import cv2
import numpy as np


def super_tresh_main(img):
    image = img

    # cv2.imshow("Image", image)

    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    # cv2.imshow("gray", gray)

    blur = cv2.GaussianBlur(gray, (5, 5), 0)
    # cv2.imshow("blur", blur)

    thresh = cv2.adaptiveThreshold(blur, 255, 1, 1, 11, 2)

    # thresh = cv2.bitwise_not(thresh)

    # cv2.imshow("thresh", thresh)
    # cv2.waitKey()
    contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

    max_area = 0
    c = 0
    for i in contours:
        area = cv2.contourArea(i)
        if area > 1000:
            if area > max_area:
                max_area = area
                best_cnt = i
                image = cv2.drawContours(image, contours, c, (0, 255, 0), 1)
        c += 1

    mask = np.zeros((gray.shape), np.uint8)
    cv2.drawContours(mask, [best_cnt], 0, 255, -1)
    cv2.drawContours(mask, [best_cnt], 0, 0, 1)
    # cv2.imshow("mask", mask)

    out = np.zeros_like(gray)
    out[mask == 255] = gray[mask == 255]
    # cv2.imshow("New image", out)

    blur = cv2.GaussianBlur(out, (5, 5), 0)
    # cv2.imshow("blur1", blur)

    thresh = cv2.adaptiveThreshold(blur, 255, 1, 1, 11, 2)
    return cv2.bitwise_not(thresh)

def super_tresh_needle(img):
    image = img

    # cv2.imshow("Image", image)

    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    # cv2.imshow("gray", gray)

    blur = cv2.GaussianBlur(gray, (5, 5), 0)
    # cv2.imshow("blur", blur)

    thresh = cv2.adaptiveThreshold(blur, 255, 1, 1, 11, 2)

    return cv2.bitwise_not(thresh)
'''
    # cv2.imshow("thresh", thresh)
    # cv2.waitKey()
    contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

    max_area = 0
    c = 0
    for i in contours:
        area = cv2.contourArea(i)
        if area > 1000:
            if area > max_area:
                max_area = area
                best_cnt = i
                image = cv2.drawContours(image, contours, c, (0, 255, 0), 1)
        c += 1

    mask = np.zeros((gray.shape), np.uint8)
    cv2.drawContours(mask, [best_cnt], 0, 255, -1)
    cv2.drawContours(mask, [best_cnt], 0, 0, 1)
    # cv2.imshow("mask", mask)

    out = np.zeros_like(gray)
    out[mask == 255] = gray[mask == 255]
    # cv2.imshow("New image", out)

    blur = cv2.GaussianBlur(out, (5, 5), 0)
    # cv2.imshow("blur1", blur)

    thresh = cv2.adaptiveThreshold(blur, 255, 1, 1, 11, 2)
    return cv2.bitwise_not(thresh)
'''