昭通市网站建设_网站建设公司_移动端适配_seo优化

1. 创建环境

conda create --name yolo_new python=3.10

2. 安装 yolo

pip install -U ultralytics

3. 编写获取视频代码

1. 环境：MacOS + iphone 摄像头

2. 导入相应的头文件

import cv2 from ultralytics import solutions

2. 获取摄像头

def open_iphone_camera_with_cv(): """ 主函数：找到并打开iPhone摄像头 """ print("正在检测iPhone摄像头...\n") # for i in range(5): cap = cv2.VideoCapture(0) if cap.isOpened(): print("打开默认摄像头 (索引0)") while True: ret, frame = cap.read() if ret: frame_corrected = cv2.flip(frame, 1) cv2.imshow(f'Camera {0} - 按q退出', frame_corrected) if cv2.waitKey(1) & 0xFF == ord('q'): break cv2.destroyAllWindows() cap.release() print("摄像头已关闭") else: print("无法打开摄像头，请检查连接。") # 运行 if __name__ == "__main__": # 方法1：自动检测并打开iPhone摄像头 open_iphone_camera_with_cv()

3. 设置视频长和宽

cap.set(cv2.CAP_PROP_FRAME_WIDTH, 2048) cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 2048)

4. 设置yolo 代码

region_points = [(20, 400), (1080, 400), (1080, 360), (20, 360)] # w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS)) # video_writer = cv2.VideoWriter("object_counting_output.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h)) counter = solutions.ObjectCounter( show=True, # display the output region=region_points, # pass region points model="yolo26s.pt", # model="yolo26n-obb.pt" for object counting with OBB model. # classes=[0, 2], # count specific classes, e.g., person and car with the COCO pretrained model. # tracker="botsort.yaml", # choose trackers, e.g., "bytetrack.yaml" )

会自动下载模型，region_points 为识别的可视野的大小

5. 处理视频中的一帧图像

results = counter(frame_corrected)

6. 完整代码

import cv2 from ultralytics import solutions def open_iphone_camera_with_cv(): """ 主函数：找到并打开iPhone摄像头 """ print("正在检测iPhone摄像头...\n") # for i in range(5): cap = cv2.VideoCapture(0) if cap.isOpened(): print("打开默认摄像头 (索引0)") cap.set(cv2.CAP_PROP_FRAME_WIDTH, 2048) cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 2048) region_points = [(20, 400), (1080, 400), (1080, 360), (20, 360)] # w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS)) # video_writer = cv2.VideoWriter("object_counting_output.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h)) counter = solutions.ObjectCounter( show=True, # display the output region=region_points, # pass region points model="yolo26s.pt", # model="yolo26n-obb.pt" for object counting with OBB model. # classes=[0, 2], # count specific classes, e.g., person and car with the COCO pretrained model. # tracker="botsort.yaml", # choose trackers, e.g., "bytetrack.yaml" ) while True: ret, frame = cap.read() if ret: frame_corrected = cv2.flip(frame, 1) results = counter(frame_corrected) cv2.imshow(f'Camera {0} - 按q退出', results.plot_im) if cv2.waitKey(1) & 0xFF == ord('q'): break cv2.destroyAllWindows() cap.release() print("摄像头已关闭") else: print("无法打开摄像头，请检查连接。") # 运行 if __name__ == "__main__": # 方法1：自动检测并打开iPhone摄像头 open_iphone_camera_with_cv()

4. 总结

1. yolo 26 安装方便

2. 占用cpu更低

3. 不同模型识别速度不同，模型越大识别越慢

yolo26n.pt 最小(5.5M)，识别最快， 在 50ms左右

yolo26s.pt 20.4M, 在75ms左右

yolo26m.pt 44.3M, 在120ms左右

yolo26l.pt 53.2M，在150ms左右

yolo26x.pt 118.7M，在170ms左右