import cv2
import numpy as np
from pdf2image import pdf2image

from cv_analysis.utils.detection import detect_large_coherent_structures
from cv_analysis.utils.display import show_mpl
from cv_analysis.utils.draw import draw_rectangles
from cv_analysis.utils.post_processing import remove_included
from cv_analysis.utils.filters import is_large_enough, has_acceptable_format
from cv_analysis.utils.text import remove_primary_text_regions


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 detect_figures(image: np.array):
    image = image.copy()
    #show_mpl(image)
    image = remove_primary_text_regions(image)
    #show_mpl(image)
    cnts = detect_large_coherent_structures(image)

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

    return list(rects)


def detect_figures_in_pdf(pdf_path, page_index=1, show=False):
    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)

    if show:
        show_mpl(page)
    else:
        return page


def figures_in_image(cropped_page):
    redaction_contours = detect_figures(cropped_page)

    if len(redaction_contours) > 0:
        return True
    else:
        return False