yolo车辆视频目标检测 python代码

以下是使用Python和YOLOv3进行车辆视频目标检测的代码示例：

import cv2
import numpy as np

# 加载YOLOv3模型
net = cv2.dnn.readNet('path/to/yolov3.weights', 'path/to/yolov3.cfg')

# 设置类别列表和颜色编码
classes = []
with open('path/to/coco.names', 'r') as f:
    classes = [line.strip() for line in f.readlines()]
colors = np.random.uniform(0, 255, size=(len(classes), 3))

# 读取输入视频
cap = cv2.VideoCapture('path/to/input/video.mp4')

# 检测车辆并标记
while True:
    # 从视频流中读取一帧
    ret, frame = cap.read()
    
    # 如果视频结束，跳出循环
    if not ret:
        break
    
    # 获取输入图像的尺寸
    height, width, _ = frame.shape
    
    # 构建一个blob，并将其输入到YOLOv3模型中
    blob = cv2.dnn.blobFromImage(frame, 1/255, (416, 416), (0, 0, 0), True, crop=False)
    net.setInput(blob)
    outs = net.forward(net.getUnconnectedOutLayersNames())
    
    # 初始化一些列表
    class_ids = []
    confidences = []
    boxes = []
    
    # 处理YOLOv3模型的输出
    for out in outs:
        for detection in out:
            scores = detection[5:]
            class_id = np.argmax(scores)
            confidence = scores[class_id]
            if confidence > 0.5:
                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)
    
    # 应用非最大抑制，并在图像中标记检测到的车辆
    indices = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
    for i in indices:
        i = i[0]
        box = boxes[i]
        x, y, w, h = box
        color = colors[class_ids[i]]
        cv2.rectangle(frame, (x, y), (x+w, y+h), color, 2)
        cv2.putText(frame, classes[class_ids[i]], (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
    
    # 显示标记后的帧
    cv2.imshow('Car Detection', frame)
    
    # 按下q键退出程序
    if cv2.waitKey(25) & 0xFF == ord('q'):
        break

# 释放视频流和窗口
cap.release()
cv2.destroyAllWindows()

在上面的代码中，我们首先加载YOLOv3模型，然后使用OpenCV的VideoCapture函数读取输入视频。接下来，我们在循环中读取每一帧视频，并将其作为输入传递给YOLOv3模型。然后，我们处理模型的输出，并使用非最大抑制算法应用阈值，以过滤掉重叠的边界框。最后，我们在每个检测到的车辆周围绘制矩形，并在窗口中显示标记后的帧。最后，我们按下q键退出程序，并释放视频流和窗口。