利用python和opencv批量去掉圖片黑邊

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import osimport cv2import numpy as npfrom scipy.stats import modeimport timeimport concurrent.futures‘‘‘    multi-process to crop pictures.‘‘‘def crop(file_path_list):    origin_path, save_path = file_path_list    img = cv2.imread(origin_path)    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)    closed_1 = cv2.erode(gray, None, iterations=4)    closed_1 = cv2.dilate(closed_1, None, iterations=4)    blurred = cv2.blur(closed_1, (9, 9))    # get the most frequent pixel    num = mode(blurred.flat)[0][0] + 1    # the threshold depends on the mode of your images‘ pixels    num = num if num <= 30 else 1    _, thresh = cv2.threshold(blurred, num, 255, cv2.THRESH_BINARY)    # you can control the size of kernel according your need.    kernel = np.ones((13, 13), np.uint8)    closed_2 = cv2.erode(thresh, kernel, iterations=4)    closed_2 = cv2.dilate(closed_2, kernel, iterations=4)    _, cnts, _ = cv2.findContours(closed_2.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)    c = sorted(cnts, key=cv2.contourArea, reverse=True)[0]    # compute the rotated bounding box of the largest contour    rect = cv2.minAreaRect(c)    box = np.int0(cv2.boxPoints(rect))    # draw a bounding box arounded the detected barcode and display the image    # cv2.drawContours(img, [box], -1, (0, 255, 0), 3)    # cv2.imshow("Image", img)    # cv2.imwrite("pic.jpg", img)    # cv2.waitKey(0)    xs = [i[0] for i in box]    ys = [i[1] for i in box]    x1 = min(xs)    x2 = max(xs)    y1 = min(ys)    y2 = max(ys)    height = y2 - y1    width = x2 - x1    crop_img = img[y1:y1 + height, x1:x1 + width]    cv2.imwrite(save_path, crop_img)    # cv2.imshow("Image", crop_img)    # cv2.waitKey(0)    print(f‘the {origin_path} finish crop, most frequent pixel is {num}‘)def multi_process_crop(input_dir):    with concurrent.futures.ProcessPoolExecutor() as executor:        executor.map(crop, input_dir)if __name__ == "__main__":    data_dir = ‘‘    save_dir = ‘‘    path_list = [(os.path.join(data_dir, o), os.path.join(save_dir, o)) for o in os.listdir(data_dir)]    start = time.time()    multi_process_crop(path_list)    print(f‘Total cost {time.time()-start} seconds‘)

 

利用python和opencv批量去掉圖片黑邊