox[0]),int(bbox[1])-10), cv2.FONT_HERSHEY_SIMPLEX,0.9, color,2) out.write(frame) cap.release() out.release()print(f\"Video detection completed and saved to {args.output}\")else:print(\"Unsupported file format.\")if __name__ ==\"__main__\": main()<\/code><\/pre>\n\n\n\n\u56db\u3001\u8fd0\u884c\u4f60\u7684\u7cfb\u7edf<\/h3>\n\n\n\n\n\u5bf9\u4e8e\u5355\u5f20\u56fe\u7247<\/strong>\uff1apython infrared_detector_cli.py --input path\/to\/image.jpg --output output.jpg --conf\u00a00.5<\/code><\/li>\n\n\n\n\u5bf9\u4e8e\u89c6\u9891\u6587\u4ef6<\/strong>\uff1apython infrared_detector_cli.py --input path\/to\/video.mp4 --output output_video.avi --con<\/code><\/li>\n<\/ul>\n","raw":"","protected":false},"excerpt":{"rendered":"\u6df1\u5ea6\u5b66\u4e60\u57fa\u4e8eYOLOv8\u6846\u67b6\u6765\u8bad\u7ec3\u7ea2\u5916\u56fe\u50cf\u5c0f\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6 \u7ea2\u5916\u56fe\u50cf\u5c0f\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6\u7684\u8bad\u7ec3\u53ca\u5e94\u7528 \u63a8\u7406\u8bc6\u522b\u68c0\u6d4b\u98de\u673a \u65e0\u4eba\u673a \u76f4\u5347\u673a 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YOLOV11\u5982\u4f55\u8bad\u7ec3\u81ea\u5df1\u7684\u6570\u636e\u96c6 YOLOv11\u8bad\u7ec3\u914d\u7f6e\u73af\u5883\u6559\u7a0b \u4ee5\u6c14\u7403\u68c0\u6d4b\u6570\u636e\u96c6\u4e3a\u4f8b","previous_post_id":6590,"previous_post_title":"YOLOV8\u6a21\u578b\u5982\u4f55\u8bad\u7ec3 \u53cc\u6a21\u6001\u7ea2\u5916-\u53ef\u89c1\u5149\u5bf9\u9f50\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6 \u7ea2\u5916\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6\u7684\u8bad\u7ec3\u53ca\u5e94\u7528 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1<\/noscript>1<\/noscript>\u7ea2\u5916\u56fe\u50cf\u4e0b\u6781\u5c0f\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6\u8fdb\u884c\u8bad\u7ec3\u3001\u63a8\u7406\u548c\u8bc4\u4f30\uff0c\u57fa\u4e8eYOLOv8\u6846\u67b6\u6765\u5b9e\u73b0\u3002\u8be6\u7ec6\u7684\u6b65\u9aa4\u548c\u4ee3\u7801\u793a\u4f8b\uff0c\u5305\u62ec\u5982\u4f55\u51c6\u5907\u6570\u636e\u3001\u8bad\u7ec3\u6a21\u578b\u3001\u8fdb\u884c\u63a8\u7406\u4ee5\u53ca\u8bc4\u4f30\u6a21\u578b\u6027\u80fd\u3002<\/p>\n\n\n\n\u4e00\u3001\u73af\u5883\u51c6\u5907<\/h3>\n\n\n\n\u786e\u4fdd\u73af\u5883\u4e2d\u5b89\u88c5\u4e86\u5fc5\u8981\u7684\u5e93\uff1a<\/p>\n\n\n\npip install torch torchvision torchaudio --index-url https:\/\/download.pytorch.org\/whl\/cu118pip install ultralytics opencv-python matplotlib tqdm<\/code><\/pre>\n\n\n\n\u4e8c\u3001\u6570\u636e\u51c6\u5907<\/h3>\n\n\n\n\u6570\u636e\u96c6\u7ec4\u7ec7<\/h4>\n\n\n\n\u6570\u636e\u96c6\u6309\u7167\u4ee5\u4e0b\u7ed3\u6784\u7ec4\u7ec7\uff1a<\/p>\n\n\n\ninfrared_dataset\/\u251c\u2500\u2500 images\/\u2502 \u251c\u2500\u2500 train\/\u2502 \u251c\u2500\u2500 val\/\u2502 \u2514\u2500\u2500 test\/\u251c\u2500\u2500 labels\/\u2502 \u251c\u2500\u2500 train\/\u2502 \u251c\u2500\u2500 val\/\u2502 \u2514\u2500\u2500 test\/\u2514\u2500\u2500 data.yaml<\/code><\/pre>\n\n\n\n\u5176\u4e2d data.yaml<\/code> \u6587\u4ef6\u5185\u5bb9\u5982\u4e0b\uff1a<\/p>\n\n\n\ntrain: .\/infrared_dataset\/images\/trainval: .\/infrared_dataset\/images\/valtest: .\/infrared_dataset\/images\/testnc:4names:['Plane','Drone','Heli','Bird']<\/code><\/pre>\n\n\n\n\u5982\u679c\u4f60\u7684\u6570\u636e\u6807\u6ce8\u4e3aVOC\u683c\u5f0f\uff0c\u8bf7\u5148\u8f6c\u6362\u4e3aYOLO\u683c\u5f0f\uff08\u5373\u6bcf\u4e2a\u6807\u7b7e\u6587\u4ef6\u5305\u542b\u6bcf\u884c\u4e00\u4e2a\u8fb9\u754c\u6846\uff0c\u683c\u5f0f\u4e3a\uff1a\u7c7b\u522bID \u4e2d\u5fc3\u70b9x \u4e2d\u5fc3\u70b9y \u5bbd \u9ad8\uff09\u3002<\/p>\n\n\n\n\u4e09\u3001\u8bad\u7ec3\u6a21\u578b<\/h3>\n\n\n\n\u8bad\u7ec3\u811a\u672c (train.py<\/code>)<\/h4>\n\n\n\nfrom ultralytics import YOLOimport torchdeftrain_model():# Check GPU availability<\/em> device =0if torch.cuda.is_available()else'cpu'print(f\"Using device: {device}\")# Load pretrained model (YOLOv8s is a good balance between speed and accuracy)<\/em> model = YOLO('yolov8s.pt')# Start training<\/em> results = model.train( data='infrared_dataset\/data.yaml', epochs=200,# Adjust based on your needs<\/em> imgsz=640,# Image size can be adjusted based on the dataset<\/em> batch=16,# Reduce if you encounter out-of-memory issues<\/em> name='infrared_yolov8s', project='runs\/detect', save=True, save_period=10,# Save every 10 epochs<\/em> device=device, workers=4, patience=50,# Early stopping after 50 epochs without improvement<\/em> optimizer='AdamW', lr0=0.001,# Initial learning rate<\/em> augment=True# Enable data augmentation<\/em>)print(\"Training completed!\")return resultsif __name__ =='__main__': train_model()<\/code><\/pre>\n\n\n\n\u56db\u3001\u63a8\u7406\u4e0e\u8bc4\u4f30<\/h3>\n\n\n\n\u63a8\u7406\u811a\u672c (detect.py<\/code>)<\/h4>\n\n\n\nfrom ultralytics import YOLOimport cv2defdetect_image(image_path, output_path='output_detection.jpg'):# Load trained model<\/em> model = YOLO('runs\/detect\/infrared_yolov8s\/weights\/best.pt')# Perform prediction<\/em> results = model.predict( source=image_path, conf=0.4,# Confidence threshold<\/em> iou=0.5, save=True, project='runs\/detect\/predict', name='result', exist_ok=True)# Extract and save results (optional)<\/em>for r in results: im = r.plot()# Plot bounding boxes and labels<\/em> cv2.imwrite(output_path, im)print(f\"Detection completed, saved to {output_path}\")# Print detected objects<\/em>for box in r.boxes: cls_name = model.names[int(box.cls)] conf =float(box.conf)print(f\"Detected: {cls_name} (Confidence: {conf:.3f})\")return output_pathif __name__ =='__main__': detect_image('test_infrared_image.jpg')<\/code><\/pre>\n\n\n\n\u8bc4\u4f30\u811a\u672c (evaluate.py<\/code>)<\/h4>\n\n\n\nfrom ultralytics import YOLOdefevaluate_model(): model = YOLO('runs\/detect\/infrared_yolov8s\/weights\/best.pt')# Evaluate on validation set<\/em> metrics = model.val( data='infrared_dataset\/data.yaml', split='val',# Or 'test' if evaluating on the test set<\/em> batch=16, imgsz=640, device=0if torch.cuda.is_available()else'cpu', save_json=True,# Save COCO format results for further analysis<\/em> conf_thres=0.001, iou_thres=0.6)# Output key metrics<\/em>print(f\"mAP@0.5: {metrics.box.map50:.4f}\")print(f\"mAP@0.5:0.95: {metrics.box.map:.4f}\")print(f\"Precision: {metrics.box.p:.4f}\")print(f\"Recall: {metrics.box.r:.4f}\")# Output AP per class<\/em>print(\"\\nAP per class:\")for i, name inenumerate(metrics.names.values()):print(f\" {name}: {metrics.box.ap[i]:.4f}\")if __name__ =='__main__': evaluate_model()<\/code><\/pre>\n\n\n\n\u6784\u5efa\u4e00\u4e2a\u53ef\u4ee5\u52a0\u8f7d\u6a21\u578b\u3001\u6267\u884c\u63a8\u7406\u5e76\u5bf9\u65b0\u56fe\u50cf\u6216\u89c6\u9891\u8fdb\u884c\u5b9e\u65f6\u5904\u7406\u7684\u5e94\u7528\u3002<\/p>\n\n\n\n\u4e00\u3001\u51c6\u5907<\/h3>\n\n\n\n\u786e\u4fdd\u5df2\u7ecf\u5b8c\u6210\u4e86\u6a21\u578b\u7684\u8bad\u7ec3\uff0c\u5e76\u4e14\u8bad\u7ec3\u597d\u7684\u6a21\u578b\u6587\u4ef6\uff08\u4f8b\u5982 best.pt<\/code>\uff09\uff0c\u4f4d\u4e8e runs\/detect\/infrared_yolov8s\/weights\/best.pt<\/code>\u3002<\/p>\n\n\n\n\u4e8c\u3001\u6a21\u578b\u5c01\u88c5<\/h3>\n\n\n\n\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5c01\u88c5\u6211\u4eec\u7684\u6a21\u578b\uff0c\u4f7f\u5176\u6613\u4e8e\u4f7f\u7528\u3002\u8fd9\u53ef\u4ee5\u901a\u8fc7\u521b\u5efa\u4e00\u4e2a\u7c7b\u6765\u5b9e\u73b0\uff0c\u8be5\u7c7b\u8d1f\u8d23\u52a0\u8f7d\u6a21\u578b\u5e76\u63d0\u4f9b\u9884\u6d4b\u65b9\u6cd5\u3002<\/p>\n\n\n\nmodel_utils.py<\/code><\/h4>\n\n\n\nfrom ultralytics import YOLOimport cv2classInfraredTargetDetector:def__init__(self, model_path='runs\/detect\/infrared_yolov8s\/weights\/best.pt'): self.model = YOLO(model_path)defpredict(self, image_path, conf=0.4):\"\"\" \u5bf9\u7ed9\u5b9a\u8def\u5f84\u7684\u56fe\u50cf\u8fdb\u884c\u9884\u6d4b\u3002 :param image_path: \u8f93\u5165\u56fe\u50cf\u7684\u8def\u5f84 :param conf: \u7f6e\u4fe1\u5ea6\u9608\u503c :return: \u68c0\u6d4b\u7ed3\u679c\u5217\u8868\uff0c\u6bcf\u4e2a\u7ed3\u679c\u5305\u542b\u7c7b\u522b\u540d\u79f0\u3001\u7f6e\u4fe1\u5ea6\u5206\u6570\u548c\u8fb9\u754c\u6846\u5750\u6807 \"\"\" results = self.model.predict(source=image_path, conf=conf) detections =[]for r in results:for box in r.boxes: cls_id =int(box.cls) label = self.model.names[cls_id] confidence =float(box.conf) bbox = box.xyxy[0].cpu().numpy().tolist()# [x1, y1, x2, y2]<\/em> detections.append({'class': label,'confidence': confidence,'bbox': bbox})return detectionsdefdetect_and_draw(self, image_path, output_path='output_detection.jpg', conf=0.4):\"\"\" \u5bf9\u56fe\u50cf\u8fdb\u884c\u68c0\u6d4b\u5e76\u5728\u539f\u56fe\u4e0a\u7ed8\u5236\u68c0\u6d4b\u7ed3\u679c\u3002 :param image_path: \u8f93\u5165\u56fe\u50cf\u7684\u8def\u5f84 :param output_path: \u8f93\u51fa\u56fe\u50cf\u7684\u4fdd\u5b58\u8def\u5f84 :param conf: \u7f6e\u4fe1\u5ea6\u9608\u503c :return: None \"\"\" detections = self.predict(image_path, conf) img = cv2.imread(image_path)for det in detections: bbox = det['bbox'] label =f\"{det['class']}: {det['confidence']:.2f}\" color =(0,255,0)# Green<\/em> cv2.rectangle(img,(int(bbox[0]),int(bbox[1])),(int(bbox[2]),int(bbox[3])), color,2) cv2.putText(img, label,(int(bbox[0]),int(bbox[1])-10), cv2.FONT_HERSHEY_SIMPLEX,0.9, color,2) cv2.imwrite(output_path, img)print(f\"Detection completed and saved to {output_path}\")# \u793a\u4f8b\u4f7f\u7528<\/em>if __name__ =='__main__': detector = InfraredTargetDetector() detector.detect_and_draw('test_infrared_image.jpg')<\/code><\/pre>\n\n\n\n\u4e09\u3001\u6784\u5efa\u547d\u4ee4\u884c\u5de5\u5177<\/h3>\n\n\n\n\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u521b\u5efa\u4e00\u4e2a\u547d\u4ee4\u884c\u5de5\u5177\uff0c\u5141\u8bb8\u7528\u6237\u901a\u8fc7\u547d\u4ee4\u884c\u5bf9\u56fe\u50cf\u6216\u89c6\u9891\u8fdb\u884c\u68c0\u6d4b\u3002<\/p>\n\n\n\ninfrared_detector_cli.py<\/code><\/h4>\n\n\n\nimport argparsefrom model_utils import InfraredTargetDetectorimport cv2defmain(): parser = argparse.ArgumentParser(description=\"Infrared Target Detection System\") parser.add_argument('--input',type=str, required=True,help='Path to input image or video file.') parser.add_argument('--output',type=str, default='output.jpg',help='Path to save the output.') parser.add_argument('--conf',type=float, default=0.4,help='Confidence threshold.') args = parser.parse_args() detector = InfraredTargetDetector()if args.input.lower().endswith(('jpg','jpeg','png')): detector.detect_and_draw(args.input, args.output, args.conf)elif args.input.lower().endswith(('mp4','avi')): cap = cv2.VideoCapture(args.input) frame_width =int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) frame_height =int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) out = cv2.VideoWriter(args.output, cv2.VideoWriter_fourcc(*'XVID'),20.0,(frame_width, frame_height))while cap.isOpened(): ret, frame = cap.read()ifnot ret:break# Convert frame to image path temporarily for prediction<\/em> temp_img_path ='temp_frame.jpg' cv2.imwrite(temp_img_path, frame) detections = detector.predict(temp_img_path, args.conf)for det in detections: bbox = det['bbox'] label =f\"{det['class']}: {det['confidence']:.2f}\" color =(0,255,0)# Green<\/em> cv2.rectangle(frame,(int(bbox[0]),int(bbox[1])),(int(bbox[2]),int(bbox[3])), color,2) cv2.putText(frame, label,(int(bbox[0]),int(bbox[1])-10), cv2.FONT_HERSHEY_SIMPLEX,0.9, color,2) out.write(frame) cap.release() out.release()print(f\"Video detection completed and saved to {args.output}\")else:print(\"Unsupported file format.\")if __name__ ==\"__main__\": main()<\/code><\/pre>\n\n\n\n\u56db\u3001\u8fd0\u884c\u4f60\u7684\u7cfb\u7edf<\/h3>\n\n\n\n\n\u5bf9\u4e8e\u5355\u5f20\u56fe\u7247<\/strong>\uff1apython infrared_detector_cli.py --input path\/to\/image.jpg --output output.jpg --conf\u00a00.5<\/code><\/li>\n\n\n\n\u5bf9\u4e8e\u89c6\u9891\u6587\u4ef6<\/strong>\uff1apython infrared_detector_cli.py --input path\/to\/video.mp4 --output output_video.avi --con<\/code><\/li>\n<\/ul>\n","raw":"","protected":false},"excerpt":{"rendered":"\u6df1\u5ea6\u5b66\u4e60\u57fa\u4e8eYOLOv8\u6846\u67b6\u6765\u8bad\u7ec3\u7ea2\u5916\u56fe\u50cf\u5c0f\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6 \u7ea2\u5916\u56fe\u50cf\u5c0f\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6\u7684\u8bad\u7ec3\u53ca\u5e94\u7528 \u63a8\u7406\u8bc6\u522b\u68c0\u6d4b\u98de\u673a \u65e0\u4eba\u673a \u76f4\u5347\u673a …<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"emotion":"","emotion_color":"","title_style":"","license":"","footnotes":""},"categories":[46],"tags":[],"class_list":["post-6592","post","type-post","status-publish","format-standard","hentry","category-yolo"],"post_thumbnail_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","content_first_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_medium_image_300":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_thumbnail_image_624":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_frist_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_medium_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_large_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_full_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_all_images":[{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","id":"image0"},{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","id":"image1"},{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKicJO37JCcKiczyLibDpRgBDXlTon1aE9dVcNEEQ2gvaQTRZOajJPzLexw\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=1","id":"image2"},{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKicJO37JCcKiczyLibDpRgBDXlTon1aE9dVcNEEQ2gvaQTRZOajJPzLexw\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=1","id":"image3"},{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKAyqwlqJhAhUaqz66Via4WKqzx8qTkKvrURcsQVImhEUmXhZAYbia279Q\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=2","id":"image4"},{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKAyqwlqJhAhUaqz66Via4WKqzx8qTkKvrURcsQVImhEUmXhZAYbia279Q\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=2","id":"image5"}],"videoAdId":"","listAd":"0","listAdId":"","listAdEvery":6,"total_comments":0,"category_name":"YOLO","post_date":"2025-09-29","like_count":"0","praiseWord":"\u9f13\u52b1","copyright_state":"","excitationAd":"0","rewardedVideoAdId":"","detailAdId":"","detailAd":"0","enterpriseMinapp":"0","audios":[],"postImageUrl":"https:\/\/wp-moto-1258805347.cos.ap-shanghai.myqcloud.com\/2023\/05\/20230519082947553.jpg","avatarurls":[],"related_posts":null,"pageviews":269,"next_post_id":6657,"next_post_title":"\u6df1\u5ea6\u5b66\u4e60\u76ee\u6807\u68c0\u6d4b YOLOV11\u5982\u4f55\u8bad\u7ec3\u81ea\u5df1\u7684\u6570\u636e\u96c6 YOLOv11\u8bad\u7ec3\u914d\u7f6e\u73af\u5883\u6559\u7a0b \u4ee5\u6c14\u7403\u68c0\u6d4b\u6570\u636e\u96c6\u4e3a\u4f8b","previous_post_id":6590,"previous_post_title":"YOLOV8\u6a21\u578b\u5982\u4f55\u8bad\u7ec3 \u53cc\u6a21\u6001\u7ea2\u5916-\u53ef\u89c1\u5149\u5bf9\u9f50\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6 \u7ea2\u5916\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6\u7684\u8bad\u7ec3\u53ca\u5e94\u7528 \u53cc\u6a21\u6001\uff08\u7ea2\u5916+\u53ef\u89c1\u5149\uff09\u6570\u636e\u96c6","_links":{"self":[{"href":"https:\/\/xinchewhd.com.cn\/index.php\/wp-json\/wp\/v2\/posts\/6592","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/xinchewhd.com.cn\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/xinchewhd.com.cn\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/xinchewhd.com.cn\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/xinchewhd.com.cn\/index.php\/wp-json\/wp\/v2\/comments?post=6592"}],"version-history":[{"count":1,"href":"https:\/\/xinchewhd.com.cn\/index.php\/wp-json\/wp\/v2\/posts\/6592\/revisions"}],"predecessor-version":[{"id":6593,"href":"https:\/\/xinchewhd.com.cn\/index.php\/wp-json\/wp\/v2\/posts\/6592\/revisions\/6593"}],"wp:attachment":[{"href":"https:\/\/xinchewhd.com.cn\/index.php\/wp-json\/wp\/v2\/media?parent=6592"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/xinchewhd.com.cn\/index.php\/wp-json\/wp\/v2\/categories?post=6592"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/xinchewhd.com.cn\/index.php\/wp-json\/wp\/v2\/tags?post=6592"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
\u786e\u4fdd\u73af\u5883\u4e2d\u5b89\u88c5\u4e86\u5fc5\u8981\u7684\u5e93\uff1a<\/p>\n\n\n\n
pip install torch torchvision torchaudio --index-url https:\/\/download.pytorch.org\/whl\/cu118pip install ultralytics opencv-python matplotlib tqdm<\/code><\/pre>\n\n\n\n\u4e8c\u3001\u6570\u636e\u51c6\u5907<\/h3>\n\n\n\n\u6570\u636e\u96c6\u7ec4\u7ec7<\/h4>\n\n\n\n\u6570\u636e\u96c6\u6309\u7167\u4ee5\u4e0b\u7ed3\u6784\u7ec4\u7ec7\uff1a<\/p>\n\n\n\ninfrared_dataset\/\u251c\u2500\u2500 images\/\u2502 \u251c\u2500\u2500 train\/\u2502 \u251c\u2500\u2500 val\/\u2502 \u2514\u2500\u2500 test\/\u251c\u2500\u2500 labels\/\u2502 \u251c\u2500\u2500 train\/\u2502 \u251c\u2500\u2500 val\/\u2502 \u2514\u2500\u2500 test\/\u2514\u2500\u2500 data.yaml<\/code><\/pre>\n\n\n\n\u5176\u4e2d data.yaml<\/code> \u6587\u4ef6\u5185\u5bb9\u5982\u4e0b\uff1a<\/p>\n\n\n\ntrain: .\/infrared_dataset\/images\/trainval: .\/infrared_dataset\/images\/valtest: .\/infrared_dataset\/images\/testnc:4names:['Plane','Drone','Heli','Bird']<\/code><\/pre>\n\n\n\n\u5982\u679c\u4f60\u7684\u6570\u636e\u6807\u6ce8\u4e3aVOC\u683c\u5f0f\uff0c\u8bf7\u5148\u8f6c\u6362\u4e3aYOLO\u683c\u5f0f\uff08\u5373\u6bcf\u4e2a\u6807\u7b7e\u6587\u4ef6\u5305\u542b\u6bcf\u884c\u4e00\u4e2a\u8fb9\u754c\u6846\uff0c\u683c\u5f0f\u4e3a\uff1a\u7c7b\u522bID \u4e2d\u5fc3\u70b9x \u4e2d\u5fc3\u70b9y \u5bbd \u9ad8\uff09\u3002<\/p>\n\n\n\n\u4e09\u3001\u8bad\u7ec3\u6a21\u578b<\/h3>\n\n\n\n\u8bad\u7ec3\u811a\u672c (train.py<\/code>)<\/h4>\n\n\n\nfrom ultralytics import YOLOimport torchdeftrain_model():# Check GPU availability<\/em> device =0if torch.cuda.is_available()else'cpu'print(f\"Using device: {device}\")# Load pretrained model (YOLOv8s is a good balance between speed and accuracy)<\/em> model = YOLO('yolov8s.pt')# Start training<\/em> results = model.train( data='infrared_dataset\/data.yaml', epochs=200,# Adjust based on your needs<\/em> imgsz=640,# Image size can be adjusted based on the dataset<\/em> batch=16,# Reduce if you encounter out-of-memory issues<\/em> name='infrared_yolov8s', project='runs\/detect', save=True, save_period=10,# Save every 10 epochs<\/em> device=device, workers=4, patience=50,# Early stopping after 50 epochs without improvement<\/em> optimizer='AdamW', lr0=0.001,# Initial learning rate<\/em> augment=True# Enable data augmentation<\/em>)print(\"Training completed!\")return resultsif __name__ =='__main__': train_model()<\/code><\/pre>\n\n\n\n\u56db\u3001\u63a8\u7406\u4e0e\u8bc4\u4f30<\/h3>\n\n\n\n\u63a8\u7406\u811a\u672c (detect.py<\/code>)<\/h4>\n\n\n\nfrom ultralytics import YOLOimport cv2defdetect_image(image_path, output_path='output_detection.jpg'):# Load trained model<\/em> model = YOLO('runs\/detect\/infrared_yolov8s\/weights\/best.pt')# Perform prediction<\/em> results = model.predict( source=image_path, conf=0.4,# Confidence threshold<\/em> iou=0.5, save=True, project='runs\/detect\/predict', name='result', exist_ok=True)# Extract and save results (optional)<\/em>for r in results: im = r.plot()# Plot bounding boxes and labels<\/em> cv2.imwrite(output_path, im)print(f\"Detection completed, saved to {output_path}\")# Print detected objects<\/em>for box in r.boxes: cls_name = model.names[int(box.cls)] conf =float(box.conf)print(f\"Detected: {cls_name} (Confidence: {conf:.3f})\")return output_pathif __name__ =='__main__': detect_image('test_infrared_image.jpg')<\/code><\/pre>\n\n\n\n\u8bc4\u4f30\u811a\u672c (evaluate.py<\/code>)<\/h4>\n\n\n\nfrom ultralytics import YOLOdefevaluate_model(): model = YOLO('runs\/detect\/infrared_yolov8s\/weights\/best.pt')# Evaluate on validation set<\/em> metrics = model.val( data='infrared_dataset\/data.yaml', split='val',# Or 'test' if evaluating on the test set<\/em> batch=16, imgsz=640, device=0if torch.cuda.is_available()else'cpu', save_json=True,# Save COCO format results for further analysis<\/em> conf_thres=0.001, iou_thres=0.6)# Output key metrics<\/em>print(f\"mAP@0.5: {metrics.box.map50:.4f}\")print(f\"mAP@0.5:0.95: {metrics.box.map:.4f}\")print(f\"Precision: {metrics.box.p:.4f}\")print(f\"Recall: {metrics.box.r:.4f}\")# Output AP per class<\/em>print(\"\\nAP per class:\")for i, name inenumerate(metrics.names.values()):print(f\" {name}: {metrics.box.ap[i]:.4f}\")if __name__ =='__main__': evaluate_model()<\/code><\/pre>\n\n\n\n\u6784\u5efa\u4e00\u4e2a\u53ef\u4ee5\u52a0\u8f7d\u6a21\u578b\u3001\u6267\u884c\u63a8\u7406\u5e76\u5bf9\u65b0\u56fe\u50cf\u6216\u89c6\u9891\u8fdb\u884c\u5b9e\u65f6\u5904\u7406\u7684\u5e94\u7528\u3002<\/p>\n\n\n\n\u4e00\u3001\u51c6\u5907<\/h3>\n\n\n\n\u786e\u4fdd\u5df2\u7ecf\u5b8c\u6210\u4e86\u6a21\u578b\u7684\u8bad\u7ec3\uff0c\u5e76\u4e14\u8bad\u7ec3\u597d\u7684\u6a21\u578b\u6587\u4ef6\uff08\u4f8b\u5982 best.pt<\/code>\uff09\uff0c\u4f4d\u4e8e runs\/detect\/infrared_yolov8s\/weights\/best.pt<\/code>\u3002<\/p>\n\n\n\n\u4e8c\u3001\u6a21\u578b\u5c01\u88c5<\/h3>\n\n\n\n\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5c01\u88c5\u6211\u4eec\u7684\u6a21\u578b\uff0c\u4f7f\u5176\u6613\u4e8e\u4f7f\u7528\u3002\u8fd9\u53ef\u4ee5\u901a\u8fc7\u521b\u5efa\u4e00\u4e2a\u7c7b\u6765\u5b9e\u73b0\uff0c\u8be5\u7c7b\u8d1f\u8d23\u52a0\u8f7d\u6a21\u578b\u5e76\u63d0\u4f9b\u9884\u6d4b\u65b9\u6cd5\u3002<\/p>\n\n\n\nmodel_utils.py<\/code><\/h4>\n\n\n\nfrom ultralytics import YOLOimport cv2classInfraredTargetDetector:def__init__(self, model_path='runs\/detect\/infrared_yolov8s\/weights\/best.pt'): self.model = YOLO(model_path)defpredict(self, image_path, conf=0.4):\"\"\" \u5bf9\u7ed9\u5b9a\u8def\u5f84\u7684\u56fe\u50cf\u8fdb\u884c\u9884\u6d4b\u3002 :param image_path: \u8f93\u5165\u56fe\u50cf\u7684\u8def\u5f84 :param conf: \u7f6e\u4fe1\u5ea6\u9608\u503c :return: \u68c0\u6d4b\u7ed3\u679c\u5217\u8868\uff0c\u6bcf\u4e2a\u7ed3\u679c\u5305\u542b\u7c7b\u522b\u540d\u79f0\u3001\u7f6e\u4fe1\u5ea6\u5206\u6570\u548c\u8fb9\u754c\u6846\u5750\u6807 \"\"\" results = self.model.predict(source=image_path, conf=conf) detections =[]for r in results:for box in r.boxes: cls_id =int(box.cls) label = self.model.names[cls_id] confidence =float(box.conf) bbox = box.xyxy[0].cpu().numpy().tolist()# [x1, y1, x2, y2]<\/em> detections.append({'class': label,'confidence': confidence,'bbox': bbox})return detectionsdefdetect_and_draw(self, image_path, output_path='output_detection.jpg', conf=0.4):\"\"\" \u5bf9\u56fe\u50cf\u8fdb\u884c\u68c0\u6d4b\u5e76\u5728\u539f\u56fe\u4e0a\u7ed8\u5236\u68c0\u6d4b\u7ed3\u679c\u3002 :param image_path: \u8f93\u5165\u56fe\u50cf\u7684\u8def\u5f84 :param output_path: \u8f93\u51fa\u56fe\u50cf\u7684\u4fdd\u5b58\u8def\u5f84 :param conf: \u7f6e\u4fe1\u5ea6\u9608\u503c :return: None \"\"\" detections = self.predict(image_path, conf) img = cv2.imread(image_path)for det in detections: bbox = det['bbox'] label =f\"{det['class']}: {det['confidence']:.2f}\" color =(0,255,0)# Green<\/em> cv2.rectangle(img,(int(bbox[0]),int(bbox[1])),(int(bbox[2]),int(bbox[3])), color,2) cv2.putText(img, label,(int(bbox[0]),int(bbox[1])-10), cv2.FONT_HERSHEY_SIMPLEX,0.9, color,2) cv2.imwrite(output_path, img)print(f\"Detection completed and saved to {output_path}\")# \u793a\u4f8b\u4f7f\u7528<\/em>if __name__ =='__main__': detector = InfraredTargetDetector() detector.detect_and_draw('test_infrared_image.jpg')<\/code><\/pre>\n\n\n\n\u4e09\u3001\u6784\u5efa\u547d\u4ee4\u884c\u5de5\u5177<\/h3>\n\n\n\n\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u521b\u5efa\u4e00\u4e2a\u547d\u4ee4\u884c\u5de5\u5177\uff0c\u5141\u8bb8\u7528\u6237\u901a\u8fc7\u547d\u4ee4\u884c\u5bf9\u56fe\u50cf\u6216\u89c6\u9891\u8fdb\u884c\u68c0\u6d4b\u3002<\/p>\n\n\n\ninfrared_detector_cli.py<\/code><\/h4>\n\n\n\nimport argparsefrom model_utils import InfraredTargetDetectorimport cv2defmain(): parser = argparse.ArgumentParser(description=\"Infrared Target Detection System\") parser.add_argument('--input',type=str, required=True,help='Path to input image or video file.') parser.add_argument('--output',type=str, default='output.jpg',help='Path to save the output.') parser.add_argument('--conf',type=float, default=0.4,help='Confidence threshold.') args = parser.parse_args() detector = InfraredTargetDetector()if args.input.lower().endswith(('jpg','jpeg','png')): detector.detect_and_draw(args.input, args.output, args.conf)elif args.input.lower().endswith(('mp4','avi')): cap = cv2.VideoCapture(args.input) frame_width =int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) frame_height =int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) out = cv2.VideoWriter(args.output, cv2.VideoWriter_fourcc(*'XVID'),20.0,(frame_width, frame_height))while cap.isOpened(): ret, frame = cap.read()ifnot ret:break# Convert frame to image path temporarily for prediction<\/em> temp_img_path ='temp_frame.jpg' cv2.imwrite(temp_img_path, frame) detections = detector.predict(temp_img_path, args.conf)for det in detections: bbox = det['bbox'] label =f\"{det['class']}: {det['confidence']:.2f}\" color =(0,255,0)# Green<\/em> cv2.rectangle(frame,(int(bbox[0]),int(bbox[1])),(int(bbox[2]),int(bbox[3])), color,2) cv2.putText(frame, label,(int(bbox[0]),int(bbox[1])-10), cv2.FONT_HERSHEY_SIMPLEX,0.9, color,2) out.write(frame) cap.release() out.release()print(f\"Video detection completed and saved to {args.output}\")else:print(\"Unsupported file format.