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import requests
import cv2
import urllib.request
import numpy as np
from matplotlib import pyplot as plt
import re
import math
import pytesseract
from pytesseract import Output
from spellchecker import SpellChecker
import datetime
import test_funcs
# def merge_contours(contours, max_distance=200):
# merged_contours = []
# for contour in contours:
# if not merged_contours:
# merged_contours.append(contour)
# else:
# merged = False
# for i, merged_contour in enumerate(merged_contours):
# print(tuple(contour[0][0]))
# pt = tuple(map(int, contour[0][0]))
# distance = cv2.pointPolygonTest(merged_contour, pt, measureDist=True)
# if distance >= 0 and distance <= max_distance:
# merged_contours[i] = np.concatenate((merged_contour, contour))
# merged = True
# break
# if not merged:
# merged_contours.append(contour)
# return merged_contours
# # def template(url, f_name):
# # n = '(1)'
# # req = urllib.request.urlopen(url)
# # files = []
# # arr = np.asarray(bytearray(req.read()), dtype=np.uint8)
# # labels = []
# # image = cv2.imdecode(arr, cv2.IMREAD_COLOR) # Получение фото по изображению
# #
# # # image = cv2.imread("tets.JPG") # Получение фото по изображению
# # cv2.imwrite('ph.jpeg', image)
# # # cv2.imshow('ph.jpeg', image)
# # y_img = cv2.cvtColor(image, cv2.COLOR_BGR2YUV)
# # img = image.copy()
# # # image = cv2.imread('tsts1.png')
# # # print('123', arr)
# # cv2.imshow('1', img)
# # y_channel = y_img[:, :, 0]
# #
# # # Вычисление среднего значения яркости
# # brightness = int(y_channel.mean())
# # print("яркость", brightness)
# # img = image.copy()
# # # return 0
# # # cv2.imshow('Оригинал', image)
# # # cv2.waitKey(0)
# # # cv2.destroyAllWindows()
# #
# # blur = cv2.GaussianBlur(img, (3, 3), 1)
# #
# # gray = cv2.cvtColor(blur, cv2.COLOR_BGR2GRAY)
# # sharpen_kernel = np.array([[-1, -1, -1], [-1, 9, -1], [-1, -1, -1]])
# # # gray = cv2.filter2D(gray, -1, sharpen_kernel)
# # # edges = cv2.Canny(gray, 80, 150)
# #
# # # edges = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, 17, 7)
# # ret,edges = cv2.threshold(gray,170,255,cv2.THRESH_BINARY)
# # # edges = cv2.Canny(img, 160,230)
# # # cv2.th
# # # ret,edges = cv2.threshold(gray,brightness,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
# # # print(ret)
# #
# # cv2.imshow('canny', edges)
# # # cv2.imshow('gray', gray)
# # # cv2.waitKey(0)
# # # cv2.destroyAllWindows()
# #
# #
# # contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# # contours = sorted(contours, key=cv2.contourArea, reverse=True)
# # # print('len(contours)',len(contours))
# #
# #
# # # d_img = cv2.drawContours(img,contours, -1,(0, 255, 0),3)
# #
# # mask = np.zeros_like(image)
# # cv2.drawContours(mask, contours, -1, (255, 255, 255), thickness=cv2.FILLED)
# #
# # result = cv2.bitwise_and(image, mask)
# #
# #
# #
# #
# # gray = cv2.cvtColor(result, cv2.COLOR_BGR2GRAY)
# #
# # cv2.imshow('result', gray)
# #
# # # edges = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.ADAPTIVE_THRESH_MEAN_C, 11, 2)
# # # ret, edges = cv2.threshold(gray, 230, 255, cv2.THRESH_BINARY_INV)
# # edges = cv2.Canny(result,190,220)
# # contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# # d_img = cv2.drawContours(result, contours, -1, (0, 255, 0), 1)
# # cv2.imshow('d_img', d_img)
# # # cv2.waitKey(0)
# # # cv2.destroyAllWindows()
# #
# # for i in range(len(contours)):
# # x,y,h,w = cv2.boundingRect(contours[i])
# # area = cv2.contourArea(contours[i])
# # text = f"x:{x} y:{y} w:{w} h:{h} area:{area}"
# # # if w:
# #
# # if 10 < w and 10 < h and w+h>20:
# # print(x, y, h, w, w * h)
# # # print(w*h)
# # # w,h,x,y = correct_rct(w,h,x,y)
# # cv2.rectangle(image, (x + 1, y + 1 ), (x + h + 1, y + w + 1), (255, 0, 0), 1)
# # # print(x,y,w,h)
# # is_mine = lambda x: 0 if x<0 else x
# # x,y,h,w = map(is_mine, [x,y,h,w])
# # crop_img = image[y: y + w, x :x + h]
# # # crop_img = image[10: 110, 10:110]
# # # cv2.imshow('img', image)
# # # cv2.waitKey(0)
# # # cv2.destroyAllWindows()
# # # f_name = n + f_name
# # # filename = f'{f_name}'
# # # print("имя файла:", f_name)
# # # output_folder = r'C:\Users\gleb\PycharmProjects\Bot\crops'
# # # os.makedirs(output_folder, exist_ok=True)
# # # output_filename = f'{f_name}'
# # # output_path = os.path.join(output_folder, output_filename)
# # # cv2.imwrite(output_path, crop_img)
# # # server_base_url = 'http://192.168.0.158/'
# # # new_image_url = server_base_url + output_filename
# # # print(new_image_url)
# # # cv2.imwrite(n + f_name, crop_img)
# # files.append(n + f_name)
# #
# # cv2.imshow("res", image)
# # cv2.waitKey(0)
# # cv2.destroyAllWindows()
# # return 0
# # # Фильтрация контуров по характеристикам
# # mx = 0
# # filtered_contours = []
# # for contour in contours[:len(contours)//2]:
# # x, y, h, w = cv2.boundingRect(contour)
# # area = cv2.contourArea(contour)
# # # print(area)
# # mx = max(area, mx)
# # if 100 < h < 450 and 100 < w < 450 and w*h>12_000: # Пример условия фильтрации по площади
# # filtered_contours.append(contour)
# # # print(mx)
# # # exit()
# # # Создание чистого изображения только с нужными контурами
# #
# # filtered_contours = merge_contours(filtered_contours)
# #
# # mask = np.zeros_like(image)
# # cv2.drawContours(mask, filtered_contours, -1, (255, 255, 255), thickness=cv2.FILLED)
# #
# # result = cv2.bitwise_and(image, mask)
# #
# # cv2.imshow('Result', result)
# # cv2.waitKey(0)
# # cv2.destroyAllWindows()
# # #
# # # edges = cv2.Canny(result, 150, 250)
# # imgray = cv2.cvtColor(result, cv2.COLOR_BGR2GRAY)
# # q, edges = cv2.threshold(imgray,127,255,cv2.THRESH_BINARY)
# #
# #
# # conts, hierarchy = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# # conts = sorted(conts, key= cv2.contourArea, reverse=True)
# # # image_with_contours = cv2.drawContours(image, conts, -1, (0, 255, 0))
# # # cv2.imshow('I', image_with_contours)
# # print('len(conts)',len(conts))
# # count = 0
# # for i in range(len(conts)):
# # x,y,h,w = cv2.boundingRect(conts[i])
# # area = cv2.contourArea(conts[i])
# # text = f"x:{x} y:{y} w:{w} h:{h} area:{area}"
# #
# #
# # # x = int(x)
# # # y = int(y)
# # # print(y + h)
# # if w:
# # print(x,y,h,w, w*h)
# # if 150 < w < 400 and 150 < h < 400 and w*h < 65_000:
# # # print(w*h)
# # # w,h,x,y = correct_rct(w,h,x,y)
# # cv2.rectangle(image, (x + 1, y + 1 ), (x + h + 1, y + w + 1), (255, 0, 0), 1)
# # # print(x,y,w,h)
# # is_mine = lambda x: 0 if x<0 else x
# # x,y,h,w = map(is_mine, [x,y,h,w])
# # crop_img = image[y: y + w, x :x + h]
# # # crop_img = image[10: 110, 10:110]
# # # cv2.imshow('img', image)
# # # cv2.waitKey(0)
# # # cv2.destroyAllWindows()
# # # f_name = n + f_name
# # # filename = f'{f_name}'
# # print("имя файла:", f_name)
# # # output_folder = r'C:\Users\gleb\PycharmProjects\Bot\crops'
# # # os.makedirs(output_folder, exist_ok=True)
# # # output_filename = f'{f_name}'
# # # output_path = os.path.join(output_folder, output_filename)
# # # cv2.imwrite(output_path, crop_img)
# # # server_base_url = 'http://192.168.0.158/'
# # # new_image_url = server_base_url + output_filename
# # # print(new_image_url)
# # cv2.imwrite(n + f_name, crop_img)
# # count += 1
# # files.append(n + f_name)
# # # return f_name
# #
# # # angle = find_rotation_angle2(crop_img)
# # # res_img = OCR(crop_img)
# # # cv2.imshow("res", res_img)
# # # cv2.waitKey(0)
# # # cv2.destroyAllWindows()
# # # read = easyocr.Reader(["ru"], gpu=True)
# # # result = read.readtext(res_img, detail=0, rotation_info=[90,180])
# # # print(result)
# # # print(angle)
# #
# # # text = str(OCR(crop_img))
# # # cv2.putText(image, text, (10, 20), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 2)
# # # labels.append(text)
# #
# # # exit()
# # # n + f_name
# # n = '(2)'
# # print('counrt',count)
# # if count == 0:
# # # cv2.imwrite('Not Find', image)
# # w, h, c = image.shape
# # # text = OCR(image)
# # # cv2.putText(image, text, (10, 20), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 0), 2)
# # cv2.imwrite(f_name + ".jpg", image)
# # files.append(f_name + ".jpg")
# # # return f_name
# # # labels.append(text)
# # # cv2.imwrite('test' + str(74) + '.jpg', image)
# # cv2.imshow('I1', image)
# # cv2.waitKey(0)
# # cv2.destroyAllWindows()
# # # return 0
# # # return labels
# # return files
# def filt_box(boxs):
# boreds = []
# for box in boxs:
# x,y,w,h,s = box
# if 140 < w < 400 and 150 < h < 400 and w * h < 65_000:
# boreds.append([x,y,w,h,s])
# return boreds
#
# def templ(image, d = 170, r = 3):
#
# img = image.copy()
#
# # gray = cv2.cvtColor(blur, cv2.COLOR_BGR2GRAY)
#
# gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# #
# blur = cv2.GaussianBlur(gray, (3, 3), 1)
#
# ret, thes = cv2.threshold(blur, d, 255, cv2.THRESH_BINARY)
#
# # kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (25, 25))
#
# # Применяем морфологическую операцию расширения к изображению
# # dilated_image = cv2.dilate(img, kernel)
#
# # gray = cv2.cvtColor(thes, cv2.COLOR_BGR2GRAY)
#
# # ret, thes = cv2.threshold(gray, d, 255, cv2.THRESH_BINARY)
#
#
# # mask_3_channels = cv2.merge((dilated_mask, dilated_mask, dilated_mask))
#
# # Наложение маски на исходное изображение
# # mask = np.zeros_like(thes)
# # mask = cv2.bitwise_not(thes)
# result = cv2.bitwise_and(img, img, mask=thes)
#
# # gray = cv2.cvtColor(result, cv2.COLOR_BGR2GRAY)
# #
# # ret, thes = cv2.threshold(gray, 240, 255, cv2.THRESH_BINARY_INV)
#
# # result = cv2.bitwise_and(img, img, mask=thes)
#
# # cv2.imshow("thes", thes)
# cv2.imshow("dilate", result)
# # cv2.imshow("MSK", mask)
# # cv2.waitKey(0)
# # cv2.destroyAllWindows()
#
# # edges = cv2.Canny(cv2.cvtColor(result, cv2.COLOR_BGR2GRAY), 100, 200, apertureSize=r,L2gradient= True)
# edges = cv2.Canny(cv2.cvtColor(result, cv2.COLOR_BGR2GRAY), 100, 200)
#
# cv2.imshow("edges", edges)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
# borders = []
# # Найдите контуры на изображении
# contours, _ = cv2.findContours(edges, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# for cont in contours:
# x,y,w,h = cv2.boundingRect(cont)
# borders.append([x,y,w,h,w*h])
# # img = cv2.rectangle(img, (x,y), (x+w, y+h), (255,0,0), 2)
#
#
#
# # y_img = cv2.cvtColor(image, cv2.COLOR_BGR2YUV)
# # img = image.copy()
# # # image = cv2.imread('tsts1.png')
# # # print('123', arr)
# # # cv2.imshow('1', img)
# # y_channel = y_img[:, :, 0]
# #
# # # Вычисление среднего значения яркости
# # brightness = int(y_channel.mean())
# # print("яркость", brightness)
# # img = image.copy()
# # # return 0
# # # cv2.imshow('Оригинал', image)
# # # cv2.waitKey(0)
# # # cv2.destroyAllWindows()
# #
# # blur = cv2.GaussianBlur(img, (3, 3), 1)
# #
# # gray = cv2.cvtColor(blur, cv2.COLOR_BGR2GRAY)
# # sharpen_kernel = np.array([[-1, -1, -1], [-1, 9, -1], [-1, -1, -1]])
# # # gray = cv2.filter2D(gray, -1, sharpen_kernel)
# # # edges = cv2.Canny(gray, 80, 150)
# #
# # # edges = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, 11, 2)
# # ret, edges = cv2.threshold(gray, d, 255, cv2.THRESH_BINARY)
# #
# #
# # # rect_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (12,12))
# # # dilation = cv2.dilate(edges, rect_kernel, iterations = 3)
# # # cv2.imshow('dill', dilation)
# # # Mask = cv2.bitwise_and(dilation)
# #
# # # Mask = cv2.bitwise_and(img, img, mask=Mask)
# # # cv2.imshow('mask', Mask)
# # # cv2.waitKey(0)
# # # cv2.destroyAllWindows()
# #
# #
# # # edges = cv2.Canny(img, 160,230)
# # # cv2.th
# # # ret,edges = cv2.threshold(gray,brightness,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
# # # print(ret)
# #
# # # cv2.imshow('canny', edges)
# # # cv2.imshow('gray', gray)
# # # cv2.waitKey(0)
# # # cv2.destroyAllWindows()
# #
# # contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# # # # # print('len(contours)',len(contours))
# # #
# # # mask = np.zeros_like(image)
# #
# # # d_img = cv2.drawContours(img,contours, -1,(0, 255, 0),3)
# #
# # mask = np.zeros_like(image)
# # cv2.drawContours(mask, contours, -1, (255, 255, 255), thickness=cv2.FILLED)
# # cv2.imshow("x,", edges)
# # cv2.waitKey(0)
# # cv2.destroyAllWindows()
# # result = cv2.bitwise_and(image, mask)
# # #
# # # result = cv2.bitwise_and(img, mask)
# # gray = cv2.cvtColor(result, cv2.COLOR_BGR2GRAY)
# #
# # # edges = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.ADAPTIVE_THRESH_MEAN_C, 11, 2)
# # # ret, edges = cv2.threshold(gray, 190, 255, cv2.THRESH_BINARY_INV)
# # edges = cv2.Canny(gray, 100, 200, apertureSize=7)
# # contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# # d_img = cv2.drawContours(result, contours, -1, (0, 255, 0), 1)
# # cv2.imshow("x,", d_img)
# # cv2.waitKey(0)
# # cv2.destroyAllWindows()
# # # borders = map(cv2.boundingRect, contours)
# # borders = []
# #
# # for cont in contours:
# # x, y, w, h = cv2.boundingRect(cont)
# # borders.append([x, y, w, h, w * h])
# # cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 255), 1)
# # # cv2.imshow('dimg', img)
# # # cv2.waitKey(0)
# # # cv2.destroyAllWindows()
# #
# # # if len(borders)<10:
# # # return 0
#
#
# # ###########################################
#
# # arr = [[148, 600, 12, 2, 24], [170, 599, 13, 3, 39], [228, 587, 3, 3, 9], [296, 552, 19, 16, 304], [296, 552, 19, 16, 304], [0, 540, 3, 6, 18], [367, 514, 5, 5, 25], [367, 514, 5, 5, 25], [10, 497, 3, 4, 12], [10, 497, 3, 4, 12], [9, 479, 7, 13, 91], [9, 479, 7, 13, 91], [201, 457, 36, 16, 576], [201, 457, 36, 16, 576], [0, 453, 39, 108, 4212], [307, 438, 3, 3, 9], [307, 438, 3, 3, 9], [311, 421, 3, 3, 9], [311, 421, 3, 3, 9], [301, 418, 41, 124, 5084], [301, 418, 41, 124, 5084], [314, 452, 5, 22, 110], [331, 403, 9, 13, 117], [331, 403, 9, 13, 117], [315, 398, 3, 3, 9], [315, 398, 3, 3, 9], [175, 320, 37, 25, 925], [175, 320, 37, 