python调用海康工业相机并用opencv显示，并进行YOLO识别

### Python调用海康威视工业相机并结合OpenCV显示图像及YOLO目标检测 #### 使用 OpenCV 调用海康威视工业相机 要通过 `cv2.VideoCapture()` 接口调用海康威视工业相机，可以使用 RTSP 协议连接到设备。以下是具体方法： ```python import cv2 url = "rtsp://admin:[email protected]/Streaming/Channels/2" cap = cv2.VideoCapture(url) if not cap.isOpened(): print("无法打开摄像头") else: while True: ret, frame = cap.read() if not ret: print("未能读取帧") break # 显示图像 cv2.imshow("Camera Feed", frame) if cv2.waitKey(1) & 0xFF == ord('q'): break cap.release() cv2.destroyAllWindows() ``` 上述代码实现了通过 RTSP 地址访问海康威视工业相机，并实时显示其捕获的画面[^2]。 --- #### 结合 YOLO 实现目标检测 为了在获取的视频流上应用 YOLO 进行目标检测，需加载预训练模型并对每一帧执行推理操作。以下是一个完整的示例代码： ```python import cv2 import numpy as np def load_yolo_model(weights_path, config_path, labels_path): net = cv2.dnn.readNetFromDarknet(config_path, weights_path) with open(labels_path, 'r') as f: classes = [line.strip() for line in f.readlines()] layer_names = net.getLayerNames() output_layers = [layer_names[i - 1] for i in net.getUnconnectedOutLayers()] return net, classes, output_layers def detect_objects(frame, net, output_layers): blob = cv2.dnn.blobFromImage(frame, scalefactor=0.00392, size=(416, 416), mean=(0, 0, 0), swapRB=True, crop=False) net.setInput(blob) outputs = net.forward(output_layers) return outputs def draw_boxes(outputs, frame, classes, confidence_threshold=0.5, nms_threshold=0.4): class_ids = [] confidences = [] boxes = [] height, width = frame.shape[:2] for output in outputs: for detection in output: scores = detection[5:] class_id = np.argmax(scores) confidence = scores[class_id] if confidence > confidence_threshold: center_x = int(detection[0] * width) center_y = int(detection[1] * height) w = int(detection[2] * width) h = int(detection[3] * height) x = int(center_x - w / 2) y = int(center_y - h / 2) boxes.append([x, y, w, h]) confidences.append(float(confidence)) class_ids.append(class_id) indices = cv2.dnn.NMSBoxes(boxes, confidences, confidence_threshold, nms_threshold) for i in indices.flatten(): box = boxes[i] color = (0, 255, 0) label = f"{classes[class_ids[i]]}: {confidences[i]:.2f}" cv2.rectangle(frame, (box[0], box[1]), (box[0]+box[2], box[1]+box[3]), color, thickness=2) cv2.putText(frame, label, (box[0], box[1]-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, thickness=2) return frame # 加载 YOLO 模型 weights_path = "yolov3.weights" config_path = "yolov3.cfg" labels_path = "coco.names" net, classes, output_layers = load_yolo_model(weights_path, config_path, labels_path) # 打开摄像头 url = "rtsp://admin:[email protected]/Streaming/Channels/2" cap = cv2.VideoCapture(url) while True: ret, frame = cap.read() if not ret: print("未能读取帧") break outputs = detect_objects(frame, net, output_layers) detected_frame = draw_boxes(outputs, frame, classes) cv2.imshow("YOLO Detection", detected_frame) if cv2.waitKey(1) & 0xFF == ord('q'): break cap.release() cv2.destroyAllWindows() ``` 此代码片段展示了如何加载 YOLOv3 模型、处理每帧数据以提取边界框和类别标签，并绘制结果于原始图像之上[^3]。 --- #### 注意事项 - **RTSP 配置**：确保 RTSP URL 正确无误，用户名密码与实际设备匹配。 - **性能优化**：对于高分辨率视频流，可考虑降低输入尺寸或调整推理频率来提升效率。 - **依赖安装**：运行前确认已安装必要库 (`opencv-python` 和 `numpy`) 及下载对应权重文件。 ---