基于图像的苹果坏果检测代码
时间: 2023-06-14 14:07:25 浏览: 51
以下是基于图像的苹果坏果检测的示例代码,使用Python和OpenCV库实现:
```python
import cv2
# 读取图像
image = cv2.imread("apple.jpg")
# 转换为HSV颜色空间
hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
# 定义绿色和棕色的HSV范围
green_lower = (25, 52, 72)
green_upper = (102, 255, 255)
brown_lower = (10, 60, 20)
brown_upper = (20, 255, 255)
# 创建掩码
green_mask = cv2.inRange(hsv, green_lower, green_upper)
brown_mask = cv2.inRange(hsv, brown_lower, brown_upper)
mask = cv2.bitwise_or(green_mask, brown_mask)
# 应用掩码
masked_image = cv2.bitwise_and(image, image, mask=mask)
# 计算苹果的面积
gray = cv2.cvtColor(masked_image, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY)
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
area = 0
for cnt in contours:
area += cv2.contourArea(cnt)
# 计算坏苹果的面积
brown_masked_image = cv2.bitwise_and(image, image, mask=brown_mask)
brown_gray = cv2.cvtColor(brown_masked_image, cv2.COLOR_BGR2GRAY)
brown_ret, brown_thresh = cv2.threshold(brown_gray, 0, 255, cv2.THRESH_BINARY)
brown_contours, brown_hierarchy = cv2.findContours(brown_thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
brown_area = 0
for cnt in brown_contours:
brown_area += cv2.contourArea(cnt)
# 计算好苹果的面积
green_masked_image = cv2.bitwise_and(image, image, mask=green_mask)
green_gray = cv2.cvtColor(green_masked_image, cv2.COLOR_BGR2GRAY)
green_ret, green_thresh = cv2.threshold(green_gray, 0, 255, cv2.THRESH_BINARY)
green_contours, green_hierarchy = cv2.findContours(green_thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
green_area = 0
for cnt in green_contours:
green_area += cv2.contourArea(cnt)
# 判断苹果是否坏掉
if brown_area / area > 0.1:
print("这个苹果坏掉了")
else:
print("这个苹果很好")
```
请注意,这只是一个示例代码,它可能需要根据具体情况进行调整和优化。