物体检测实战（使用 OpenCV 进行 YOLO 对象检测） python

loop over frames from the video file stream while True:

# 从文件中读取下一帧 (grabbed, frame) = vs.read() # 如果帧没有被抓取，那么已经到了流的末尾 if not grabbed: break # 如果框架尺寸为空，则给他们赋值 if W is None or H is None: (H, W) = frame.shape[:2] # 从输入帧构造一个 blob，然后执行 YOLO 对象检测器的前向传递，得到边界框和相关概率 blob = cv2.dnn.blobFromImage(frame, 1 / 255.0, (416, 416), swapRB=True, crop=False) net.setInput(blob) start = time.time() layerOutputs = net.forward(outInfo) end = time.time()

# 分别初始化检测到的边界框、置信度和类 ID 的列表

boxes = [] confidences = [] classIDs = [] # 循环输出 for output in layerOutputs: # 遍历每个检测结果 for detection in output: # 提取物体检测的类ID和置信度（即概率） scores = detection[5:] classID = np.argmax(scores) confidence = scores[classID] # 过滤精度低的结果 if confidence > confidence_t: # 缩放边界框坐标，计算 YOLO 边界框的中心 (x, y) 坐标，然后是框的宽度和高度 box = detection[0:4] * np.array([W, H, W, H]) (centerX, centerY, width, height) = box.astype("int") # 使用中心 (x, y) 坐标导出边界框的上角和左角 x = int(centerX - (width / 2)) y = int(centerY - (height / 2)) # 更新边界框坐标、[股指期货](https://www.gendan5.com/ff/sf.html)置信度和类 ID 列表 boxes.append([x, y, int(width), int(height)]) confidences.append(float(confidence)) classIDs.append(classID) # 使用非极大值抑制来抑制弱的、重叠的边界框 idxs = cv2.dnn.NMSBoxes(boxes, confidences, confidence_t, threshold) # 确保至少存在一个检测 if len(idxs) > 0: # 遍历保存的索引 for i in idxs.flatten(): # 在图像上绘制一个边界框矩形和标签 (x, y) = (boxes[i][0], boxes[i][1]) (w, h) = (boxes[i][2], boxes[i][3]) # 确保至少存在一个检测 color = [int(c) for c in COLORS[classIDs[i]]] cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2) text = "{}: {:.4f}".format(LABELS[classIDs[i]], confidences[i]) cv2.putText(frame, text, (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2) cv2.imshow("Frame", frame) key = cv2.waitKey(1) & 0xFF # check if the video writer is None if writer is None: # initialize our video writer fourcc = cv2.VideoWriter_fourcc(*'XVID') writer = cv2.VideoWriter('output.avi', fourcc, 30, (int(frame.shape[1]), int(frame.shape[0]))) # some information on processing single frame if total > 0: elap = (end - start) print("[INFO] single frame took {:.4f} seconds".format(elap)) print("[INFO] estimated total time to finish: {:.4f}".format( elap * total)) # write the output frame to disk writer.write(frame)

release the file pointers 【物体检测实战（使用 OpenCV 进行 YOLO 对象检测）】print("[INFO] cleaning up...")
writer.release()
vs.release()