用python将图像二值化，形态学运算，轮廓提取，精确扣取前景，使用罩层更换背景图像的代码

以下是用Python将图像二值化，形态学运算，轮廓提取，精确扣取前景，使用罩层更换背景图像的代码：

import cv2

# Load image
img = cv2.imread('input.jpg')

# Convert image to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# Apply binary threshold
ret, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY_INV)

# Apply morphological closing
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5,5))
closed = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)

# Find external contours
contours, hierarchy = cv2.findContours(closed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

# Find largest contour
max_contour = max(contours, key=cv2.contourArea)

# Create mask from largest contour
mask = cv2.drawContours(img.copy(), [max_contour], -1, (255,255,255), cv2.FILLED)

# Apply mask to original image
result = cv2.bitwise_and(img, mask)

# Load background image
bg_img = cv2.imread('background.jpg')

# Resize background image to match original image size
bg_img = cv2.resize(bg_img, (img.shape[1], img.shape[0]))

# Invert mask to create background mask
bg_mask = cv2.bitwise_not(mask)

# Apply background mask to background image
bg_result = cv2.bitwise_and(bg_img, bg_mask)

# Combine masked original image and masked background image
final_result = cv2.add(result, bg_result)

# Save final result
cv2.imwrite('output.jpg', final_result)

此代码为将输入图像二值化并使用形态学运算和轮廓提取来精确分离前景和背景。然后，使用罩层将前景放入新的背景图像中。请注意，此代码不适用于高度复杂或具有重叠元素的图像。