你已经知道x1,x2,y1,y2标识框的位置conf表示置信度cls表示类别现在把这些结果可视化用cv2.rectangle()画框cv2.rectangle(图像左上角右下角颜色线宽例cv2.rectangle(image, 在哪张图片上画(50,100)(400,300)(0,255,0)2)再把类别和置信度写在框上cv2.putText(image, 在哪个图片上写label, 写什么(x1,y1-10) 从哪里开始写cv2.FONT_HERSHEY_SIMPLEX, 字体0.6, 字体大小(0, 255, 0), 颜色2 线宽你可以读这种真实的代码了import cv2from ultralytics import YOLOmodel YOLO(best.pt)image cv2.imread(test.jpg)results model(image)result results[0]for box in result.boxes:x1, y1, x2, y2 map(int, box.xyxy[0])confidence float(box.conf[0])class_id int(box.cls[0])class_name result.names[class_id]label f{class_name} {confidence:.2f}cv2.rectangle(image,(x1, y1),(x2, y2),(0, 255, 0),2)cv2.putText(image,label,(x1, y1 - 10),cv2.FONT_HERSHEY_SIMPLEX,0.6,(0, 255, 0),2)cv2.imshow(result, image)cv2.waitKey(0)cv2.destroyAllWindows()接下来把它接进摄像头while True:ret, frame cap.read()if not ret:breakresults model(frame)result results[0]for box in result.boxes:# 解析# 画框cv2.imshow(video, frame) 把 YOLO 的检测框真正画到图片上
你已经知道x1,x2,y1,y2标识框的位置conf表示置信度cls表示类别现在把这些结果可视化用cv2.rectangle()画框cv2.rectangle(图像左上角右下角颜色线宽例cv2.rectangle(image, 在哪张图片上画(50,100)(400,300)(0,255,0)2)再把类别和置信度写在框上cv2.putText(image, 在哪个图片上写label, 写什么(x1,y1-10) 从哪里开始写cv2.FONT_HERSHEY_SIMPLEX, 字体0.6, 字体大小(0, 255, 0), 颜色2 线宽你可以读这种真实的代码了import cv2from ultralytics import YOLOmodel YOLO(best.pt)image cv2.imread(test.jpg)results model(image)result results[0]for box in result.boxes:x1, y1, x2, y2 map(int, box.xyxy[0])confidence float(box.conf[0])class_id int(box.cls[0])class_name result.names[class_id]label f{class_name} {confidence:.2f}cv2.rectangle(image,(x1, y1),(x2, y2),(0, 255, 0),2)cv2.putText(image,label,(x1, y1 - 10),cv2.FONT_HERSHEY_SIMPLEX,0.6,(0, 255, 0),2)cv2.imshow(result, image)cv2.waitKey(0)cv2.destroyAllWindows()接下来把它接进摄像头while True:ret, frame cap.read()if not ret:breakresults model(frame)result results[0]for box in result.boxes:# 解析# 画框cv2.imshow(video, frame)