\")if __name__ ==\"__main__\": main()<\/code><\/pre>\n\n\n\n\u56db\u3001\u8fd0\u884c\u4f60\u7684\u7cfb\u7edf<\/h3>\n\n\n\n\n\u5bf9\u4e8e\u5355\u5f20\u56fe\u7247<\/strong>\uff1apython infrared_detector_cli.py --input path\/to\/image.jpg --output output.jpg --conf\u00a00.5<\/code><\/li>\n\n\n\n\u5bf9\u4e8e\u89c6\u9891\u6587\u4ef6<\/strong>\uff1apython infrared_detector_cli.py --input path\/to\/video.mp4 --output output_video.avi --con<\/code><\/li>\n<\/ul>\n","raw":"","protected":false},"excerpt":{"rendered":"\u6df1\u5ea6\u5b66\u4e60\u57fa\u4e8eYOLOv8\u6846\u67b6\u6765\u8bad\u7ec3\u7ea2\u5916\u56fe\u50cf\u5c0f\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6 \u7ea2\u5916\u56fe\u50cf\u5c0f\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6\u7684\u8bad\u7ec3\u53ca\u5e94\u7528 \u63a8\u7406\u8bc6\u522b\u68c0\u6d4b\u98de\u673a \u65e0\u4eba\u673a \u76f4\u5347\u673a …<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"emotion":"","emotion_color":"","title_style":"","license":"","footnotes":""},"categories":[46],"tags":[],"class_list":["post-6592","post","type-post","status-publish","format-standard","hentry","category-yolo"],"post_thumbnail_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","content_first_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_medium_image_300":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_thumbnail_image_624":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_frist_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_medium_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_large_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_full_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_all_images":[{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","id":"image0"},{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","id":"image1"},{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKicJO37JCcKiczyLibDpRgBDXlTon1aE9dVcNEEQ2gvaQTRZOajJPzLexw\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=1","id":"image2"},{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKicJO37JCcKiczyLibDpRgBDXlTon1aE9dVcNEEQ2gvaQTRZOajJPzLexw\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=1","id":"image3"},{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKAyqwlqJhAhUaqz66Via4WKqzx8qTkKvrURcsQVImhEUmXhZAYbia279Q\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=2","id":"image4"},{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKAyqwlqJhAhUaqz66Via4WKqzx8qTkKvrURcsQVImhEUmXhZAYbia279Q\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=2","id":"image5"}],"videoAdId":"","listAd":"0","listAdId":"","listAdEvery":6,"total_comments":0,"category_name":"YOLO","post_date":"2025-09-29","like_count":"0","praiseWord":"\u9f13\u52b1","copyright_state":"","excitationAd":"0","rewardedVideoAdId":"","detailAdId":"","detailAd":"0","enterpriseMinapp":"0","audios":[],"postImageUrl":"https:\/\/wp-moto-1258805347.cos.ap-shanghai.myqcloud.com\/2023\/05\/20230519082947553.jpg","avatarurls":[],"related_posts":null,"pageviews":269,"next_post_id":6657,"next_post_title":"\u6df1\u5ea6\u5b66\u4e60\u76ee\u6807\u68c0\u6d4b YOLOV11\u5982\u4f55\u8bad\u7ec3\u81ea\u5df1\u7684\u6570\u636e\u96c6 YOLOv11\u8bad\u7ec3\u914d\u7f6e\u73af\u5883\u6559\u7a0b \u4ee5\u6c14\u7403\u68c0\u6d4b\u6570\u636e\u96c6\u4e3a\u4f8b","previous_post_id":6590,"previous_post_title":"YOLOV8\u6a21\u578b\u5982\u4f55\u8bad\u7ec3 \u53cc\u6a21\u6001\u7ea2\u5916-\u53ef\u89c1\u5149\u5bf9\u9f50\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6 \u7ea2\u5916\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6\u7684\u8bad\u7ec3\u53ca\u5e94\u7528 \u53cc\u6a21\u6001\uff08\u7ea2\u5916+\u53ef\u89c1\u5149\uff09\u6570\u636e\u96c6","_links":{"self":[{"href":"https:\/\/xinchewhd.com.cn\/index.php\/wp-json\/wp\/v2\/posts\/6592","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/xinchewhd.com.cn\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/xinchewhd.com.cn\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/xinchewhd.com.cn\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/xinchewhd.com.cn\/index.php\/wp-json\/wp\/v2\/comments?post=6592"}],"version-history":[{"count":1,"href":"https:\/\/xinchewhd.com.cn\/index.php\/wp-json\/wp\/v2\/posts\/6592\/revisions"}],"predecessor-version":[{"id":6593,"href":"https:\/\/xinchewhd.com.cn\/index.php\/wp-json\/wp\/v2\/posts\/6592\/revisions\/6593"}],"wp:attachment":[{"href":"https:\/\/xinchewhd.com.cn\/index.php\/wp-json\/wp\/v2\/media?parent=6592"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/xinchewhd.com.cn\/index.php\/wp-json\/wp\/v2\/categories?post=6592"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/xinchewhd.com.cn\/index.php\/wp-json\/wp\/v2\/tags?post=6592"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
\u6570\u636e\u96c6\u6309\u7167\u4ee5\u4e0b\u7ed3\u6784\u7ec4\u7ec7\uff1a<\/p>\n\n\n\n
infrared_dataset\/\u251c\u2500\u2500 images\/\u2502 \u251c\u2500\u2500 train\/\u2502 \u251c\u2500\u2500 val\/\u2502 \u2514\u2500\u2500 test\/\u251c\u2500\u2500 labels\/\u2502 \u251c\u2500\u2500 train\/\u2502 \u251c\u2500\u2500 val\/\u2502 \u2514\u2500\u2500 test\/\u2514\u2500\u2500 data.yaml<\/code><\/pre>\n\n\n\n\u5176\u4e2d data.yaml<\/code> \u6587\u4ef6\u5185\u5bb9\u5982\u4e0b\uff1a<\/p>\n\n\n\ntrain: .\/infrared_dataset\/images\/trainval: .\/infrared_dataset\/images\/valtest: .\/infrared_dataset\/images\/testnc:4names:['Plane','Drone','Heli','Bird']<\/code><\/pre>\n\n\n\n\u5982\u679c\u4f60\u7684\u6570\u636e\u6807\u6ce8\u4e3aVOC\u683c\u5f0f\uff0c\u8bf7\u5148\u8f6c\u6362\u4e3aYOLO\u683c\u5f0f\uff08\u5373\u6bcf\u4e2a\u6807\u7b7e\u6587\u4ef6\u5305\u542b\u6bcf\u884c\u4e00\u4e2a\u8fb9\u754c\u6846\uff0c\u683c\u5f0f\u4e3a\uff1a\u7c7b\u522bID \u4e2d\u5fc3\u70b9x \u4e2d\u5fc3\u70b9y \u5bbd \u9ad8\uff09\u3002<\/p>\n\n\n\n\u4e09\u3001\u8bad\u7ec3\u6a21\u578b<\/h3>\n\n\n\n\u8bad\u7ec3\u811a\u672c (train.py<\/code>)<\/h4>\n\n\n\nfrom ultralytics import YOLOimport torchdeftrain_model():# Check GPU availability<\/em> device =0if torch.cuda.is_available()else'cpu'print(f\"Using device: {device}\")# Load pretrained model (YOLOv8s is a good balance between speed and accuracy)<\/em> model = YOLO('yolov8s.pt')# Start training<\/em> results = model.train( data='infrared_dataset\/data.yaml', epochs=200,# Adjust based on your needs<\/em> imgsz=640,# Image size can be adjusted based on the dataset<\/em> batch=16,# Reduce if you encounter out-of-memory issues<\/em> name='infrared_yolov8s', project='runs\/detect', save=True, save_period=10,# Save every 10 epochs<\/em> device=device, workers=4, patience=50,# Early stopping after 50 epochs without improvement<\/em> optimizer='AdamW', lr0=0.001,# Initial learning rate<\/em> augment=True# Enable data augmentation<\/em>)print(\"Training completed!\")return resultsif __name__ =='__main__': train_model()<\/code><\/pre>\n\n\n\n\u56db\u3001\u63a8\u7406\u4e0e\u8bc4\u4f30<\/h3>\n\n\n\n\u63a8\u7406\u811a\u672c (detect.py<\/code>)<\/h4>\n\n\n\nfrom ultralytics import YOLOimport cv2defdetect_image(image_path, output_path='output_detection.jpg'):# Load trained model<\/em> model = YOLO('runs\/detect\/infrared_yolov8s\/weights\/best.pt')# Perform prediction<\/em> results = model.predict( source=image_path, conf=0.4,# Confidence threshold<\/em> iou=0.5, save=True, project='runs\/detect\/predict', name='result', exist_ok=True)# Extract and save results (optional)<\/em>for r in results: im = r.plot()# Plot bounding boxes and labels<\/em> cv2.imwrite(output_path, im)print(f\"Detection completed, saved to {output_path}\")# Print detected objects<\/em>for box in r.boxes: cls_name = model.names[int(box.cls)] conf =float(box.conf)print(f\"Detected: {cls_name} (Confidence: {conf:.3f})\")return output_pathif __name__ =='__main__': detect_image('test_infrared_image.jpg')<\/code><\/pre>\n\n\n\n\u8bc4\u4f30\u811a\u672c (evaluate.py<\/code>)<\/h4>\n\n\n\nfrom ultralytics import YOLOdefevaluate_model(): model = YOLO('runs\/detect\/infrared_yolov8s\/weights\/best.pt')# Evaluate on validation set<\/em> metrics = model.val( data='infrared_dataset\/data.yaml', split='val',# Or 'test' if evaluating on the test set<\/em> batch=16, imgsz=640, device=0if torch.cuda.is_available()else'cpu', save_json=True,# Save COCO format results for further analysis<\/em> conf_thres=0.001, iou_thres=0.6)# Output key metrics<\/em>print(f\"mAP@0.5: {metrics.box.map50:.4f}\")print(f\"mAP@0.5:0.95: {metrics.box.map:.4f}\")print(f\"Precision: {metrics.box.p:.4f}\")print(f\"Recall: {metrics.box.r:.4f}\")# Output AP per class<\/em>print(\"\\nAP per class:\")for i, name inenumerate(metrics.names.values()):print(f\" {name}: {metrics.box.ap[i]:.4f}\")if __name__ =='__main__': evaluate_model()<\/code><\/pre>\n\n\n\n\u6784\u5efa\u4e00\u4e2a\u53ef\u4ee5\u52a0\u8f7d\u6a21\u578b\u3001\u6267\u884c\u63a8\u7406\u5e76\u5bf9\u65b0\u56fe\u50cf\u6216\u89c6\u9891\u8fdb\u884c\u5b9e\u65f6\u5904\u7406\u7684\u5e94\u7528\u3002<\/p>\n\n\n\n\u4e00\u3001\u51c6\u5907<\/h3>\n\n\n\n\u786e\u4fdd\u5df2\u7ecf\u5b8c\u6210\u4e86\u6a21\u578b\u7684\u8bad\u7ec3\uff0c\u5e76\u4e14\u8bad\u7ec3\u597d\u7684\u6a21\u578b\u6587\u4ef6\uff08\u4f8b\u5982 best.pt<\/code>\uff09\uff0c\u4f4d\u4e8e runs\/detect\/infrared_yolov8s\/weights\/best.pt<\/code>\u3002<\/p>\n\n\n\n\u4e8c\u3001\u6a21\u578b\u5c01\u88c5<\/h3>\n\n\n\n\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5c01\u88c5\u6211\u4eec\u7684\u6a21\u578b\uff0c\u4f7f\u5176\u6613\u4e8e\u4f7f\u7528\u3002\u8fd9\u53ef\u4ee5\u901a\u8fc7\u521b\u5efa\u4e00\u4e2a\u7c7b\u6765\u5b9e\u73b0\uff0c\u8be5\u7c7b\u8d1f\u8d23\u52a0\u8f7d\u6a21\u578b\u5e76\u63d0\u4f9b\u9884\u6d4b\u65b9\u6cd5\u3002<\/p>\n\n\n\nmodel_utils.py<\/code><\/h4>\n\n\n\nfrom ultralytics import YOLOimport cv2classInfraredTargetDetector:def__init__(self, model_path='runs\/detect\/infrared_yolov8s\/weights\/best.pt'): self.model = YOLO(model_path)defpredict(self, image_path, conf=0.4):\"\"\" \u5bf9\u7ed9\u5b9a\u8def\u5f84\u7684\u56fe\u50cf\u8fdb\u884c\u9884\u6d4b\u3002 :param image_path: \u8f93\u5165\u56fe\u50cf\u7684\u8def\u5f84 :param conf: \u7f6e\u4fe1\u5ea6\u9608\u503c :return: \u68c0\u6d4b\u7ed3\u679c\u5217\u8868\uff0c\u6bcf\u4e2a\u7ed3\u679c\u5305\u542b\u7c7b\u522b\u540d\u79f0\u3001\u7f6e\u4fe1\u5ea6\u5206\u6570\u548c\u8fb9\u754c\u6846\u5750\u6807 \"\"\" results = self.model.predict(source=image_path, conf=conf) detections =[]for r in results:for box in r.boxes: cls_id =int(box.cls) label = self.model.names[cls_id] confidence =float(box.conf) bbox = box.xyxy[0].cpu().numpy().tolist()# [x1, y1, x2, y2]<\/em> detections.append({'class': label,'confidence': confidence,'bbox': bbox})return detectionsdefdetect_and_draw(self, image_path, output_path='output_detection.jpg', conf=0.4):\"\"\" \u5bf9\u56fe\u50cf\u8fdb\u884c\u68c0\u6d4b\u5e76\u5728\u539f\u56fe\u4e0a\u7ed8\u5236\u68c0\u6d4b\u7ed3\u679c\u3002 :param image_path: \u8f93\u5165\u56fe\u50cf\u7684\u8def\u5f84 :param output_path: \u8f93\u51fa\u56fe\u50cf\u7684\u4fdd\u5b58\u8def\u5f84 :param conf: \u7f6e\u4fe1\u5ea6\u9608\u503c :return: None \"\"\" detections = self.predict(image_path, conf) img = cv2.imread(image_path)for det in detections: bbox = det['bbox'] label =f\"{det['class']}: {det['confidence']:.2f}\" color =(0,255,0)# Green<\/em> cv2.rectangle(img,(int(bbox[0]),int(bbox[1])),(int(bbox[2]),int(bbox[3])), color,2) cv2.putText(img, label,(int(bbox[0]),int(bbox[1])-10), cv2.FONT_HERSHEY_SIMPLEX,0.9, color,2) cv2.imwrite(output_path, img)print(f\"Detection completed and saved to {output_path}\")# \u793a\u4f8b\u4f7f\u7528<\/em>if __name__ =='__main__': detector = InfraredTargetDetector() detector.detect_and_draw('test_infrared_image.jpg')<\/code><\/pre>\n\n\n\n\u4e09\u3001\u6784\u5efa\u547d\u4ee4\u884c\u5de5\u5177<\/h3>\n\n\n\n\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u521b\u5efa\u4e00\u4e2a\u547d\u4ee4\u884c\u5de5\u5177\uff0c\u5141\u8bb8\u7528\u6237\u901a\u8fc7\u547d\u4ee4\u884c\u5bf9\u56fe\u50cf\u6216\u89c6\u9891\u8fdb\u884c\u68c0\u6d4b\u3002<\/p>\n\n\n\ninfrared_detector_cli.py<\/code><\/h4>\n\n\n\nimport argparsefrom model_utils import InfraredTargetDetectorimport cv2defmain(): parser = argparse.ArgumentParser(description=\"Infrared Target Detection System\") parser.add_argument('--input',type=str, required=True,help='Path to input image or video file.') parser.add_argument('--output',type=str, default='output.jpg',help='Path to save the output.') parser.add_argument('--conf',type=float, default=0.4,help='Confidence threshold.') args = parser.parse_args() detector = InfraredTargetDetector()if args.input.lower().endswith(('jpg','jpeg','png')): detector.detect_and_draw(args.input, args.output, args.conf)elif args.input.lower().endswith(('mp4','avi')): cap = cv2.VideoCapture(args.input) frame_width =int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) frame_height =int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) out = cv2.VideoWriter(args.output, cv2.VideoWriter_fourcc(*'XVID'),20.0,(frame_width, frame_height))while cap.isOpened(): ret, frame = cap.read()ifnot ret:break# Convert frame to image path temporarily for prediction<\/em> temp_img_path ='temp_frame.jpg' cv2.imwrite(temp_img_path, frame) detections = detector.predict(temp_img_path, args.conf)for det in detections: bbox = det['bbox'] label =f\"{det['class']}: {det['confidence']:.2f}\" color =(0,255,0)# Green<\/em> cv2.rectangle(frame,(int(bbox[0]),int(bbox[1])),(int(bbox[2]),int(bbox[3])), color,2) cv2.putText(frame, label,(int(bbox[0]),int(bbox[1])-10), cv2.FONT_HERSHEY_SIMPLEX,0.9, color,2) out.write(frame) cap.release() out.release()print(f\"Video detection completed and saved to {args.output}\")else:print(\"Unsupported file format.\")if __name__ ==\"__main__\": main()<\/code><\/pre>\n\n\n\n\u56db\u3001\u8fd0\u884c\u4f60\u7684\u7cfb\u7edf<\/h3>\n\n\n\n\n\u5bf9\u4e8e\u5355\u5f20\u56fe\u7247<\/strong>\uff1apython infrared_detector_cli.py --input path\/to\/image.jpg --output output.jpg --conf\u00a00.5<\/code><\/li>\n\n\n\n\u5bf9\u4e8e\u89c6\u9891\u6587\u4ef6<\/strong>\uff1apython infrared_detector_cli.py --input path\/to\/video.mp4 --output output_video.avi --con<\/code><\/li>\n<\/ul>\n","raw":"","protected":false},"excerpt":{"rendered":"\u6df1\u5ea6\u5b66\u4e60\u57fa\u4e8eYOLOv8\u6846\u67b6\u6765\u8bad\u7ec3\u7ea2\u5916\u56fe\u50cf\u5c0f\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6 \u7ea2\u5916\u56fe\u50cf\u5c0f\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6\u7684\u8bad\u7ec3\u53ca\u5e94\u7528 \u63a8\u7406\u8bc6\u522b\u68c0\u6d4b\u98de\u673a \u65e0\u4eba\u673a \u76f4\u5347\u673a 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YOLOV11\u5982\u4f55\u8bad\u7ec3\u81ea\u5df1\u7684\u6570\u636e\u96c6 YOLOv11\u8bad\u7ec3\u914d\u7f6e\u73af\u5883\u6559\u7a0b \u4ee5\u6c14\u7403\u68c0\u6d4b\u6570\u636e\u96c6\u4e3a\u4f8b","previous_post_id":6590,"previous_post_title":"YOLOV8\u6a21\u578b\u5982\u4f55\u8bad\u7ec3 \u53cc\u6a21\u6001\u7ea2\u5916-\u53ef\u89c1\u5149\u5bf9\u9f50\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6 \u7ea2\u5916\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6\u7684\u8bad\u7ec3\u53ca\u5e94\u7528 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\u5176\u4e2d data.yaml<\/code> \u6587\u4ef6\u5185\u5bb9\u5982\u4e0b\uff1a<\/p>\n\n\n\ntrain: .\/infrared_dataset\/images\/trainval: .\/infrared_dataset\/images\/valtest: .\/infrared_dataset\/images\/testnc:4names:['Plane','Drone','Heli','Bird']<\/code><\/pre>\n\n\n\n\u5982\u679c\u4f60\u7684\u6570\u636e\u6807\u6ce8\u4e3aVOC\u683c\u5f0f\uff0c\u8bf7\u5148\u8f6c\u6362\u4e3aYOLO\u683c\u5f0f\uff08\u5373\u6bcf\u4e2a\u6807\u7b7e\u6587\u4ef6\u5305\u542b\u6bcf\u884c\u4e00\u4e2a\u8fb9\u754c\u6846\uff0c\u683c\u5f0f\u4e3a\uff1a\u7c7b\u522bID \u4e2d\u5fc3\u70b9x \u4e2d\u5fc3\u70b9y \u5bbd \u9ad8\uff09\u3002<\/p>\n\n\n\n\u4e09\u3001\u8bad\u7ec3\u6a21\u578b<\/h3>\n\n\n\n\u8bad\u7ec3\u811a\u672c (train.py<\/code>)<\/h4>\n\n\n\nfrom ultralytics import YOLOimport torchdeftrain_model():# Check GPU availability<\/em> device =0if torch.cuda.is_available()else'cpu'print(f\"Using device: {device}\")# Load pretrained model (YOLOv8s is a good balance between speed and accuracy)<\/em> model = YOLO('yolov8s.pt')# Start training<\/em> results = model.train( data='infrared_dataset\/data.yaml', epochs=200,# Adjust based on your needs<\/em> imgsz=640,# Image size can be adjusted based on the dataset<\/em> batch=16,# Reduce if you encounter out-of-memory issues<\/em> name='infrared_yolov8s', project='runs\/detect', save=True, save_period=10,# Save every 10 epochs<\/em> device=device, workers=4, patience=50,# Early stopping after 50 epochs without improvement<\/em> optimizer='AdamW', lr0=0.001,# Initial learning rate<\/em> augment=True# Enable data augmentation<\/em>)print(\"Training completed!