kek3

from numpy import zeros, squeeze
from skimage.morphology import disk
from skimage.io import imread, imsave
from skimage.exposure import adjust_log, adjust_gamma, equalize_hist
from skimage.filters import denoise_bilateral, threshold_otsu, threshold_adaptive,gaussian_filter, median, rank,inverse
from scipy import ndimage
from math import gamma
from sklearn import svm, datasets, feature_extraction
from skimage.segmentation import felzenszwalb, clear_border
from skimage import morphology
import matplotlib
import matplotlib.pyplot as plt
from itertools import permutations
from skimage.transform import resize
from skimage import data
from skimage.filters import rank
from skimage.util import img_as_ubyte, img_as_float
from skimage.feature import hog
from os import listdir

def filtering(image):
    image = denoise_bilateral(rank.autolevel(image, disk(5)))
    black_white = threshold_otsu(image)
    for i in range(len(image)):
        for j in range(len(image[1])):
            if image[i][j] >= black_white:
                image[i][j] = 1
            else:
                image[i][j] = 0
    return image

def generate_template(digit_dir_path):
    ans=[]
    for name in listdir(digit_dir_path):
        img = imread(digit_dir_path + "/" + name)
        img = resize(img, (25, 15))
        img = img.ravel()
        ans.append(img)
    return ans

def recognize(image, digit_templates):
    image = filtering(image)
    image = image >= rank.otsu(image, disk(20))
    image = image == False
    image = clear_border(image)
    morphology.remove_small_objects(image, min_size=60, connectivity=1, in_place=True)
    marked, objects = ndimage.label(image)
    founded_obj =[]
    for obj in range(objects):
        temp = ndimage.find_objects(marked)[obj]
        founded_obj.append((marked[temp], temp))
    for numb in permutations(founded_obj, 3):
        calibrate_numbers = numb[0][1][1].start < numb[1][1][1].start < numb[2][1][1].start
        calibrate_height = abs(len(numb[0][0]) - len(numb[1][0])) < 5 and abs(len(numb[1][0]) - len(numb[2][0])) < 5 and abs(len(numb[0][0]) - len(numb[2][0])) < 5
        calibrate_width = abs(len(numb[0][0][0]) - len(numb[1][0][0])) < 5 and abs(len(numb[1][0][0]) - len(numb[2][0][0])) < 5 and abs(len(numb[0][0][0]) - len(numb[2][0][0])) < 5
        if calibrate_numbers == True and calibrate_width == True and calibrate_height == True:
            numbers = numb
            break
    image = img_as_ubyte(image)
    dataset = []

    for i in range(0, 10):
        for number in digit_templates:
            dataset.append([i, number])

    goal = [array[0] for array in dataset]
    attemt = [array[1] for array in dataset]

    kek = svm.SVC(kernel = "poly", degree = 2)
    kek.fit(attemt, goal)
    try:
        one = img_as_ubyte(numbers[0][0] == 0)
        two = img_as_ubyte(numbers[1][0] == 0)
        three = img_as_ubyte(numbers[2][0] == 0)
    except:
        return -1
    predict1 = kek.predict(resize(one, (25,15)).ravel())[0]
    predict2 = kek.predict(resize(two, (25,15)).ravel())[0]
    predict3 = kek.predict(resize(three, (25,15)).ravel())[0]
    return predict1, predict2, predict3