from itertools import count

import cv2
import numpy as np
import pdf2image
from matplotlib import pyplot as plt
from timeit import timeit


def parse(image: np.array):
    gray_scale = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    th1, img_bin = cv2.threshold(gray_scale, 200, 255, cv2.THRESH_BINARY)
    img_bin = ~img_bin

    line_min_width = 5
    kernel_h = np.ones((1, line_min_width), np.uint8)
    kernel_v = np.ones((line_min_width, 1), np.uint8)

    img_bin_h = cv2.morphologyEx(img_bin, cv2.MORPH_OPEN, kernel_h)
    img_bin_v = cv2.morphologyEx(img_bin, cv2.MORPH_OPEN, kernel_v)
    # print([cv2.countNonZero(row) for row in img_bin_v])

    img_bin_final = img_bin_h | img_bin_v

    find_and_close_edges(img_bin_final)

    _, labels, stats, _ = cv2.connectedComponentsWithStats(~img_bin_final, connectivity=8, ltype=cv2.CV_32S)
    return labels, stats


# def filter_unconnected_cells(stats):
#     filtered_cells = []
#     for left, middle, right in zip(stats[0:], stats[1:], list(stats[2:])+[None]):
#         x, y, w, h, area = middle
#         if w > 35 and h > 13 and area > 500:
#             if y == left[1] or y == right[1]:
#                 filtered_cells.append(middle)
#     return filtered_cells

def filter_unconnected_cells(stats):
    filtered_cells = []
    # print(stats)
    for left, middle, right in zip(stats[0:], stats[1:], list(stats[2:]) + [np.array([None, None, None, None, None])]):
        x, y, w, h, area = middle
        if w > 35 and h > 13 and area > 500:
            if right[1] is None:
                if y == left[1] or x == left[0]:
                    filtered_cells.append(middle)
            else:
                if y == left[1] or y == right[1] or x == left[0] or x == right[0]:
                    filtered_cells.append(middle)
    return filtered_cells


# def annotate_image(image, stats):
#     stats = filter_unconnected_cells(stats)
#     for i,val in enumerate(stats):
#         x, y, w, h, area = stats[i][0], stats[i][1], stats[i][2], stats[i][3], stats[i][4]
#         cv2.rectangle(image, (x, y), (x + w, y + h), (255, 0, 255), 2)
#
#         for i, (s, v) in enumerate(zip(["x", "y", "w", "h"], [x, y, w, h])):
#             anno = f"{s} = {v}"
#             xann = int(x + 5)
#             yann = int(y + h - (20 * (i + 1)))
#             cv2.putText(image, anno, (xann, yann), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 0, 255), 2)
#
#     return image
def annotate_image(image, stats):
    stats = filter_unconnected_cells(stats)
    for stat in stats:
        x, y, w, h, area = stat
        cv2.rectangle(image, (x, y), (x + w, y + h), (255, 0, 255), 2)
        for i, (s, v) in enumerate(zip(["x", "y", "w", "h"], [x, y, w, h])):
            anno = f"{s} = {v}"
            xann = int(x + 5)
            yann = int(y + h - (20 * (i + 1)))
            cv2.putText(image, anno, (xann, yann), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 0, 255), 2)

    return image


def find_and_close_edges(img_bin_final):
    contours, hierarchy = cv2.findContours(img_bin_final, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
    for cnt in contours:
        missing_external_edges = True
        left = tuple(cnt[cnt[:, :, 0].argmin()][0])
        right = tuple(cnt[cnt[:, :, 0].argmax()][0])
        top = tuple(cnt[cnt[:, :, 1].argmin()][0])
        bottom = tuple(cnt[cnt[:, :, 1].argmax()][0])
        topleft = [left[0] + 1, top[1]]
        # print(cnt, left, top, topleft)
        bottomright = [right[0] - 1, bottom[1]]
        for arr in cnt:
            if np.array_equal(arr, np.array([bottomright])) or np.array_equal(arr, np.array([topleft])):
                missing_external_edges = False
                break

        if missing_external_edges and (bottomright[0]-topleft[0])*(bottomright[1]-topleft[1])>= 50000:
            topleft[0] -= 1
            bottomright[0] += 1
            cv2.rectangle(img_bin_final, tuple(topleft), tuple(bottomright), (255,255,255) , 2)
            print("missing cell detectet rectangle drawn")

    return img_bin_final


def parse_tables_in_pdf(pages):
    return zip(map(parse, pages), count())


def annotate_tables_in_pdf(pdf_path, page_index=1):
    timeit()
    page = pdf2image.convert_from_path(pdf_path, first_page=page_index + 1, last_page=page_index + 1)[0]
    page = np.array(page)

    _, stats = parse(page)
    page = annotate_image(page, stats)
    print(timeit())
    fig, ax = plt.subplots(1, 1)
    fig.set_size_inches(20, 20)
    ax.imshow(page)
    plt.show()