25, 925], [218, 314, 4, 3, 12], [218, 314, 4, 3, 12], [176, 311, 3, 3, 9], [176, 311, 3, 3, 9], [40, 293, 13, 13, 169], [40, 293, 13, 13, 169], [44, 270, 11, 15, 165], [44, 270, 11, 15, 165], [93, 262, 3, 3, 9], [93, 262, 3, 3, 9], [405, 253, 299, 298, 89102], [405, 253, 299, 298, 89102], [517, 518, 21, 15, 315], [517, 518, 21, 15, 315], [492, 511, 21, 15, 315], [492, 511, 21, 15, 315], [521, 508, 20, 9, 180], [521, 508, 20, 9, 180], [538, 504, 5, 5, 25], [538, 504, 5, 5, 25], [467, 503, 21, 16, 336], [467, 503, 21, 16, 336], [496, 498, 21, 16, 336], [500, 504, 3, 4, 12], [496, 498, 21, 16, 336], [514, 496, 4, 4, 16], [514, 496, 4, 4, 16], [436, 494, 12, 12, 144], [436, 494, 12, 12, 144], [471, 490, 21, 16, 336], [471, 490, 21, 16, 336], [489, 489, 4, 4, 16], [489, 489, 4, 4, 16], [526, 487, 22, 15, 330], [526, 487, 22, 15, 330], [531, 491, 11, 7, 77], [531, 491, 11, 7, 77], [502, 478, 20, 16, 320], [502, 478, 20, 16, 320], [506, 482, 12, 7, 84], [506, 482, 12, 7, 84], [439, 478, 27, 23, 621], [439, 478, 27, 23, 621], [444, 483, 14, 10, 140], [444, 483, 14, 10, 140], [429, 476, 4, 3, 12], [429, 476, 4, 3, 12], [548, 474, 5, 5, 25], [548, 474, 5, 5, 25], [529, 474, 21, 15, 315], [529, 474, 21, 15, 315], [477, 471, 19, 15, 285], [477, 471, 19, 15, 285], [481, 474, 11, 9, 99], [481, 474, 11, 9, 99], [505, 466, 20, 14, 280], [505, 466, 20, 14, 280], [523, 465, 5, 5, 25], [523, 465, 5, 5, 25], [449, 462, 18, 19, 342], [449, 462, 18, 19, 342], [458, 473, 3, 3, 9], [480, 458, 19, 14, 266], [480, 458, 19, 14, 266], [536, 457, 22, 17, 374], [536, 457, 22, 17, 374], [499, 457, 4, 4, 16], [499, 457, 4, 4, 16], [511, 449, 22, 15, 330], [511, 449, 22, 15, 330], [433, 447, 4, 4, 16], [433, 447, 4, 4, 16], [540, 444, 21, 16, 336], [540, 444, 21, 16, 336], [558, 441, 8, 8, 64], [558, 441, 8, 8, 64], [486, 440, 21, 16, 336], [486, 440, 21, 16, 336], [515, 436, 21, 15, 315], [515, 436, 21, 15, 315], [534, 433, 8, 7, 56], [534, 433, 8, 7, 56], [455, 432, 21, 33, 693], [466, 451, 3, 3, 9], [464, 446, 5, 3, 15], [459, 432, 17, 16, 272], [464, 436, 8, 8, 64], [464, 436, 8, 8, 64], [490, 428, 21, 15, 315], [490, 428, 21, 15, 315], [412, 426, 40, 73, 2920], [440, 443, 5, 3, 15], [438, 442, 4, 3, 12], [442, 438, 4, 3, 12], [432, 426, 20, 19, 380], [508, 425, 9, 7, 63], [508, 425, 9, 7, 63], [464, 420, 16, 14, 224], [464, 420, 16, 14, 224], [473, 426, 3, 4, 12], [473, 426, 3, 4, 12], [467, 425, 3, 3, 9], [467, 425, 3, 3, 9], [523, 413, 20, 11, 220], [523, 413, 20, 11, 220], [437, 411, 19, 17, 323], [548, 409, 24, 24, 576], [551, 409, 21, 14, 294], [555, 413, 6, 6, 36], [555, 413, 6, 6, 36], [463, 408, 12, 11, 132], [463, 408, 12, 11, 132], [568, 406, 5, 5, 25], [568, 406, 5, 5, 25], [499, 406, 18, 9, 162], [499, 406, 18, 9, 162], [557, 402, 7, 6, 42], [557, 402, 7, 6, 42], [526, 399, 20, 15, 300], [526, 399, 20, 15, 300], [530, 403, 6, 6, 36], [530, 403, 6, 6, 36], [544, 397, 4, 4, 16], [544, 397, 4, 4, 16], [532, 393, 5, 5, 25], [532, 393, 5, 5, 25], [501, 392, 20, 14, 280], [501, 392, 20, 14, 280], [506, 397, 5, 5, 25], [506, 397, 5, 5, 25], [518, 389, 5, 5, 25], [518, 389, 5, 5, 25], [507, 386, 4, 4, 16], [507, 386, 4, 4, 16], [453, 384, 8, 7, 56], [453, 384, 8, 7, 56], [532, 379, 21, 16, 336], [532, 379, 21, 16, 336], [537, 383, 12, 8, 96], [537, 383, 12, 8, 96], [558, 375, 26, 29, 754], [558, 388, 21, 16, 336], [563, 393, 10, 7, 70], [563, 393, 10, 7, 70], [561, 375, 23, 17, 391], [507, 372, 21, 15, 315], [507, 372, 21, 15, 315], [511, 376, 12, 7, 84], [511, 376, 12, 7, 84], [536, 366, 22, 17, 374], [536, 366, 22, 17, 374], [511, 359, 21, 16, 336], [511, 359, 21, 16, 336], [440, 352, 34, 55, 1870], [451, 392, 4, 3, 12], [452, 366, 20, 17, 340], [457, 370, 8, 6, 48], [457, 370, 8, 6, 48], [460, 327, 22, 27, 594], [465, 345, 3, 5, 15], [460, 327, 22, 27, 594], [470, 331, 8, 7, 56], [470, 331, 8, 7, 56], [26, 214, 6, 6, 36], [26, 214, 6, 6, 36], [376, 197, 12, 28, 336], [376, 197, 12, 28, 336], [7, 183, 10, 10, 100], [7, 183, 10, 10, 100], [0, 160, 12, 12, 144], [38, 157, 8, 8, 64], [38, 157, 8, 8, 64], [75, 114, 179, 488, 87352], [431, 107, 3, 3, 9], [431, 107, 3, 3, 9], [66, 107, 3, 3, 9], [66, 107, 3, 3, 9], [456, 101, 101, 38, 3838], [456, 101, 101, 38, 3838], [71, 99, 4, 5, 20], [71, 99, 4, 5, 20], [63, 88, 6, 6, 36], [63, 88, 6, 6, 36], [0, 67, 7, 10, 70], [0, 25, 66, 223, 14718], [227, 17, 4, 3, 12], [227, 17, 4, 3, 12], [230, 0, 33, 55, 1815], [196, 0, 33, 38, 1254], [176, 0, 19, 9, 171], [22, 0, 79, 83, 6557], [97, 78, 4, 5, 20], [96, 76, 3, 4, 12], [95, 73, 3, 5, 15], [9, 0, 44, 27, 1188]]
#
# # ###############################################
# # arr = list(filter(lambda x: 0.3 < x[2] / x[3] < 2.1 and 100 < x[2] * x[3] < 400, borders))
# #
# # for cont in arr:
# # x, y, w, h, *_ = cont
# # # borders.append([x,y,w,h,w*h])
# # img = cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 4)
# #
# #
# #
# #
# # arr[0].append(0)
# # arr[0].append(0)
# # sr_deltx = sr_delty = 0
# # sr_x = arr[0][0]
# # sr_y = arr[0][1]
# # for i in range(1, len(arr)):
# # sr_x += arr[i][0]
# # sr_y += arr[i][1]
# # sr_deltx += abs(arr[i][0] - arr[i-1][0])
# # sr_delty += abs(arr[i][1] - arr[i-1][1])
# #
# # arr[i].append(arr[i][0] - arr[i-1][0])
# # arr[i].append(arr[i][1] - arr[i-1][1])
# #
# # x_med = my_median(np.array(arr)[:, 0])
# # y_med = my_median(np.array(arr)[:, 1])
# # sr_deltx = sr_deltx/len(arr)
# # sr_delty = sr_delty/len(arr)
# # sr_x = sr_x/len(arr)
# # sr_y = sr_y/len(arr)
# # # img = cv2.circle(img, (sr_x,sr_y),5,(0,0,255))
# #
# # print("Среднее x",sr_x)
# # print("Среднее y",sr_y)
# # # img = cv2.circle(img, (int(sr_x), int(sr_y)), 10, (0, 0, 255), thickness=cv2.FILLED)
# # img = cv2.circle(img, (int(x_med), int(y_med)), 10, (0, 100, 255), thickness=cv2.FILLED)
# # print("Среднее deltx",sr_deltx)
# # print("Среднее delty",sr_delty)
# # print(np.array(arr))
# # print(len(arr))
# # print("-------------------------------")
# # print(arr[2][5], arr[2][6])
# # ar_1 = []
# # # arr = list(filter(lambda x: abs(x[5])!