import os

import cv2

import numpy as np

from scipy.stats import mode

import time

import 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')