请帮我更改yolov5的detect.py文件,将其功能封装为输入为rtsp流地址、检测返回阈值、指定帧数,能够调用模型对rtsp流进行对应的目标检测,同时在模型检测到目标物体之后,对于高于阈值的时刻,能够实时返回对应帧的图片以及指定几帧内的视频的函数,最后将示例代码包括细节全部呈现出来
时间: 2023-02-12 10:54:03 浏览: 175
我可以帮你修改 yolov5 的 detect.py 文件,使其能够读取 rtsp 流地址、检测返回阈值、指定帧数,并调用模型对 rtsp 流进行目标检测。当模型检测到目标物体时,如果置信度高于阈值,则可以实时返回对应帧的图片以及指定几帧内的视频。
下面是一份示例代码:
```python
import cv2
import numpy as np
from yolov5.models import *
from yolov5.utils.datasets import *
from yolov5.utils.utils import *
class RTSP_Object_Detector:
def __init__(self, model_path, rtsp_stream, threshold=0.5, frames_to_return=5):
self.model = load_model(model_path, device='cpu')
self.rtsp_stream = rtsp_stream
self.threshold = threshold
self.frames_to_return = frames_to_return
self.frames = []
def detect(self):
# Open the RTSP stream
cap = cv2.VideoCapture(self.rtsp_stream)
while True:
_, frame = cap.read()
# Run object detection on the current frame
img = torch.from_numpy(frame).float()
img = img.to(device)
pred = self.model(img, augment=False)[0]
detections = non_max_suppression(pred, self.threshold, 0.5, classes=None, agnostic=False)
# Draw bounding boxes on the frame
for x1, y1, x2, y2, conf, cls_conf, cls_pred in detections[0]:
if conf >= self.threshold:
cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
# Add the current frame to the list of frames to return
self.frames.append(frame)
if len(self.frames) > self.frames_to_return:
self.frames.pop(0)
# Show the current frame
cv2.imshow("Frame", frame)
key = cv2.waitKey(1)
if key == 27:
break
# Release the RTSP stream and destroy the window
cap.release()
cv2.destroyAllWindows()
def get_frames(self):
return self.frames
#