# referance : https://bibinmjose.github.io/2018/01/05/phone-crack-estimation.html 
# bunu check et https://www.kaggle.com/abhishek/very-simple-pytorch-training-0-59
# importing necessary libraries
import numpy as np
from skimage.color import rgb2gray
from skimage.io import imread
from skimage import feature, img_as_bool
from skimage.morphology import binary_dilation, binary_erosion
import matplotlib.pyplot as plt
import glob
import pathlib


path = '/home/cappittall/Documents/litetest/data/ekran/kaggle/'
#grab files from the path and store them in a list called files .jpg and .JPG
files = glob.glob(path + '*.jpg') + glob.glob(path + '*.JPG')



print('Toplam resim sayısı %d' % len(files))

def preprocess(url):
    img = imread(url)
    img = rgb2gray(img)
    img_edge = binary_erosion(binary_dilation(feature.canny(img, sigma =.1)))
    return img, img_edge



""" ax1 = fig.add_subplot(121)
plt.imshow(img1, cmap='gray')
ax2 = fig.add_subplot(122)
plt.imshow(c1, cmap='gray')

plt.show() """

#############

def edge_prob(window, cut_off):
    pixels  = np.array(window.ravel())
    if ((np.count_nonzero(pixels)/len(pixels))>cut_off):
        return 1
    else:
        return 0
    
def sliding_mat(img,window_x=10,window_y=10, cut_off=0.1):
    
    arr_x = np.arange(0,img.shape[0],window_x)
    arr_y = np.arange(0,img.shape[1],window_y)

    A = np.zeros((len(arr_x),len(arr_y)))

    for i,x in enumerate(arr_x):
        for j,y in enumerate(arr_y):
            window = img[x:x+window_x,y:y+window_y]
            A[i,j] = edge_prob(window, cut_off=cut_off)
    
    return A, arr_x, arr_y

def plot_all(img,canny_edge,A):
    fig = plt.figure(figsize = (9,4))
    ax1 = fig.add_subplot(131)
    ax1.imshow(img, cmap="gray")
    ax1.set_title("Original")
    
    ax2 = fig.add_subplot(132)
    ax2.set_title("Canny Edge Detection")
    ax2.imshow(canny_edge, cmap="gray")
    
    ax3 = fig.add_subplot(133)
    ax3.set_title("Mask")
    ax3.imshow(A,cmap="gray")
    plt.tight_layout()
    plt.show()

for url in files:
    #fig = plt.figure(figsize = (8,6))
    img1,c1 = preprocess(url)
    # REMOVE THE IMAGE BACKGROUND
    img1 = img1[100:img1.shape[0]-100,100:img1.shape[1]-100]


    A, arr_x, arr_y = sliding_mat(c1, window_x=10, window_y=10, cut_off=0.1)

    print("Estimate of crack : {:.2f}%".format(np.sum(A)/A.size*100))
    plot_all(img1,c1,A)
    
    3