\")return resultsif __name__ =='__main__': train_model()<\/code><\/pre>\n\n\n\n\u56db\u3001\u63a8\u7406\u4e0e\u8bc4\u4f30<\/h3>\n\n\n\n\u63a8\u7406\u811a\u672c (detect.py<\/code>)<\/h4>\n\n\n\nfrom ultralytics import YOLOimport cv2defdetect_image(image_path, output_path='output_detection.jpg'):# Load trained model<\/em> model = YOLO('runs\/detect\/infrared_yolov8s\/weights\/best.pt')# Perform prediction<\/em> results = model.predict( source=image_path, conf=0.4,# Confidence threshold<\/em> iou=0.5, save=True, project='runs\/detect\/predict', name='result', exist_ok=True)# Extract and save results (optional)<\/em>for r in results: im = r.plot()# Plot bounding boxes and labels<\/em> cv2.imwrite(output_path, im)print(f\"Detection completed, saved to {output_path}\")# Print detected objects<\/em>for box in r.boxes: cls_name = model.names[int(box.cls)] conf =float(box.conf)print(f\"Detected: {cls_name} (Confidence: {conf:.3f})\")return output_pathif __name__ =='__main__': detect_image('test_infrared_image.jpg')<\/code><\/pre>\n\n\n\n\u8bc4\u4f30\u811a\u672c (evaluate.py<\/code>)<\/h4>\n\n\n\nfrom ultralytics import YOLOdefevaluate_model(): model = YOLO('runs\/detect\/infrared_yolov8s\/weights\/best.pt')# Evaluate on validation set<\/em> metrics = model.val( data='infrared_dataset\/data.yaml', split='val',# Or 'test' if evaluating on the test set<\/em> batch=16, imgsz=640, device=0if torch.cuda.is_available()else'cpu', save_json=True,# Save COCO format results for further analysis<\/em> conf_thres=0.001, iou_thres=0.6)# Output key metrics<\/em>print(f\"mAP@0.5: {metrics.box.map50:.4f}\")print(f\"mAP@0.5:0.95: {metrics.box.map:.4f}\")print(f\"Precision: {metrics.box.p:.4f}\")print(f\"Recall: {metrics.box.r:.4f}\")# Output AP per class<\/em>print(\"\\nAP per class:\")for i, name inenumerate(metrics.names.values()):print(f\" {name}: {metrics.box.ap[i]:.4f}\")if __name__ =='__main__': evaluate_model()<\/code><\/pre>\n\n\n\n\u6784\u5efa\u4e00\u4e2a\u53ef\u4ee5\u52a0\u8f7d\u6a21\u578b\u3001\u6267\u884c\u63a8\u7406\u5e76\u5bf9\u65b0\u56fe\u50cf\u6216\u89c6\u9891\u8fdb\u884c\u5b9e\u65f6\u5904\u7406\u7684\u5e94\u7528\u3002<\/p>\n\n\n\n\u4e00\u3001\u51c6\u5907<\/h3>\n\n\n\n\u786e\u4fdd\u5df2\u7ecf\u5b8c\u6210\u4e86\u6a21\u578b\u7684\u8bad\u7ec3\uff0c\u5e76\u4e14\u8bad\u7ec3\u597d\u7684\u6a21\u578b\u6587\u4ef6\uff08\u4f8b\u5982 best.pt<\/code>\uff09\uff0c\u4f4d\u4e8e runs\/detect\/infrared_yolov8s\/weights\/best.pt<\/code>\u3002<\/p>\n\n\n\n\u4e8c\u3001\u6a21\u578b\u5c01\u88c5<\/h3>\n\n\n\n\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5c01\u88c5\u6211\u4eec\u7684\u6a21\u578b\uff0c\u4f7f\u5176\u6613\u4e8e\u4f7f\u7528\u3002\u8fd9\u53ef\u4ee5\u901a\u8fc7\u521b\u5efa\u4e00\u4e2a\u7c7b\u6765\u5b9e\u73b0\uff0c\u8be5\u7c7b\u8d1f\u8d23\u52a0\u8f7d\u6a21\u578b\u5e76\u63d0\u4f9b\u9884\u6d4b\u65b9\u6cd5\u3002<\/p>\n\n\n\nmodel_utils.py<\/code><\/h4>\n\n\n\nfrom ultralytics import YOLOimport cv2classInfraredTargetDetector:def__init__(self, model_path='runs\/detect\/infrared_yolov8s\/weights\/best.pt'): self.model = YOLO(model_path)defpredict(self, image_path, conf=0.4):\"\"\" \u5bf9\u7ed9\u5b9a\u8def\u5f84\u7684\u56fe\u50cf\u8fdb\u884c\u9884\u6d4b\u3002 :param image_path: \u8f93\u5165\u56fe\u50cf\u7684\u8def\u5f84 :param conf: \u7f6e\u4fe1\u5ea6\u9608\u503c :return: \u68c0\u6d4b\u7ed3\u679c\u5217\u8868\uff0c\u6bcf\u4e2a\u7ed3\u679c\u5305\u542b\u7c7b\u522b\u540d\u79f0\u3001\u7f6e\u4fe1\u5ea6\u5206\u6570\u548c\u8fb9\u754c\u6846\u5750\u6807 \"\"\" results = self.model.predict(source=image_path, conf=conf) detections =[]for r in results:for box in r.boxes: cls_id =int(box.cls) label = self.model.names[cls_id] confidence =float(box.conf) bbox = box.xyxy[0].cpu().numpy().tolist()# [x1, y1, x2, y2]<\/em> detections.append({'class': label,'confidence': confidence,'bbox': bbox})return detectionsdefdetect_and_draw(self, image_path, output_path='output_detection.jpg', conf=0.4):\"\"\" \u5bf9\u56fe\u50cf\u8fdb\u884c\u68c0\u6d4b\u5e76\u5728\u539f\u56fe\u4e0a\u7ed8\u5236\u68c0\u6d4b\u7ed3\u679c\u3002 :param image_path: \u8f93\u5165\u56fe\u50cf\u7684\u8def\u5f84 :param output_path: \u8f93\u51fa\u56fe\u50cf\u7684\u4fdd\u5b58\u8def\u5f84 :param conf: \u7f6e\u4fe1\u5ea6\u9608\u503c :return: None \"\"\" detections = self.predict(image_path, conf) img = cv2.imread(image_path)for det in detections: bbox = det['bbox'] label =f\"{det['class']}: {det['confidence']:.2f}\" color =(0,255,0)# Green<\/em> cv2.rectangle(img,(int(bbox[0]),int(bbox[1])),(int(bbox[2]),int(bbox[3])), color,2) cv2.putText(img, label,(int(bbox[0]),int(bbox[1])-10), cv2.FONT_HERSHEY_SIMPLEX,0.9, color,2) cv2.imwrite(output_path, img)print(f\"Detection completed and saved to {output_path}\")# \u793a\u4f8b\u4f7f\u7528<\/em>if __name__ =='__main__': detector = InfraredTargetDetector() detector.detect_and_draw('test_infrared_image.jpg')<\/code><\/pre>\n\n\n\n\u4e09\u3001\u6784\u5efa\u547d\u4ee4\u884c\u5de5\u5177<\/h3>\n\n\n\n\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u521b\u5efa\u4e00\u4e2a\u547d\u4ee4\u884c\u5de5\u5177\uff0c\u5141\u8bb8\u7528\u6237\u901a\u8fc7\u547d\u4ee4\u884c\u5bf9\u56fe\u50cf\u6216\u89c6\u9891\u8fdb\u884c\u68c0\u6d4b\u3002<\/p>\n\n\n\ninfrared_detector_cli.py<\/code><\/h4>\n\n\n\nimport argparsefrom model_utils import InfraredTargetDetectorimport cv2defmain(): parser = argparse.ArgumentParser(description=\"Infrared Target Detection System\") parser.add_argument('--input',type=str, required=True,help='Path to input image or video file.') parser.add_argument('--output',type=str, default='output.jpg',help='Path to save the output.') parser.add_argument('--conf',type=float, default=0.4,help='Confidence threshold.') args = parser.parse_args() detector = InfraredTargetDetector()if args.input.lower().endswith(('jpg','jpeg','png')): detector.detect_and_draw(args.input, args.output, args.conf)elif args.input.lower().endswith(('mp4','avi')): cap = cv2.VideoCapture(args.input) frame_width =int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) frame_height =int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) out = cv2.VideoWriter(args.output, cv2.VideoWriter_fourcc(*'XVID'),20.0,(frame_width, frame_height))while cap.isOpened(): ret, frame = cap.read()ifnot ret:break# Convert frame to image path temporarily for prediction<\/em> temp_img_path ='temp_frame.jpg' cv2.imwrite(temp_img_path, frame) detections = detector.predict(temp_img_path, args.conf)for det in detections: bbox = det['bbox'] label =f\"{det['class']}: {det['confidence']:.2f}\" color =(0,255,0)# Green<\/em> cv2.rectangle(frame,(int(bbox[0]),int(bbox[1])),(int(bbox[2]),int(bbox[3])), color,2) cv2.putText(frame, label,(int(bbox[0]),int(bbox[1])-10), cv2.FONT_HERSHEY_SIMPLEX,0.9, color,2) out.write(frame) cap.release() out.release()print(f\"Video detection completed and saved to {args.output}\")else:print(\"Unsupported file format.\")if __name__ ==\"__main__\": main()<\/code><\/pre>\n\n\n\n\u56db\u3001\u8fd0\u884c\u4f60\u7684\u7cfb\u7edf<\/h3>\n\n\n\n\n\u5bf9\u4e8e\u5355\u5f20\u56fe\u7247<\/strong>\uff1apython infrared_detector_cli.py --input path\/to\/image.jpg --output output.jpg --conf\u00a00.5<\/code><\/li>\n\n\n\n\u5bf9\u4e8e\u89c6\u9891\u6587\u4ef6<\/strong>\uff1apython infrared_detector_cli.py --input path\/to\/video.mp4 --output output_video.avi --con<\/code><\/li>\n<\/ul>\n","raw":"","protected":false},"excerpt":{"rendered":"\u6df1\u5ea6\u5b66\u4e60\u57fa\u4e8eYOLOv8\u6846\u67b6\u6765\u8bad\u7ec3\u7ea2\u5916\u56fe\u50cf\u5c0f\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6 \u7ea2\u5916\u56fe\u50cf\u5c0f\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6\u7684\u8bad\u7ec3\u53ca\u5e94\u7528 \u63a8\u7406\u8bc6\u522b\u68c0\u6d4b\u98de\u673a \u65e0\u4eba\u673a \u76f4\u5347\u673a 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YOLOV11\u5982\u4f55\u8bad\u7ec3\u81ea\u5df1\u7684\u6570\u636e\u96c6 YOLOv11\u8bad\u7ec3\u914d\u7f6e\u73af\u5883\u6559\u7a0b \u4ee5\u6c14\u7403\u68c0\u6d4b\u6570\u636e\u96c6\u4e3a\u4f8b","previous_post_id":6590,"previous_post_title":"YOLOV8\u6a21\u578b\u5982\u4f55\u8bad\u7ec3 \u53cc\u6a21\u6001\u7ea2\u5916-\u53ef\u89c1\u5149\u5bf9\u9f50\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6 \u7ea2\u5916\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6\u7684\u8bad\u7ec3\u53ca\u5e94\u7528 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data.yaml<\/code> \u6587\u4ef6\u5185\u5bb9\u5982\u4e0b\uff1a<\/p>\n\n\n\ntrain: .\/infrared_dataset\/images\/trainval: .\/infrared_dataset\/images\/valtest: .\/infrared_dataset\/images\/testnc:4names:['Plane','Drone','Heli','Bird']<\/code><\/pre>\n\n\n\n\u5982\u679c\u4f60\u7684\u6570\u636e\u6807\u6ce8\u4e3aVOC\u683c\u5f0f\uff0c\u8bf7\u5148\u8f6c\u6362\u4e3aYOLO\u683c\u5f0f\uff08\u5373\u6bcf\u4e2a\u6807\u7b7e\u6587\u4ef6\u5305\u542b\u6bcf\u884c\u4e00\u4e2a\u8fb9\u754c\u6846\uff0c\u683c\u5f0f\u4e3a\uff1a\u7c7b\u522bID \u4e2d\u5fc3\u70b9x \u4e2d\u5fc3\u70b9y \u5bbd \u9ad8\uff09\u3002<\/p>\n\n\n\n\u4e09\u3001\u8bad\u7ec3\u6a21\u578b<\/h3>\n\n\n\n\u8bad\u7ec3\u811a\u672c (train.py<\/code>)<\/h4>\n\n\n\nfrom ultralytics import YOLOimport torchdeftrain_model():# Check GPU availability<\/em> device =0if torch.cuda.is_available()else'cpu'print(f\"Using device: {device}\")# Load pretrained model (YOLOv8s is a good balance between speed and accuracy)<\/em> model = YOLO('yolov8s.pt')# Start training<\/em> results = model.train( data='infrared_dataset\/data.yaml', epochs=200,# Adjust based on your needs<\/em> imgsz=640,# Image size can be adjusted based on the dataset<\/em> batch=16,# Reduce if you encounter out-of-memory issues<\/em> name='infrared_yolov8s', project='runs\/detect', save=True, save_period=10,# Save every 10 epochs<\/em> device=device, workers=4, patience=50,# Early stopping after 50 epochs without improvement<\/em> optimizer='AdamW', lr0=0.001,# Initial learning rate<\/em> augment=True# Enable data augmentation<\/em>)print(\"Training completed!\")return resultsif __name__ =='__main__': train_model()<\/code><\/pre>\n\n\n\n\u56db\u3001\u63a8\u7406\u4e0e\u8bc4\u4f30<\/h3>\n\n\n\n\u63a8\u7406\u811a\u672c (detect.py<\/code>)<\/h4>\n\n\n\nfrom ultralytics import YOLOimport cv2defdetect_image(image_path, output_path='output_detection.jpg'):# Load trained model<\/em> model = YOLO('runs\/detect\/infrared_yolov8s\/weights\/best.pt')# Perform prediction<\/em> results = model.predict( source=image_path, conf=0.4,# Confidence threshold<\/em> iou=0.5, save=True, project='runs\/detect\/predict', name='result', exist_ok=True)# Extract and save results (optional)<\/em>for r in results: im = r.plot()# Plot bounding boxes and labels<\/em> cv2.imwrite(output_path, im)print(f\"Detection completed, saved to {output_path}\")# Print detected objects<\/em>for box in r.boxes: cls_name = model.names[int(box.cls)] conf =float(box.conf)print(f\"Detected: {cls_name} (Confidence: {conf:.3f})\")return output_pathif __name__ =='__main__': detect_image('test_infrared_image.jpg')<\/code><\/pre>\n\n\n\n\u8bc4\u4f30\u811a\u672c (evaluate.py<\/code>)<\/h4>\n\n\n\nfrom ultralytics import YOLOdefevaluate_model(): model = YOLO('runs\/detect\/infrared_yolov8s\/weights\/best.pt')# Evaluate on validation set<\/em> metrics = model.val( data='infrared_dataset\/data.yaml', split='val',# Or 'test' if evaluating on the test set<\/em> batch=16, imgsz=640, device=0if torch.cuda.is_available()else'cpu', save_json=True,# Save COCO format results for further analysis<\/em> conf_thres=0.001, iou_thres=0.6)# Output key metrics<\/em>print(f\"mAP@0.5: {metrics.box.map50:.4f}\")print(f\"mAP@0.5:0.95: {metrics.box.map:.4f}\")print(f\"Precision: {metrics.box.p:.4f}\")print(f\"Recall: {metrics.box.r:.4f}\")# Output AP per class<\/em>print(\"\\nAP per class:\")for i, name inenumerate(metrics.names.values()):print(f\" {name}: {metrics.box.ap[i]:.4f}\")if __name__ =='__main__': evaluate_model()<\/code><\/pre>\n\n\n\n\u6784\u5efa\u4e00\u4e2a\u53ef\u4ee5\u52a0\u8f7d\u6a21\u578b\u3001\u6267\u884c\u63a8\u7406\u5e76\u5bf9\u65b0\u56fe\u50cf\u6216\u89c6\u9891\u8fdb\u884c\u5b9e\u65f6\u5904\u7406\u7684\u5e94\u7528\u3002<\/p>\n\n\n\n\u4e00\u3001\u51c6\u5907<\/h3>\n\n\n\n\u786e\u4fdd\u5df2\u7ecf\u5b8c\u6210\u4e86\u6a21\u578b\u7684\u8bad\u7ec3\uff0c\u5e76\u4e14\u8bad\u7ec3\u597d\u7684\u6a21\u578b\u6587\u4ef6\uff08\u4f8b\u5982 best.pt<\/code>\uff09\uff0c\u4f4d\u4e8e runs\/detect\/infrared_yolov8s\/weights\/best.pt<\/code>\u3002<\/p>\n\n\n\n\u4e8c\u3001\u6a21\u578b\u5c01\u88c5<\/h3>\n\n\n\n\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5c01\u88c5\u6211\u4eec\u7684\u6a21\u578b\uff0c\u4f7f\u5176\u6613\u4e8e\u4f7f\u7528\u3002\u8fd9\u53ef\u4ee5\u901a\u8fc7\u521b\u5efa\u4e00\u4e2a\u7c7b\u6765\u5b9e\u73b0\uff0c\u8be5\u7c7b\u8d1f\u8d23\u52a0\u8f7d\u6a21\u578b\u5e76\u63d0\u4f9b\u9884\u6d4b\u65b9\u6cd5\u3002<\/p>\n\n\n\nmodel_utils.py<\/code><\/h4>\n\n\n\nfrom ultralytics import YOLOimport cv2classInfraredTargetDetector:def__init__(self, model_path='runs\/detect\/infrared_yolov8s\/weights\/best.pt'): self.model = YOLO(model_path)defpredict(self, image_path, conf=0.4):\"\"\" \u5bf9\u7ed9\u5b9a\u8def\u5f84\u7684\u56fe\u50cf\u8fdb\u884c\u9884\u6d4b\u3002 :param image_path: \u8f93\u5165\u56fe\u50cf\u7684\u8def\u5f84 :param conf: \u7f6e\u4fe1\u5ea6\u9608\u503c :return: \u68c0\u6d4b\u7ed3\u679c\u5217\u8868\uff0c\u6bcf\u4e2a\u7ed3\u679c\u5305\u542b\u7c7b\u522b\u540d\u79f0\u3001\u7f6e\u4fe1\u5ea6\u5206\u6570\u548c\u8fb9\u754c\u6846\u5750\u6807 \"\"\" results = self.model.predict(source=image_path, conf=conf) detections =[]for r in results:for box in r.boxes: cls_id =int(box.cls) label = self.model.names[cls_id] confidence =float(box.conf) bbox = box.xyxy[0].cpu().numpy().tolist()# [x1, y1, x2, y2]<\/em> detections.append({'class': label,'confidence': confidence,'bbox': bbox})return detectionsdefdetect_and_draw(self, image_path, output_path='output_detection.jpg', conf=0.4):\"\"\" \u5bf9\u56fe\u50cf\u8fdb\u884c\u68c0\u6d4b\u5e76\u5728\u539f\u56fe\u4e0a\u7ed8\u5236\u68c0\u6d4b\u7ed3\u679c\u3002 :param image_path: \u8f93\u5165\u56fe\u50cf\u7684\u8def\u5f84 :param output_path: \u8f93\u51fa\u56fe\u50cf\u7684\u4fdd\u5b58\u8def\u5f84 :param conf: \u7f6e\u4fe1\u5ea6\u9608\u503c :return: None \"\"\" detections = self.predict(image_path, conf) img = cv2.imread(image_path)for det in detections: bbox = det['bbox'] label =f\"{det['class']}: {det['confidence']:.2f}\" color =(0,255,0)# Green<\/em> cv2.rectangle(img,(int(bbox[0]),int(bbox[1])),(int(bbox[2]),int(bbox[3])), color,2) cv2.putText(img, label,(int(bbox[0]),int(bbox[1])-10), cv2.FONT_HERSHEY_SIMPLEX,0.9, color,2) cv2.imwrite(output_path, img)print(f\"Detection completed and saved to {output_path}\")# \u793a\u4f8b\u4f7f\u7528<\/em>if __name__ =='__main__': detector = InfraredTargetDetector() detector.detect_and_draw('test_infrared_image.jpg')<\/code><\/pre>\n\n\n\n\u4e09\u3001\u6784\u5efa\u547d\u4ee4\u884c\u5de5\u5177<\/h3>\n\n\n\n\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u521b\u5efa\u4e00\u4e2a\u547d\u4ee4\u884c\u5de5\u5177\uff0c\u5141\u8bb8\u7528\u6237\u901a\u8fc7\u547d\u4ee4\u884c\u5bf9\u56fe\u50cf\u6216\u89c6\u9891\u8fdb\u884c\u68c0\u6d4b\u3002<\/p>\n\n\n\ninfrared_detector_cli.py<\/code><\/h4>\n\n\n\nimport argparsefrom model_utils import InfraredTargetDetectorimport cv2defmain(): parser = argparse.ArgumentParser(description=\"Infrared Target Detection System\") parser.add_argument('--input',type=str, required=True,help='Path to input image or video file.') parser.add_argument('--output',type=str, default='output.jpg',help='Path to save the output.') parser.add_argument('--conf',type=float, default=0.4,help='Confidence threshold.') args = parser.parse_args() detector = InfraredTargetDetector()if args.input.lower().endswith(('jpg','jpeg','png')): detector.detect_and_draw(args.input, args.output, args.conf)elif args.input.lower().endswith(('mp4','avi')): cap = cv2.VideoCapture(args.input) frame_width =int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) frame_height =int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) out = cv2.VideoWriter(args.output, cv2.VideoWriter_fourcc(*'XVID'),20.0,(frame_width, frame_height))while cap.isOpened(): ret, frame = cap.read()ifnot ret:break# Convert frame to image path temporarily for prediction<\/em> temp_img_path ='temp_frame.jpg' cv2.imwrite(temp_img_path, frame) detections = detector.predict(temp_img_path, args.conf)for det in detections: bbox = det['bbox'] label =f\"{det['class']}: {det['confidence']:.2f}\" color =(0,255,0)# Green<\/em> cv2.rectangle(frame,(int(bbox[0]),int(bbox[1])),(int(bbox[2]),int(bbox[3])), color,2) cv2.putText(frame, label,(int(bbox[0]),int(bbox[1])-10), cv2.FONT_HERSHEY_SIMPLEX,0.9, color,2) out.write(frame) cap.release() out.release()print(f\"Video detection completed and saved to {args.output}\")else:print(\"Unsupported file format.\")if __name__ ==\"__main__\": main()<\/code><\/pre>\n\n\n\n\u56db\u3001\u8fd0\u884c\u4f60\u7684\u7cfb\u7edf<\/h3>\n\n\n\n\n\u5bf9\u4e8e\u5355\u5f20\u56fe\u7247<\/strong>\uff1apython infrared_detector_cli.py --input path\/to\/image.jpg --output output.jpg --conf\u00a00.5<\/code><\/li>\n\n\n\n\u5bf9\u4e8e\u89c6\u9891\u6587\u4ef6<\/strong>\uff1apython infrared_detector_cli.py --input path\/to\/video.mp4 --output output_video.avi --con<\/code><\/li>\n<\/ul>\n","raw":"","protected":false},"excerpt":{"rendered":"\u6df1\u5ea6\u5b66\u4e60\u57fa\u4e8eYOLOv8\u6846\u67b6\u6765\u8bad\u7ec3\u7ea2\u5916\u56fe\u50cf\u5c0f\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6 \u7ea2\u5916\u56fe\u50cf\u5c0f\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6\u7684\u8bad\u7ec3\u53ca\u5e94\u7528 \u63a8\u7406\u8bc6\u522b\u68c0\u6d4b\u98de\u673a \u65e0\u4eba\u673a \u76f4\u5347\u673a 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YOLOV11\u5982\u4f55\u8bad\u7ec3\u81ea\u5df1\u7684\u6570\u636e\u96c6 YOLOv11\u8bad\u7ec3\u914d\u7f6e\u73af\u5883\u6559\u7a0b \u4ee5\u6c14\u7403\u68c0\u6d4b\u6570\u636e\u96c6\u4e3a\u4f8b","previous_post_id":6590,"previous_post_title":"YOLOV8\u6a21\u578b\u5982\u4f55\u8bad\u7ec3 \u53cc\u6a21\u6001\u7ea2\u5916-\u53ef\u89c1\u5149\u5bf9\u9f50\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6 \u7ea2\u5916\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6\u7684\u8bad\u7ec3\u53ca\u5e94\u7528 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train: .\/infrared_dataset\/images\/trainval: .\/infrared_dataset\/images\/valtest: .\/infrared_dataset\/images\/testnc:4names:['Plane','Drone','Heli','Bird']<\/code><\/pre>\n\n\n\n\u5982\u679c\u4f60\u7684\u6570\u636e\u6807\u6ce8\u4e3aVOC\u683c\u5f0f\uff0c\u8bf7\u5148\u8f6c\u6362\u4e3aYOLO\u683c\u5f0f\uff08\u5373\u6bcf\u4e2a\u6807\u7b7e\u6587\u4ef6\u5305\u542b\u6bcf\u884c\u4e00\u4e2a\u8fb9\u754c\u6846\uff0c\u683c\u5f0f\u4e3a\uff1a\u7c7b\u522bID \u4e2d\u5fc3\u70b9x \u4e2d\u5fc3\u70b9y \u5bbd \u9ad8\uff09\u3002<\/p>\n\n\n\n\u4e09\u3001\u8bad\u7ec3\u6a21\u578b<\/h3>\n\n\n\n\u8bad\u7ec3\u811a\u672c (train.py<\/code>)<\/h4>\n\n\n\nfrom ultralytics import YOLOimport torchdeftrain_model():# Check GPU availability<\/em> device =0if torch.cuda.is_available()else'cpu'print(f\"Using device: {device}\")# Load pretrained model (YOLOv8s is a good balance between speed and accuracy)<\/em> model = YOLO('yolov8s.pt')# Start training<\/em> results = model.train( data='infrared_dataset\/data.yaml', epochs=200,# Adjust based on your needs<\/em> imgsz=640,# Image size can be adjusted based on the dataset<\/em> batch=16,# Reduce if you encounter out-of-memory issues<\/em> name='infrared_yolov8s', project='runs\/detect', save=True, save_period=10,# Save every 10 epochs<\/em> device=device, workers=4, patience=50,# Early stopping after 50 epochs without improvement<\/em> optimizer='AdamW', lr0=0.001,# Initial learning rate<\/em> augment=True# Enable data augmentation<\/em>)print(\"Training completed!\")return resultsif __name__ =='__main__': train_model()<\/code><\/pre>\n\n\n\n\u56db\u3001\u63a8\u7406\u4e0e\u8bc4\u4f30<\/h3>\n\n\n\n\u63a8\u7406\u811a\u672c (detect.py<\/code>)<\/h4>\n\n\n\nfrom ultralytics import YOLOimport cv2defdetect_image(image_path, output_path='output_detection.jpg'):# Load trained model<\/em> model = YOLO('runs\/detect\/infrared_yolov8s\/weights\/best.pt')# Perform prediction<\/em> results = model.predict( source=image_path, conf=0.4,# Confidence threshold<\/em> iou=0.5, save=True, project='runs\/detect\/predict', name='result', exist_ok=True)# Extract and save results (optional)<\/em>for r in results: im = r.plot()# Plot bounding boxes and labels<\/em> cv2.imwrite(output_path, im)print(f\"Detection completed, saved to {output_path}\")# Print detected objects<\/em>for box in r.boxes: cls_name = model.names[int(box.cls)] conf =float(box.conf)print(f\"Detected: {cls_name} (Confidence: {conf:.3f})\")return output_pathif __name__ =='__main__': detect_image('test_infrared_image.jpg')<\/code><\/pre>\n\n\n\n\u8bc4\u4f30\u811a\u672c (evaluate.py<\/code>)<\/h4>\n\n\n\nfrom ultralytics import YOLOdefevaluate_model(): model = YOLO('runs\/detect\/infrared_yolov8s\/weights\/best.pt')# Evaluate on validation set<\/em> metrics = model.val( data='infrared_dataset\/data.yaml', split='val',# Or 'test' if evaluating on the test set<\/em> batch=16, imgsz=640, device=0if torch.cuda.is_available()else'cpu', save_json=True,# Save COCO format results for further analysis<\/em> conf_thres=0.001, iou_thres=0.6)# Output key metrics<\/em>print(f\"mAP@0.5: {metrics.box.map50:.4f}\")print(f\"mAP@0.5:0.95: {metrics.box.map:.4f}\")print(f\"Precision: {metrics.box.p:.4f}\")print(f\"Recall: {metrics.box.r:.4f}\")# Output AP per class<\/em>print(\"\\nAP per class:\")for i, name inenumerate(metrics.names.values()):print(f\" {name}: {metrics.box.ap[i]:.4f}\")if __name__ =='__main__': evaluate_model()<\/code><\/pre>\n\n\n\n\u6784\u5efa\u4e00\u4e2a\u53ef\u4ee5\u52a0\u8f7d\u6a21\u578b\u3001\u6267\u884c\u63a8\u7406\u5e76\u5bf9\u65b0\u56fe\u50cf\u6216\u89c6\u9891\u8fdb\u884c\u5b9e\u65f6\u5904\u7406\u7684\u5e94\u7528\u3002<\/p>\n\n\n\n\u4e00\u3001\u51c6\u5907<\/h3>\n\n\n\n\u786e\u4fdd\u5df2\u7ecf\u5b8c\u6210\u4e86\u6a21\u578b\u7684\u8bad\u7ec3\uff0c\u5e76\u4e14\u8bad\u7ec3\u597d\u7684\u6a21\u578b\u6587\u4ef6\uff08\u4f8b\u5982 best.pt<\/code>\uff09\uff0c\u4f4d\u4e8e runs\/detect\/infrared_yolov8s\/weights\/best.pt<\/code>\u3002<\/p>\n\n\n\n\u4e8c\u3001\u6a21\u578b\u5c01\u88c5<\/h3>\n\n\n\n\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5c01\u88c5\u6211\u4eec\u7684\u6a21\u578b\uff0c\u4f7f\u5176\u6613\u4e8e\u4f7f\u7528\u3002\u8fd9\u53ef\u4ee5\u901a\u8fc7\u521b\u5efa\u4e00\u4e2a\u7c7b\u6765\u5b9e\u73b0\uff0c\u8be5\u7c7b\u8d1f\u8d23\u52a0\u8f7d\u6a21\u578b\u5e76\u63d0\u4f9b\u9884\u6d4b\u65b9\u6cd5\u3002<\/p>\n\n\n\nmodel_utils.py<\/code><\/h4>\n\n\n\nfrom ultralytics import YOLOimport cv2classInfraredTargetDetector:def__init__(self, model_path='runs\/detect\/infrared_yolov8s\/weights\/best.pt'): self.model = YOLO(model_path)defpredict(self, image_path, conf=0.4):\"\"\" \u5bf9\u7ed9\u5b9a\u8def\u5f84\u7684\u56fe\u50cf\u8fdb\u884c\u9884\u6d4b\u3002 :param image_path: \u8f93\u5165\u56fe\u50cf\u7684\u8def\u5f84 :param conf: \u7f6e\u4fe1\u5ea6\u9608\u503c :return: \u68c0\u6d4b\u7ed3\u679c\u5217\u8868\uff0c\u6bcf\u4e2a\u7ed3\u679c\u5305\u542b\u7c7b\u522b\u540d\u79f0\u3001\u7f6e\u4fe1\u5ea6\u5206\u6570\u548c\u8fb9\u754c\u6846\u5750\u6807 \"\"\" results = self.model.predict(source=image_path, conf=conf) detections =[]for r in results:for box in r.boxes: cls_id =int(box.cls) label = self.model.names[cls_id] confidence =float(box.conf) bbox = box.xyxy[0].cpu().numpy().tolist()# [x1, y1, x2, y2]<\/em> detections.append({'class': label,'confidence': confidence,'bbox': bbox})return detectionsdefdetect_and_draw(self, image_path, output_path='output_detection.jpg', conf=0.4):\"\"\" \u5bf9\u56fe\u50cf\u8fdb\u884c\u68c0\u6d4b\u5e76\u5728\u539f\u56fe\u4e0a\u7ed8\u5236\u68c0\u6d4b\u7ed3\u679c\u3002 :param image_path: \u8f93\u5165\u56fe\u50cf\u7684\u8def\u5f84 :param output_path: \u8f93\u51fa\u56fe\u50cf\u7684\u4fdd\u5b58\u8def\u5f84 :param conf: \u7f6e\u4fe1\u5ea6\u9608\u503c :return: None \"\"\" detections = self.predict(image_path, conf) img = cv2.imread(image_path)for det in detections: bbox = det['bbox'] label =f\"{det['class']}: {det['confidence']:.2f}\" color =(0,255,0)# Green<\/em> cv2.rectangle(img,(int(bbox[0]),int(bbox[1])),(int(bbox[2]),int(bbox[3])), color,2) cv2.putText(img, label,(int(bbox[0]),int(bbox[1])-10), cv2.FONT_HERSHEY_SIMPLEX,0.9, color,2) cv2.imwrite(output_path, img)print(f\"Detection completed and saved to {output_path}\")# \u793a\u4f8b\u4f7f\u7528<\/em>if __name__ =='__main__': detector = InfraredTargetDetector() detector.detect_and_draw('test_infrared_image.jpg')<\/code><\/pre>\n\n\n\n\u4e09\u3001\u6784\u5efa\u547d\u4ee4\u884c\u5de5\u5177<\/h3>\n\n\n\n\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u521b\u5efa\u4e00\u4e2a\u547d\u4ee4\u884c\u5de5\u5177\uff0c\u5141\u8bb8\u7528\u6237\u901a\u8fc7\u547d\u4ee4\u884c\u5bf9\u56fe\u50cf\u6216\u89c6\u9891\u8fdb\u884c\u68c0\u6d4b\u3002<\/p>\n\n\n\ninfrared_detector_cli.py<\/code><\/h4>\n\n\n\nimport argparsefrom model_utils import InfraredTargetDetectorimport cv2defmain(): parser = argparse.ArgumentParser(description=\"Infrared Target Detection System\") parser.add_argument('--input',type=str, required=True,help='Path to input image or video file.') parser.add_argument('--output',type=str, default='output.jpg',help='Path to save the output.') parser.add_argument('--conf',type=float, default=0.4,help='Confidence threshold.') args = parser.parse_args() detector = InfraredTargetDetector()if args.input.lower().endswith(('jpg','jpeg','png')): detector.detect_and_draw(args.input, args.output, args.conf)elif args.input.lower().endswith(('mp4','avi')): cap = cv2.VideoCapture(args.input) frame_width =int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) frame_height =int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) out = cv2.VideoWriter(args.output, cv2.VideoWriter_fourcc(*'XVID'),20.0,(frame_width, frame_height))while cap.isOpened(): ret, frame = cap.read()ifnot ret:break# Convert frame to image path temporarily for prediction<\/em> temp_img_path ='temp_frame.jpg' cv2.imwrite(temp_img_path, frame) detections = detector.predict(temp_img_path, args.conf)for det in detections: bbox = det['bbox'] label =f\"{det['class']}: {det['confidence']:.2f}\" color =(0,255,0)# Green<\/em> cv2.rectangle(frame,(int(bbox[0]),int(bbox[1])),(int(bbox[2]),int(bbox[3])), color,2) cv2.putText(frame, label,(int(bbox[0]),int(bbox[1])-10), cv2.FONT_HERSHEY_SIMPLEX,0.9, color,2) out.write(frame) cap.release() out.release()print(f\"Video detection completed and saved to {args.output}\")else:print(\"Unsupported file format.