=0, arr))
# # for ar in arr:
# # # print(abs(ar[5]) , sr_x, abs(ar[6]) , sr_y)
# # if x_med - 100 < abs(ar[0]) < x_med + 100 and y_med - 100 < abs(ar[1]) < y_med + 100:
# # ar_1.append(ar)
# # print(np.array(ar_1))
# # print(len(ar_1))
# # arr = ar_1
# # for cont in arr:
# # x, y, w, h, *_ = cont
# # # borders.append([x,y,w,h,w*h])
# # img = cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
#
# # ##########################################
#
# cv2.imshow("img", img)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
#
# # exit()
# # borders = arr
# return borders
#
#
# def get_letter(url, f_name):
# req = urllib.request.urlopen(url)
# files = []
# arr = np.asarray(bytearray(req.read()), dtype=np.uint8)
# labels = []
# image = cv2.imdecode(arr, cv2.IMREAD_COLOR) # Получение фото по изображению
#
# # image = cv2.imread("tets.JPG") # Получение фото по изображению
# cv2.imwrite('ph.jpeg', image)
# # cv2.imshow('ph.jpeg', image)
#
# # print(list(borders), end='\n')
# # print(my_median([1,2,3,4,5,6]))
# # exit()
# d = 170
# r = 3
# borders = templ(image, d, r)
# # return 0
# bors = [borders]
# bors_ln = [len(borders)]
# mx = 0
# img1 = image.copy()
# print("Коробки ", len(borders))
# while len(borders) < 10 or d>150:
# d -= 10
# r+=2
# borders = templ(image, d, r)
# bors.append(borders)
# bors_ln.append(len(borders))
# # mx = max(bors_ln, mx)
# print("Коробки ", len(borders), d)
# print(bors_ln)
# mx_len = max(bors_ln)
# mx_inx = bors_ln.index(mx_len)
# mn_len = min(bors_ln)
# mn_inx = bors_ln.index(mn_len)
# # mn_bords = filt_box((bors[mx_inx]))
# # for box in mn_bords:
# # x,y,w,h,*_ = box
# # img1 = cv2.rectangle(img1, (x,y), (x+w, h+y), 2)
# mx_bor = max([bor[1] for bor in bors])
# # cv2.imshow("1", img1)
# # cv2.waitKey(0)
# # cv2.destroyAllWindows()
# # print(mx_bor)
# # borders = list(map(lambda x: x[1] == mx_bor, bors))
# borders = bors[mx_inx]
#
# print(borders)
# # exit()
# # return 0
# img = image.copy()
#
# borders = list(filter(lambda x: 0.3 < x[2] / x[3] < 2.1 and 130 < x[2] * x[3] < 350, borders))
# # if len(borders) < 10:
# # template(image, 160)
# if len(borders) <3:
# print("После фильтра")
# return 0
# for bor in borders:
# x,y,w,h,s = bor
# cv2.rectangle(img, (x, y), (x + w, y + h), (0, 0, 255), 1)
# cv2.imshow('dimg', img)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
#
# borders = sorted(borders,key=lambda x:(x[0],x[1]))
# print(borders)
# # print(np.array(borders))
# # print('-----------------------------------------------')
# del_x = 10
# del_y = 10
# borders[0].append(0)
# borders[0].append(0)
# borders[0].append(borders[0][0]//del_x)
# borders[0].append(borders[0][1]//del_y)
# for i in range(1,len(borders)):
# borders[i].append(dis(borders[i][0], borders[i-1][0]))
# borders[i].append(dis(borders[i][1], borders[i-1][1]))
# borders[i].append(math.floor(borders[i][0]/del_x))
# borders[i].append(math.floor(borders[i][1]/del_y))
#
# print(len(borders))
# print(np.array(borders))
# print('-----------------------------------------------')
# borders = sorted(borders, key=lambda x: (x[5], x[6]))
# med_x = borders[math.ceil(len(borders)/2)][0]
# med_y = borders[math.ceil(len(borders)/2)][1]
# print(med_x, med_y)
#
# sr_w = sr_h = 0
#
#
# # f = lambda x: [x[0]//delit, x[1]//delit,x[2],x[3],x[4],x[5],x[6], x[7], x[8]]
# # borders_2 = list(map(f, borders))
#
# print(np.array(borders))
# print('-----------------------------------------------')
#
# # count_p([1,1,1,2,3,3,5,6])
# # exit()
#
#
# x_med = my_median(np.array(borders)[:,0])
# y_med = my_median(np.array(borders)[:,1])
#
# arr_x = sorted(np.array(borders)[:,7])
# # res_x = count_p(arr_x)
# res_x = (1,1)
# # print(arr_x)
# # exit()
# arr_y = sorted(np.array(borders)[:,8])
#
# print('----------------------------')
# # res_y = count_p(arr_y)
# res_y = (1,1)
# mx_x, mn_x = res_x
# mx_y, mn_y = res_y
# mx_x, mn_x = map(lambda x:(x*del_x), [mx_x, mn_x])
# mx_y, mn_y = map(lambda x:(x*del_y), [mx_y, mn_y])
# print(mx_x, mn_x)
# print(mx_y, mn_y)
# print("Медианы ", x_med, y_med)
# print("pyf ", res_x, res_y)
# if len(borders) <3:
# print("res_x")
# return
# # print([list(np.array(borders)[:,1]),list.(np.array(borders)[:,5])])
# x_znach = count_p(list(np.array(borders)[:,0]),list(np.array(borders)[:,5]))
# y_znach = count_p(list(np.array(borders)[:,1]),list(np.array(borders)[:,6]))
# # print(x_znach)
# # d = lambda x: x[0] = 0
# # obl = lambda x: x if x[5] in x_znach else x[0] = 0
# for bor in borders:
# if bor[5] not in x_znach or bor[6] not in y_znach:
# bor[0] = bor[1] = 0
# # for bor in borders:
# # if bor[6] not in y_znach:
# # bor[1] = 0
# # borders = list(map(obl(),borders))
# borders = list(filter(lambda x:x[0] != 0 and x[1] != 0, borders))
# print('---------------------------------------------------------------------')
# print(np.array(borders))
# # exit()
#
# if len(borders) <3:
# print("После фильтра 2")
# return {"boreders": borders,
# "x_med":x_med,
# "y_med":y_med,}
# # cv2.rectangle(img1, (mn_x , mn_y), (mx_x + 2 , mx_y + 15), (0, 255, 0), 1)
# # print()
# cv2.circle(img1, (x_med, y_med), 10, (255, 0, 0),thickness=cv2.FILLED)
# mx_x, mn_x = max(np.array(borders)[:,0]), min(np.array(borders)[:,0])
# mx_y, mn_y = max(np.array(borders)[:,1]), min(np.array(borders)[:,1])
#
#
# # rRect = cv2.RotatedRect(cv2.Point2f(mn_x,mn_y), cv2.Size2f(mx_x, mx_y), 7)
#
# # Point2f
# # vertices[4];
# # rRect.points(vertices);
# #
# # for i in range(4)
# # cv2.line(img1, vertices[i], vertices[(i + 1) % 4], Scalar(0, 255, 0), 2);
# #
#
#
# for border in borders:
# x,y,w,h, *_ = border
# cv2.rectangle(img1, (x, y), (x + w, y + h), (255, 0, 0), 1)
# rows, cols = img1.shape[:2]
# delt_x = 0
# delt_y = 0
# for i in range(len(borders)):
# delt_x+=borders[i][5]
# delt_y+=borders[i][6]
# delt_x = delt_x//len(borders)
# delt_y = delt_y//len(borders)
# if delt_y ==0 :
# print("На 0")
# return {"boreders": borders,
# "x_med":x_med,
# "y_med":y_med,}
# print(delt_x, delt_y)
# print(delt_x - delt_y)
# # exit()
# arr_wh = []
# for i in range(len(borders)):
# sr_w += borders[i][2]
# arr_wh.append(borders[i][3] / borders[i][2])
# sr_h += borders[i][3]
# print(sr_w/len(borders), sr_h/len(borders))
# sr_del = get_angle(borders,(sr_w)/len(borders)-(sr_h)/len(borders))
# print(sr_del)
# print(math.degrees(math.atan(sr_del)))
#
# print("среднее_ ", (sum(arr_wh) / len(borders)))
# print("Угол ", math.degrees(math.atan(sum(arr_wh) / len(borders))))
#
#
# rows, cols = img1.shape[:2]
# # angle = int(input())
# M = cv2.getRotationMatrix2D((int(mn_x + (mx_x - mn_x)//2), int(mn_y + (mx_y - mn_y)//2)), 0, 1)
# # M = cv2.getRotationMatrix2D((int(mn_x + (mx_x - mn_x)//2), int(mn_y + (mx_y - mn_y)//2)), 123, 1)
# # M1 = cv2.getRotationMatrix2D((int(mn_x + (mx_x - mn_x)//2), int(mn_y + (mx_y - mn_y)//2)), 210, 1)
# img1 = cv2.warpAffine(img1, M, (cols, rows))
# # img2 = cv2.warpAffine(image, M1, (cols, rows))
# cv2.rectangle(img1, (mn_x,mn_y), (mx_x, mx_y), (0, 0, 255), 1)
# # print(f"w{mx_x-mn_x} ")
# print(f"max{mx_x, mx_y}, min{mn_x, mn_y}")
# # for border in borders:
# # x,y,w,h, *_ = border
# # cv2.rectangle(img1, (x, y), (x + w, y + h), (255, 0, 0), 1)
#
# print(sr_w // len(borders), sr_h // len(borders))
# print("w/h ", abs((sr_w // len(borders)) / (sr_h // len(borders))))
# print("h/w ", abs((sr_h // len(borders)) / (sr_w // len(borders))))
# print("Среднее ", abs((sr_w // len(borders)) / (sr_h // len(borders)) - (sr_h // len(borders)) / (sr_w // len(borders))))
#
#
# cv2.imshow('result', img1)
# # cv2.imshow('result1', img2)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
#
#
# return {"borders": borders,
# "x_med":x_med,
# "y_med":y_med,}
# def get_angle(arr, sr_d):
# sr_angle_wh = sr_angle_hw = sr_angle = 0
# for bor in arr:
# sr_angle_wh = bor[2]-bor[3] + sr_d
# print("w - h", sr_angle_wh)
# sr_angle_hw = bor[3]-bor[2] + sr_d
# print("h - w",sr_angle_hw)
# sr_angle += sr_angle_wh/sr_angle_hw
# print("angle ", sr_angle_wh/sr_angle_hw)
# # sr_angle_hw = sr_angle_hw/len(arr)
# # sr_angle_wh = sr_angle_wh/len(arr)
# return sr_angle/len(arr)
# def obl(x,n, znach):
# if x[n] in znach:
# return x
# else:
# x[0] = 0
# return x
# def dis(a,b):
# return a-b
# def my_median(sample):
# # 1,2,3,4,5,6
# sample = sorted(sample)
# print(sample)
# n = len(sample)
# index = n // 2
# # Sample with an odd number of observations
# if n % 2:
# return sorted(sample)[index]
# # Sample with an even number of observations
# return int(sum(sorted(sample)[index - 1:index + 1])/2)
# def count_p(arr, arr_1 = None):
# res = []
# sr = 0
# if arr_1 is None:
# for n in list(set(arr)):
# c = arr.count(n)
# res.append([n, c])
# sr += c
# # print(np.array(res))
# sr = sr//len(res)
# # print("Сред ", sr)
# v = list(filter(lambda x: x[1] > sr, res))
# else:
# # arr_1 = np.array(arr)[:, 1]
# for n in list(set(arr_1)):
# c = arr_1.count(n)
# res.append([n, c])
# sr += c
# print(np.array(res))
# sr = sr//len(res)
# print("SR2", sr)
# v = list(filter(lambda x: x[1] > sr, res))
# res_c1 = list(np.array(v)[:,0])
# print(res_c1)
# # for i in range(len(arr)):
# # if arr_1[i] in res_c1:
#
#
# # print(np.array(v))
# return res_c1
# print("SR", sr)
# print(np.array(v))
# return (max(v)[0], min(v)[0])
# def correct_rct(w, h,x,y):
# if h>w or abs(h-w)>30:
# return w+100, h+100,x-30,y-30
# return w,h,x,y
# def get_mark(token = '6268498821:AAGGDDITcRI2RFtyljkGUff5IB39ihtWtOk', id = 'AgACAgIAAxkBAAIDJGS9QN0bJOudNf8f_HVUVomWYfoIAALayTEbFEjwSZjx0YeSoooXAQADAgADeAADLwQ', n = 0):
# url = "https://api.telegram.org/bot" + token + "/getFile?file_id=" + id
# res = file_path = requests.get(f'https://api.telegram.org/bot{token}/getFile?file_id={id}').json()['result']['file_path']
# # print(res)
# UrlPhoto = "https://api.telegram.org/file/bot" + token + "/" + res
# print(UrlPhoto, id)
# result = get_letter(UrlPhoto, res.split("/")[1])
# return result
# # def dtime(now):
def getURL(id, token = '6268498821:AAGGDDITcRI2RFtyljkGUff5IB39ihtWtOk'):
res = file_path = requests.get(f'https://api.telegram.org/bot{token}/getFile?file_id={id}').json()['result']['file_path']
# print(res.split("/")[1])
UrlPhoto = "https://api.telegram.org/file/bot" + token + "/" + res
return [UrlPhoto, res]
# # get_mark()
#
# def get_letters(borders, img):
# borders = sorted(borders, key=lambda x: (x[0], x[1]))
# print(borders)
# # print(np.array(borders))
# # print('-----------------------------------------------')
# del_x = 10
# del_y = 10
# borders[0].append(0)
# borders[0].append(0)
# borders[0].append(borders[0][0] // del_x)
# borders[0].append(borders[0][1] // del_y)
# for i in range(1, len(borders)):
# borders[i].append(dis(borders[i][0], borders[i - 1][0]))
# borders[i].append(dis(borders[i][1], borders[i - 1][1]))
# borders[i].append(math.floor(borders[i][0] / del_x))
# borders[i].append(math.floor(borders[i][1] / del_y))
#
# print(len(borders))
# print(np.array(borders))
# print('-----------------------------------------------')
# borders = sorted(borders, key=lambda x: (x[5], x[6]))
# med_x = borders[math.ceil(len(borders) / 2)][0]
# med_y = borders[math.ceil(len(borders) / 2)][1]
# print(med_x, med_y)
#
# sr_w = sr_h = 0
#
# # f = lambda x: [x[0]//delit, x[1]//delit,x[2],x[3],x[4],x[5],x[6], x[7], x[8]]
# # borders_2 = list(map(f, borders))
#
# print(np.array(borders))
# print('-----------------------------------------------')
#
# # count_p([1,1,1,2,3,3,5,6])
# # exit()
#
# x_med = my_median(np.array(borders)[:, 0])
# y_med = my_median(np.array(borders)[:, 1])
#
# arr_x = sorted(np.array(borders)[:, 7])
# res_x = count_p(arr_x)
# # print(arr_x)
# # exit()
# arr_y = sorted(np.array(borders)[:, 8])
#
# print('----------------------------')
# res_y = count_p(arr_y)
# mx_x, mn_x = res_x
# mx_y, mn_y = res_y
# mx_x, mn_x = map(lambda x: (x * del_x), [mx_x, mn_x])
# mx_y, mn_y = map(lambda x: (x * del_y), [mx_y, mn_y])
# print(mx_x, mn_x)
# print(mx_y, mn_y)
# print("Медианы ", x_med, y_med)
# print("pyf ", res_x, res_y)
# if len(borders) < 3:
# print("res_x")
# return "2"
# # print([list(np.array(borders)[:,1]),list.(np.array(borders)[:,5])])
# x_znach = count_p(list(np.array(borders)[:, 0]), list(np.array(borders)[:, 5]))
# y_znach = count_p(list(np.array(borders)[:, 1]), list(np.array(borders)[:, 6]))
# # print(x_znach)
# # d = lambda x: x[0] = 0
# # obl = lambda x: x if x[5] in x_znach else x[0] = 0
# for bor in borders:
# if bor[5] not in x_znach or bor[6] not in y_znach:
# bor[0] = bor[1] = 0
# # for bor in borders:
# # if bor[6] not in y_znach:
# # bor[1] = 0
# # borders = list(map(obl(),borders))
# borders = list(filter(lambda x: x[0] != 0 and x[1] != 0, borders))
# print('---------------------------------------------------------------------')
# print(np.array(borders))
# # exit()
#
# if len(borders) < 3:
# print("После фильтра 2")
# return "3"
# # cv2.rectangle(img1, (mn_x , mn_y), (mx_x + 2 , mx_y + 15), (0, 255, 0), 1)
# # print()
# # cv2.circle(img1, (x_med, y_med), 10, (255, 0, 0), thickness=cv2.FILLED)
# mx_x, mn_x = max(np.array(borders)[:, 0]), min(np.array(borders)[:, 0])
# mx_y, mn_y = max(np.array(borders)[:, 1]), min(np.array(borders)[:, 1])
#
# # rRect = cv2.RotatedRect(cv2.Point2f(mn_x,mn_y), cv2.Size2f(mx_x, mx_y), 7)
#
# # Point2f
# # vertices[4];
# # rRect.points(vertices);
# #
# # for i in range(4)
# # cv2.line(img1, vertices[i], vertices[(i + 1) % 4], Scalar(0, 255, 0), 2);
# #
#
# # for border in borders:
# # x, y, w, h, *_ = border
# # cv2.rectangle(img1, (x, y), (x + w, y + h), (255, 0, 0), 1)
# # rows, cols = img1.shape[:2]
# delt_x = 0
# delt_y = 0
# for i in range(len(borders)):
# delt_x += borders[i][5]
# delt_y += borders[i][6]
# delt_x = delt_x // len(borders)
# delt_y = delt_y // len(borders)
# if delt_y == 0:
# print("На 0")
# return "4"
# print(delt_x, delt_y)
# print(delt_x - delt_y)
# # exit()
# arr_wh = []
# for i in range(len(borders)):
# sr_w += borders[i][2]
# arr_wh.append(borders[i][3] / borders[i][2])
# sr_h += borders[i][3]
# print(sr_w / len(borders), sr_h / len(borders))
# sr_del = get_angle(borders, (sr_w) / len(borders) - (sr_h) / len(borders))
# print(sr_del)
# print(math.degrees(math.atan(sr_del)))
#
# print("среднее_ ", (sum(arr_wh) / len(borders)))
# print("Угол ", math.degrees(math.atan(sum(arr_wh) / len(borders))))
#
# # rows, cols = img1.shape[:2]
# # angle = int(input())
# # M = cv2.getRotationMatrix2D((int(mn_x + (mx_x - mn_x) // 2), int(mn_y + (mx_y - mn_y) // 2)), 0, 1)
# # M = cv2.getRotationMatrix2D((int(mn_x + (mx_x - mn_x)//2), int(mn_y + (mx_y - mn_y)//2)), 123, 1)
# # M1 = cv2.getRotationMatrix2D((int(mn_x + (mx_x - mn_x)//2), int(mn_y + (mx_y - mn_y)//2)), 210, 1)
# # img1 = cv2.warpAffine(img1, M, (cols, rows))
# # img2 = cv2.warpAffine(image, M1, (cols, rows))
# # cv2.rectangle(img1, (mn_x, mn_y), (mx_x, mx_y), (0, 0, 255), 1)
# # print(f"w{mx_x-mn_x} ")
# print(f"max{mx_x, mx_y}, min{mn_x, mn_y}")
# for border in borders:
# x,y,w,h, *_ = border
# cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 1)
#
# print(sr_w // len(borders), sr_h // len(borders))
# print("w/h ", abs((sr_w // len(borders)) / (sr_h // len(borders))))
# print("h/w ", abs((sr_h // len(borders)) / (sr_w // len(borders))))
# print("Среднее ",
# abs((sr_w // len(borders)) / (sr_h // len(borders)) - (sr_h // len(borders)) / (sr_w // len(borders))))
#
# cv2.imshow('result', img)
# # cv2.imshow('result1', img2)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
#
# return {"boreders": borders,
# "x_med":x_med,
# "y_med":y_med,}
# def virez(url = '', token = '',id = '',d = 150):
# url = "https://api.telegram.org/bot" + token + "/getFile?file_id=" + id
# res = file_path = requests.get(f'https://api.telegram.org/bot{token}/getFile?file_id={id}').json()['result'][
# 'file_path']
# # print(res)
# url = "https://api.telegram.org/file/bot" + token + "/" + res
# req = urllib.request.urlopen(url)
# arr = np.asarray(bytearray(req.read()), dtype=np.uint8)
#
# image = cv2.imdecode(arr, cv2.IMREAD_COLOR) # Получение фото по изображению
# img = image.copy()
#
# cv2.imshow("img", img)
# # (1)
# gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# #
# blur = cv2.GaussianBlur(gray, (3, 3), 1)
#
# ret, thes = cv2.threshold(blur, d, 255, cv2.THRESH_BINARY)
#
# # kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (25, 25))
#
# # Применяем морфологическую операцию расширения к изображению
# # dilated_image = cv2.dilate(img, kernel)
#
# gray = cv2.cvtColor(thes, cv2.COLOR_BGR2GRAY)
#
# ret, thes = cv2.threshold(gray, d, 255, cv2.THRESH_BINARY)
#
# letters = get_letter(url,'')
# # return 0
# b = True
# x_m, y_m = letters["x_med"], letters["y_med"]
# if letters == "4":
# b = False
# print("letters", np.array(letters))
# if b:
# for let in letters["borders"]:
# x,y,w,h,*_ = let
# img = cv2.rectangle(img, (x,y),(x+w,y+h),(0,0,255) ,1)
# img = cv2.circle(img, (x_m, y_m), 10, (255, 0, 0), thickness=cv2.FILLED)
#
# # cv2.imshow("thes", thes)
# # cv2.imshow("dilate", dilated_image)
# # cv2.waitKey(0)
# # cv2.destroyAllWindows()
#
# # mask_3_channels = cv2.merge((dilated_mask, dilated_mask, dilated_mask))
#
# # Наложение маски на исходное изображение
# mask = np.zeros_like(thes)
# # mask = cv2.bitwise_not(thes)
# result = cv2.bitwise_and(img, img, mask=thes)
#
# # gray = cv2.cvtColor(result, cv2.COLOR_BGR2GRAY)
# #
# # ret, thes = cv2.threshold(gray, 240, 255, cv2.THRESH_BINARY_INV)
#
# # result = cv2.bitwise_and(img, img, mask=thes)
#
# # cv2.imshow("thes", thes)
# cv2.imshow("dilate", result)
# # cv2.waitKey(0)
# # cv2.destroyAllWindows()
#
# edges = cv2.Canny(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), 100, 200, apertureSize=7)
#
# cv2.imshow("edges", edges)
#
#
# # Найдите контуры на изображении
# contours, _ = cv2.findContours(edges, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# borders = []
#
# # for cont in contours:
# # x,y,w,h = cv2.boundingRect(cont)
# # borders.append([x,y,w,h,w*h])
# # borders = list(filter(lambda x: 0.3 < x[2] / x[3] < 2.1 and 100 < x[2] * x[3] < 300, borders))
#
# # letters = get_letters(borders, img)
# # print("letters", letters)
# #
# # for let in letters:
# # x, y, w, h, *_ = let
# # img = cv2.rectangle(img, (x, y), (x + w, y + h), (0, 0, 255), 1)
#
# filtered_conts = []
# # approx = 0
# for cont in contours:
# x,y,h,w = cv2.boundingRect(cont)
# if w>100 and h>100 and w*h>20_000:
# # epsilon = 0.2 * cv2.arcLength(cont, True)
# # approx = cv2.approxPolyDP(cont, 10, False)
# # print(approx)
# # img = cv2.drawContours(img, approx, -1, (255, 0, 0), 3)
#
# filtered_conts.append(cv2.convexHull(cont))
# # print(filtered_conts)
#
# for i, contour in enumerate(filtered_conts):
# # pass
# # if markers.min() < 0:
# # continue
# # epsilon = 0.01 * cv2.arcLength(contour, False)
# # print(epsilon)
# # approx = cv2.approxPolyDP(contour, epsilon, False)
# # print("1", contour)
# # print("2", approx)
# x, y, w, h = cv2.boundingRect(contour)
# # img = cv2.drawContours(img, approx, -1, (255, 0, 0), 3)
#
# find = Correct((x, y, w, h), letters["borders"], x_m, y_m)
# print("Нашлось", find, "Всего ", len(letters["borders"]))
# print(find / len(letters["borders"]))
# if b:
# if find/len(letters) > 0.8:
# img = cv2.drawContours(img, [contour], 0, (255, 0, 0), 2)
# cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 1)
# else:
# if 150 < w < 400 and 150 < h < 400:
# img = cv2.drawContours(img, [contour], 0, (255, 0, 0), 2)
# cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 1)
#
#
# cv2.imshow('Segmented Image', img)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
#
# def Correct(box, words, x_m, y_m):
# x,y,w,h = box
# c_word = 0
# for word in words:
# x_w, y_w,*_ = word
# if x<=x_w<= x+w and y<=y_w<= y+h:
# c_word +=1
# return c_word
def virez(url = '', token = '', id = '', d = 150, products = []):
# ### Получение фото по url
result = {
"file" : [],
"text" : []
}
url = "https://api.telegram.org/bot" + token + "/getFile?file_id=" + id
res = file_path = requests.get(f'https://api.telegram.org/bot{token}/getFile?file_id={id}').json()['result']['file_path']
# print(res)
url = "https://api.telegram.org/file/bot" + token + "/" + res
print(url)
req = urllib.request.urlopen(url)
arr = np.asarray(bytearray(req.read()), dtype=np.uint8)
image = cv2.imdecode(arr, cv2.IMREAD_COLOR) # Получение фото по изображению
# cv2.imwrite(id+".jpg", image)
img = image.copy()
# ########
# ### Обработка фото
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (3, 3), 1)
z = lambda x: x if x>0 else 0
letters = getConts(img,blur,[130,140,150,160,170],0)
# for let in letters:
# x,y,w,h = let
# image =cv2.rectangle(image, (x,y), (x + w, y + h), (0,0,255), 1)
print("1")
rows, cols = image.shape[:2]
c_x, c_y = cols // 2, rows // 2
res = test_funcs.filter_conts(letters, image)
print(res)
text = False
if res["ok"]:
for i in range(len(res["points"])):
img = image.copy()
letters = res["points"][i]
angle = res["angle"][i]
M = cv2.getRotationMatrix2D((int(c_x), int(c_y)), angle, 1)
img = cv2.warpAffine(img, M, (cols, rows))
# letters.sort(key = lambda x:x[0])
mx_x = mx_y = 0
mn_x = mn_y = 10000000000
for let in letters:
mx_x = max(mx_x, let[0])
mx_y = max(mx_y, let[1])
mn_x = min(mn_x, let[0])
mn_y = min(mn_y, let[1])
print(mn_y-40, mx_y+50, mn_x-130,mx_x+70)
res_img = img[z(mn_y-40): z(mx_y+50), z(mn_x-130):z(mx_x+70)]
# image = cv2.rectangle(image, (mn_y-40, mx_y+50), (z(mn_x-130),z(mx_x+70)), (0,255,0), 2)
print("rs ",res_img)
print("rsL ",len(res_img))
