import pathlib
import sys

import cv2 as cv
import numpy as np


def read_image(image):
    img = cv.imread(image)
    # if img.shape[1] > 1000:
    #     img = cv.resize(img, (img.shape[1]//2, img.shape[0]//2),
    #                     interpolation=cv.INTER_CUBIC)
    return img

def get_corners(image):
    gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
    # blur = cv.GaussianBlur(gray, (5,5), cv.BORDER_DEFAULT)
    # canny = cv.Canny(blur, 125, 175)
    # cv.imshow('Canny', canny)
    _, thresh = cv.threshold(gray, 125, 255, cv.THRESH_BINARY)
    # cv.imshow('Thresh', thresh)

    # https://docs.opencv.org/4.x/dd...orial_py_contour_features.html
    contours, _ = cv.findContours(thresh, cv.RETR_LIST,
                                  cv.CHAIN_APPROX_SIMPLE)  # cv.CHAIN_APPROX_NONE
    contours = sorted(contours, key=cv.contourArea, reverse=True)

    for contour in contours:
        # approximate the contour
        perimeter = 0.05 * cv.arcLength(contour, True)
        corners = cv.approxPolyDP(contour, perimeter, True)
        # if contour with 4 points founded, break the loop
        if len(corners) == 4:
            break

    return corners


# Copy/Paste from
# https://pyimagesearch.com/2014...perspective-transform-example/
# or install: https://pypi.org/project/imutils/
def order_points(pts):
     # initialzie a list of coordinates that will be ordered
     # such that the first entry in the list is the top-left,
     # the second entry is the top-right, the third is the
     # bottom-right, and the fourth is the bottom-left
     rect = np.zeros((4, 2), dtype = "float32")
     # the top-left point will have the smallest sum, whereas
     # the bottom-right point will have the largest sum
     s = pts.sum(axis = 1)
     rect[0] = pts[np.argmin(s)]
     rect[2] = pts[np.argmax(s)]
     # now, compute the difference between the points, the
     # top-right point will have the smallest difference,
     # whereas the bottom-left will have the largest difference
     diff = np.diff(pts, axis = 1)
     rect[1] = pts[np.argmin(diff)]
     rect[3] = pts[np.argmax(diff)]
     # return the ordered coordinates
     return rect


def four_point_transform(image, pts):
     # obtain a consistent order of the points and unpack them
     # individually
     rect = order_points(pts)
     (tl, tr, br, bl) = rect
     # compute the width of the new image, which will be the
     # maximum distance between bottom-right and bottom-left
     # x-coordiates or the top-right and top-left x-coordinates
     widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))
     widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))
     maxWidth = max(int(widthA), int(widthB))
     # compute the height of the new image, which will be the
     # maximum distance between the top-right and bottom-right
     # y-coordinates or the top-left and bottom-left y-coordinates
     heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))
     heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))
     maxHeight = max(int(heightA), int(heightB))
     # now that we have the dimensions of the new image, construct
     # the set of destination points to obtain a "birds eye view",
     # (i.e. top-down view) of the image, again specifying points
     # in the top-left, top-right, bottom-right, and bottom-left
     # order
     dst = np.array([
          [0, 0],
          [maxWidth - 1, 0],
          [maxWidth - 1, maxHeight - 1],
          [0, maxHeight - 1]], dtype = "float32")
     # compute the perspective transform matrix and then apply it
     M = cv.getPerspectiveTransform(rect, dst)
     warped = cv.warpPerspective(image, M, (maxWidth, maxHeight))
     # return the warped image
     return warped


if __name__ == '__main__':
    if len(sys.argv) > 1:
        img_path = pathlib.Path(sys.argv[1])
    else:
        img_path = pathlib.Path(__file__).parent

    output_dir = f'{img_path.as_posix()}/output'
    pathlib.Path(output_dir).mkdir(exist_ok=True)

    for img in img_path.iterdir():
        if img.suffix.lower() in ('.png', '.jpg', '.jpeg'):
            image = read_image(img.as_posix())
            corners = get_corners(image)
            new_image = four_point_transform(image, corners.reshape(4, 2))
            image_name = f'{img.name.removesuffix(img.suffix)}_cropped{img.suffix}'
            cv.imwrite(f'{output_dir}/{image_name}', new_image)