import numpy as np
from shapely import geometry
import cv2
import imutils

def filter_contours_area_of_image(image, contours, hirarchy, max_area, min_area):
    found_polygons_early = list()

    jv = 0
    for c in contours:
        if len(c) < 3:  # A polygon cannot have less than 3 points
            continue

        polygon = geometry.Polygon([point[0] for point in c])
        area = polygon.area
        if area >= min_area * np.prod(image.shape[:2]) and area <= max_area * np.prod(image.shape[:2]) and hirarchy[0][jv][3] == -1:  # and hirarchy[0][jv][3]==-1 :
            found_polygons_early.append(np.array([[point] for point in polygon.exterior.coords], dtype=np.uint))
        jv += 1
    return found_polygons_early

def filter_contours_area_of_image_interiors(image, contours, hirarchy, max_area, min_area):
    found_polygons_early = list()

    jv = 0
    for c in contours:
        if len(c) < 3:  # A polygon cannot have less than 3 points
            continue

        polygon = geometry.Polygon([point[0] for point in c])
        area = polygon.area
        if area >= min_area * np.prod(image.shape[:2]) and area <= max_area * np.prod(image.shape[:2]) and hirarchy[0][jv][3] != -1:
            # print(c[0][0][1])
            found_polygons_early.append(np.array([point for point in polygon.exterior.coords], dtype=np.uint))
        jv += 1
    return found_polygons_early


def filter_contours_area_of_image_tables(image, contours, hirarchy, max_area, min_area):
    found_polygons_early = list()

    jv = 0
    for c in contours:
        if len(c) < 3:  # A polygon cannot have less than 3 points
            continue

        polygon = geometry.Polygon([point[0] for point in c])
        # area = cv2.contourArea(c)
        area = polygon.area
        ##print(np.prod(thresh.shape[:2]))
        # Check that polygon has area greater than minimal area
        # print(hirarchy[0][jv][3],hirarchy )
        if area >= min_area * np.prod(image.shape[:2]) and area <= max_area * np.prod(image.shape[:2]):  # and hirarchy[0][jv][3]==-1 :
            # print(c[0][0][1])
            found_polygons_early.append(np.array([[point] for point in polygon.exterior.coords], dtype=np.int32))
        jv += 1
    return found_polygons_early

def resize_image(img_in, input_height, input_width):
    return cv2.resize(img_in, (input_width, input_height), interpolation=cv2.INTER_NEAREST)

def rotatedRectWithMaxArea(w, h, angle):
    if w <= 0 or h <= 0:
        return 0, 0

    width_is_longer = w >= h
    side_long, side_short = (w, h) if width_is_longer else (h, w)

    # since the solutions for angle, -angle and 180-angle are all the same,
    # if suffices to look at the first quadrant and the absolute values of sin,cos:
    sin_a, cos_a = abs(math.sin(angle)), abs(math.cos(angle))
    if side_short <= 2.0 * sin_a * cos_a * side_long or abs(sin_a - cos_a) < 1e-10:
        # half constrained case: two crop corners touch the longer side,
        #   the other two corners are on the mid-line parallel to the longer line
        x = 0.5 * side_short
        wr, hr = (x / sin_a, x / cos_a) if width_is_longer else (x / cos_a, x / sin_a)
    else:
        # fully constrained case: crop touches all 4 sides
        cos_2a = cos_a * cos_a - sin_a * sin_a
        wr, hr = (w * cos_a - h * sin_a) / cos_2a, (h * cos_a - w * sin_a) / cos_2a

    return wr, hr

def rotate_max_area_new(image, rotated, angle):
    wr, hr = rotatedRectWithMaxArea(image.shape[1], image.shape[0], math.radians(angle))
    h, w, _ = rotated.shape
    y1 = h // 2 - int(hr / 2)
    y2 = y1 + int(hr)
    x1 = w // 2 - int(wr / 2)
    x2 = x1 + int(wr)
    return rotated[y1:y2, x1:x2]

def rotation_image_new(img, thetha):
    rotated = imutils.rotate(img, thetha)
    return rotate_max_area_new(img, rotated, thetha)

def rotate_image(img_patch, slope):
    (h, w) = img_patch.shape[:2]
    center = (w // 2, h // 2)
    M = cv2.getRotationMatrix2D(center, slope, 1.0)
    return cv2.warpAffine(img_patch, M, (w, h), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE)

def rotyate_image_different( img, slope):
    # img = cv2.imread('images/input.jpg')
    num_rows, num_cols = img.shape[:2]

    rotation_matrix = cv2.getRotationMatrix2D((num_cols / 2, num_rows / 2), slope, 1)
    img_rotation = cv2.warpAffine(img, rotation_matrix, (num_cols, num_rows))
    return img_rotation

def crop_image_inside_box(box, img_org_copy):
    image_box = img_org_copy[box[1] : box[1] + box[3], box[0] : box[0] + box[2]]
    return image_box, [box[1], box[1] + box[3], box[0], box[0] + box[2]]

def otsu_copy(img):
    img_r = np.zeros(img.shape)
    img1 = img[:, :, 0]
    img2 = img[:, :, 1]
    img3 = img[:, :, 2]
    # print(img.min())
    # print(img[:,:,0].min())
    # blur = cv2.GaussianBlur(img,(5,5))
    # ret3,th3 = cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
    retval1, threshold1 = cv2.threshold(img1, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
    retval2, threshold2 = cv2.threshold(img2, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
    retval3, threshold3 = cv2.threshold(img3, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)

    img_r[:, :, 0] = threshold1
    img_r[:, :, 1] = threshold1
    img_r[:, :, 2] = threshold1
    return img_r

def otsu_copy_binary(img):
    img_r = np.zeros((img.shape[0], img.shape[1], 3))
    img1 = img[:, :, 0]

    retval1, threshold1 = cv2.threshold(img1, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)

    img_r[:, :, 0] = threshold1
    img_r[:, :, 1] = threshold1
    img_r[:, :, 2] = threshold1

    img_r = img_r / float(np.max(img_r)) * 255
    return img_r