\")if __name__ ==\"__main__\": main()<\/code><\/pre>\n\n\n\n\u56db\u3001\u8fd0\u884c\u4f60\u7684\u7cfb\u7edf<\/h3>\n\n\n\n\n\u5bf9\u4e8e\u5355\u5f20\u56fe\u7247<\/strong>\uff1apython infrared_detector_cli.py --input path\/to\/image.jpg --output output.jpg --conf\u00a00.5<\/code><\/li>\n\n\n\n\u5bf9\u4e8e\u89c6\u9891\u6587\u4ef6<\/strong>\uff1apython infrared_detector_cli.py --input path\/to\/video.mp4 --output output_video.avi --con<\/code><\/li>\n<\/ul>\n","raw":"","protected":false},"excerpt":{"rendered":"\u6df1\u5ea6\u5b66\u4e60\u57fa\u4e8eYOLOv8\u6846\u67b6\u6765\u8bad\u7ec3\u7ea2\u5916\u56fe\u50cf\u5c0f\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6 \u7ea2\u5916\u56fe\u50cf\u5c0f\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6\u7684\u8bad\u7ec3\u53ca\u5e94\u7528 \u63a8\u7406\u8bc6\u522b\u68c0\u6d4b\u98de\u673a \u65e0\u4eba\u673a \u76f4\u5347\u673a …<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"emotion":"","emotion_color":"","title_style":"","license":"","footnotes":""},"categories":[46],"tags":[],"class_list":["post-6592","post","type-post","status-publish","format-standard","hentry","category-yolo"],"post_thumbnail_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","content_first_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_medium_image_300":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_thumbnail_image_624":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_frist_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_medium_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_large_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_full_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_all_images":[{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","id":"image0"},{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","id":"image1"},{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKicJO37JCcKiczyLibDpRgBDXlTon1aE9dVcNEEQ2gvaQTRZOajJPzLexw\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=1","id":"image2"},{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKicJO37JCcKiczyLibDpRgBDXlTon1aE9dVcNEEQ2gvaQTRZOajJPzLexw\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=1","id":"image3"},{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKAyqwlqJhAhUaqz66Via4WKqzx8qTkKvrURcsQVImhEUmXhZAYbia279Q\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=2","id":"image4"},{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKAyqwlqJhAhUaqz66Via4WKqzx8qTkKvrURcsQVImhEUmXhZAYbia279Q\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=2","id":"image5"}],"videoAdId":"","listAd":"0","listAdId":"","listAdEvery":6,"total_comments":0,"category_name":"YOLO","post_date":"2025-09-29","like_count":"0","praiseWord":"\u9f13\u52b1","copyright_state":"","excitationAd":"0","rewardedVideoAdId":"","detailAdId":"","detailAd":"0","enterpriseMinapp":"0","audios":[],"postImageUrl":"https:\/\/wp-moto-1258805347.cos.ap-shanghai.myqcloud.com\/2023\/05\/20230519082947553.jpg","avatarurls":[],"related_posts":null,"pageviews":269,"next_post_id":6657,"next_post_title":"\u6df1\u5ea6\u5b66\u4e60\u76ee\u6807\u68c0\u6d4b YOLOV11\u5982\u4f55\u8bad\u7ec3\u81ea\u5df1\u7684\u6570\u636e\u96c6 YOLOv11\u8bad\u7ec3\u914d\u7f6e\u73af\u5883\u6559\u7a0b \u4ee5\u6c14\u7403\u68c0\u6d4b\u6570\u636e\u96c6\u4e3a\u4f8b","previous_post_id":6590,"previous_post_title":"YOLOV8\u6a21\u578b\u5982\u4f55\u8bad\u7ec3 \u53cc\u6a21\u6001\u7ea2\u5916-\u53ef\u89c1\u5149\u5bf9\u9f50\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6 \u7ea2\u5916\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6\u7684\u8bad\u7ec3\u53ca\u5e94\u7528 \u53cc\u6a21\u6001\uff08\u7ea2\u5916+\u53ef\u89c1\u5149\uff09\u6570\u636e\u96c6","_links":{"self":[{"href":"https:\/\/xinchewhd.com.cn\/index.php\/wp-json\/wp\/v2\/posts\/6592","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/xinchewhd.com.cn\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/xinchewhd.com.cn\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/xinchewhd.com.cn\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/xinchewhd.com.cn\/index.php\/wp-json\/wp\/v2\/comments?post=6592"}],"version-history":[{"count":1,"href":"https:\/\/xinchewhd.com.cn\/index.php\/wp-json\/wp\/v2\/posts\/6592\/revisions"}],"predecessor-version":[{"id":6593,"href":"https:\/\/xinchewhd.com.cn\/index.php\/wp-json\/wp\/v2\/posts\/6592\/revisions\/6593"}],"wp:attachment":[{"href":"https:\/\/xinchewhd.com.cn\/index.php\/wp-json\/wp\/v2\/media?parent=6592"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/xinchewhd.com.cn\/index.php\/wp-json\/wp\/v2\/categories?post=6592"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/xinchewhd.com.cn\/index.php\/wp-json\/wp\/v2\/tags?post=6592"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
\u5982\u679c\u4f60\u7684\u6570\u636e\u6807\u6ce8\u4e3aVOC\u683c\u5f0f\uff0c\u8bf7\u5148\u8f6c\u6362\u4e3aYOLO\u683c\u5f0f\uff08\u5373\u6bcf\u4e2a\u6807\u7b7e\u6587\u4ef6\u5305\u542b\u6bcf\u884c\u4e00\u4e2a\u8fb9\u754c\u6846\uff0c\u683c\u5f0f\u4e3a\uff1a\u7c7b\u522bID \u4e2d\u5fc3\u70b9x \u4e2d\u5fc3\u70b9y \u5bbd \u9ad8\uff09\u3002<\/p>\n\n\n\n
train.py<\/code>)<\/h4>\n\n\n\nfrom ultralytics import YOLOimport torchdeftrain_model():# Check GPU availability<\/em> device =0if torch.cuda.is_available()else'cpu'print(f\"Using device: {device}\")# Load pretrained model (YOLOv8s is a good balance between speed and accuracy)<\/em> model = YOLO('yolov8s.pt')# Start training<\/em> results = model.train( data='infrared_dataset\/data.yaml', epochs=200,# Adjust based on your needs<\/em> imgsz=640,# Image size can be adjusted based on the dataset<\/em> batch=16,# Reduce if you encounter out-of-memory issues<\/em> name='infrared_yolov8s', project='runs\/detect', save=True, save_period=10,# Save every 10 epochs<\/em> device=device, workers=4, patience=50,# Early stopping after 50 epochs without improvement<\/em> optimizer='AdamW', lr0=0.001,# Initial learning rate<\/em> augment=True# Enable data augmentation<\/em>)print(\"Training completed!\")return resultsif __name__ =='__main__': train_model()<\/code><\/pre>\n\n\n\n\u56db\u3001\u63a8\u7406\u4e0e\u8bc4\u4f30<\/h3>\n\n\n\n\u63a8\u7406\u811a\u672c (detect.py<\/code>)<\/h4>\n\n\n\nfrom ultralytics import YOLOimport cv2defdetect_image(image_path, output_path='output_detection.jpg'):# Load trained model<\/em> model = YOLO('runs\/detect\/infrared_yolov8s\/weights\/best.pt')# Perform prediction<\/em> results = model.predict( source=image_path, conf=0.4,# Confidence threshold<\/em> iou=0.5, save=True, project='runs\/detect\/predict', name='result', exist_ok=True)# Extract and save results (optional)<\/em>for r in results: im = r.plot()# Plot bounding boxes and labels<\/em> cv2.imwrite(output_path, im)print(f\"Detection completed, saved to {output_path}\")# Print detected objects<\/em>for box in r.boxes: cls_name = model.names[int(box.cls)] conf =float(box.conf)print(f\"Detected: {cls_name} (Confidence: {conf:.3f})\")return output_pathif __name__ =='__main__': detect_image('test_infrared_image.jpg')<\/code><\/pre>\n\n\n\n\u8bc4\u4f30\u811a\u672c (evaluate.py<\/code>)<\/h4>\n\n\n\nfrom ultralytics import YOLOdefevaluate_model(): model = YOLO('runs\/detect\/infrared_yolov8s\/weights\/best.pt')# Evaluate on validation set<\/em> metrics = model.val( data='infrared_dataset\/data.yaml', split='val',# Or 'test' if evaluating on the test set<\/em> batch=16, imgsz=640, device=0if torch.cuda.is_available()else'cpu', save_json=True,# Save COCO format results for further analysis<\/em> conf_thres=0.001, iou_thres=0.6)# Output key metrics<\/em>print(f\"mAP@0.5: {metrics.box.map50:.4f}\")print(f\"mAP@0.5:0.95: {metrics.box.map:.4f}\")print(f\"Precision: {metrics.box.p:.4f}\")print(f\"Recall: {metrics.box.r:.4f}\")# Output AP per class<\/em>print(\"\\nAP per class:\")for i, name inenumerate(metrics.names.values()):print(f\" {name}: {metrics.box.ap[i]:.4f}\")if __name__ =='__main__': evaluate_model()<\/code><\/pre>\n\n\n\n\u6784\u5efa\u4e00\u4e2a\u53ef\u4ee5\u52a0\u8f7d\u6a21\u578b\u3001\u6267\u884c\u63a8\u7406\u5e76\u5bf9\u65b0\u56fe\u50cf\u6216\u89c6\u9891\u8fdb\u884c\u5b9e\u65f6\u5904\u7406\u7684\u5e94\u7528\u3002<\/p>\n\n\n\n\u4e00\u3001\u51c6\u5907<\/h3>\n\n\n\n\u786e\u4fdd\u5df2\u7ecf\u5b8c\u6210\u4e86\u6a21\u578b\u7684\u8bad\u7ec3\uff0c\u5e76\u4e14\u8bad\u7ec3\u597d\u7684\u6a21\u578b\u6587\u4ef6\uff08\u4f8b\u5982 best.pt<\/code>\uff09\uff0c\u4f4d\u4e8e runs\/detect\/infrared_yolov8s\/weights\/best.pt<\/code>\u3002<\/p>\n\n\n\n\u4e8c\u3001\u6a21\u578b\u5c01\u88c5<\/h3>\n\n\n\n\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5c01\u88c5\u6211\u4eec\u7684\u6a21\u578b\uff0c\u4f7f\u5176\u6613\u4e8e\u4f7f\u7528\u3002\u8fd9\u53ef\u4ee5\u901a\u8fc7\u521b\u5efa\u4e00\u4e2a\u7c7b\u6765\u5b9e\u73b0\uff0c\u8be5\u7c7b\u8d1f\u8d23\u52a0\u8f7d\u6a21\u578b\u5e76\u63d0\u4f9b\u9884\u6d4b\u65b9\u6cd5\u3002<\/p>\n\n\n\nmodel_utils.py<\/code><\/h4>\n\n\n\nfrom ultralytics import YOLOimport cv2classInfraredTargetDetector:def__init__(self, model_path='runs\/detect\/infrared_yolov8s\/weights\/best.pt'): self.model = YOLO(model_path)defpredict(self, image_path, conf=0.4):\"\"\" \u5bf9\u7ed9\u5b9a\u8def\u5f84\u7684\u56fe\u50cf\u8fdb\u884c\u9884\u6d4b\u3002 :param image_path: \u8f93\u5165\u56fe\u50cf\u7684\u8def\u5f84 :param conf: \u7f6e\u4fe1\u5ea6\u9608\u503c :return: \u68c0\u6d4b\u7ed3\u679c\u5217\u8868\uff0c\u6bcf\u4e2a\u7ed3\u679c\u5305\u542b\u7c7b\u522b\u540d\u79f0\u3001\u7f6e\u4fe1\u5ea6\u5206\u6570\u548c\u8fb9\u754c\u6846\u5750\u6807 \"\"\" results = self.model.predict(source=image_path, conf=conf) detections =[]for r in results:for box in r.boxes: cls_id =int(box.cls) label = self.model.names[cls_id] confidence =float(box.conf) bbox = box.xyxy[0].cpu().numpy().tolist()# [x1, y1, x2, y2]<\/em> detections.append({'class': label,'confidence': confidence,'bbox': bbox})return detectionsdefdetect_and_draw(self, image_path, output_path='output_detection.jpg', conf=0.4):\"\"\" \u5bf9\u56fe\u50cf\u8fdb\u884c\u68c0\u6d4b\u5e76\u5728\u539f\u56fe\u4e0a\u7ed8\u5236\u68c0\u6d4b\u7ed3\u679c\u3002 :param image_path: \u8f93\u5165\u56fe\u50cf\u7684\u8def\u5f84 :param output_path: \u8f93\u51fa\u56fe\u50cf\u7684\u4fdd\u5b58\u8def\u5f84 :param conf: \u7f6e\u4fe1\u5ea6\u9608\u503c :return: None \"\"\" detections = self.predict(image_path, conf) img = cv2.imread(image_path)for det in detections: bbox = det['bbox'] label =f\"{det['class']}: {det['confidence']:.2f}\" color =(0,255,0)# Green<\/em> cv2.rectangle(img,(int(bbox[0]),int(bbox[1])),(int(bbox[2]),int(bbox[3])), color,2) cv2.putText(img, label,(int(bbox[0]),int(bbox[1])-10), cv2.FONT_HERSHEY_SIMPLEX,0.9, color,2) cv2.imwrite(output_path, img)print(f\"Detection completed and saved to {output_path}\")# \u793a\u4f8b\u4f7f\u7528<\/em>if __name__ =='__main__': detector = InfraredTargetDetector() detector.detect_and_draw('test_infrared_image.jpg')<\/code><\/pre>\n\n\n\n\u4e09\u3001\u6784\u5efa\u547d\u4ee4\u884c\u5de5\u5177<\/h3>\n\n\n\n\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u521b\u5efa\u4e00\u4e2a\u547d\u4ee4\u884c\u5de5\u5177\uff0c\u5141\u8bb8\u7528\u6237\u901a\u8fc7\u547d\u4ee4\u884c\u5bf9\u56fe\u50cf\u6216\u89c6\u9891\u8fdb\u884c\u68c0\u6d4b\u3002<\/p>\n\n\n\ninfrared_detector_cli.py<\/code><\/h4>\n\n\n\nimport argparsefrom model_utils import InfraredTargetDetectorimport cv2defmain(): parser = argparse.ArgumentParser(description=\"Infrared Target Detection System\") parser.add_argument('--input',type=str, required=True,help='Path to input image or video file.') parser.add_argument('--output',type=str, default='output.jpg',help='Path to save the output.') parser.add_argument('--conf',type=float, default=0.4,help='Confidence threshold.') args = parser.parse_args() detector = InfraredTargetDetector()if args.input.lower().endswith(('jpg','jpeg','png')): detector.detect_and_draw(args.input, args.output, args.conf)elif args.input.lower().endswith(('mp4','avi')): cap = cv2.VideoCapture(args.input) frame_width =int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) frame_height =int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) out = cv2.VideoWriter(args.output, cv2.VideoWriter_fourcc(*'XVID'),20.0,(frame_width, frame_height))while cap.isOpened(): ret, frame = cap.read()ifnot ret:break# Convert frame to image path temporarily for prediction<\/em> temp_img_path ='temp_frame.jpg' cv2.imwrite(temp_img_path, frame) detections = detector.predict(temp_img_path, args.conf)for det in detections: bbox = det['bbox'] label =f\"{det['class']}: {det['confidence']:.2f}\" color =(0,255,0)# Green<\/em> cv2.rectangle(frame,(int(bbox[0]),int(bbox[1])),(int(bbox[2]),int(bbox[3])), color,2) cv2.putText(frame, label,(int(bbox[0]),int(bbox[1])-10), cv2.FONT_HERSHEY_SIMPLEX,0.9, color,2) out.write(frame) cap.release() out.release()print(f\"Video detection completed and saved to {args.output}\")else:print(\"Unsupported file format.\")if __name__ ==\"__main__\": main()<\/code><\/pre>\n\n\n\n\u56db\u3001\u8fd0\u884c\u4f60\u7684\u7cfb\u7edf<\/h3>\n\n\n\n\n\u5bf9\u4e8e\u5355\u5f20\u56fe\u7247<\/strong>\uff1apython infrared_detector_cli.py --input path\/to\/image.jpg --output output.jpg --conf\u00a00.5<\/code><\/li>\n\n\n\n\u5bf9\u4e8e\u89c6\u9891\u6587\u4ef6<\/strong>\uff1apython infrared_detector_cli.py --input path\/to\/video.mp4 --output output_video.avi --con<\/code><\/li>\n<\/ul>\n","raw":"","protected":false},"excerpt":{"rendered":"\u6df1\u5ea6\u5b66\u4e60\u57fa\u4e8eYOLOv8\u6846\u67b6\u6765\u8bad\u7ec3\u7ea2\u5916\u56fe\u50cf\u5c0f\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6 \u7ea2\u5916\u56fe\u50cf\u5c0f\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6\u7684\u8bad\u7ec3\u53ca\u5e94\u7528 \u63a8\u7406\u8bc6\u522b\u68c0\u6d4b\u98de\u673a \u65e0\u4eba\u673a \u76f4\u5347\u673a 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YOLOV11\u5982\u4f55\u8bad\u7ec3\u81ea\u5df1\u7684\u6570\u636e\u96c6 YOLOv11\u8bad\u7ec3\u914d\u7f6e\u73af\u5883\u6559\u7a0b \u4ee5\u6c14\u7403\u68c0\u6d4b\u6570\u636e\u96c6\u4e3a\u4f8b","previous_post_id":6590,"previous_post_title":"YOLOV8\u6a21\u578b\u5982\u4f55\u8bad\u7ec3 \u53cc\u6a21\u6001\u7ea2\u5916-\u53ef\u89c1\u5149\u5bf9\u9f50\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6 \u7ea2\u5916\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6\u7684\u8bad\u7ec3\u53ca\u5e94\u7528 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from ultralytics import YOLOimport torchdeftrain_model():# Check GPU availability<\/em> device =0if torch.cuda.is_available()else'cpu'print(f\"Using device: {device}\")# Load pretrained model (YOLOv8s is a good balance between speed and accuracy)<\/em> model = YOLO('yolov8s.pt')# Start training<\/em> results = model.train( data='infrared_dataset\/data.yaml', epochs=200,# Adjust based on your needs<\/em> imgsz=640,# Image size can be adjusted based on the dataset<\/em> batch=16,# Reduce if you encounter out-of-memory issues<\/em> name='infrared_yolov8s', project='runs\/detect', save=True, save_period=10,# Save every 10 epochs<\/em> device=device, workers=4, patience=50,# Early stopping after 50 epochs without improvement<\/em> optimizer='AdamW', lr0=0.001,# Initial learning rate<\/em> augment=True# Enable data augmentation<\/em>)print(\"Training completed!\")return resultsif __name__ =='__main__': train_model()<\/code><\/pre>\n\n\n\n\u56db\u3001\u63a8\u7406\u4e0e\u8bc4\u4f30<\/h3>\n\n\n\n\u63a8\u7406\u811a\u672c (detect.py<\/code>)<\/h4>\n\n\n\nfrom ultralytics import YOLOimport cv2defdetect_image(image_path, output_path='output_detection.jpg'):# Load trained model<\/em> model = YOLO('runs\/detect\/infrared_yolov8s\/weights\/best.pt')# Perform prediction<\/em> results = model.predict( source=image_path, conf=0.4,# Confidence threshold<\/em> iou=0.5, save=True, project='runs\/detect\/predict', name='result', exist_ok=True)# Extract and save results (optional)<\/em>for r in results: im = r.plot()# Plot bounding boxes and labels<\/em> cv2.imwrite(output_path, im)print(f\"Detection completed, saved to {output_path}\")# Print detected objects<\/em>for box in r.boxes: cls_name = model.names[int(box.cls)] conf =float(box.conf)print(f\"Detected: {cls_name} (Confidence: {conf:.3f})\")return output_pathif __name__ =='__main__': detect_image('test_infrared_image.jpg')<\/code><\/pre>\n\n\n\n\u8bc4\u4f30\u811a\u672c (evaluate.py<\/code>)<\/h4>\n\n\n\nfrom ultralytics import YOLOdefevaluate_model(): model = YOLO('runs\/detect\/infrared_yolov8s\/weights\/best.pt')# Evaluate on validation set<\/em> metrics = model.val( data='infrared_dataset\/data.yaml', split='val',# Or 'test' if evaluating on the test set<\/em> batch=16, imgsz=640, device=0if torch.cuda.is_available()else'cpu', save_json=True,# Save COCO format results for further analysis<\/em> conf_thres=0.001, iou_thres=0.6)# Output key metrics<\/em>print(f\"mAP@0.5: {metrics.box.map50:.4f}\")print(f\"mAP@0.5:0.95: {metrics.box.map:.4f}\")print(f\"Precision: {metrics.box.p:.4f}\")print(f\"Recall: {metrics.box.r:.4f}\")# Output AP per class<\/em>print(\"\\nAP per class:\")for i, name inenumerate(metrics.names.values()):print(f\" {name}: {metrics.box.ap[i]:.4f}\")if __name__ =='__main__': evaluate_model()<\/code><\/pre>\n\n\n\n\u6784\u5efa\u4e00\u4e2a\u53ef\u4ee5\u52a0\u8f7d\u6a21\u578b\u3001\u6267\u884c\u63a8\u7406\u5e76\u5bf9\u65b0\u56fe\u50cf\u6216\u89c6\u9891\u8fdb\u884c\u5b9e\u65f6\u5904\u7406\u7684\u5e94\u7528\u3002<\/p>\n\n\n\n\u4e00\u3001\u51c6\u5907<\/h3>\n\n\n\n\u786e\u4fdd\u5df2\u7ecf\u5b8c\u6210\u4e86\u6a21\u578b\u7684\u8bad\u7ec3\uff0c\u5e76\u4e14\u8bad\u7ec3\u597d\u7684\u6a21\u578b\u6587\u4ef6\uff08\u4f8b\u5982 best.pt<\/code>\uff09\uff0c\u4f4d\u4e8e runs\/detect\/infrared_yolov8s\/weights\/best.pt<\/code>\u3002<\/p>\n\n\n\n\u4e8c\u3001\u6a21\u578b\u5c01\u88c5<\/h3>\n\n\n\n\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5c01\u88c5\u6211\u4eec\u7684\u6a21\u578b\uff0c\u4f7f\u5176\u6613\u4e8e\u4f7f\u7528\u3002\u8fd9\u53ef\u4ee5\u901a\u8fc7\u521b\u5efa\u4e00\u4e2a\u7c7b\u6765\u5b9e\u73b0\uff0c\u8be5\u7c7b\u8d1f\u8d23\u52a0\u8f7d\u6a21\u578b\u5e76\u63d0\u4f9b\u9884\u6d4b\u65b9\u6cd5\u3002<\/p>\n\n\n\nmodel_utils.py<\/code><\/h4>\n\n\n\nfrom ultralytics import YOLOimport cv2classInfraredTargetDetector:def__init__(self, model_path='runs\/detect\/infrared_yolov8s\/weights\/best.pt'): self.model = YOLO(model_path)defpredict(self, image_path, conf=0.4):\"\"\" \u5bf9\u7ed9\u5b9a\u8def\u5f84\u7684\u56fe\u50cf\u8fdb\u884c\u9884\u6d4b\u3002 :param image_path: \u8f93\u5165\u56fe\u50cf\u7684\u8def\u5f84 :param conf: \u7f6e\u4fe1\u5ea6\u9608\u503c :return: \u68c0\u6d4b\u7ed3\u679c\u5217\u8868\uff0c\u6bcf\u4e2a\u7ed3\u679c\u5305\u542b\u7c7b\u522b\u540d\u79f0\u3001\u7f6e\u4fe1\u5ea6\u5206\u6570\u548c\u8fb9\u754c\u6846\u5750\u6807 \"\"\" results = self.model.predict(source=image_path, conf=conf) detections =[]for r in results:for box in r.boxes: cls_id =int(box.cls) label = self.model.names[cls_id] confidence =float(box.conf) bbox = box.xyxy[0].cpu().numpy().tolist()# [x1, y1, x2, y2]<\/em> detections.append({'class': label,'confidence': confidence,'bbox': bbox})return detectionsdefdetect_and_draw(self, image_path, output_path='output_detection.jpg', conf=0.4):\"\"\" \u5bf9\u56fe\u50cf\u8fdb\u884c\u68c0\u6d4b\u5e76\u5728\u539f\u56fe\u4e0a\u7ed8\u5236\u68c0\u6d4b\u7ed3\u679c\u3002 :param image_path: \u8f93\u5165\u56fe\u50cf\u7684\u8def\u5f84 :param output_path: \u8f93\u51fa\u56fe\u50cf\u7684\u4fdd\u5b58\u8def\u5f84 :param conf: \u7f6e\u4fe1\u5ea6\u9608\u503c :return: None \"\"\" detections = self.predict(image_path, conf) img = cv2.imread(image_path)for det in detections: bbox = det['bbox'] label =f\"{det['class']}: {det['confidence']:.2f}\" color =(0,255,0)# Green<\/em> cv2.rectangle(img,(int(bbox[0]),int(bbox[1])),(int(bbox[2]),int(bbox[3])), color,2) cv2.putText(img, label,(int(bbox[0]),int(bbox[1])-10), cv2.FONT_HERSHEY_SIMPLEX,0.9, color,2) cv2.imwrite(output_path, img)print(f\"Detection completed and saved to {output_path}\")# \u793a\u4f8b\u4f7f\u7528<\/em>if __name__ =='__main__': detector = InfraredTargetDetector() detector.detect_and_draw('test_infrared_image.jpg')<\/code><\/pre>\n\n\n\n\u4e09\u3001\u6784\u5efa\u547d\u4ee4\u884c\u5de5\u5177<\/h3>\n\n\n\n\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u521b\u5efa\u4e00\u4e2a\u547d\u4ee4\u884c\u5de5\u5177\uff0c\u5141\u8bb8\u7528\u6237\u901a\u8fc7\u547d\u4ee4\u884c\u5bf9\u56fe\u50cf\u6216\u89c6\u9891\u8fdb\u884c\u68c0\u6d4b\u3002<\/p>\n\n\n\ninfrared_detector_cli.py<\/code><\/h4>\n\n\n\nimport argparsefrom model_utils import InfraredTargetDetectorimport cv2defmain(): parser = argparse.ArgumentParser(description=\"Infrared Target Detection System\") parser.add_argument('--input',type=str, required=True,help='Path to input image or video file.') parser.add_argument('--output',type=str, default='output.jpg',help='Path to save the output.') parser.add_argument('--conf',type=float, default=0.4,help='Confidence threshold.') args = parser.parse_args() detector = InfraredTargetDetector()if args.input.lower().endswith(('jpg','jpeg','png')): detector.detect_and_draw(args.input, args.output, args.conf)elif args.input.lower().endswith(('mp4','avi')): cap = cv2.VideoCapture(args.input) frame_width =int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) frame_height =int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) out = cv2.VideoWriter(args.output, cv2.VideoWriter_fourcc(*'XVID'),20.0,(frame_width, frame_height))while cap.isOpened(): ret, frame = cap.read()ifnot ret:break# Convert frame to image path temporarily for prediction<\/em> temp_img_path ='temp_frame.jpg' cv2.imwrite(temp_img_path, frame) detections = detector.predict(temp_img_path, args.conf)for det in detections: bbox = det['bbox'] label =f\"{det['class']}: {det['confidence']:.2f}\" color =(0,255,0)# Green<\/em> cv2.rectangle(frame,(int(bbox[0]),int(bbox[1])),(int(bbox[2]),int(bbox[3])), color,2) cv2.putText(frame, label,(int(bbox[0]),int(bbox[1])-10), cv2.FONT_HERSHEY_SIMPLEX,0.9, color,2) out.write(frame) cap.release() out.release()print(f\"Video detection completed and saved to {args.output}\")else:print(\"Unsupported file format.