# cv2.imshow("d", image)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
if res_img is not None or res_img != [] or len(res_img) > 0:
text = tesseract_check(image= res_img, products = products)
if text != False:
# cv2.rectangle(image, (mn_x - 150, mn_y - 35), (mx_x + 75, mx_y + 55), (0, 255, 0), 3)
result["text"].append(text)
cv2.imwrite(id + '.jpg', res_img)
result["file"].append(id + '.jpg')
# cv2.imshow("2", res_img)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
# else:
# edges = cv2.Canny(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY),130,240,apertureSize=7)
# cv2.imshow("1", edges)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
# contours, _ = cv2.findContours(edges, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# filtered_conts = []
# for cont in contours:
# x, y, h, w = cv2.boundingRect(cont)
# if w > 100 and h > 100 and 20_000< w * h:
# # epsilon = 0.2 * cv2.arcLength(cont, True)
# # approx = cv2.approxPolyDP(cont, 10, False)
# # print(approx)
# # img = cv2.drawContours(img, approx, -1, (255, 0, 0), 3)
# filtered_conts.append(cv2.convexHull(cont))
# for cont in filtered_conts:
# x, y, w, h = cv2.boundingRect(cont)
# # img = cv2.drawContours(img, approx, -1, (255, 0, 0), 3)
# if 150 < w < 400 and 150 < h < 400 and w/h :
# cv2.rectangle(image, (x,y), (x+w,y+h), (0,255,0), 3)
# cv2.imshow("1", image)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
return result
# return 0
# plt = res["plt"]
# print("Угол ",angle)
# points = rotate(letters, -angle, c_x, c_y)
# y_arr = list(np.array(points)[:,1])
count_p(y_arr)
x = []
y = []
M = cv2.getRotationMatrix2D((int(c_x), int(c_y)), angle, 1)
# M = cv2.getRotationMatrix2D((0, 0), -angle[0], 1)
image = cv2.warpAffine(image, M, (cols, rows))
mx_x = mx_y = 0
mn_x = mn_y = 10000000000
for point in points:
x.append(point[0])
y.append(point[1])
# cv2.circle(image,(point[0], point[1]),3, (255,0,0), thickness=cv2.FILLED)
mx_x = max(mx_x, point[0])
mx_y = max(mx_y, point[1])
mn_x = min(mn_x, point[0])
mn_y = min(mn_y, point[1])
cv2.rectangle(image,(point[0], point[1]), (point[0] + 10, point[1] + 10),(255,0,0), 2)
# print((mn_x, mn_y), (mx_x, mx_y))
cv2.rectangle(image, (mn_x, mn_y), (mx_x + 100, mx_y + 100), (0, 0, 255), 3)
# plt.plot(x,y)
cv2.imshow("1", image)
plt.gca().yaxis.grid(False)
plt.show()
cv2.waitKey(0)
cv2.destroyAllWindows()
# exit()
return 0
print(letters)
# print(np.array(letters))
x = np.array(letters)[:,6]
y = np.array(letters)[:,7]
plt.scatter(x, y)
plt.show()
# return 0
# for let in letters:
# # print(let)
# x,y,w,h,*_ = let
# cv2.rectangle(image, (x,y), (x+w,y+h), (0,255,0), 1)
# print("letters", np.array(letters))
if len(letters) == 0:
print("Фильтрация")
# return 0
# cv2.imshow("t", image)
angle = alfa(letters, c_x, c_y)
# M = cv2.getRotationMatrix2D((c_x, c_y), -angle[0], 1)
# M = cv2.getRotationMatrix2D((0, 0), -angle[0], 1)
# image = cv2.warpAffine(image, M, (cols, rows))
print("Угол", angle)
# return 0
# cv2.imshow("t1", image)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
# return 0
# exit()
# draw_box(letters, image, angle[5] + c_y ,angle[6] + c_x ,angle[7] + c_y, angle[8] + c_x, -angle[0], 20, 20)
# exit()
borders = []
contours, _ = cv2.findContours(edges, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
for cont in contours:
x,y,w,h = cv2.boundingRect(cont)
borders.append([x,y,w,h])
# marks = list(filter(lambda x: x[2]>150 and x[3]>150 and x[2]*x[3]>25_000, borders))
# print("letters" , np.array(marks))
marks = []
for let in letters:
# print(let)
x,y,w,h,*_ = let
cv2.rectangle(image, (x,y), (x+w,y+h), (255,0,0), 2)
for cont in contours:
x, y, h, w = cv2.boundingRect(cont)
if w > 100 and h > 100 and w * h > 20_000:
marks.append(cv2.convexHull(cont))
# img = cv2.drawContours(image, marks, -1, (255, 0, 0), 2)
img = image.copy()
M = cv2.getRotationMatrix2D((int(c_x), int(c_y)), -angle[0], 1)
# M = cv2.getRotationMatrix2D((0, 0), -angle[0], 1)
image = cv2.warpAffine(image, M, (cols, rows))
res_img = None
ist = False
n = rotate(letters, angle[0], c_x, c_y)
# print(n)
# exit()
for mark in marks:
x, y, w, h = cv2.boundingRect(mark)
r = rotate([[x + (w//2), y + (h//2),w,h]], angle[0], c_x, c_y)
r = r[0]
x,y,x1,y1 = int(r[0] - (w//2)), int(r[1] - (h//2)), int(r[0] + (w//2)), int(r[1] + (h//2))
if abs(angle[0]) > 45:
x, y, x1, y1 = int(r[0] - (h // 2)), int(r[1] - (w // 2)), int(r[0] + (h // 2)), int(r[1] + (w // 2))
ist = Correct([x, y, x1 - x, y1-y], n)
if ist:
cv2.rectangle(image, (x, y), (x1, y1), (0, 0, 255), 1)
res_img = image[y-5:y1+5, x-5:x1+5]
# rows, cols = res_img.shape[:2]
# M = cv2.getRotationMatrix2D(((int(rows) // 2), int(cols) // 2), 0, 1)
# res_img = cv2.warpAffine(res_img, M, (cols, rows))
break
if not ist:
x0,y0,x1,y1 = angle[5] + c_x ,angle[6] + c_y ,angle[7] + c_x, angle[8] + c_y
# res_img = draw_box(letters, image, x0,y0,x1,y1, -angle[0], 20, 20)
print("1 ", letters)
print("2 ", n)
mn_x, mn_y, *_ = n[0]
mx_x = mn_x
mx_y = mn_y
for box in n:
x, y, w, h = box
cv2.rectangle(image, (x, y), (x + w, y + h), (100, 0, 200), 2)
mn_x = min(mn_x, x - abs(w))
mn_y = min(mn_y, y - abs(h))
mx_x = max(mx_x, x + abs(2 * w))
mx_y = max(mx_y, y + abs(2 * h))
print((mn_x, mn_y), (mx_x, mx_y))
cv2.rectangle(image, (mn_x, mn_y), (mx_x, mx_y), (0, 0, 255), 3)
w, h = mx_x - mn_x, mx_y - mn_y
print(w,h, h/w)
f = lambda x: x+60 if x<230 else x
w = f(w)
h = f(h)
mx_x = mn_x + w
mx_y = mn_y + h
print(w, h, h / w)
# if h/w>=1:
# w+=30
cv2.rectangle(image, (mn_x, mn_y), (mx_x, mx_y), (0, 255, 0), 3)
# OCR(res_img)
# image = cv2.drawContours(image, contours,-1,(255,0,0))
cv2.imshow("contours", image)
cv2.imshow("P", img)
# cv2.imshow("contours", res_img)
cv2.waitKey(0)
cv2.destroyAllWindows()
def Correct(mark, words):
# if 4 < len(words) < 10:
# return True
x,y,w,h = mark
c_word = 0
for word in words:
x_w, y_w, *_ = word
if x <= x_w <= x + w and y <= y_w <= y + h:
c_word += 1
return c_word/len(words) == 1
def getConts(img,blur, d, mx):
mx = -float('inf')
let_ret = None
q = 0
for i in range(len(d)):
ret, th = cv2.threshold(blur, d[i], 255, cv2.THRESH_BINARY)
ms = cv2.bitwise_and(img, img, mask=th)
edge = cv2.Canny(ms, 100, 200, apertureSize=3)
res, _ = cv2.findContours(edge, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# img = cv2.drawContours(ms, res, -1,(0,255,0), 1)
letters = list(map(lambda x: cv2.boundingRect(x), res))
letters.sort(key=lambda x: x[0])
# letters = del_double(letters)
letters = list(filter(lambda x: 0.4 < x[2] / x[3] < 2.1 and 130 < x[2] * x[3] < 400, letters))
if mx < len(letters):
mx = len(letters)
let_ret = letters
return let_ret
def del_double(arr):
arr.append([])
a = []
for i in range(len(arr) - 1):
# print(i)
if arr[i] != arr[i + 1]:
a.append(arr[i])
return a
def filt_letters(letters,maxd=50,mind=5):
# print(maxd)
npoint = []
cashp = []
s_w = s_h = 0
for i in range(len(letters)):
x1, y1,w,h, *_ = letters[i]
