import cv2
import numpy as np
from pdf2image import pdf2image

from vidocp.utils import draw_contours, show_mpl, draw_rectangles, remove_included, remove_overlapping, show_cv2


def is_large_enough(cont, min_area=10000):
    return cv2.contourArea(cont, False) > min_area


def has_acceptable_format(cont, max_width_to_hight_ratio=6):
    _, _, w, h = cv2.boundingRect(cont)
    return max_width_to_hight_ratio >= w / h >= (1 / max_width_to_hight_ratio)


def is_likely_figure(cont, min_area=5000, max_width_to_hight_ratio=6):
    return is_large_enough(cont, min_area) and has_acceptable_format(cont, max_width_to_hight_ratio)


def remove_primary_text_regions(image):
    """Removes regions of primary text, meaning no figure descriptions for example, but main text body paragraphs.

    Args:
        image: Image to remove primary text from.

    Returns:
        Image with primary text removed.

    References:
        https://stackoverflow.com/questions/58349726/opencv-how-to-remove-text-from-background
    """

    def filter_likely_primary_text_segments(cnts):
        for c in cnts:
            area = cv2.contourArea(c)
            if area > 800 and area < 15000:
                yield cv2.boundingRect(c)

    image = image.copy()

    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    thresh = cv2.threshold(gray, 253, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]

    close_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (20, 3))
    close = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, close_kernel, iterations=1)

    dilate_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 3))
    dilate = cv2.dilate(close, dilate_kernel, iterations=1)

    cnts, _ = cv2.findContours(dilate, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)

    for rect in filter_likely_primary_text_segments(cnts):
        x, y, w, h = rect
        cv2.rectangle(image, (x, y), (x + w, y + h), (255, 255, 255), -1)

    return image


def __detect_large_coherent_structures(image: np.array):
    """Detects large coherent structures on an image.
   
    References:
         https://stackoverflow.com/questions/60259169/how-to-group-nearby-contours-in-opencv-python-zebra-crossing-detection
    """
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    thresh = cv2.threshold(gray, 253, 255, cv2.THRESH_BINARY)[1]

    dilate_kernel = cv2.getStructuringElement(cv2.MORPH_OPEN, (5, 5))
    dilate = cv2.dilate(~thresh, dilate_kernel, iterations=4)

    close_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (20, 20))
    close = cv2.morphologyEx(dilate, cv2.MORPH_CLOSE, close_kernel, iterations=1)

    cnts, _ = cv2.findContours(close, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

    return cnts


def detect_figures(image: np.array):

    image = image.copy()

    image = remove_primary_text_regions(image)
    cnts = __detect_large_coherent_structures(image)

    cnts = filter(is_likely_figure, cnts)
    rects = map(cv2.boundingRect, cnts)
    rects = remove_included(rects)

    return rects


def remove_text_in_pdf(pdf_path, page_index=1):

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

    redaction_contours = detect_figures(page)
    page = draw_rectangles(page, redaction_contours)

    show_mpl(page)