\")if __name__ ==\"__main__\": main()<\/code><\/pre>\n\n\n\n\u56db\u3001\u8fd0\u884c\u4f60\u7684\u7cfb\u7edf<\/h3>\n\n\n\n\n\u5bf9\u4e8e\u5355\u5f20\u56fe\u7247<\/strong>\uff1apython infrared_detector_cli.py --input path\/to\/image.jpg --output output.jpg --conf\u00a00.5<\/code><\/li>\n\n\n\n\u5bf9\u4e8e\u89c6\u9891\u6587\u4ef6<\/strong>\uff1apython infrared_detector_cli.py --input path\/to\/video.mp4 --output output_video.avi --con<\/code><\/li>\n<\/ul>\n","raw":"","protected":false},"excerpt":{"rendered":"\u6df1\u5ea6\u5b66\u4e60\u57fa\u4e8eYOLOv8\u6846\u67b6\u6765\u8bad\u7ec3\u7ea2\u5916\u56fe\u50cf\u5c0f\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6 \u7ea2\u5916\u56fe\u50cf\u5c0f\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6\u7684\u8bad\u7ec3\u53ca\u5e94\u7528 \u63a8\u7406\u8bc6\u522b\u68c0\u6d4b\u98de\u673a \u65e0\u4eba\u673a \u76f4\u5347\u673a 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YOLOV11\u5982\u4f55\u8bad\u7ec3\u81ea\u5df1\u7684\u6570\u636e\u96c6 YOLOv11\u8bad\u7ec3\u914d\u7f6e\u73af\u5883\u6559\u7a0b \u4ee5\u6c14\u7403\u68c0\u6d4b\u6570\u636e\u96c6\u4e3a\u4f8b","previous_post_id":6590,"previous_post_title":"YOLOV8\u6a21\u578b\u5982\u4f55\u8bad\u7ec3 \u53cc\u6a21\u6001\u7ea2\u5916-\u53ef\u89c1\u5149\u5bf9\u9f50\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6 \u7ea2\u5916\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6\u7684\u8bad\u7ec3\u53ca\u5e94\u7528 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detect.py<\/code>)<\/h4>\n\n\n\nfrom ultralytics import YOLOimport cv2defdetect_image(image_path, output_path='output_detection.jpg'):# Load trained model<\/em> model = YOLO('runs\/detect\/infrared_yolov8s\/weights\/best.pt')# Perform prediction<\/em> results = model.predict( source=image_path, conf=0.4,# Confidence threshold<\/em> iou=0.5, save=True, project='runs\/detect\/predict', name='result', exist_ok=True)# Extract and save results (optional)<\/em>for r in results: im = r.plot()# Plot bounding boxes and labels<\/em> cv2.imwrite(output_path, im)print(f\"Detection completed, saved to {output_path}\")# Print detected objects<\/em>for box in r.boxes: cls_name = model.names[int(box.cls)] conf =float(box.conf)print(f\"Detected: {cls_name} (Confidence: {conf:.3f})\")return output_pathif __name__ =='__main__': detect_image('test_infrared_image.jpg')<\/code><\/pre>\n\n\n\n\u8bc4\u4f30\u811a\u672c (evaluate.py<\/code>)<\/h4>\n\n\n\nfrom ultralytics import YOLOdefevaluate_model(): model = YOLO('runs\/detect\/infrared_yolov8s\/weights\/best.pt')# Evaluate on validation set<\/em> metrics = model.val( data='infrared_dataset\/data.yaml', split='val',# Or 'test' if evaluating on the test set<\/em> batch=16, imgsz=640, device=0if torch.cuda.is_available()else'cpu', save_json=True,# Save COCO format results for further analysis<\/em> conf_thres=0.001, iou_thres=0.6)# Output key metrics<\/em>print(f\"mAP@0.5: {metrics.box.map50:.4f}\")print(f\"mAP@0.5:0.95: {metrics.box.map:.4f}\")print(f\"Precision: {metrics.box.p:.4f}\")print(f\"Recall: {metrics.box.r:.4f}\")# Output AP per class<\/em>print(\"\\nAP per class:\")for i, name inenumerate(metrics.names.values()):print(f\" {name}: {metrics.box.ap[i]:.4f}\")if __name__ =='__main__': evaluate_model()<\/code><\/pre>\n\n\n\n\u6784\u5efa\u4e00\u4e2a\u53ef\u4ee5\u52a0\u8f7d\u6a21\u578b\u3001\u6267\u884c\u63a8\u7406\u5e76\u5bf9\u65b0\u56fe\u50cf\u6216\u89c6\u9891\u8fdb\u884c\u5b9e\u65f6\u5904\u7406\u7684\u5e94\u7528\u3002<\/p>\n\n\n\n\u4e00\u3001\u51c6\u5907<\/h3>\n\n\n\n\u786e\u4fdd\u5df2\u7ecf\u5b8c\u6210\u4e86\u6a21\u578b\u7684\u8bad\u7ec3\uff0c\u5e76\u4e14\u8bad\u7ec3\u597d\u7684\u6a21\u578b\u6587\u4ef6\uff08\u4f8b\u5982 best.pt<\/code>\uff09\uff0c\u4f4d\u4e8e runs\/detect\/infrared_yolov8s\/weights\/best.pt<\/code>\u3002<\/p>\n\n\n\n\u4e8c\u3001\u6a21\u578b\u5c01\u88c5<\/h3>\n\n\n\n\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5c01\u88c5\u6211\u4eec\u7684\u6a21\u578b\uff0c\u4f7f\u5176\u6613\u4e8e\u4f7f\u7528\u3002\u8fd9\u53ef\u4ee5\u901a\u8fc7\u521b\u5efa\u4e00\u4e2a\u7c7b\u6765\u5b9e\u73b0\uff0c\u8be5\u7c7b\u8d1f\u8d23\u52a0\u8f7d\u6a21\u578b\u5e76\u63d0\u4f9b\u9884\u6d4b\u65b9\u6cd5\u3002<\/p>\n\n\n\nmodel_utils.py<\/code><\/h4>\n\n\n\nfrom ultralytics import YOLOimport cv2classInfraredTargetDetector:def__init__(self, model_path='runs\/detect\/infrared_yolov8s\/weights\/best.pt'): self.model = YOLO(model_path)defpredict(self, image_path, conf=0.4):\"\"\" \u5bf9\u7ed9\u5b9a\u8def\u5f84\u7684\u56fe\u50cf\u8fdb\u884c\u9884\u6d4b\u3002 :param image_path: \u8f93\u5165\u56fe\u50cf\u7684\u8def\u5f84 :param conf: \u7f6e\u4fe1\u5ea6\u9608\u503c :return: \u68c0\u6d4b\u7ed3\u679c\u5217\u8868\uff0c\u6bcf\u4e2a\u7ed3\u679c\u5305\u542b\u7c7b\u522b\u540d\u79f0\u3001\u7f6e\u4fe1\u5ea6\u5206\u6570\u548c\u8fb9\u754c\u6846\u5750\u6807 \"\"\" results = self.model.predict(source=image_path, conf=conf) detections =[]for r in results:for box in r.boxes: cls_id =int(box.cls) label = self.model.names[cls_id] confidence =float(box.conf) bbox = box.xyxy[0].cpu().numpy().tolist()# [x1, y1, x2, y2]<\/em> detections.append({'class': label,'confidence': confidence,'bbox': bbox})return detectionsdefdetect_and_draw(self, image_path, output_path='output_detection.jpg', conf=0.4):\"\"\" \u5bf9\u56fe\u50cf\u8fdb\u884c\u68c0\u6d4b\u5e76\u5728\u539f\u56fe\u4e0a\u7ed8\u5236\u68c0\u6d4b\u7ed3\u679c\u3002 :param image_path: \u8f93\u5165\u56fe\u50cf\u7684\u8def\u5f84 :param output_path: \u8f93\u51fa\u56fe\u50cf\u7684\u4fdd\u5b58\u8def\u5f84 :param conf: \u7f6e\u4fe1\u5ea6\u9608\u503c :return: None \"\"\" detections = self.predict(image_path, conf) img = cv2.imread(image_path)for det in detections: bbox = det['bbox'] label =f\"{det['class']}: {det['confidence']:.2f}\" color =(0,255,0)# Green<\/em> cv2.rectangle(img,(int(bbox[0]),int(bbox[1])),(int(bbox[2]),int(bbox[3])), color,2) cv2.putText(img, label,(int(bbox[0]),int(bbox[1])-10), cv2.FONT_HERSHEY_SIMPLEX,0.9, color,2) cv2.imwrite(output_path, img)print(f\"Detection completed and saved to {output_path}\")# \u793a\u4f8b\u4f7f\u7528<\/em>if __name__ =='__main__': detector = InfraredTargetDetector() detector.detect_and_draw('test_infrared_image.jpg')<\/code><\/pre>\n\n\n\n\u4e09\u3001\u6784\u5efa\u547d\u4ee4\u884c\u5de5\u5177<\/h3>\n\n\n\n\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u521b\u5efa\u4e00\u4e2a\u547d\u4ee4\u884c\u5de5\u5177\uff0c\u5141\u8bb8\u7528\u6237\u901a\u8fc7\u547d\u4ee4\u884c\u5bf9\u56fe\u50cf\u6216\u89c6\u9891\u8fdb\u884c\u68c0\u6d4b\u3002<\/p>\n\n\n\ninfrared_detector_cli.py<\/code><\/h4>\n\n\n\nimport argparsefrom model_utils import InfraredTargetDetectorimport cv2defmain(): parser = argparse.ArgumentParser(description=\"Infrared Target Detection System\") parser.add_argument('--input',type=str, required=True,help='Path to input image or video file.') parser.add_argument('--output',type=str, default='output.jpg',help='Path to save the output.') parser.add_argument('--conf',type=float, default=0.4,help='Confidence threshold.') args = parser.parse_args() detector = InfraredTargetDetector()if args.input.lower().endswith(('jpg','jpeg','png')): detector.detect_and_draw(args.input, args.output, args.conf)elif args.input.lower().endswith(('mp4','avi')): cap = cv2.VideoCapture(args.input) frame_width =int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) frame_height =int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) out = cv2.VideoWriter(args.output, cv2.VideoWriter_fourcc(*'XVID'),20.0,(frame_width, frame_height))while cap.isOpened(): ret, frame = cap.read()ifnot ret:break# Convert frame to image path temporarily for prediction<\/em> temp_img_path ='temp_frame.jpg' cv2.imwrite(temp_img_path, frame) detections = detector.predict(temp_img_path, args.conf)for det in detections: bbox = det['bbox'] label =f\"{det['class']}: {det['confidence']:.2f}\" color =(0,255,0)# Green<\/em> cv2.rectangle(frame,(int(bbox[0]),int(bbox[1])),(int(bbox[2]),int(bbox[3])), color,2) cv2.putText(frame, label,(int(bbox[0]),int(bbox[1])-10), cv2.FONT_HERSHEY_SIMPLEX,0.9, color,2) out.write(frame) cap.release() out.release()print(f\"Video detection completed and saved to {args.output}\")else:print(\"Unsupported file format.\")if __name__ ==\"__main__\": main()<\/code><\/pre>\n\n\n\n\u56db\u3001\u8fd0\u884c\u4f60\u7684\u7cfb\u7edf<\/h3>\n\n\n\n\n\u5bf9\u4e8e\u5355\u5f20\u56fe\u7247<\/strong>\uff1apython infrared_detector_cli.py --input path\/to\/image.jpg --output output.jpg --conf\u00a00.5<\/code><\/li>\n\n\n\n\u5bf9\u4e8e\u89c6\u9891\u6587\u4ef6<\/strong>\uff1apython infrared_detector_cli.py --input path\/to\/video.mp4 --output output_video.avi --con<\/code><\/li>\n<\/ul>\n","raw":"","protected":false},"excerpt":{"rendered":"\u6df1\u5ea6\u5b66\u4e60\u57fa\u4e8eYOLOv8\u6846\u67b6\u6765\u8bad\u7ec3\u7ea2\u5916\u56fe\u50cf\u5c0f\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6 \u7ea2\u5916\u56fe\u50cf\u5c0f\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6\u7684\u8bad\u7ec3\u53ca\u5e94\u7528 \u63a8\u7406\u8bc6\u522b\u68c0\u6d4b\u98de\u673a \u65e0\u4eba\u673a \u76f4\u5347\u673a 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YOLOV11\u5982\u4f55\u8bad\u7ec3\u81ea\u5df1\u7684\u6570\u636e\u96c6 YOLOv11\u8bad\u7ec3\u914d\u7f6e\u73af\u5883\u6559\u7a0b \u4ee5\u6c14\u7403\u68c0\u6d4b\u6570\u636e\u96c6\u4e3a\u4f8b","previous_post_id":6590,"previous_post_title":"YOLOV8\u6a21\u578b\u5982\u4f55\u8bad\u7ec3 \u53cc\u6a21\u6001\u7ea2\u5916-\u53ef\u89c1\u5149\u5bf9\u9f50\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6 \u7ea2\u5916\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6\u7684\u8bad\u7ec3\u53ca\u5e94\u7528 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from ultralytics import YOLOimport cv2defdetect_image(image_path, output_path='output_detection.jpg'):# Load trained model<\/em> model = YOLO('runs\/detect\/infrared_yolov8s\/weights\/best.pt')# Perform prediction<\/em> results = model.predict( source=image_path, conf=0.4,# Confidence threshold<\/em> iou=0.5, save=True, project='runs\/detect\/predict', name='result', exist_ok=True)# Extract and save results (optional)<\/em>for r in results: im = r.plot()# Plot bounding boxes and labels<\/em> cv2.imwrite(output_path, im)print(f\"Detection completed, saved to {output_path}\")# Print detected objects<\/em>for box in r.boxes: cls_name = model.names[int(box.cls)] conf =float(box.conf)print(f\"Detected: {cls_name} (Confidence: {conf:.3f})\")return output_pathif __name__ =='__main__': detect_image('test_infrared_image.jpg')<\/code><\/pre>\n\n\n\n\u8bc4\u4f30\u811a\u672c (evaluate.py<\/code>)<\/h4>\n\n\n\nfrom ultralytics import YOLOdefevaluate_model(): model = YOLO('runs\/detect\/infrared_yolov8s\/weights\/best.pt')# Evaluate on validation set<\/em> metrics = model.val( data='infrared_dataset\/data.yaml', split='val',# Or 'test' if evaluating on the test set<\/em> batch=16, imgsz=640, device=0if torch.cuda.is_available()else'cpu', save_json=True,# Save COCO format results for further analysis<\/em> conf_thres=0.001, iou_thres=0.6)# Output key metrics<\/em>print(f\"mAP@0.5: {metrics.box.map50:.4f}\")print(f\"mAP@0.5:0.95: {metrics.box.map:.4f}\")print(f\"Precision: {metrics.box.p:.4f}\")print(f\"Recall: {metrics.box.r:.4f}\")# Output AP per class<\/em>print(\"\\nAP per class:\")for i, name inenumerate(metrics.names.values()):print(f\" {name}: {metrics.box.ap[i]:.4f}\")if __name__ =='__main__': evaluate_model()<\/code><\/pre>\n\n\n\n\u6784\u5efa\u4e00\u4e2a\u53ef\u4ee5\u52a0\u8f7d\u6a21\u578b\u3001\u6267\u884c\u63a8\u7406\u5e76\u5bf9\u65b0\u56fe\u50cf\u6216\u89c6\u9891\u8fdb\u884c\u5b9e\u65f6\u5904\u7406\u7684\u5e94\u7528\u3002<\/p>\n\n\n\n\u4e00\u3001\u51c6\u5907<\/h3>\n\n\n\n\u786e\u4fdd\u5df2\u7ecf\u5b8c\u6210\u4e86\u6a21\u578b\u7684\u8bad\u7ec3\uff0c\u5e76\u4e14\u8bad\u7ec3\u597d\u7684\u6a21\u578b\u6587\u4ef6\uff08\u4f8b\u5982 best.pt<\/code>\uff09\uff0c\u4f4d\u4e8e runs\/detect\/infrared_yolov8s\/weights\/best.pt<\/code>\u3002<\/p>\n\n\n\n\u4e8c\u3001\u6a21\u578b\u5c01\u88c5<\/h3>\n\n\n\n\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5c01\u88c5\u6211\u4eec\u7684\u6a21\u578b\uff0c\u4f7f\u5176\u6613\u4e8e\u4f7f\u7528\u3002\u8fd9\u53ef\u4ee5\u901a\u8fc7\u521b\u5efa\u4e00\u4e2a\u7c7b\u6765\u5b9e\u73b0\uff0c\u8be5\u7c7b\u8d1f\u8d23\u52a0\u8f7d\u6a21\u578b\u5e76\u63d0\u4f9b\u9884\u6d4b\u65b9\u6cd5\u3002<\/p>\n\n\n\nmodel_utils.py<\/code><\/h4>\n\n\n\nfrom ultralytics import YOLOimport cv2classInfraredTargetDetector:def__init__(self, model_path='runs\/detect\/infrared_yolov8s\/weights\/best.pt'): self.model = YOLO(model_path)defpredict(self, image_path, conf=0.4):\"\"\" \u5bf9\u7ed9\u5b9a\u8def\u5f84\u7684\u56fe\u50cf\u8fdb\u884c\u9884\u6d4b\u3002 :param image_path: \u8f93\u5165\u56fe\u50cf\u7684\u8def\u5f84 :param conf: \u7f6e\u4fe1\u5ea6\u9608\u503c :return: \u68c0\u6d4b\u7ed3\u679c\u5217\u8868\uff0c\u6bcf\u4e2a\u7ed3\u679c\u5305\u542b\u7c7b\u522b\u540d\u79f0\u3001\u7f6e\u4fe1\u5ea6\u5206\u6570\u548c\u8fb9\u754c\u6846\u5750\u6807 \"\"\" results = self.model.predict(source=image_path, conf=conf) detections =[]for r in results:for box in r.boxes: cls_id =int(box.cls) label = self.model.names[cls_id] confidence =float(box.conf) bbox = box.xyxy[0].cpu().numpy().tolist()# [x1, y1, x2, y2]<\/em> detections.append({'class': label,'confidence': confidence,'bbox': bbox})return detectionsdefdetect_and_draw(self, image_path, output_path='output_detection.jpg', conf=0.4):\"\"\" \u5bf9\u56fe\u50cf\u8fdb\u884c\u68c0\u6d4b\u5e76\u5728\u539f\u56fe\u4e0a\u7ed8\u5236\u68c0\u6d4b\u7ed3\u679c\u3002 :param image_path: \u8f93\u5165\u56fe\u50cf\u7684\u8def\u5f84 :param output_path: \u8f93\u51fa\u56fe\u50cf\u7684\u4fdd\u5b58\u8def\u5f84 :param conf: \u7f6e\u4fe1\u5ea6\u9608\u503c :return: None \"\"\" detections = self.predict(image_path, conf) img = cv2.imread(image_path)for det in detections: bbox = det['bbox'] label =f\"{det['class']}: {det['confidence']:.2f}\" color =(0,255,0)# Green<\/em> cv2.rectangle(img,(int(bbox[0]),int(bbox[1])),(int(bbox[2]),int(bbox[3])), color,2) cv2.putText(img, label,(int(bbox[0]),int(bbox[1])-10), cv2.FONT_HERSHEY_SIMPLEX,0.9, color,2) cv2.imwrite(output_path, img)print(f\"Detection completed and saved to {output_path}\")# \u793a\u4f8b\u4f7f\u7528<\/em>if __name__ =='__main__': detector = InfraredTargetDetector() detector.detect_and_draw('test_infrared_image.jpg')<\/code><\/pre>\n\n\n\n\u4e09\u3001\u6784\u5efa\u547d\u4ee4\u884c\u5de5\u5177<\/h3>\n\n\n\n\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u521b\u5efa\u4e00\u4e2a\u547d\u4ee4\u884c\u5de5\u5177\uff0c\u5141\u8bb8\u7528\u6237\u901a\u8fc7\u547d\u4ee4\u884c\u5bf9\u56fe\u50cf\u6216\u89c6\u9891\u8fdb\u884c\u68c0\u6d4b\u3002<\/p>\n\n\n\ninfrared_detector_cli.py<\/code><\/h4>\n\n\n\nimport argparsefrom model_utils import InfraredTargetDetectorimport cv2defmain(): parser = argparse.ArgumentParser(description=\"Infrared Target Detection System\") parser.add_argument('--input',type=str, required=True,help='Path to input image or video file.') parser.add_argument('--output',type=str, default='output.jpg',help='Path to save the output.') parser.add_argument('--conf',type=float, default=0.4,help='Confidence threshold.') args = parser.parse_args() detector = InfraredTargetDetector()if args.input.lower().endswith(('jpg','jpeg','png')): detector.detect_and_draw(args.input, args.output, args.conf)elif args.input.lower().endswith(('mp4','avi')): cap = cv2.VideoCapture(args.input) frame_width =int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) frame_height =int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) out = cv2.VideoWriter(args.output, cv2.VideoWriter_fourcc(*'XVID'),20.0,(frame_width, frame_height))while cap.isOpened(): ret, frame = cap.read()ifnot ret:break# Convert frame to image path temporarily for prediction<\/em> temp_img_path ='temp_frame.jpg' cv2.imwrite(temp_img_path, frame) detections = detector.predict(temp_img_path, args.conf)for det in detections: bbox = det['bbox'] label =f\"{det['class']}: {det['confidence']:.2f}\" color =(0,255,0)# Green<\/em> cv2.rectangle(frame,(int(bbox[0]),int(bbox[1])),(int(bbox[2]),int(bbox[3])), color,2) cv2.putText(frame, label,(int(bbox[0]),int(bbox[1])-10), cv2.FONT_HERSHEY_SIMPLEX,0.9, color,2) out.write(frame) cap.release() out.release()print(f\"Video detection completed and saved to {args.output}\")else:print(\"Unsupported file format.\")if __name__ ==\"__main__\": main()<\/code><\/pre>\n\n\n\n\u56db\u3001\u8fd0\u884c\u4f60\u7684\u7cfb\u7edf<\/h3>\n\n\n\n\n\u5bf9\u4e8e\u5355\u5f20\u56fe\u7247<\/strong>\uff1apython infrared_detector_cli.py --input path\/to\/image.jpg --output output.jpg --conf\u00a00.5<\/code><\/li>\n\n\n\n\u5bf9\u4e8e\u89c6\u9891\u6587\u4ef6<\/strong>\uff1apython infrared_detector_cli.py --input path\/to\/video.mp4 --output output_video.avi --con<\/code><\/li>\n<\/ul>\n","raw":"","protected":false},"excerpt":{"rendered":"\u6df1\u5ea6\u5b66\u4e60\u57fa\u4e8eYOLOv8\u6846\u67b6\u6765\u8bad\u7ec3\u7ea2\u5916\u56fe\u50cf\u5c0f\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6 \u7ea2\u5916\u56fe\u50cf\u5c0f\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6\u7684\u8bad\u7ec3\u53ca\u5e94\u7528 \u63a8\u7406\u8bc6\u522b\u68c0\u6d4b\u98de\u673a \u65e0\u4eba\u673a \u76f4\u5347\u673a …<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"emotion":"","emotion_color":"","title_style":"","license":"","footnotes":""},"categories":[46],"tags":[],"class_list":["post-6592","post","type-post","status-publish","format-standard","hentry","category-yolo"],"post_thumbnail_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","content_first_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_medium_image_300":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_thumbnail_image_624":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_frist_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_medium_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_large_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_full_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_all_images":[{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","id":"image0"},{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","id":"image1"},{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKicJO37JCcKiczyLibDpRgBDXlTon1aE9dVcNEEQ2gvaQTRZOajJPzLexw\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=1","id":"image2"},{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKicJO37JCcKiczyLibDpRgBDXlTon1aE9dVcNEEQ2gvaQTRZOajJPzLexw\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=1","id":"image3"},{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKAyqwlqJhAhUaqz66Via4WKqzx8qTkKvrURcsQVImhEUmXhZAYbia279Q\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=2","id":"image4"},{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKAyqwlqJhAhUaqz66Via4WKqzx8qTkKvrURcsQVImhEUmXhZAYbia279Q\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=2","id":"image5"}],"videoAdId":"","listAd":"0","listAdId":"","listAdEvery":6,"total_comments":0,"category_name":"YOLO","post_date":"2025-09-29","like_count":"0","praiseWord":"\u9f13\u52b1","copyright_state":"","excitationAd":"0","rewardedVideoAdId":"","detailAdId":"","detailAd":"0","enterpriseMinapp":"0","audios":[],"postImageUrl":"https:\/\/wp-moto-1258805347.cos.ap-shanghai.myqcloud.com\/2023\/05\/20230519082947553.jpg","avatarurls":[],"related_posts":null,"pageviews":269,"next_post_id":6657,"next_post_title":"\u6df1\u5ea6\u5b66\u4e60\u76ee\u6807\u68c0\u6d4b YOLOV11\u5982\u4f55\u8bad\u7ec3\u81ea\u5df1\u7684\u6570\u636e\u96c6 YOLOv11\u8bad\u7ec3\u914d\u7f6e\u73af\u5883\u6559\u7a0b \u4ee5\u6c14\u7403\u68c0\u6d4b\u6570\u636e\u96c6\u4e3a\u4f8b","previous_post_id":6590,"previous_post_title":"YOLOV8\u6a21\u578b\u5982\u4f55\u8bad\u7ec3 \u53cc\u6a21\u6001\u7ea2\u5916-\u53ef\u89c1\u5149\u5bf9\u9f50\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6 \u7ea2\u5916\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6\u7684\u8bad\u7ec3\u53ca\u5e94\u7528 \u53cc\u6a21\u6001\uff08\u7ea2\u5916+\u53ef\u89c1\u5149\uff09\u6570\u636e\u96c6","_links":{"self":[{"href":"https:\/\/xinchewhd.com.cn\/index.php\/wp-json\/wp\/v2\/posts\/6592","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/xinchewhd.com.cn\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/xinchewhd.com.cn\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/xinchewhd.com.cn\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/xinchewhd.com.cn\/index.php\/wp-json\/wp\/v2\/comments?post=6592"}],"version-history":[{"count":1,"href":"https:\/\/xinchewhd.com.cn\/index.php\/wp-json\/wp\/v2\/posts\/6592\/revisions"}],"predecessor-version":[{"id":6593,"href":"https:\/\/xinchewhd.com.cn\/index.php\/wp-json\/wp\/v2\/posts\/6592\/revisions\/6593"}],"wp:attachment":[{"href":"https:\/\/xinchewhd.com.cn\/index.php\/wp-json\/wp\/v2\/media?parent=6592"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/xinchewhd.com.cn\/index.php\/wp-json\/wp\/v2\/categories?post=6592"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/xinchewhd.com.cn\/index.php\/wp-json\/wp\/v2\/tags?post=6592"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
