基于图像处理判断稻种个数的操作步骤并显示相应的代码

操作步骤如下：

1. 读取图片并转为灰度图像

import cv2

img = cv2.imread('rice.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

2. 进行二值化处理

# 使用自适应阈值进行二值化
thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 11, 2)

3. 进行形态学操作

# 进行形态学操作，去除噪点
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
thresh = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=2)

4. 寻找轮廓并计算稻粒个数

# 寻找轮廓
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

# 绘制轮廓
cv2.drawContours(img, contours, -1, (0, 0, 255), 2)

# 计算稻粒个数
print('稻粒个数为：', len(contours))

# 显示结果
cv2.imshow('result', img)
cv2.waitKey(0)
cv2.destroyAllWindows()

完整代码如下：

import cv2

img = cv2.imread('rice.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# 使用自适应阈值进行二值化
thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 11, 2)

# 进行形态学操作，去除噪点
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
thresh = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=2)

# 寻找轮廓
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

# 绘制轮廓
cv2.drawContours(img, contours, -1, (0, 0, 255), 2)

# 计算稻粒个数
print('稻粒个数为：', len(contours))

# 显示结果
cv2.imshow('result', img)
cv2.waitKey(0)
cv2.destroyAllWindows()