点击下方“深度学习爱好者”，选择加"星标"或“置顶”
OpenCV：用于视频处理和显示结果。 Ultralytics YOLO：一个流行且高效的深度学习模型，用于目标检测。
pip install opencv-pythonpip install ultralytics
加载YOLO模型：我们加载一个预训练的YOLO模型（best.pt）用于眼睛检测。这个模型被训练来识别两种状态：“闭合”和“打开”的眼睛。下载模型 读取输入视频：使用OpenCV逐帧读取视频。 执行目标检测：使用YOLO检测每帧中眼睛的状态。 跟踪眼睛闭合持续时间：如果检测到眼睛“闭合”超过指定阈值，将显示警告。 显示结果：结果被写入输出视频文件，如果需要，警告消息会被叠加在帧上。
import cv2from ultralytics import YOLO# Load the pre-trained YOLO modelmodel = YOLO("best.pt")names = model.names# Open the video filecap = cv2.VideoCapture("video.mp4")assert cap.isOpened(), "Error reading video file"w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))# Initialize video writer to save the outputvideo_writer = cv2.VideoWriter("output.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))# Initialize variables for eye closure detectioneye_closed_frames = 0eye_closed_threshold_seconds = 1 # Threshold in secondseye_closed_threshold_frames = eye_closed_threshold_seconds * fps # Convert seconds to frameswhile cap.isOpened():success, im0 = cap.read()if not success:print("Video frame is empty or video processing has been successfully completed.")break# Predict the state of the eyes using YOLOresults = model.predict(im0, show=False)boxes = results[0].boxes.xyxy.cpu().tolist()clss = results[0].boxes.cls.cpu().tolist()annotator = Annotator(im0, line_width=2, example=names)eye_closed = False # Flag to check if the eye is closed in the current frameIf boxes are not None:for box, cls in zip(boxes, clss):clsName = names[int(cls)]xmax = int(box[0])ymin = int(box[1])xmin = int(box[2])ymax = int(box[3])# Set color based on the class nameif clsName == 'closed':clr = (0, 0, 255)eye_closed = True # Mark eye as closedelif clsName == 'opened':clr = (0, 255, 0)# Draw the bounding box and labelcv2.FONT_HERSHEY_SIMPLEXFont_scale = 1Font_thickness = 2tw, th = cv2.getTextSize(clsName, font, font_scale, font_thickness)[0]cv2.rectangle(im0, (xmin, ymin), (xmax, ymax), color=clr, thickness=2)cv2.putText(im0, clsName, (xmax, ymin - 5), font, font_scale, color=clr, thickness=font_thickness)# Check for eye closure durationif eye_closed:Eye_closed_frames += 1else:# Reset counter if the eye is not closed# Display warning if eye has been closed for more than the thresholdif eye_closed_frames > eye_closed_threshold_frames:print("Warning: Eye has been closed for more than 2 seconds!")cv2.putText(im0, "WARNING: Eye closed for more than 2 seconds!", (50, 50), font, font_scale, (0, 0, 255), font_thickness)# Write the processed frame to the output videovideo_writer.write(im0)# Release resourcescap.release()video_writer.release()
捕获实时视频：将视频文件输入更改为实时视频流，方法是将cv2.VideoCapture参数更改为0（默认网络摄像头）。
cap = cv2.VideoCapture(0) # Use 0 for the default camera, or 1, 2, etc. for other cameras为确保流畅的实时性能，你可能需要通过使用较小的模型（如YOLOv5s）或在可用的情况下在GPU上运行来优化模型推理速度。
使用cv2.imshow()实时显示视频流：
cv2.imshow("Eye Closure Detection", im0) if cv2.waitKey(1) & 0xFF == ord('q'): # Press 'q' to quit break下载1：Pytoch常用函数手册 在「深度学习爱好者」公众号后台回复：Pytorch常用函数手册，即可下载全网第一份Pytorch常用函数手册，涵盖Tensors介绍、基础函数介绍、数据处理函数、优化函数、CUDA编程、多线程处理等十四章章内容。 下载2：Python视觉实战项目52讲 在「小白学视觉」公众号后台回复：Python视觉实战项目，即可下载包括图像分割、口罩检测、车道线检测、车辆计数、添加眼线、车牌识别、字符识别、情绪检测、文本内容提取、面部识别等31个视觉实战项目，助力快速学校计算机视觉。 交流群 欢迎加入公众号读者群一起和同行交流，目前有SLAM、三维视觉驶、计算摄影、检测、分割、识别、医学影像、GAN、算法竞赛等微信群（以后会逐渐细分），请扫描下面微信号加群，备注：”昵称+学校/公司+研究方向“，例如：”张三 + 上海交大 + 视觉SLAM“。请按照格式备注，否则不予通过。添加成功后会根据研究方向邀请进入相关微信群。请勿在群内发送广告，否则会请出群，谢谢理解~