evaluate.py<\/code>)<\/h4>\n\n\n\nfrom ultralytics import YOLOdefevaluate_model(): model = YOLO('runs\/detect\/infrared_yolov8s\/weights\/best.pt')# Evaluate on validation set<\/em> metrics = model.val( data='infrared_dataset\/data.yaml', split='val',# Or 'test' if evaluating on the test set<\/em> batch=16, imgsz=640, device=0if torch.cuda.is_available()else'cpu', save_json=True,# Save COCO format results for further analysis<\/em> conf_thres=0.001, iou_thres=0.6)# Output key metrics<\/em>print(f\"mAP@0.5: {metrics.box.map50:.4f}\")print(f\"mAP@0.5:0.95: {metrics.box.map:.4f}\")print(f\"Precision: {metrics.box.p:.4f}\")print(f\"Recall: {metrics.box.r:.4f}\")# Output AP per class<\/em>print(\"\\nAP per class:\")for i, name inenumerate(metrics.names.values()):print(f\" {name}: {metrics.box.ap[i]:.4f}\")if __name__ =='__main__': evaluate_model()<\/code><\/pre>\n\n\n\n\u6784\u5efa\u4e00\u4e2a\u53ef\u4ee5\u52a0\u8f7d\u6a21\u578b\u3001\u6267\u884c\u63a8\u7406\u5e76\u5bf9\u65b0\u56fe\u50cf\u6216\u89c6\u9891\u8fdb\u884c\u5b9e\u65f6\u5904\u7406\u7684\u5e94\u7528\u3002<\/p>\n\n\n\n\u4e00\u3001\u51c6\u5907<\/h3>\n\n\n\n\u786e\u4fdd\u5df2\u7ecf\u5b8c\u6210\u4e86\u6a21\u578b\u7684\u8bad\u7ec3\uff0c\u5e76\u4e14\u8bad\u7ec3\u597d\u7684\u6a21\u578b\u6587\u4ef6\uff08\u4f8b\u5982 best.pt<\/code>\uff09\uff0c\u4f4d\u4e8e runs\/detect\/infrared_yolov8s\/weights\/best.pt<\/code>\u3002<\/p>\n\n\n\n\u4e8c\u3001\u6a21\u578b\u5c01\u88c5<\/h3>\n\n\n\n\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5c01\u88c5\u6211\u4eec\u7684\u6a21\u578b\uff0c\u4f7f\u5176\u6613\u4e8e\u4f7f\u7528\u3002\u8fd9\u53ef\u4ee5\u901a\u8fc7\u521b\u5efa\u4e00\u4e2a\u7c7b\u6765\u5b9e\u73b0\uff0c\u8be5\u7c7b\u8d1f\u8d23\u52a0\u8f7d\u6a21\u578b\u5e76\u63d0\u4f9b\u9884\u6d4b\u65b9\u6cd5\u3002<\/p>\n\n\n\nmodel_utils.py<\/code><\/h4>\n\n\n\nfrom ultralytics import YOLOimport cv2classInfraredTargetDetector:def__init__(self, model_path='runs\/detect\/infrared_yolov8s\/weights\/best.pt'): self.model = YOLO(model_path)defpredict(self, image_path, conf=0.4):\"\"\" \u5bf9\u7ed9\u5b9a\u8def\u5f84\u7684\u56fe\u50cf\u8fdb\u884c\u9884\u6d4b\u3002 :param image_path: \u8f93\u5165\u56fe\u50cf\u7684\u8def\u5f84 :param conf: \u7f6e\u4fe1\u5ea6\u9608\u503c :return: \u68c0\u6d4b\u7ed3\u679c\u5217\u8868\uff0c\u6bcf\u4e2a\u7ed3\u679c\u5305\u542b\u7c7b\u522b\u540d\u79f0\u3001\u7f6e\u4fe1\u5ea6\u5206\u6570\u548c\u8fb9\u754c\u6846\u5750\u6807 \"\"\" results = self.model.predict(source=image_path, conf=conf) detections =[]for r in results:for box in r.boxes: cls_id =int(box.cls) label = self.model.names[cls_id] confidence =float(box.conf) bbox = box.xyxy[0].cpu().numpy().tolist()# [x1, y1, x2, y2]<\/em> detections.append({'class': label,'confidence': confidence,'bbox': bbox})return detectionsdefdetect_and_draw(self, image_path, output_path='output_detection.jpg', conf=0.4):\"\"\" \u5bf9\u56fe\u50cf\u8fdb\u884c\u68c0\u6d4b\u5e76\u5728\u539f\u56fe\u4e0a\u7ed8\u5236\u68c0\u6d4b\u7ed3\u679c\u3002 :param image_path: \u8f93\u5165\u56fe\u50cf\u7684\u8def\u5f84 :param output_path: \u8f93\u51fa\u56fe\u50cf\u7684\u4fdd\u5b58\u8def\u5f84 :param conf: \u7f6e\u4fe1\u5ea6\u9608\u503c :return: None \"\"\" detections = self.predict(image_path, conf) img = cv2.imread(image_path)for det in detections: bbox = det['bbox'] label =f\"{det['class']}: {det['confidence']:.2f}\" color =(0,255,0)# Green<\/em> cv2.rectangle(img,(int(bbox[0]),int(bbox[1])),(int(bbox[2]),int(bbox[3])), color,2) cv2.putText(img, label,(int(bbox[0]),int(bbox[1])-10), cv2.FONT_HERSHEY_SIMPLEX,0.9, color,2) cv2.imwrite(output_path, img)print(f\"Detection completed and saved to {output_path}\")# \u793a\u4f8b\u4f7f\u7528<\/em>if __name__ =='__main__': detector = InfraredTargetDetector() detector.detect_and_draw('test_infrared_image.jpg')<\/code><\/pre>\n\n\n\n\u4e09\u3001\u6784\u5efa\u547d\u4ee4\u884c\u5de5\u5177<\/h3>\n\n\n\n\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u521b\u5efa\u4e00\u4e2a\u547d\u4ee4\u884c\u5de5\u5177\uff0c\u5141\u8bb8\u7528\u6237\u901a\u8fc7\u547d\u4ee4\u884c\u5bf9\u56fe\u50cf\u6216\u89c6\u9891\u8fdb\u884c\u68c0\u6d4b\u3002<\/p>\n\n\n\ninfrared_detector_cli.py<\/code><\/h4>\n\n\n\nimport argparsefrom model_utils import InfraredTargetDetectorimport cv2defmain(): parser = argparse.ArgumentParser(description=\"Infrared Target Detection System\") parser.add_argument('--input',type=str, required=True,help='Path to input image or video file.') parser.add_argument('--output',type=str, default='output.jpg',help='Path to save the output.') parser.add_argument('--conf',type=float, default=0.4,help='Confidence threshold.') args = parser.parse_args() detector = InfraredTargetDetector()if args.input.lower().endswith(('jpg','jpeg','png')): detector.detect_and_draw(args.input, args.output, args.conf)elif args.input.lower().endswith(('mp4','avi')): cap = cv2.VideoCapture(args.input) frame_width =int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) frame_height =int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) out = cv2.VideoWriter(args.output, cv2.VideoWriter_fourcc(*'XVID'),20.0,(frame_width, frame_height))while cap.isOpened(): ret, frame = cap.read()ifnot ret:break# Convert frame to image path temporarily for prediction<\/em> temp_img_path ='temp_frame.jpg' cv2.imwrite(temp_img_path, frame) detections = detector.predict(temp_img_path, args.conf)for det in detections: bbox = det['bbox'] label =f\"{det['class']}: {det['confidence']:.2f}\" color =(0,255,0)# Green<\/em> cv2.rectangle(frame,(int(bbox[0]),int(bbox[1])),(int(bbox[2]),int(bbox[3])), color,2) cv2.putText(frame, label,(int(bbox[0]),int(bbox[1])-10), cv2.FONT_HERSHEY_SIMPLEX,0.9, color,2) out.write(frame) cap.release() out.release()print(f\"Video detection completed and saved to {args.output}\")else:print(\"Unsupported file format.\")if __name__ ==\"__main__\": main()<\/code><\/pre>\n\n\n\n\u56db\u3001\u8fd0\u884c\u4f60\u7684\u7cfb\u7edf<\/h3>\n\n\n\n\n\u5bf9\u4e8e\u5355\u5f20\u56fe\u7247<\/strong>\uff1apython infrared_detector_cli.py --input path\/to\/image.jpg --output output.jpg --conf\u00a00.5<\/code><\/li>\n\n\n\n\u5bf9\u4e8e\u89c6\u9891\u6587\u4ef6<\/strong>\uff1apython infrared_detector_cli.py --input path\/to\/video.mp4 --output output_video.avi --con<\/code><\/li>\n<\/ul>\n","raw":"","protected":false},"excerpt":{"rendered":"\u6df1\u5ea6\u5b66\u4e60\u57fa\u4e8eYOLOv8\u6846\u67b6\u6765\u8bad\u7ec3\u7ea2\u5916\u56fe\u50cf\u5c0f\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6 \u7ea2\u5916\u56fe\u50cf\u5c0f\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6\u7684\u8bad\u7ec3\u53ca\u5e94\u7528 \u63a8\u7406\u8bc6\u522b\u68c0\u6d4b\u98de\u673a \u65e0\u4eba\u673a \u76f4\u5347\u673a 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YOLOV11\u5982\u4f55\u8bad\u7ec3\u81ea\u5df1\u7684\u6570\u636e\u96c6 YOLOv11\u8bad\u7ec3\u914d\u7f6e\u73af\u5883\u6559\u7a0b \u4ee5\u6c14\u7403\u68c0\u6d4b\u6570\u636e\u96c6\u4e3a\u4f8b","previous_post_id":6590,"previous_post_title":"YOLOV8\u6a21\u578b\u5982\u4f55\u8bad\u7ec3 \u53cc\u6a21\u6001\u7ea2\u5916-\u53ef\u89c1\u5149\u5bf9\u9f50\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6 \u7ea2\u5916\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6\u7684\u8bad\u7ec3\u53ca\u5e94\u7528 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from ultralytics import YOLOdefevaluate_model(): model = YOLO('runs\/detect\/infrared_yolov8s\/weights\/best.pt')# Evaluate on validation set<\/em> metrics = model.val( data='infrared_dataset\/data.yaml', split='val',# Or 'test' if evaluating on the test set<\/em> batch=16, imgsz=640, device=0if torch.cuda.is_available()else'cpu', save_json=True,# Save COCO format results for further analysis<\/em> conf_thres=0.001, iou_thres=0.6)# Output key metrics<\/em>print(f\"mAP@0.5: {metrics.box.map50:.4f}\")print(f\"mAP@0.5:0.95: {metrics.box.map:.4f}\")print(f\"Precision: {metrics.box.p:.4f}\")print(f\"Recall: {metrics.box.r:.4f}\")# Output AP per class<\/em>print(\"\\nAP per class:\")for i, name inenumerate(metrics.names.values()):print(f\" {name}: {metrics.box.ap[i]:.4f}\")if __name__ =='__main__': evaluate_model()<\/code><\/pre>\n\n\n\n\u6784\u5efa\u4e00\u4e2a\u53ef\u4ee5\u52a0\u8f7d\u6a21\u578b\u3001\u6267\u884c\u63a8\u7406\u5e76\u5bf9\u65b0\u56fe\u50cf\u6216\u89c6\u9891\u8fdb\u884c\u5b9e\u65f6\u5904\u7406\u7684\u5e94\u7528\u3002<\/p>\n\n\n\n\u4e00\u3001\u51c6\u5907<\/h3>\n\n\n\n\u786e\u4fdd\u5df2\u7ecf\u5b8c\u6210\u4e86\u6a21\u578b\u7684\u8bad\u7ec3\uff0c\u5e76\u4e14\u8bad\u7ec3\u597d\u7684\u6a21\u578b\u6587\u4ef6\uff08\u4f8b\u5982 best.pt<\/code>\uff09\uff0c\u4f4d\u4e8e runs\/detect\/infrared_yolov8s\/weights\/best.pt<\/code>\u3002<\/p>\n\n\n\n\u4e8c\u3001\u6a21\u578b\u5c01\u88c5<\/h3>\n\n\n\n\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5c01\u88c5\u6211\u4eec\u7684\u6a21\u578b\uff0c\u4f7f\u5176\u6613\u4e8e\u4f7f\u7528\u3002\u8fd9\u53ef\u4ee5\u901a\u8fc7\u521b\u5efa\u4e00\u4e2a\u7c7b\u6765\u5b9e\u73b0\uff0c\u8be5\u7c7b\u8d1f\u8d23\u52a0\u8f7d\u6a21\u578b\u5e76\u63d0\u4f9b\u9884\u6d4b\u65b9\u6cd5\u3002<\/p>\n\n\n\nmodel_utils.py<\/code><\/h4>\n\n\n\nfrom ultralytics import YOLOimport cv2classInfraredTargetDetector:def__init__(self, model_path='runs\/detect\/infrared_yolov8s\/weights\/best.pt'): self.model = YOLO(model_path)defpredict(self, image_path, conf=0.4):\"\"\" \u5bf9\u7ed9\u5b9a\u8def\u5f84\u7684\u56fe\u50cf\u8fdb\u884c\u9884\u6d4b\u3002 :param image_path: \u8f93\u5165\u56fe\u50cf\u7684\u8def\u5f84 :param conf: \u7f6e\u4fe1\u5ea6\u9608\u503c :return: \u68c0\u6d4b\u7ed3\u679c\u5217\u8868\uff0c\u6bcf\u4e2a\u7ed3\u679c\u5305\u542b\u7c7b\u522b\u540d\u79f0\u3001\u7f6e\u4fe1\u5ea6\u5206\u6570\u548c\u8fb9\u754c\u6846\u5750\u6807 \"\"\" results = self.model.predict(source=image_path, conf=conf) detections =[]for r in results:for box in r.boxes: cls_id =int(box.cls) label = self.model.names[cls_id] confidence =float(box.conf) bbox = box.xyxy[0].cpu().numpy().tolist()# [x1, y1, x2, y2]<\/em> detections.append({'class': label,'confidence': confidence,'bbox': bbox})return detectionsdefdetect_and_draw(self, image_path, output_path='output_detection.jpg', conf=0.4):\"\"\" \u5bf9\u56fe\u50cf\u8fdb\u884c\u68c0\u6d4b\u5e76\u5728\u539f\u56fe\u4e0a\u7ed8\u5236\u68c0\u6d4b\u7ed3\u679c\u3002 :param image_path: \u8f93\u5165\u56fe\u50cf\u7684\u8def\u5f84 :param output_path: \u8f93\u51fa\u56fe\u50cf\u7684\u4fdd\u5b58\u8def\u5f84 :param conf: \u7f6e\u4fe1\u5ea6\u9608\u503c :return: None \"\"\" detections = self.predict(image_path, conf) img = cv2.imread(image_path)for det in detections: bbox = det['bbox'] label =f\"{det['class']}: {det['confidence']:.2f}\" color =(0,255,0)# Green<\/em> cv2.rectangle(img,(int(bbox[0]),int(bbox[1])),(int(bbox[2]),int(bbox[3])), color,2) cv2.putText(img, label,(int(bbox[0]),int(bbox[1])-10), cv2.FONT_HERSHEY_SIMPLEX,0.9, color,2) cv2.imwrite(output_path, img)print(f\"Detection completed and saved to {output_path}\")# \u793a\u4f8b\u4f7f\u7528<\/em>if __name__ =='__main__': detector = InfraredTargetDetector() detector.detect_and_draw('test_infrared_image.jpg')<\/code><\/pre>\n\n\n\n\u4e09\u3001\u6784\u5efa\u547d\u4ee4\u884c\u5de5\u5177<\/h3>\n\n\n\n\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u521b\u5efa\u4e00\u4e2a\u547d\u4ee4\u884c\u5de5\u5177\uff0c\u5141\u8bb8\u7528\u6237\u901a\u8fc7\u547d\u4ee4\u884c\u5bf9\u56fe\u50cf\u6216\u89c6\u9891\u8fdb\u884c\u68c0\u6d4b\u3002<\/p>\n\n\n\ninfrared_detector_cli.py<\/code><\/h4>\n\n\n\nimport argparsefrom model_utils import InfraredTargetDetectorimport cv2defmain(): parser = argparse.ArgumentParser(description=\"Infrared Target Detection System\") parser.add_argument('--input',type=str, required=True,help='Path to input image or video file.') parser.add_argument('--output',type=str, default='output.jpg',help='Path to save the output.') parser.add_argument('--conf',type=float, default=0.4,help='Confidence threshold.') args = parser.parse_args() detector = InfraredTargetDetector()if args.input.lower().endswith(('jpg','jpeg','png')): detector.detect_and_draw(args.input, args.output, args.conf)elif args.input.lower().endswith(('mp4','avi')): cap = cv2.VideoCapture(args.input) frame_width =int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) frame_height =int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) out = cv2.VideoWriter(args.output, cv2.VideoWriter_fourcc(*'XVID'),20.0,(frame_width, frame_height))while cap.isOpened(): ret, frame = cap.read()ifnot ret:break# Convert frame to image path temporarily for prediction<\/em> temp_img_path ='temp_frame.jpg' cv2.imwrite(temp_img_path, frame) detections = detector.predict(temp_img_path, args.conf)for det in detections: bbox = det['bbox'] label =f\"{det['class']}: {det['confidence']:.2f}\" color =(0,255,0)# Green<\/em> cv2.rectangle(frame,(int(bbox[0]),int(bbox[1])),(int(bbox[2]),int(bbox[3])), color,2) cv2.putText(frame, label,(int(bbox[0]),int(bbox[1])-10), cv2.FONT_HERSHEY_SIMPLEX,0.9, color,2) out.write(frame) cap.release() out.release()print(f\"Video detection completed and saved to {args.output}\")else:print(\"Unsupported file format.\")if __name__ ==\"__main__\": main()<\/code><\/pre>\n\n\n\n\u56db\u3001\u8fd0\u884c\u4f60\u7684\u7cfb\u7edf<\/h3>\n\n\n\n\n\u5bf9\u4e8e\u5355\u5f20\u56fe\u7247<\/strong>\uff1apython infrared_detector_cli.py --input path\/to\/image.jpg --output output.jpg --conf\u00a00.5<\/code><\/li>\n\n\n\n\u5bf9\u4e8e\u89c6\u9891\u6587\u4ef6<\/strong>\uff1apython infrared_detector_cli.py --input path\/to\/video.mp4 --output output_video.avi --con<\/code><\/li>\n<\/ul>\n","raw":"","protected":false},"excerpt":{"rendered":"\u6df1\u5ea6\u5b66\u4e60\u57fa\u4e8eYOLOv8\u6846\u67b6\u6765\u8bad\u7ec3\u7ea2\u5916\u56fe\u50cf\u5c0f\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6 \u7ea2\u5916\u56fe\u50cf\u5c0f\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6\u7684\u8bad\u7ec3\u53ca\u5e94\u7528 \u63a8\u7406\u8bc6\u522b\u68c0\u6d4b\u98de\u673a \u65e0\u4eba\u673a \u76f4\u5347\u673a …<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"emotion":"","emotion_color":"","title_style":"","license":"","footnotes":""},"categories":[46],"tags":[],"class_list":["post-6592","post","type-post","status-publish","format-standard","hentry","category-yolo"],"post_thumbnail_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","content_first_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_medium_image_300":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_thumbnail_image_624":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_frist_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_medium_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_large_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_full_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_all_images":[{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","id":"image0"},{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","id":"image1"},{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKicJO37JCcKiczyLibDpRgBDXlTon1aE9dVcNEEQ2gvaQTRZOajJPzLexw\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=1","id":"image2"},{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKicJO37JCcKiczyLibDpRgBDXlTon1aE9dVcNEEQ2gvaQTRZOajJPzLexw\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=1","id":"image3"},{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKAyqwlqJhAhUaqz66Via4WKqzx8qTkKvrURcsQVImhEUmXhZAYbia279Q\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=2","id":"image4"},{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKAyqwlqJhAhUaqz66Via4WKqzx8qTkKvrURcsQVImhEUmXhZAYbia279Q\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=2","id":"image5"}],"videoAdId":"","listAd":"0","listAdId":"","listAdEvery":6,"total_comments":0,"category_name":"YOLO","post_date":"2025-09-29","like_count":"0","praiseWord":"\u9f13\u52b1","copyright_state":"","excitationAd":"0","rewardedVideoAdId":"","detailAdId":"","detailAd":"0","enterpriseMinapp":"0","audios":[],"postImageUrl":"https:\/\/wp-moto-1258805347.cos.ap-shanghai.myqcloud.com\/2023\/05\/20230519082947553.jpg","avatarurls":[],"related_posts":null,"pageviews":269,"next_post_id":6657,"next_post_title":"\u6df1\u5ea6\u5b66\u4e60\u76ee\u6807\u68c0\u6d4b YOLOV11\u5982\u4f55\u8bad\u7ec3\u81ea\u5df1\u7684\u6570\u636e\u96c6 YOLOv11\u8bad\u7ec3\u914d\u7f6e\u73af\u5883\u6559\u7a0b \u4ee5\u6c14\u7403\u68c0\u6d4b\u6570\u636e\u96c6\u4e3a\u4f8b","previous_post_id":6590,"previous_post_title":"YOLOV8\u6a21\u578b\u5982\u4f55\u8bad\u7ec3 \u53cc\u6a21\u6001\u7ea2\u5916-\u53ef\u89c1\u5149\u5bf9\u9f50\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6 \u7ea2\u5916\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6\u7684\u8bad\u7ec3\u53ca\u5e94\u7528 \u53cc\u6a21\u6001\uff08\u7ea2\u5916+\u53ef\u89c1\u5149\uff09\u6570\u636e\u96c6","_links":{"self":[{"href":"https:\/\/xinchewhd.com.cn\/index.php\/wp-json\/wp\/v2\/posts\/6592","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/xinchewhd.com.cn\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/xinchewhd.com.cn\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/xinchewhd.com.cn\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/xinchewhd.com.cn\/index.php\/wp-json\/wp\/v2\/comments?post=6592"}],"version-history":[{"count":1,"href":"https:\/\/xinchewhd.com.cn\/index.php\/wp-json\/wp\/v2\/posts\/6592\/revisions"}],"predecessor-version":[{"id":6593,"href":"https:\/\/xinchewhd.com.cn\/index.php\/wp-json\/wp\/v2\/posts\/6592\/revisions\/6593"}],"wp:attachment":[{"href":"https:\/\/xinchewhd.com.cn\/index.php\/wp-json\/wp\/v2\/media?parent=6592"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/xinchewhd.com.cn\/index.php\/wp-json\/wp\/v2\/categories?post=6592"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/xinchewhd.com.cn\/index.php\/wp-json\/wp\/v2\/tags?post=6592"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
\u6784\u5efa\u4e00\u4e2a\u53ef\u4ee5\u52a0\u8f7d\u6a21\u578b\u3001\u6267\u884c\u63a8\u7406\u5e76\u5bf9\u65b0\u56fe\u50cf\u6216\u89c6\u9891\u8fdb\u884c\u5b9e\u65f6\u5904\u7406\u7684\u5e94\u7528\u3002<\/p>\n\n\n\n
\u786e\u4fdd\u5df2\u7ecf\u5b8c\u6210\u4e86\u6a21\u578b\u7684\u8bad\u7ec3\uff0c\u5e76\u4e14\u8bad\u7ec3\u597d\u7684\u6a21\u578b\u6587\u4ef6\uff08\u4f8b\u5982 best.pt<\/code>\uff09\uff0c\u4f4d\u4e8e runs\/detect\/infrared_yolov8s\/weights\/best.pt<\/code>\u3002<\/p>\n\n\n\n\u4e8c\u3001\u6a21\u578b\u5c01\u88c5<\/h3>\n\n\n\n\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5c01\u88c5\u6211\u4eec\u7684\u6a21\u578b\uff0c\u4f7f\u5176\u6613\u4e8e\u4f7f\u7528\u3002\u8fd9\u53ef\u4ee5\u901a\u8fc7\u521b\u5efa\u4e00\u4e2a\u7c7b\u6765\u5b9e\u73b0\uff0c\u8be5\u7c7b\u8d1f\u8d23\u52a0\u8f7d\u6a21\u578b\u5e76\u63d0\u4f9b\u9884\u6d4b\u65b9\u6cd5\u3002<\/p>\n\n\n\nmodel_utils.py<\/code><\/h4>\n\n\n\nfrom ultralytics import YOLOimport cv2classInfraredTargetDetector:def__init__(self, model_path='runs\/detect\/infrared_yolov8s\/weights\/best.pt'): self.model = YOLO(model_path)defpredict(self, image_path, conf=0.4):\"\"\" \u5bf9\u7ed9\u5b9a\u8def\u5f84\u7684\u56fe\u50cf\u8fdb\u884c\u9884\u6d4b\u3002 :param image_path: \u8f93\u5165\u56fe\u50cf\u7684\u8def\u5f84 :param conf: \u7f6e\u4fe1\u5ea6\u9608\u503c :return: \u68c0\u6d4b\u7ed3\u679c\u5217\u8868\uff0c\u6bcf\u4e2a\u7ed3\u679c\u5305\u542b\u7c7b\u522b\u540d\u79f0\u3001\u7f6e\u4fe1\u5ea6\u5206\u6570\u548c\u8fb9\u754c\u6846\u5750\u6807 \"\"\" results = self.model.predict(source=image_path, conf=conf) detections =[]for r in results:for box in r.boxes: cls_id =int(box.cls) label = self.model.names[cls_id] confidence =float(box.conf) bbox = box.xyxy[0].cpu().numpy().tolist()# [x1, y1, x2, y2]<\/em> detections.append({'class': label,'confidence': confidence,'bbox': bbox})return detectionsdefdetect_and_draw(self, image_path, output_path='output_detection.jpg', conf=0.4):\"\"\" \u5bf9\u56fe\u50cf\u8fdb\u884c\u68c0\u6d4b\u5e76\u5728\u539f\u56fe\u4e0a\u7ed8\u5236\u68c0\u6d4b\u7ed3\u679c\u3002 :param image_path: \u8f93\u5165\u56fe\u50cf\u7684\u8def\u5f84 :param output_path: \u8f93\u51fa\u56fe\u50cf\u7684\u4fdd\u5b58\u8def\u5f84 :param conf: \u7f6e\u4fe1\u5ea6\u9608\u503c :return: None \"\"\" detections = self.predict(image_path, conf) img = cv2.imread(image_path)for det in detections: bbox = det['bbox'] label =f\"{det['class']}: {det['confidence']:.2f}\" color =(0,255,0)# Green<\/em> cv2.rectangle(img,(int(bbox[0]),int(bbox[1])),(int(bbox[2]),int(bbox[3])), color,2) cv2.putText(img, label,(int(bbox[0]),int(bbox[1])-10), cv2.FONT_HERSHEY_SIMPLEX,0.9, color,2) cv2.imwrite(output_path, img)print(f\"Detection completed and saved to {output_path}\")# \u793a\u4f8b\u4f7f\u7528<\/em>if __name__ =='__main__': detector = InfraredTargetDetector() detector.detect_and_draw('test_infrared_image.jpg')<\/code><\/pre>\n\n\n\n\u4e09\u3001\u6784\u5efa\u547d\u4ee4\u884c\u5de5\u5177<\/h3>\n\n\n\n\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u521b\u5efa\u4e00\u4e2a\u547d\u4ee4\u884c\u5de5\u5177\uff0c\u5141\u8bb8\u7528\u6237\u901a\u8fc7\u547d\u4ee4\u884c\u5bf9\u56fe\u50cf\u6216\u89c6\u9891\u8fdb\u884c\u68c0\u6d4b\u3002<\/p>\n\n\n\ninfrared_detector_cli.py<\/code><\/h4>\n\n\n\nimport argparsefrom model_utils import InfraredTargetDetectorimport cv2defmain(): parser = argparse.ArgumentParser(description=\"Infrared Target Detection System\") parser.add_argument('--input',type=str, required=True,help='Path to input image or video file.') parser.add_argument('--output',type=str, default='output.jpg',help='Path to save the output.') parser.add_argument('--conf',type=float, default=0.4,help='Confidence threshold.') args = parser.parse_args() detector = InfraredTargetDetector()if args.input.lower().endswith(('jpg','jpeg','png')): detector.detect_and_draw(args.input, args.output, args.conf)elif args.input.lower().endswith(('mp4','avi')): cap = cv2.VideoCapture(args.input) frame_width =int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) frame_height =int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) out = cv2.VideoWriter(args.output, cv2.VideoWriter_fourcc(*'XVID'),20.0,(frame_width, frame_height))while cap.isOpened(): ret, frame = cap.read()ifnot ret:break# Convert frame to image path temporarily for prediction<\/em> temp_img_path ='temp_frame.jpg' cv2.imwrite(temp_img_path, frame) detections = detector.predict(temp_img_path, args.conf)for det in detections: bbox = det['bbox'] label =f\"{det['class']}: {det['confidence']:.2f}\" color =(0,255,0)# Green<\/em> cv2.rectangle(frame,(int(bbox[0]),int(bbox[1])),(int(bbox[2]),int(bbox[3])), color,2) cv2.putText(frame, label,(int(bbox[0]),int(bbox[1])-10), cv2.FONT_HERSHEY_SIMPLEX,0.9, color,2) out.write(frame) cap.release() out.release()print(f\"Video detection completed and saved to {args.output}\")else:print(\"Unsupported file format.