python实现运动探测动的地方用绿框画出并根据绿框图显示检测到并展开另一个转为轻度灰度照片
时间: 2024-03-12 20:43:14 浏览: 59
一种基于RGB图像的复杂背景下的运动检测方法
好的,我理解您的问题了。为了实现您的需求,我们需要使用Python中的OpenCV库来进行图像处理和检测。下面是实现的步骤:
1. 导入必要的库:
```python
import cv2
import numpy as np
```
2. 加载原始图像:
```python
img = cv2.imread('input.jpg')
```
3. 将图像转换为灰度图像:
```python
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
```
4. 定义运动物体检测器:
```python
detector = cv2.createBackgroundSubtractorMOG2(history=100, varThreshold=50, detectShadows=False)
```
5. 对灰度图像进行运动物体检测:
```python
mask = detector.apply(gray)
```
6. 对检测到的运动物体进行轮廓检测:
```python
contours, hierarchy = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
```
7. 绘制矩形框并展示检测结果:
```python
for c in contours:
x, y, w, h = cv2.boundingRect(c)
cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
cv2.imshow('Result', img)
```
8. 将检测到的运动物体区域转换为轻度灰度照片并保存:
```python
for i, c in enumerate(contours):
x, y, w, h = cv2.boundingRect(c)
roi = img[y:y+h, x:x+w]
roi_gray = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
cv2.imwrite(f'output_{i}.jpg', roi_gray)
```
完整代码如下:
```python
import cv2
import numpy as np
img = cv2.imread('input.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
detector = cv2.createBackgroundSubtractorMOG2(history=100, varThreshold=50, detectShadows=False)
mask = detector.apply(gray)
contours, hierarchy = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
for c in contours:
x, y, w, h = cv2.boundingRect(c)
cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
roi = img[y:y+h, x:x+w]
roi_gray = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
cv2.imwrite(f'output_{i}.jpg', roi_gray)
cv2.imshow('Result', img)
cv2.waitKey(0)
cv2.destroyAllWindows()
```
运行代码后,检测到的运动物体部分将会用绿框画出,并保存为灰度图像。
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