帮我写一个工人危险区域入侵的程序,采用传统图像处理的方法不用深度学习
时间: 2024-02-18 12:01:53 浏览: 19
好的,以下是一个基于传统图像处理方法的工人危险区域入侵的程序,具体实现细节可能需要根据具体情况进行调整和优化。
1. 获取视频流或者视频文件
```python
import cv2
video = cv2.VideoCapture('video.mp4') #或者使用摄像头 cv2.VideoCapture(0)
```
2. 进行帧差法背景建模,提取前后两帧之间的差异,得到前景图像
```python
_, frame = video.read()
gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray,(5,5),0)
while True:
_, frame = video.read()
if frame is None:
break
frame_gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
frame_gray = cv2.GaussianBlur(frame_gray,(5,5),0)
diff = cv2.absdiff(gray,frame_gray)
_, diff = cv2.threshold(diff,25,255,cv2.THRESH_BINARY)
cv2.imshow('diff',diff)
gray = frame_gray
k = cv2.waitKey(30)
if k == 27:
break
cv2.destroyAllWindows()
```
3. 对前景图像进行形态学运算,去除噪点,得到二值图像
```python
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3,3))
diff = cv2.morphologyEx(diff,cv2.MORPH_OPEN,kernel)
diff = cv2.morphologyEx(diff,cv2.MORPH_CLOSE,kernel)
```
4. 对二值图像进行轮廓检测,得到轮廓
```python
contours, hierarchy = cv2.findContours(diff,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
```
5. 对每一个轮廓进行面积和中心点的计算,判断是否进入危险区域。
```python
for cnt in contours:
area = cv2.contourArea(cnt) # 计算轮廓面积
if area < 500: # 去除面积过小的轮廓
continue
(x,y,w,h) = cv2.boundingRect(cnt)
cv2.rectangle(frame,(x,y),(x+w,y+h),(0,255,0),2)
# 计算轮廓中心点坐标
M = cv2.moments(cnt)
cx = int(M['m10']/M['m00'])
cy = int(M['m01']/M['m00'])
# 判断是否进入危险区域
if cx > 400 and cy > 400:
print('Worker is in danger area!')
```
6. 进行结果展示
```python
cv2.imshow('frame',frame)
cv2.imshow('diff',diff)
k = cv2.waitKey(30)
if k == 27:
break
cv2.destroyAllWindows()
video.release()
```
注意:这只是一个简单的程序框架,实际应用中需要进行更多的优化和改进,例如:图像增强、背景更新、目标跟踪等。