\")if __name__ ==\"__main__\": main()<\/code><\/pre>\n\n\n\n\u56db\u3001\u8fd0\u884c\u4f60\u7684\u7cfb\u7edf<\/h3>\n\n\n\n\n\u5bf9\u4e8e\u5355\u5f20\u56fe\u7247<\/strong>\uff1apython infrared_detector_cli.py --input path\/to\/image.jpg --output output.jpg --conf\u00a00.5<\/code><\/li>\n\n\n\n\u5bf9\u4e8e\u89c6\u9891\u6587\u4ef6<\/strong>\uff1apython infrared_detector_cli.py --input path\/to\/video.mp4 --output output_video.avi --con<\/code><\/li>\n<\/ul>\n","raw":"","protected":false},"excerpt":{"rendered":"\u6df1\u5ea6\u5b66\u4e60\u57fa\u4e8eYOLOv8\u6846\u67b6\u6765\u8bad\u7ec3\u7ea2\u5916\u56fe\u50cf\u5c0f\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6 \u7ea2\u5916\u56fe\u50cf\u5c0f\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6\u7684\u8bad\u7ec3\u53ca\u5e94\u7528 \u63a8\u7406\u8bc6\u522b\u68c0\u6d4b\u98de\u673a \u65e0\u4eba\u673a \u76f4\u5347\u673a …<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"emotion":"","emotion_color":"","title_style":"","license":"","footnotes":""},"categories":[46],"tags":[],"class_list":["post-6592","post","type-post","status-publish","format-standard","hentry","category-yolo"],"post_thumbnail_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","content_first_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_medium_image_300":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_thumbnail_image_624":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_frist_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_medium_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_large_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_full_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_all_images":[{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","id":"image0"},{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","id":"image1"},{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKicJO37JCcKiczyLibDpRgBDXlTon1aE9dVcNEEQ2gvaQTRZOajJPzLexw\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=1","id":"image2"},{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKicJO37JCcKiczyLibDpRgBDXlTon1aE9dVcNEEQ2gvaQTRZOajJPzLexw\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=1","id":"image3"},{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKAyqwlqJhAhUaqz66Via4WKqzx8qTkKvrURcsQVImhEUmXhZAYbia279Q\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=2","id":"image4"},{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKAyqwlqJhAhUaqz66Via4WKqzx8qTkKvrURcsQVImhEUmXhZAYbia279Q\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=2","id":"image5"}],"videoAdId":"","listAd":"0","listAdId":"","listAdEvery":6,"total_comments":0,"category_name":"YOLO","post_date":"2025-09-29","like_count":"0","praiseWord":"\u9f13\u52b1","copyright_state":"","excitationAd":"0","rewardedVideoAdId":"","detailAdId":"","detailAd":"0","enterpriseMinapp":"0","audios":[],"postImageUrl":"https:\/\/wp-moto-1258805347.cos.ap-shanghai.myqcloud.com\/2023\/05\/20230519082947553.jpg","avatarurls":[],"related_posts":null,"pageviews":269,"next_post_id":6657,"next_post_title":"\u6df1\u5ea6\u5b66\u4e60\u76ee\u6807\u68c0\u6d4b YOLOV11\u5982\u4f55\u8bad\u7ec3\u81ea\u5df1\u7684\u6570\u636e\u96c6 YOLOv11\u8bad\u7ec3\u914d\u7f6e\u73af\u5883\u6559\u7a0b \u4ee5\u6c14\u7403\u68c0\u6d4b\u6570\u636e\u96c6\u4e3a\u4f8b","previous_post_id":6590,"previous_post_title":"YOLOV8\u6a21\u578b\u5982\u4f55\u8bad\u7ec3 \u53cc\u6a21\u6001\u7ea2\u5916-\u53ef\u89c1\u5149\u5bf9\u9f50\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6 \u7ea2\u5916\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6\u7684\u8bad\u7ec3\u53ca\u5e94\u7528 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best.pt<\/code>\uff09\uff0c\u4f4d\u4e8e runs\/detect\/infrared_yolov8s\/weights\/best.pt<\/code>\u3002<\/p>\n\n\n\n\u4e8c\u3001\u6a21\u578b\u5c01\u88c5<\/h3>\n\n\n\n\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5c01\u88c5\u6211\u4eec\u7684\u6a21\u578b\uff0c\u4f7f\u5176\u6613\u4e8e\u4f7f\u7528\u3002\u8fd9\u53ef\u4ee5\u901a\u8fc7\u521b\u5efa\u4e00\u4e2a\u7c7b\u6765\u5b9e\u73b0\uff0c\u8be5\u7c7b\u8d1f\u8d23\u52a0\u8f7d\u6a21\u578b\u5e76\u63d0\u4f9b\u9884\u6d4b\u65b9\u6cd5\u3002<\/p>\n\n\n\nmodel_utils.py<\/code><\/h4>\n\n\n\nfrom ultralytics import YOLOimport cv2classInfraredTargetDetector:def__init__(self, model_path='runs\/detect\/infrared_yolov8s\/weights\/best.pt'): self.model = YOLO(model_path)defpredict(self, image_path, conf=0.4):\"\"\" \u5bf9\u7ed9\u5b9a\u8def\u5f84\u7684\u56fe\u50cf\u8fdb\u884c\u9884\u6d4b\u3002 :param image_path: \u8f93\u5165\u56fe\u50cf\u7684\u8def\u5f84 :param conf: \u7f6e\u4fe1\u5ea6\u9608\u503c :return: \u68c0\u6d4b\u7ed3\u679c\u5217\u8868\uff0c\u6bcf\u4e2a\u7ed3\u679c\u5305\u542b\u7c7b\u522b\u540d\u79f0\u3001\u7f6e\u4fe1\u5ea6\u5206\u6570\u548c\u8fb9\u754c\u6846\u5750\u6807 \"\"\" results = self.model.predict(source=image_path, conf=conf) detections =[]for r in results:for box in r.boxes: cls_id =int(box.cls) label = self.model.names[cls_id] confidence =float(box.conf) bbox = box.xyxy[0].cpu().numpy().tolist()# [x1, y1, x2, y2]<\/em> detections.append({'class': label,'confidence': confidence,'bbox': bbox})return detectionsdefdetect_and_draw(self, image_path, output_path='output_detection.jpg', conf=0.4):\"\"\" \u5bf9\u56fe\u50cf\u8fdb\u884c\u68c0\u6d4b\u5e76\u5728\u539f\u56fe\u4e0a\u7ed8\u5236\u68c0\u6d4b\u7ed3\u679c\u3002 :param image_path: \u8f93\u5165\u56fe\u50cf\u7684\u8def\u5f84 :param output_path: \u8f93\u51fa\u56fe\u50cf\u7684\u4fdd\u5b58\u8def\u5f84 :param conf: \u7f6e\u4fe1\u5ea6\u9608\u503c :return: None \"\"\" detections = self.predict(image_path, conf) img = cv2.imread(image_path)for det in detections: bbox = det['bbox'] label =f\"{det['class']}: {det['confidence']:.2f}\" color =(0,255,0)# Green<\/em> cv2.rectangle(img,(int(bbox[0]),int(bbox[1])),(int(bbox[2]),int(bbox[3])), color,2) cv2.putText(img, label,(int(bbox[0]),int(bbox[1])-10), cv2.FONT_HERSHEY_SIMPLEX,0.9, color,2) cv2.imwrite(output_path, img)print(f\"Detection completed and saved to {output_path}\")# \u793a\u4f8b\u4f7f\u7528<\/em>if __name__ =='__main__': detector = InfraredTargetDetector() detector.detect_and_draw('test_infrared_image.jpg')<\/code><\/pre>\n\n\n\n\u4e09\u3001\u6784\u5efa\u547d\u4ee4\u884c\u5de5\u5177<\/h3>\n\n\n\n\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u521b\u5efa\u4e00\u4e2a\u547d\u4ee4\u884c\u5de5\u5177\uff0c\u5141\u8bb8\u7528\u6237\u901a\u8fc7\u547d\u4ee4\u884c\u5bf9\u56fe\u50cf\u6216\u89c6\u9891\u8fdb\u884c\u68c0\u6d4b\u3002<\/p>\n\n\n\ninfrared_detector_cli.py<\/code><\/h4>\n\n\n\nimport argparsefrom model_utils import InfraredTargetDetectorimport cv2defmain(): parser = argparse.ArgumentParser(description=\"Infrared Target Detection System\") parser.add_argument('--input',type=str, required=True,help='Path to input image or video file.') parser.add_argument('--output',type=str, default='output.jpg',help='Path to save the output.') parser.add_argument('--conf',type=float, default=0.4,help='Confidence threshold.') args = parser.parse_args() detector = InfraredTargetDetector()if args.input.lower().endswith(('jpg','jpeg','png')): detector.detect_and_draw(args.input, args.output, args.conf)elif args.input.lower().endswith(('mp4','avi')): cap = cv2.VideoCapture(args.input) frame_width =int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) frame_height =int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) out = cv2.VideoWriter(args.output, cv2.VideoWriter_fourcc(*'XVID'),20.0,(frame_width, frame_height))while cap.isOpened(): ret, frame = cap.read()ifnot ret:break# Convert frame to image path temporarily for prediction<\/em> temp_img_path ='temp_frame.jpg' cv2.imwrite(temp_img_path, frame) detections = detector.predict(temp_img_path, args.conf)for det in detections: bbox = det['bbox'] label =f\"{det['class']}: {det['confidence']:.2f}\" color =(0,255,0)# Green<\/em> cv2.rectangle(frame,(int(bbox[0]),int(bbox[1])),(int(bbox[2]),int(bbox[3])), color,2) cv2.putText(frame, label,(int(bbox[0]),int(bbox[1])-10), cv2.FONT_HERSHEY_SIMPLEX,0.9, color,2) out.write(frame) cap.release() out.release()print(f\"Video detection completed and saved to {args.output}\")else:print(\"Unsupported file format.\")if __name__ ==\"__main__\": main()<\/code><\/pre>\n\n\n\n\u56db\u3001\u8fd0\u884c\u4f60\u7684\u7cfb\u7edf<\/h3>\n\n\n\n\n\u5bf9\u4e8e\u5355\u5f20\u56fe\u7247<\/strong>\uff1apython infrared_detector_cli.py --input path\/to\/image.jpg --output output.jpg --conf\u00a00.5<\/code><\/li>\n\n\n\n\u5bf9\u4e8e\u89c6\u9891\u6587\u4ef6<\/strong>\uff1apython infrared_detector_cli.py --input path\/to\/video.mp4 --output output_video.avi --con<\/code><\/li>\n<\/ul>\n","raw":"","protected":false},"excerpt":{"rendered":"\u6df1\u5ea6\u5b66\u4e60\u57fa\u4e8eYOLOv8\u6846\u67b6\u6765\u8bad\u7ec3\u7ea2\u5916\u56fe\u50cf\u5c0f\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6 \u7ea2\u5916\u56fe\u50cf\u5c0f\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6\u7684\u8bad\u7ec3\u53ca\u5e94\u7528 \u63a8\u7406\u8bc6\u522b\u68c0\u6d4b\u98de\u673a \u65e0\u4eba\u673a \u76f4\u5347\u673a …<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"emotion":"","emotion_color":"","title_style":"","license":"","footnotes":""},"categories":[46],"tags":[],"class_list":["post-6592","post","type-post","status-publish","format-standard","hentry","category-yolo"],"post_thumbnail_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","content_first_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_medium_image_300":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_thumbnail_image_624":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_frist_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_medium_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_large_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_full_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_all_images":[{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","id":"image0"},{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","id":"image1"},{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKicJO37JCcKiczyLibDpRgBDXlTon1aE9dVcNEEQ2gvaQTRZOajJPzLexw\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=1","id":"image2"},{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKicJO37JCcKiczyLibDpRgBDXlTon1aE9dVcNEEQ2gvaQTRZOajJPzLexw\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=1","id":"image3"},{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKAyqwlqJhAhUaqz66Via4WKqzx8qTkKvrURcsQVImhEUmXhZAYbia279Q\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=2","id":"image4"},{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKAyqwlqJhAhUaqz66Via4WKqzx8qTkKvrURcsQVImhEUmXhZAYbia279Q\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=2","id":"image5"}],"videoAdId":"","listAd":"0","listAdId":"","listAdEvery":6,"total_comments":0,"category_name":"YOLO","post_date":"2025-09-29","like_count":"0","praiseWord":"\u9f13\u52b1","copyright_state":"","excitationAd":"0","rewardedVideoAdId":"","detailAdId":"","detailAd":"0","enterpriseMinapp":"0","audios":[],"postImageUrl":"https:\/\/wp-moto-1258805347.cos.ap-shanghai.myqcloud.com\/2023\/05\/20230519082947553.jpg","avatarurls":[],"related_posts":null,"pageviews":269,"next_post_id":6657,"next_post_title":"\u6df1\u5ea6\u5b66\u4e60\u76ee\u6807\u68c0\u6d4b YOLOV11\u5982\u4f55\u8bad\u7ec3\u81ea\u5df1\u7684\u6570\u636e\u96c6 YOLOv11\u8bad\u7ec3\u914d\u7f6e\u73af\u5883\u6559\u7a0b \u4ee5\u6c14\u7403\u68c0\u6d4b\u6570\u636e\u96c6\u4e3a\u4f8b","previous_post_id":6590,"previous_post_title":"YOLOV8\u6a21\u578b\u5982\u4f55\u8bad\u7ec3 \u53cc\u6a21\u6001\u7ea2\u5916-\u53ef\u89c1\u5149\u5bf9\u9f50\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6 \u7ea2\u5916\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6\u7684\u8bad\u7ec3\u53ca\u5e94\u7528 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runs\/detect\/infrared_yolov8s\/weights\/best.pt<\/code>\u3002<\/p>\n\n\n\n\u4e8c\u3001\u6a21\u578b\u5c01\u88c5<\/h3>\n\n\n\n\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5c01\u88c5\u6211\u4eec\u7684\u6a21\u578b\uff0c\u4f7f\u5176\u6613\u4e8e\u4f7f\u7528\u3002\u8fd9\u53ef\u4ee5\u901a\u8fc7\u521b\u5efa\u4e00\u4e2a\u7c7b\u6765\u5b9e\u73b0\uff0c\u8be5\u7c7b\u8d1f\u8d23\u52a0\u8f7d\u6a21\u578b\u5e76\u63d0\u4f9b\u9884\u6d4b\u65b9\u6cd5\u3002<\/p>\n\n\n\nmodel_utils.py<\/code><\/h4>\n\n\n\nfrom ultralytics import YOLOimport cv2classInfraredTargetDetector:def__init__(self, model_path='runs\/detect\/infrared_yolov8s\/weights\/best.pt'): self.model = YOLO(model_path)defpredict(self, image_path, conf=0.4):\"\"\" \u5bf9\u7ed9\u5b9a\u8def\u5f84\u7684\u56fe\u50cf\u8fdb\u884c\u9884\u6d4b\u3002 :param image_path: \u8f93\u5165\u56fe\u50cf\u7684\u8def\u5f84 :param conf: \u7f6e\u4fe1\u5ea6\u9608\u503c :return: \u68c0\u6d4b\u7ed3\u679c\u5217\u8868\uff0c\u6bcf\u4e2a\u7ed3\u679c\u5305\u542b\u7c7b\u522b\u540d\u79f0\u3001\u7f6e\u4fe1\u5ea6\u5206\u6570\u548c\u8fb9\u754c\u6846\u5750\u6807 \"\"\" results = self.model.predict(source=image_path, conf=conf) detections =[]for r in results:for box in r.boxes: cls_id =int(box.cls) label = self.model.names[cls_id] confidence =float(box.conf) bbox = box.xyxy[0].cpu().numpy().tolist()# [x1, y1, x2, y2]<\/em> detections.append({'class': label,'confidence': confidence,'bbox': bbox})return detectionsdefdetect_and_draw(self, image_path, output_path='output_detection.jpg', conf=0.4):\"\"\" \u5bf9\u56fe\u50cf\u8fdb\u884c\u68c0\u6d4b\u5e76\u5728\u539f\u56fe\u4e0a\u7ed8\u5236\u68c0\u6d4b\u7ed3\u679c\u3002 :param image_path: \u8f93\u5165\u56fe\u50cf\u7684\u8def\u5f84 :param output_path: \u8f93\u51fa\u56fe\u50cf\u7684\u4fdd\u5b58\u8def\u5f84 :param conf: \u7f6e\u4fe1\u5ea6\u9608\u503c :return: None \"\"\" detections = self.predict(image_path, conf) img = cv2.imread(image_path)for det in detections: bbox = det['bbox'] label =f\"{det['class']}: {det['confidence']:.2f}\" color =(0,255,0)# Green<\/em> cv2.rectangle(img,(int(bbox[0]),int(bbox[1])),(int(bbox[2]),int(bbox[3])), color,2) cv2.putText(img, label,(int(bbox[0]),int(bbox[1])-10), cv2.FONT_HERSHEY_SIMPLEX,0.9, color,2) cv2.imwrite(output_path, img)print(f\"Detection completed and saved to {output_path}\")# \u793a\u4f8b\u4f7f\u7528<\/em>if __name__ =='__main__': detector = InfraredTargetDetector() detector.detect_and_draw('test_infrared_image.jpg')<\/code><\/pre>\n\n\n\n\u4e09\u3001\u6784\u5efa\u547d\u4ee4\u884c\u5de5\u5177<\/h3>\n\n\n\n\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u521b\u5efa\u4e00\u4e2a\u547d\u4ee4\u884c\u5de5\u5177\uff0c\u5141\u8bb8\u7528\u6237\u901a\u8fc7\u547d\u4ee4\u884c\u5bf9\u56fe\u50cf\u6216\u89c6\u9891\u8fdb\u884c\u68c0\u6d4b\u3002<\/p>\n\n\n\ninfrared_detector_cli.py<\/code><\/h4>\n\n\n\nimport argparsefrom model_utils import InfraredTargetDetectorimport cv2defmain(): parser = argparse.ArgumentParser(description=\"Infrared Target Detection System\") parser.add_argument('--input',type=str, required=True,help='Path to input image or video file.') parser.add_argument('--output',type=str, default='output.jpg',help='Path to save the output.') parser.add_argument('--conf',type=float, default=0.4,help='Confidence threshold.') args = parser.parse_args() detector = InfraredTargetDetector()if args.input.lower().endswith(('jpg','jpeg','png')): detector.detect_and_draw(args.input, args.output, args.conf)elif args.input.lower().endswith(('mp4','avi')): cap = cv2.VideoCapture(args.input) frame_width =int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) frame_height =int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) out = cv2.VideoWriter(args.output, cv2.VideoWriter_fourcc(*'XVID'),20.0,(frame_width, frame_height))while cap.isOpened(): ret, frame = cap.read()ifnot ret:break# Convert frame to image path temporarily for prediction<\/em> temp_img_path ='temp_frame.jpg' cv2.imwrite(temp_img_path, frame) detections = detector.predict(temp_img_path, args.conf)for det in detections: bbox = det['bbox'] label =f\"{det['class']}: {det['confidence']:.2f}\" color =(0,255,0)# Green<\/em> cv2.rectangle(frame,(int(bbox[0]),int(bbox[1])),(int(bbox[2]),int(bbox[3])), color,2) cv2.putText(frame, label,(int(bbox[0]),int(bbox[1])-10), cv2.FONT_HERSHEY_SIMPLEX,0.9, color,2) out.write(frame) cap.release() out.release()print(f\"Video detection completed and saved to {args.output}\")else:print(\"Unsupported file format.\")if __name__ ==\"__main__\": main()<\/code><\/pre>\n\n\n\n\u56db\u3001\u8fd0\u884c\u4f60\u7684\u7cfb\u7edf<\/h3>\n\n\n\n\n\u5bf9\u4e8e\u5355\u5f20\u56fe\u7247<\/strong>\uff1apython infrared_detector_cli.py --input path\/to\/image.jpg --output output.jpg --conf\u00a00.5<\/code><\/li>\n\n\n\n\u5bf9\u4e8e\u89c6\u9891\u6587\u4ef6<\/strong>\uff1apython infrared_detector_cli.py --input path\/to\/video.mp4 --output output_video.avi --con<\/code><\/li>\n<\/ul>\n","raw":"","protected":false},"excerpt":{"rendered":"\u6df1\u5ea6\u5b66\u4e60\u57fa\u4e8eYOLOv8\u6846\u67b6\u6765\u8bad\u7ec3\u7ea2\u5916\u56fe\u50cf\u5c0f\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6 \u7ea2\u5916\u56fe\u50cf\u5c0f\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6\u7684\u8bad\u7ec3\u53ca\u5e94\u7528 \u63a8\u7406\u8bc6\u522b\u68c0\u6d4b\u98de\u673a \u65e0\u4eba\u673a \u76f4\u5347\u673a …<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"emotion":"","emotion_color":"","title_style":"","license":"","footnotes":""},"categories":[46],"tags":[],"class_list":["post-6592","post","type-post","status-publish","format-standard","hentry","category-yolo"],"post_thumbnail_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","content_first_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_medium_image_300":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_thumbnail_image_624":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_frist_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_medium_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_large_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_full_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_all_images":[{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","id":"image0"},{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","id":"image1"},{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKicJO37JCcKiczyLibDpRgBDXlTon1aE9dVcNEEQ2gvaQTRZOajJPzLexw\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=1","id":"image2"},{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKicJO37JCcKiczyLibDpRgBDXlTon1aE9dVcNEEQ2gvaQTRZOajJPzLexw\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=1","id":"image3"},{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKAyqwlqJhAhUaqz66Via4WKqzx8qTkKvrURcsQVImhEUmXhZAYbia279Q\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=2","id":"image4"},{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKAyqwlqJhAhUaqz66Via4WKqzx8qTkKvrURcsQVImhEUmXhZAYbia279Q\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=2","id":"image5"}],"videoAdId":"","listAd":"0","listAdId":"","listAdEvery":6,"total_comments":0,"category_name":"YOLO","post_date":"2025-09-29","like_count":"0","praiseWord":"\u9f13\u52b1","copyright_state":"","excitationAd":"0","rewardedVideoAdId":"","detailAdId":"","detailAd":"0","enterpriseMinapp":"0","audios":[],"postImageUrl":"https:\/\/wp-moto-1258805347.cos.ap-shanghai.myqcloud.com\/2023\/05\/20230519082947553.jpg","avatarurls":[],"related_posts":null,"pageviews":269,"next_post_id":6657,"next_post_title":"\u6df1\u5ea6\u5b66\u4e60\u76ee\u6807\u68c0\u6d4b YOLOV11\u5982\u4f55\u8bad\u7ec3\u81ea\u5df1\u7684\u6570\u636e\u96c6 YOLOv11\u8bad\u7ec3\u914d\u7f6e\u73af\u5883\u6559\u7a0b \u4ee5\u6c14\u7403\u68c0\u6d4b\u6570\u636e\u96c6\u4e3a\u4f8b","previous_post_id":6590,"previous_post_title":"YOLOV8\u6a21\u578b\u5982\u4f55\u8bad\u7ec3 \u53cc\u6a21\u6001\u7ea2\u5916-\u53ef\u89c1\u5149\u5bf9\u9f50\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6 \u7ea2\u5916\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6\u7684\u8bad\u7ec3\u53ca\u5e94\u7528 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\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5c01\u88c5\u6211\u4eec\u7684\u6a21\u578b\uff0c\u4f7f\u5176\u6613\u4e8e\u4f7f\u7528\u3002\u8fd9\u53ef\u4ee5\u901a\u8fc7\u521b\u5efa\u4e00\u4e2a\u7c7b\u6765\u5b9e\u73b0\uff0c\u8be5\u7c7b\u8d1f\u8d23\u52a0\u8f7d\u6a21\u578b\u5e76\u63d0\u4f9b\u9884\u6d4b\u65b9\u6cd5\u3002<\/p>\n\n\n\n
