yolo读取视频并检测&cv

这是一段示例代码，用于跑通基础验证，不可用于生产，除非你的视频路数少于4路

from time import sleep
import cv2 as cv, torch
from ultralytics import YOLO
import logging
import time

logging.basicConfig(
 level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
)
log = logging.getLogger(__name__)

# log.info("-- OpenCV Info --")
# log.info(cv.getBuildInformation())
# log.info("-" * 50)

# device = "cuda:0" if torch.cuda.is_available() else "cpu"
# log.info(f"Using device: {device}")

yolo_model = YOLO("./yolo_model/yolo11s.pt")
image_size = 640
yolo_model.overrides["imgsz"] = image_size

cls_map = {
 0: "person",
 1: "bicycle",
 2: "car",
 3: "motorcycle",
 5: "bus",
 7: "truck",
}

# 帧率
FPS_TARGET = 12
# 帧间隔
T_INTERVAL = 1.0 / FPS_TARGET
# 上一次时间
last_t = time.time()
# 检测间隔
detect_every = 3
# 检测计数
detect_count = 0

cap = cv.VideoCapture(
 "https://smart.saas.vppark.cn/oss/1.mp4",
)
 

while cap.isOpened():
 ret, frame = cap.read()
 if not ret:
 break
 # if time.time() - last_t < T_INTERVAL:
 # continue
 # last_t = time.time()

 
 detect_count += 1
 if detect_count % detect_every == 0 or True:
 # 手动 resize
 h0, w0 = frame.shape[:2]
 scale = min(image_size / h0, image_size / w0)
 h1, w1 = int(h0 * scale), int(w0 * scale)
 frame_resize = cv.resize(frame, (w1, h1), interpolation=cv.INTER_LINEAR)

 # 执行检测
 results = yolo_model(
 frame_resize, imgsz=image_size, classes=list(cls_map.keys())
 )
 # 把框映射回原图
 for result in results:
 boxes_data = result.boxes.data.clone()
 boxes_data[..., :4] /= scale
 result.boxes.data = boxes_data
 react_frame = result.plot(img=frame, line_width=2)
 cv.imshow("frame", react_frame)
 else:
 cv.imshow("frame", frame)
 # if cv.waitKey(int(1000 / FPS_TARGET)) & 0xFF == ord("q"):
 if cv.waitKey(1) & 0xFF == ord("q"):
 break

cap.release()
cv.destroyAllWindows()