from typing import Iterable

import cv2
import numpy as np

from cv_analysis.figure_detection.text import remove_primary_text_regions, apply_threshold_to_image
from cv_analysis.table_parsing import preprocess, isolate_vertical_and_horizontal_components, \
    turn_connected_components_into_rects
from cv_analysis.utils.display import show_image_mpl


def area_is_bigger_than(rect: tuple, maxarea=100000):
    x, y, w, h = rect
    return w * h >= maxarea


def define_rect(rect_img, original_position):
    show_image_mpl(rect_img)
    xo,yo,wo,ho = original_position
    if is_header(yo+ho):
        print(original_position, " is header")
        return "header"
    elif is_footer(yo):
        print(original_position, " is footer")
        return "footer"
    elif is_table(rect_img):
        print(original_position, " is table")
        return "table"
    elif is_text(rect_img):
        print(original_position, " is text")
        return "text"


def is_table(rect_img):
    rect_img_inv = preprocess(rect_img)
    grid_inv = isolate_vertical_and_horizontal_components(rect_img_inv)
    cnts, _ = cv2.findContours(image=grid_inv, mode=cv2.RETR_EXTERNAL, method=cv2.CHAIN_APPROX_SIMPLE)
    if cnts:
        rects = turn_connected_components_into_rects(grid_inv)
        rects = map(lambda r: r.xywh(), rects)
        bbox = list((cv2.boundingRect(c) for c in cnts))
        if len(list(rects)) > 1 and len(bbox) == 1:
            x, y, w, h = bbox[0]
            w_img, h_img = rect_img.shape
            if w * h / (w_img * h_img) >= 0.75:
                print("is table")
                return True
            else:
                print(" table detected but to small for layout rect, so cant be table, maybe figure?")
                return False

        else:
            print("single cell or no connected components, maybe figure?")
            return False

    else:
        print("not a table, but text?")
        return False

def is_header(y):
    return y < 200


def is_footer(y):
    return y > 2150


def is_text(img):
    show_image_mpl(img)
    cleaned = remove_primary_text_regions(img)
    show_image_mpl(cleaned)
    return pixel_density(cleaned) < 0.05


def pixel_density(img):
    pixels = np.count_nonzero(img)
    density = pixels / img.size
    return density


def annotate_rect(rect, rect_img):
    pass


def label_rects(rects: Iterable[tuple], image: np.array):
    labeled_rects = {}
    for rect in rects:
        x, y, w, h = rect
        labeled_rects[rect] = define_rect(image[y:y + h, x:x + w], rect)