import itertools
from itertools import compress
from itertools import starmap
from operator import __and__

import cv2
import numpy as np


from cv_analysis.utils.connect_rects import connect_related_rects
from cv_analysis.utils.display import show_image_mpl
from cv_analysis.utils.draw import draw_rectangles
from cv_analysis.utils.label_rects import label_rects
from cv_analysis.utils.structures import Rectangle
from cv_analysis.utils.postprocessing import (
    remove_overlapping,
    remove_included,
    has_no_parent,
)
from cv_analysis.utils.visual_logging import vizlogger

#could be dynamic parameter is the scan is noisy
def is_likely_segment(rect, min_area=100):
    return cv2.contourArea(rect, False) > min_area


def find_segments(image):
    contours, hierarchies = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    mask1 = map(is_likely_segment, contours)
    mask2 = map(has_no_parent, hierarchies[0])
    mask = starmap(__and__, zip(mask1, mask2))
    contours = compress(contours, mask)

    rectangles = (cv2.boundingRect(c) for c in contours)

    return rectangles


def dilate_page_components(image):
    #if text is detected in words make kernel bigger
    image = cv2.GaussianBlur(image, (7, 7), 0)
    thresh = cv2.threshold(image, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]
    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
    return cv2.dilate(thresh, kernel, iterations=4)


def fill_in_component_area(image, rect):
    x, y, w, h = rect
    cv2.rectangle(image, (x, y), (x + w, y + h), (0, 0, 0), -1)
    cv2.rectangle(image, (x, y), (x + w, y + h), (255, 255, 255), 7)
    _, image = cv2.threshold(image, 254, 255, cv2.THRESH_BINARY)
    return ~image



def parse_layout(image: np.array):
    image = image.copy()
    image_ = image.copy()
    #show_image_mpl(image)
    if len(image_.shape) > 2:
        image_ = cv2.cvtColor(image_, cv2.COLOR_BGR2GRAY)

    dilate = dilate_page_components(image_)
    # show_mpl(dilate)

    rects = list(find_segments(dilate))

    # -> Run meta detection on the previous detections TODO: refactor
    for rect in rects:
        x, y, w, h = rect
        cv2.rectangle(image, (x, y), (x + w, y + h), (0, 0, 0), -1)
        cv2.rectangle(image, (x, y), (x + w, y + h), (255, 255, 255), 7)
    # show_mpl(image)
    _, image = cv2.threshold(image, 254, 255, cv2.THRESH_BINARY)
    image = ~image
    # show_mpl(image)
    if len(image.shape) > 2:
        image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    rects = find_segments(image)
    # <- End of meta detection
    rects = list(map(Rectangle.from_xywh, rects))
    rects = remove_included(rects)

    rects = map(lambda r: r.xywh(), rects)
    rects = connect_related_rects(rects)
    rects = list(map(Rectangle.from_xywh, rects))
    # rects = remove_included(rects)
    return rects