### Python OpenCV 实现摄像头目标检测并设置置信度阈值 以下是通过 Python 和 OpenCV 调用摄像头进行实时目标检测的代码示例。该代码设置了置信度阈值为 0.5，只有当检测结果的置信度超过此阈值时才会显示。 #### 完整代码实现 ```python import cv2 import numpy as np # 加载预训练模型 (YOLOv3) weights_path = "yolov3.weights" config_path = "yolov3.cfg" net = cv2.dnn.readNet(weights_path, config_path) # 获取类别名称 with open("coco.names", "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()] colors = np.random.uniform(0, 255, size=(len(classes), 3)) # 设置视频捕获设备 cap = cv2.VideoCapture(0) # 使用默认摄像头 confidence_threshold = 0.5 # 置信度阈值 while True: ret, frame = cap.read() if not ret: break height, width, channels = frame.shape # 将帧转换为blob blob = cv2.dnn.blobFromImage(frame, 0.00392, (416, 416), (0, 0, 0), True, crop=False) # 输入网络 net.setInput(blob) outs = net.forward(output_layers) class_ids = [] confidences = [] boxes = [] # 解析输出 for out in outs: for detection in out: scores = detection[5:] class_id = np.argmax(scores) confidence = scores[class_id] if confidence > confidence_threshold: # 只有置信度高于阈值的结果会被保留 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) # 非极大抑制消除冗余框 indexes = cv2.dnn.NMSBoxes(boxes, confidences, confidence_threshold, 0.4) font = cv2.FONT_HERSHEY_PLAIN for i in range(len(boxes)): if i in indexes: x, y, w, h = boxes[i] label = str(classes[class_ids[i]]) color = colors[i % len(colors)] cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2) cv2.putText(frame, f"{label} {confidences[i]:.2f}", (x, y - 5), font, 1, color, 1) # 显示结果 cv2.imshow("Object Detection", frame) key = cv2.waitKey(1) if key == 27: # 按ESC键退出 break cap.release() cv2.destroyAllWindows() ``` --- ### 关键点说明 1. **加载预训练模型** 使用 `cv2.dnn.readNet` 方法加载 YOLOv3 的权重文件和配置文件[^1]。这些文件可以从官方资源下载或自定义训练得到。 2. **读取类别名称** 类别列表存储在 `coco.names` 文件中，每一行代表一个对象类别[^2]。 3. **设置置信度阈值** 在代码中设定 `confidence_threshold = 0.5`，仅当检测结果的置信度大于等于这个值时才绘制边界框[^3]。 4. **非极大值抑制（NMS）** 利用 `cv2.dnn.NMSBoxes` 函数去除重叠过多的候选框，从而提高检测质量[^4]。 5. **实时处理摄像头输入** 使用 `cv2.VideoCapture(0)` 打开默认摄像头，并逐帧处理图像数据。 --- ###