model_utils.py<\/code><\/h4>\n\n\n\nfrom ultralytics import YOLOimport cv2classInfraredTargetDetector:def__init__(self, model_path='runs\/detect\/infrared_yolov8s\/weights\/best.pt'): self.model = YOLO(model_path)defpredict(self, image_path, conf=0.4):\"\"\" \u5bf9\u7ed9\u5b9a\u8def\u5f84\u7684\u56fe\u50cf\u8fdb\u884c\u9884\u6d4b\u3002 :param image_path: \u8f93\u5165\u56fe\u50cf\u7684\u8def\u5f84 :param conf: \u7f6e\u4fe1\u5ea6\u9608\u503c :return: \u68c0\u6d4b\u7ed3\u679c\u5217\u8868\uff0c\u6bcf\u4e2a\u7ed3\u679c\u5305\u542b\u7c7b\u522b\u540d\u79f0\u3001\u7f6e\u4fe1\u5ea6\u5206\u6570\u548c\u8fb9\u754c\u6846\u5750\u6807 \"\"\" results = self.model.predict(source=image_path, conf=conf) detections =[]for r in results:for box in r.boxes: cls_id =int(box.cls) label = self.model.names[cls_id] confidence =float(box.conf) bbox = box.xyxy[0].cpu().numpy().tolist()# [x1, y1, x2, y2]<\/em> detections.append({'class': label,'confidence': confidence,'bbox': bbox})return detectionsdefdetect_and_draw(self, image_path, output_path='output_detection.jpg', conf=0.4):\"\"\" \u5bf9\u56fe\u50cf\u8fdb\u884c\u68c0\u6d4b\u5e76\u5728\u539f\u56fe\u4e0a\u7ed8\u5236\u68c0\u6d4b\u7ed3\u679c\u3002 :param image_path: \u8f93\u5165\u56fe\u50cf\u7684\u8def\u5f84 :param output_path: \u8f93\u51fa\u56fe\u50cf\u7684\u4fdd\u5b58\u8def\u5f84 :param conf: \u7f6e\u4fe1\u5ea6\u9608\u503c :return: None \"\"\" detections = self.predict(image_path, conf) img = cv2.imread(image_path)for det in detections: bbox = det['bbox'] label =f\"{det['class']}: {det['confidence']:.2f}\" color =(0,255,0)# Green<\/em> cv2.rectangle(img,(int(bbox[0]),int(bbox[1])),(int(bbox[2]),int(bbox[3])), color,2) cv2.putText(img, label,(int(bbox[0]),int(bbox[1])-10), cv2.FONT_HERSHEY_SIMPLEX,0.9, color,2) cv2.imwrite(output_path, img)print(f\"Detection completed and saved to {output_path}\")# \u793a\u4f8b\u4f7f\u7528<\/em>if __name__ =='__main__': detector = InfraredTargetDetector() detector.detect_and_draw('test_infrared_image.jpg')<\/code><\/pre>\n\n\n\n\u4e09\u3001\u6784\u5efa\u547d\u4ee4\u884c\u5de5\u5177<\/h3>\n\n\n\n\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u521b\u5efa\u4e00\u4e2a\u547d\u4ee4\u884c\u5de5\u5177\uff0c\u5141\u8bb8\u7528\u6237\u901a\u8fc7\u547d\u4ee4\u884c\u5bf9\u56fe\u50cf\u6216\u89c6\u9891\u8fdb\u884c\u68c0\u6d4b\u3002<\/p>\n\n\n\ninfrared_detector_cli.py<\/code><\/h4>\n\n\n\nimport argparsefrom model_utils import InfraredTargetDetectorimport cv2defmain(): parser = argparse.ArgumentParser(description=\"Infrared Target Detection System\") parser.add_argument('--input',type=str, required=True,help='Path to input image or video file.') parser.add_argument('--output',type=str, default='output.jpg',help='Path to save the output.') parser.add_argument('--conf',type=float, default=0.4,help='Confidence threshold.') args = parser.parse_args() detector = InfraredTargetDetector()if args.input.lower().endswith(('jpg','jpeg','png')): detector.detect_and_draw(args.input, args.output, args.conf)elif args.input.lower().endswith(('mp4','avi')): cap = cv2.VideoCapture(args.input) frame_width =int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) frame_height =int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) out = cv2.VideoWriter(args.output, cv2.VideoWriter_fourcc(*'XVID'),20.0,(frame_width, frame_height))while cap.isOpened(): ret, frame = cap.read()ifnot ret:break# Convert frame to image path temporarily for prediction<\/em> temp_img_path ='temp_frame.jpg' cv2.imwrite(temp_img_path, frame) detections = detector.predict(temp_img_path, args.conf)for det in detections: bbox = det['bbox'] label =f\"{det['class']}: {det['confidence']:.2f}\" color =(0,255,0)# Green<\/em> cv2.rectangle(frame,(int(bbox[0]),int(bbox[1])),(int(bbox[2]),int(bbox[3])), color,2) cv2.putText(frame, label,(int(bbox[0]),int(bbox[1])-10), cv2.FONT_HERSHEY_SIMPLEX,0.9, color,2) out.write(frame) cap.release() out.release()print(f\"Video detection completed and saved to {args.output}\")else:print(\"Unsupported file format.\")if __name__ ==\"__main__\": main()<\/code><\/pre>\n\n\n\n\u56db\u3001\u8fd0\u884c\u4f60\u7684\u7cfb\u7edf<\/h3>\n\n\n\n\n\u5bf9\u4e8e\u5355\u5f20\u56fe\u7247<\/strong>\uff1apython infrared_detector_cli.py --input path\/to\/image.jpg --output output.jpg --conf\u00a00.5<\/code><\/li>\n\n\n\n\u5bf9\u4e8e\u89c6\u9891\u6587\u4ef6<\/strong>\uff1apython infrared_detector_cli.py --input path\/to\/video.mp4 --output output_video.avi --con<\/code><\/li>\n<\/ul>\n","raw":"","protected":false},"excerpt":{"rendered":"\u6df1\u5ea6\u5b66\u4e60\u57fa\u4e8eYOLOv8\u6846\u67b6\u6765\u8bad\u7ec3\u7ea2\u5916\u56fe\u50cf\u5c0f\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6 \u7ea2\u5916\u56fe\u50cf\u5c0f\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6\u7684\u8bad\u7ec3\u53ca\u5e94\u7528 \u63a8\u7406\u8bc6\u522b\u68c0\u6d4b\u98de\u673a \u65e0\u4eba\u673a \u76f4\u5347\u673a 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from ultralytics import YOLOimport cv2classInfraredTargetDetector:def__init__(self, model_path='runs\/detect\/infrared_yolov8s\/weights\/best.pt'): self.model = YOLO(model_path)defpredict(self, image_path, conf=0.4):\"\"\" \u5bf9\u7ed9\u5b9a\u8def\u5f84\u7684\u56fe\u50cf\u8fdb\u884c\u9884\u6d4b\u3002 :param image_path: \u8f93\u5165\u56fe\u50cf\u7684\u8def\u5f84 :param conf: \u7f6e\u4fe1\u5ea6\u9608\u503c :return: \u68c0\u6d4b\u7ed3\u679c\u5217\u8868\uff0c\u6bcf\u4e2a\u7ed3\u679c\u5305\u542b\u7c7b\u522b\u540d\u79f0\u3001\u7f6e\u4fe1\u5ea6\u5206\u6570\u548c\u8fb9\u754c\u6846\u5750\u6807 \"\"\" results = self.model.predict(source=image_path, conf=conf) detections =[]for r in results:for box in r.boxes: cls_id =int(box.cls) label = self.model.names[cls_id] confidence =float(box.conf) bbox = box.xyxy[0].cpu().numpy().tolist()# [x1, y1, x2, y2]<\/em> detections.append({'class': label,'confidence': confidence,'bbox': bbox})return detectionsdefdetect_and_draw(self, image_path, output_path='output_detection.jpg', conf=0.4):\"\"\" \u5bf9\u56fe\u50cf\u8fdb\u884c\u68c0\u6d4b\u5e76\u5728\u539f\u56fe\u4e0a\u7ed8\u5236\u68c0\u6d4b\u7ed3\u679c\u3002 :param image_path: \u8f93\u5165\u56fe\u50cf\u7684\u8def\u5f84 :param output_path: \u8f93\u51fa\u56fe\u50cf\u7684\u4fdd\u5b58\u8def\u5f84 :param conf: \u7f6e\u4fe1\u5ea6\u9608\u503c :return: None \"\"\" detections = self.predict(image_path, conf) img = cv2.imread(image_path)for det in detections: bbox = det['bbox'] label =f\"{det['class']}: {det['confidence']:.2f}\" color =(0,255,0)# Green<\/em> cv2.rectangle(img,(int(bbox[0]),int(bbox[1])),(int(bbox[2]),int(bbox[3])), color,2) cv2.putText(img, label,(int(bbox[0]),int(bbox[1])-10), cv2.FONT_HERSHEY_SIMPLEX,0.9, color,2) cv2.imwrite(output_path, img)print(f\"Detection completed and saved to {output_path}\")# \u793a\u4f8b\u4f7f\u7528<\/em>if __name__ =='__main__': detector = InfraredTargetDetector() detector.detect_and_draw('test_infrared_image.jpg')<\/code><\/pre>\n\n\n\n\u4e09\u3001\u6784\u5efa\u547d\u4ee4\u884c\u5de5\u5177<\/h3>\n\n\n\n\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u521b\u5efa\u4e00\u4e2a\u547d\u4ee4\u884c\u5de5\u5177\uff0c\u5141\u8bb8\u7528\u6237\u901a\u8fc7\u547d\u4ee4\u884c\u5bf9\u56fe\u50cf\u6216\u89c6\u9891\u8fdb\u884c\u68c0\u6d4b\u3002<\/p>\n\n\n\ninfrared_detector_cli.py<\/code><\/h4>\n\n\n\nimport argparsefrom model_utils import InfraredTargetDetectorimport cv2defmain(): parser = argparse.ArgumentParser(description=\"Infrared Target Detection System\") parser.add_argument('--input',type=str, required=True,help='Path to input image or video file.') parser.add_argument('--output',type=str, default='output.jpg',help='Path to save the output.') parser.add_argument('--conf',type=float, default=0.4,help='Confidence threshold.') args = parser.parse_args() detector = InfraredTargetDetector()if args.input.lower().endswith(('jpg','jpeg','png')): detector.detect_and_draw(args.input, args.output, args.conf)elif args.input.lower().endswith(('mp4','avi')): cap = cv2.VideoCapture(args.input) frame_width =int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) frame_height =int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) out = cv2.VideoWriter(args.output, cv2.VideoWriter_fourcc(*'XVID'),20.0,(frame_width, frame_height))while cap.isOpened(): ret, frame = cap.read()ifnot ret:break# Convert frame to image path temporarily for prediction<\/em> temp_img_path ='temp_frame.jpg' cv2.imwrite(temp_img_path, frame) detections = detector.predict(temp_img_path, args.conf)for det in detections: bbox = det['bbox'] label =f\"{det['class']}: {det['confidence']:.2f}\" color =(0,255,0)# Green<\/em> cv2.rectangle(frame,(int(bbox[0]),int(bbox[1])),(int(bbox[2]),int(bbox[3])), color,2) cv2.putText(frame, label,(int(bbox[0]),int(bbox[1])-10), cv2.FONT_HERSHEY_SIMPLEX,0.9, color,2) out.write(frame) cap.release() out.release()print(f\"Video detection completed and saved to {args.output}\")else:print(\"Unsupported file format.\")if __name__ ==\"__main__\": main()<\/code><\/pre>\n\n\n\n\u56db\u3001\u8fd0\u884c\u4f60\u7684\u7cfb\u7edf<\/h3>\n\n\n\n\n\u5bf9\u4e8e\u5355\u5f20\u56fe\u7247<\/strong>\uff1apython infrared_detector_cli.py --input path\/to\/image.jpg --output output.jpg --conf\u00a00.5<\/code><\/li>\n\n\n\n\u5bf9\u4e8e\u89c6\u9891\u6587\u4ef6<\/strong>\uff1apython infrared_detector_cli.py --input path\/to\/video.mp4 --output output_video.avi --con<\/code><\/li>\n<\/ul>\n","raw":"","protected":false},"excerpt":{"rendered":"\u6df1\u5ea6\u5b66\u4e60\u57fa\u4e8eYOLOv8\u6846\u67b6\u6765\u8bad\u7ec3\u7ea2\u5916\u56fe\u50cf\u5c0f\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6 \u7ea2\u5916\u56fe\u50cf\u5c0f\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6\u7684\u8bad\u7ec3\u53ca\u5e94\u7528 \u63a8\u7406\u8bc6\u522b\u68c0\u6d4b\u98de\u673a \u65e0\u4eba\u673a \u76f4\u5347\u673a …<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"emotion":"","emotion_color":"","title_style":"","license":"","footnotes":""},"categories":[46],"tags":[],"class_list":["post-6592","post","type-post","status-publish","format-standard","hentry","category-yolo"],"post_thumbnail_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","content_first_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_medium_image_300":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_thumbnail_image_624":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_frist_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_medium_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_large_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_full_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_all_images":[{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","id":"image0"},{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","id":"image1"},{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKicJO37JCcKiczyLibDpRgBDXlTon1aE9dVcNEEQ2gvaQTRZOajJPzLexw\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=1","id":"image2"},{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKicJO37JCcKiczyLibDpRgBDXlTon1aE9dVcNEEQ2gvaQTRZOajJPzLexw\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=1","id":"image3"},{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKAyqwlqJhAhUaqz66Via4WKqzx8qTkKvrURcsQVImhEUmXhZAYbia279Q\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=2","id":"image4"},{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKAyqwlqJhAhUaqz66Via4WKqzx8qTkKvrURcsQVImhEUmXhZAYbia279Q\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=2","id":"image5"}],"videoAdId":"","listAd":"0","listAdId":"","listAdEvery":6,"total_comments":0,"category_name":"YOLO","post_date":"2025-09-29","like_count":"0","praiseWord":"\u9f13\u52b1","copyright_state":"","excitationAd":"0","rewardedVideoAdId":"","detailAdId":"","detailAd":"0","enterpriseMinapp":"0","audios":[],"postImageUrl":"https:\/\/wp-moto-1258805347.cos.ap-shanghai.myqcloud.com\/2023\/05\/20230519082947553.jpg","avatarurls":[],"related_posts":null,"pageviews":269,"next_post_id":6657,"next_post_title":"\u6df1\u5ea6\u5b66\u4e60\u76ee\u6807\u68c0\u6d4b 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\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u521b\u5efa\u4e00\u4e2a\u547d\u4ee4\u884c\u5de5\u5177\uff0c\u5141\u8bb8\u7528\u6237\u901a\u8fc7\u547d\u4ee4\u884c\u5bf9\u56fe\u50cf\u6216\u89c6\u9891\u8fdb\u884c\u68c0\u6d4b\u3002<\/p>\n\n\n\n
infrared_detector_cli.py<\/code><\/h4>\n\n\n\nimport argparsefrom model_utils import InfraredTargetDetectorimport cv2defmain(): parser = argparse.ArgumentParser(description=\"Infrared Target Detection System\") parser.add_argument('--input',type=str, required=True,help='Path to input image or video file.') parser.add_argument('--output',type=str, default='output.jpg',help='Path to save the output.') parser.add_argument('--conf',type=float, default=0.4,help='Confidence threshold.') args = parser.parse_args() detector = InfraredTargetDetector()if args.input.lower().endswith(('jpg','jpeg','png')): detector.detect_and_draw(args.input, args.output, args.conf)elif args.input.lower().endswith(('mp4','avi')): cap = cv2.VideoCapture(args.input) frame_width =int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) frame_height =int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) out = cv2.VideoWriter(args.output, cv2.VideoWriter_fourcc(*'XVID'),20.0,(frame_width, frame_height))while cap.isOpened(): ret, frame = cap.read()ifnot ret:break# Convert frame to image path temporarily for prediction<\/em> temp_img_path ='temp_frame.jpg' cv2.imwrite(temp_img_path, frame) detections = detector.predict(temp_img_path, args.conf)for det in detections: bbox = det['bbox'] label =f\"{det['class']}: {det['confidence']:.2f}\" color =(0,255,0)# Green<\/em> cv2.rectangle(frame,(int(bbox[0]),int(bbox[1])),(int(bbox[2]),int(bbox[3])), color,2) cv2.putText(frame, label,(int(bbox[0]),int(bbox[1])-10), cv2.FONT_HERSHEY_SIMPLEX,0.9, color,2) out.write(frame) cap.release() out.release()print(f\"Video detection completed and saved to {args.output}\")else:print(\"Unsupported file format.\")if __name__ ==\"__main__\": main()<\/code><\/pre>\n\n\n\n\u56db\u3001\u8fd0\u884c\u4f60\u7684\u7cfb\u7edf<\/h3>\n\n\n\n\n\u5bf9\u4e8e\u5355\u5f20\u56fe\u7247<\/strong>\uff1apython infrared_detector_cli.py --input path\/to\/image.jpg --output output.jpg --conf\u00a00.5<\/code><\/li>\n\n\n\n\u5bf9\u4e8e\u89c6\u9891\u6587\u4ef6<\/strong>\uff1apython infrared_detector_cli.py --input path\/to\/video.mp4 --output output_video.avi --con<\/code><\/li>\n<\/ul>\n","raw":"","protected":false},"excerpt":{"rendered":"\u6df1\u5ea6\u5b66\u4e60\u57fa\u4e8eYOLOv8\u6846\u67b6\u6765\u8bad\u7ec3\u7ea2\u5916\u56fe\u50cf\u5c0f\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6 \u7ea2\u5916\u56fe\u50cf\u5c0f\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6\u7684\u8bad\u7ec3\u53ca\u5e94\u7528 \u63a8\u7406\u8bc6\u522b\u68c0\u6d4b\u98de\u673a \u65e0\u4eba\u673a \u76f4\u5347\u673a …<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"emotion":"","emotion_color":"","title_style":"","license":"","footnotes":""},"categories":[46],"tags":[],"class_list":["post-6592","post","type-post","status-publish","format-standard","hentry","category-yolo"],"post_thumbnail_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","content_first_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_medium_image_300":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_thumbnail_image_624":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_frist_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_medium_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_large_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_full_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_all_images":[{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","id":"image0"},{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","id":"image1"},{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKicJO37JCcKiczyLibDpRgBDXlTon1aE9dVcNEEQ2gvaQTRZOajJPzLexw\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=1","id":"image2"},{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKicJO37JCcKiczyLibDpRgBDXlTon1aE9dVcNEEQ2gvaQTRZOajJPzLexw\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=1","id":"image3"},{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKAyqwlqJhAhUaqz66Via4WKqzx8qTkKvrURcsQVImhEUmXhZAYbia279Q\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=2","id":"image4"},{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKAyqwlqJhAhUaqz66Via4WKqzx8qTkKvrURcsQVImhEUmXhZAYbia279Q\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=2","id":"image5"}],"videoAdId":"","listAd":"0","listAdId":"","listAdEvery":6,"total_comments":0,"category_name":"YOLO","post_date":"2025-09-29","like_count":"0","praiseWord":"\u9f13\u52b1","copyright_state":"","excitationAd":"0","rewardedVideoAdId":"","detailAdId":"","detailAd":"0","enterpriseMinapp":"0","audios":[],"postImageUrl":"https:\/\/wp-moto-1258805347.cos.ap-shanghai.myqcloud.com\/2023\/05\/20230519082947553.jpg","avatarurls":[],"related_posts":null,"pageviews":269,"next_post_id":6657,"next_post_title":"\u6df1\u5ea6\u5b66\u4e60\u76ee\u6807\u68c0\u6d4b 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import argparsefrom model_utils import InfraredTargetDetectorimport cv2defmain(): parser = argparse.ArgumentParser(description=\"Infrared Target Detection System\") parser.add_argument('--input',type=str, required=True,help='Path to input image or video file.') parser.add_argument('--output',type=str, default='output.jpg',help='Path to save the output.') parser.add_argument('--conf',type=float, default=0.4,help='Confidence threshold.') args = parser.parse_args() detector = InfraredTargetDetector()if args.input.lower().endswith(('jpg','jpeg','png')): detector.detect_and_draw(args.input, args.output, args.conf)elif args.input.lower().endswith(('mp4','avi')): cap = cv2.VideoCapture(args.input) frame_width =int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) frame_height =int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) out = cv2.VideoWriter(args.output, cv2.VideoWriter_fourcc(*'XVID'),20.0,(frame_width, frame_height))while cap.isOpened(): ret, frame = cap.read()ifnot ret:break# Convert frame to image path temporarily for prediction<\/em> temp_img_path ='temp_frame.jpg' cv2.imwrite(temp_img_path, frame) detections = detector.predict(temp_img_path, args.conf)for det in detections: bbox = det['bbox'] label =f\"{det['class']}: {det['confidence']:.2f}\" color =(0,255,0)# Green<\/em> cv2.rectangle(frame,(int(bbox[0]),int(bbox[1])),(int(bbox[2]),int(bbox[3])), color,2) cv2.putText(frame, label,(int(bbox[0]),int(bbox[1])-10), cv2.FONT_HERSHEY_SIMPLEX,0.9, color,2) out.write(frame) cap.release() out.release()print(f\"Video detection completed and saved to {args.output}\")else:print(\"Unsupported file format.\")if __name__ ==\"__main__\": main()<\/code><\/pre>\n\n\n\n\u56db\u3001\u8fd0\u884c\u4f60\u7684\u7cfb\u7edf<\/h3>\n\n\n\n\n\u5bf9\u4e8e\u5355\u5f20\u56fe\u7247<\/strong>\uff1apython infrared_detector_cli.py --input path\/to\/image.jpg --output output.jpg --conf\u00a00.5<\/code><\/li>\n\n\n\n\u5bf9\u4e8e\u89c6\u9891\u6587\u4ef6<\/strong>\uff1apython infrared_detector_cli.py --input path\/to\/video.mp4 --output output_video.avi --con<\/code><\/li>\n<\/ul>\n","raw":"","protected":false},"excerpt":{"rendered":"\u6df1\u5ea6\u5b66\u4e60\u57fa\u4e8eYOLOv8\u6846\u67b6\u6765\u8bad\u7ec3\u7ea2\u5916\u56fe\u50cf\u5c0f\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6 \u7ea2\u5916\u56fe\u50cf\u5c0f\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6\u7684\u8bad\u7ec3\u53ca\u5e94\u7528 \u63a8\u7406\u8bc6\u522b\u68c0\u6d4b\u98de\u673a \u65e0\u4eba\u673a \u76f4\u5347\u673a …<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"emotion":"","emotion_color":"","title_style":"","license":"","footnotes":""},"categories":[46],"tags":[],"class_list":["post-6592","post","type-post","status-publish","format-standard","hentry","category-yolo"],"post_thumbnail_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","content_first_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_medium_image_300":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_thumbnail_image_624":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_frist_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_medium_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_large_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_full_image":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","post_all_images":[{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","id":"image0"},{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKApoG7syjtSb77WzKMbrdlxyCOicUJuXdbfrqNsQice5dSrtdurSVpdRQ\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=0","id":"image1"},{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKicJO37JCcKiczyLibDpRgBDXlTon1aE9dVcNEEQ2gvaQTRZOajJPzLexw\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=1","id":"image2"},{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKicJO37JCcKiczyLibDpRgBDXlTon1aE9dVcNEEQ2gvaQTRZOajJPzLexw\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=1","id":"image3"},{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKAyqwlqJhAhUaqz66Via4WKqzx8qTkKvrURcsQVImhEUmXhZAYbia279Q\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=2","id":"image4"},{"imagesurl":"https:\/\/mmbiz.qpic.cn\/mmbiz_png\/3aq7xqHVYu3lOF8mVaLAO3D2XLuLXDCKAyqwlqJhAhUaqz66Via4WKqzx8qTkKvrURcsQVImhEUmXhZAYbia279Q\/640?wx_fmt=png&from=appmsg&watermark=1&tp=wxpic&wxfrom=5&wx_lazy=1#imgIndex=2","id":"image5"}],"videoAdId":"","listAd":"0","listAdId":"","listAdEvery":6,"total_comments":0,"category_name":"YOLO","post_date":"2025-09-29","like_count":"0","praiseWord":"\u9f13\u52b1","copyright_state":"","excitationAd":"0","rewardedVideoAdId":"","detailAdId":"","detailAd":"0","enterpriseMinapp":"0","audios":[],"postImageUrl":"https:\/\/wp-moto-1258805347.cos.ap-shanghai.myqcloud.com\/2023\/05\/20230519082947553.jpg","avatarurls":[],"related_posts":null,"pageviews":269,"next_post_id":6657,"next_post_title":"\u6df1\u5ea6\u5b66\u4e60\u76ee\u6807\u68c0\u6d4b 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python infrared_detector_cli.py --input path\/to\/image.jpg --output output.jpg --conf\u00a00.5<\/code><\/li>\n\n\n\n\u5bf9\u4e8e\u89c6\u9891\u6587\u4ef6<\/strong>\uff1apython infrared_detector_cli.py --input path\/to\/video.mp4 --output output_video.avi --con<\/code><\/li>\n<\/ul>\n","raw":"","protected":false},"excerpt":{"rendered":"\u6df1\u5ea6\u5b66\u4e60\u57fa\u4e8eYOLOv8\u6846\u67b6\u6765\u8bad\u7ec3\u7ea2\u5916\u56fe\u50cf\u5c0f\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6 \u7ea2\u5916\u56fe\u50cf\u5c0f\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6\u7684\u8bad\u7ec3\u53ca\u5e94\u7528 \u63a8\u7406\u8bc6\u522b\u68c0\u6d4b\u98de\u673a \u65e0\u4eba\u673a \u76f4\u5347\u673a 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python infrared_detector_cli.py --input path\/to\/video.mp4 --output output_video.avi --con<\/code><\/li>\n<\/ul>\n","raw":"","protected":false},"excerpt":{"rendered":"\u6df1\u5ea6\u5b66\u4e60\u57fa\u4e8eYOLOv8\u6846\u67b6\u6765\u8bad\u7ec3\u7ea2\u5916\u56fe\u50cf\u5c0f\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6 \u7ea2\u5916\u56fe\u50cf\u5c0f\u76ee\u6807\u68c0\u6d4b\u6570\u636e\u96c6\u7684\u8bad\u7ec3\u53ca\u5e94\u7528 \u63a8\u7406\u8bc6\u522b\u68c0\u6d4b\u98de\u673a \u65e0\u4eba\u673a \u76f4\u5347\u673a 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