先看对比效果:

再上代码:

import cv2
import numpy as np


def remove_gray_background(input_image_path, output_image_path, threshold=180):
    # Load the input image
    image = cv2.imread(input_image_path)

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

    # Create a binary mask based on the threshold value
    _, mask = cv2.threshold(gray, threshold, 255, cv2.THRESH_BINARY)

    # Invert the mask (black for the region to keep, white for the background)
    inverted_mask = cv2.bitwise_not(mask)

    # Create a white background image
    white_background = np.full_like(image, 130)

    # Use the mask to extract the object from the original image
    object_without_background = cv2.bitwise_or(image, image, mask=inverted_mask)

    # Combine the object with the white background
    result = cv2.bitwise_or(
        white_background, object_without_background, mask=inverted_mask
    )

    inverted_image = 255 - result

    # Save the resulting image
    cv2.imwrite(output_image_path, inverted_image)


if __name__ == "__main__":
    input_image_path = "raw.jpg"
    remove_gray_background(input_image_path, 'new.jpg')

最后附上依赖安装:

pip install numpy opencv-python