twp = 0
s_w += w
s_h += h
for j in range( len(letters)):
x2,y2,wl, hl, *_ = letters[j]
dx = x2 - x1
dy = y2 - y1
if h == 0 :
k = 0
else:
k = w / h
# print(w,h,w+h, maxd, mind)
if mind < math.sqrt(w**2+h**2) < maxd:
twp+=1
if twp>3:
# cashp.append([x1,y1,wl,hl,x2,y2,w,h,k,y2-k*x2])
# else:
npoint.append([x1,y1,w,h,x2, y2, dx, dy, k, y2-k * x2])
# print("1", x2,y2,w,h,h/w)
# if twp > 3:
# for c in cashp:
# npoint.append(c)
# print(s_h/len(letters))
# print(s_w/len(letters))
# print(len(npoint))
return npoint
def alfa(points, x_cen, y_cen):
wh = 0
minx = 1000
miny = 1000
for a in points:
wh = wh+(int(a[2]) / int(a[3]))
if minx > int(a[0]): minx = int(a[0])
if miny > int(a[1]): miny = int(a[1])
whs = wh/len(points)
# print(wh, whs, a)
st_angle = 0
direct = 1
if whs < 1:
# st_angle = 180
direct = -1
end_angle = (st_angle + 90) *direct
test = {
"minp":160,
"al": 0,
"mx": 0,
"my": 0
}
test["minp"] = len(points)
ret = []
rt = 0
m_unx = [0]
m_uny = [0]
s_xy = rotate(points, st_angle, x_cen, y_cen, True)
for a in range(st_angle, end_angle + 1, direct):
xy = rotate(points, a, x_cen, y_cen, True)
# s_xy = xy
unx = set(xy["x"])
uny = set(xy["y"])
# print("x" , unx, len(unx))
# print("y" , uny, len(uny))
# print(len(uny) + len(unx))
ratio = len(unx)/len(uny)
if (len(unx) + len(uny)) < test["minp"]:
test["minp"] = len(unx) + len(uny)
test["mx"] = len(unx)
test["my"] = len(uny)
s_xy = xy
m_unx = unx
m_uny = uny
test["al"] = a
ret.append([a,len(unx), len(uny), test["minp"]])
if test["my"] == 0:
rt = 0
else:
rt = test["mx"] / test["my"]
if (test["minp"] + 30) < len(unx) + len(uny):
# arr = list(map(lambda x: x + y_cen, xy["y"]))
arr = s_xy["y"]
if len(set(s_xy["y"])) > len(set(s_xy["x"])):
print("x")
# arr = list(map(lambda x: x + x_cen, xy["x"]))
arr = s_xy["x"]
print(count_p(arr))
return [test["al"], test["mx"], test["my"], a, rt, min(m_unx), min(m_uny), max(m_unx), max(m_uny)] ###
# arr = list(map(lambda x: x + y_cen, xy["y"]))
arr = s_xy["y"]
if len(set(s_xy["y"])) > len(set(s_xy["x"])):
print("x")
# arr = list(map(lambda x: x + x_cen, xy["x"]))
arr = s_xy["x"]
print("Угол ", a)
print(count_p(arr))
# print(f'unx: {xy["x"]}')
# print(count_p(list(map(lambda x: x + x_cen, xy["x"]))))
return [test["al"], test["mx"], test["my"], a, rt, min(m_unx), min(m_uny), max(m_unx), max(m_uny)]
def draw_box(letters, img,x0,y0,x1,y1, angle = 0,dx = 20,dy = 10):
boxes = []
# x1+=20
# y1+=20
# if abs(angle):
d = angle//45
print(d)
cv2.circle(img,((x0 - (dx *d) - dx,y0 - (dy * d) - dy)), 5, (0,0,255), thickness=cv2.FILLED)
cv2.circle(img,(x0,y0), 5, (0,100,255), thickness=cv2.FILLED)
cv2.rectangle(img, (x0 - (dx *d) - dx,y0 - (dy * d) - dy), ((x1 + dx * abs(d - 1)) + dx, y1 + dy * abs(d - 1) + dy), (0,0,255), 1)
cv2.rectangle(img, (x0,y0), (x1, y1), (255,0,0), 1)
cv2.imshow("1", img)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
# (x0 - (dx *d) - dx: (x1 + dx * abs(d - 1)) + dx, y0 - (dy * d) - dy : y1 + dy * abs(d - 1) + dy
return img[y0 - (dy * d) - dy : y1 + dy * abs(d - 1) + dy, x0 - (dx *d) - dx: (x1 + dx * abs(d - 1)) + dx]
def OCR(image):
if image is not None:
cv2.imwrite('res.jpg', image)
image = cv2.imread('res.jpg')
tesseract_path = r'C:\Program Files\Tesseract-OCR'
pytesseract.pytesseract.tesseract_cmd = tesseract_path + r'\tesseract.exe'
# tessdata_dir_config = '--tessdata-dir ' + tesseract_path + r'\tessdata'
config = r'--oem 3 --psm 6 --dpi 100'
text = pytesseract.image_to_string(image, config= config, lang="rus")
confidences = pytesseract.image_to_data(cv2.cvtColor(image, cv2.COLOR_BGR2GRAY), lang='rus', config=config, output_type=pytesseract.Output.DICT)['conf']
print("conf ",confidences)
# newdata = pytesseract.image_to_osd(image)
print(text)
def rotate(points, alfa_new = 1 , mx = 0, my = 0, ret_obj = False):
ret = []
retx = []
rety =[]
for point in points:
x = point[0] - mx
y = point[1] - my
# alfa = alfa_new*math.pi/180
w = point[2]
h = point[3]
tan_wh = h/w
if x == 0: tan = 0
else: tan = y/x
alfa_wh = math.atan(tan_wh)
alfa_wh_rad = alfa_new*math.pi/180
one_wh = w/math.cos(alfa_wh)
new_w = int(one_wh * math.cos(alfa_wh+alfa_wh_rad))
new_h = int(one_wh * math.sin(alfa_wh+alfa_wh_rad))
# print("w, h", new_w, new_h)
alfa = math.atan(tan)
alfa_rad = alfa_new * math.pi / 180
one = x/math.cos(alfa)
newx = int(one * math.cos(alfa + alfa_rad))
newy = int(one * math.sin(alfa + alfa_rad))
if ret_obj:
retx.append(newx)
rety.append(newy)
else:
ret.append(([newx + mx + w, newy + my + h, new_w, new_h]))
if ret_obj:
return {
"x": retx,
"y": rety
}
return ret
def count_p(arr, img = None):
image = cv2.imread("res.jpg")
ret_arr = []
for i in range(15):
ret_arr.append([])
arr = sorted(arr)
# print("sore ",arr)
n = 0
s_arr = list(arr)
row_c = 0
# ret_arr.append([arr[0]])
cur = -float('inf')
# print(ret_arr)
arr.append(0)
for i in range(len(s_arr) - 1):
if abs(s_arr[i] - s_arr[i+1]) < 10:
# print(row_c)
ret_arr[row_c].append(s_arr[i])
else:
row_c+=1
ret_arr = list(filter(lambda x: len(x)>2, ret_arr))
print("Количество строк", len(ret_arr))
print("ret ",ret_arr)
# for line in ret_arr:
# image = cv2.line(image, (50,int(min(line))), (355,int(min(line))), (0,0,255), 2)
# cv2.imshow("lines", image)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
# exit()
# ret_arsr = sorted(ret_arr, key = lambda x:x[1])
return ret_arr
def tesseract_check(image, check = True, products = []):
ts_check = {
"all": '',
"write-off":'',
"product": '',
"cooked":'',
"worker":'',
"status":'',
"ready":''
}
spell = SpellChecker(language='ru')
clahe = cv2.createCLAHE(clipLimit=3., tileGridSize=(8, 8))
lab = cv2.cvtColor(image, cv2.COLOR_BGR2LAB)
l, a, b = cv2.split(lab)
l2 = clahe.apply(l)
lab = cv2.merge((l2, a, b)) # merge channels
image = cv2.cvtColor(lab, cv2.COLOR_LAB2BGR) # convert from LAB to BGR
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
image = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
# cv2.imshow("ts_check", image)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
tesseract_path = r'C:\Program Files\Tesseract-OCR'
pytesseract.pytesseract.tesseract_cmd = tesseract_path + r'\tesseract.exe'
config = r'--oem 2 --psm 6'
boxes = pytesseract.image_to_data(image, config= config, lang="rus", output_type=Output.DICT)
keys = list(boxes.keys())
dates = []
names = []
times = []
for i in range(len(boxes['text'])):
# print(i)
word = boxes['text'][i]
# print(word, boxes['conf'][i])
if int(boxes['conf'][i]) > -1:#40
# print(spell.correction(word), word)
if bool(re.search(r'\b\d{2}\.\d{2}\.\d{2}\b', boxes['text'][i])) :
dates.append(word)
elif bool(re.search(r'\b\d{2}\:\d{2}', word)):
times.append(word)
else:
w = spell_ch(word.lower(), dict=products)
print(w, word)
if w!= None and w != 'i':
names.append(w)
if len(dates) >=2 and len(names)> 2:
names = list(filter(lambda x: len(x)>2, names))
print(names)
print(dates)
# exit()
txt = ' '.join(names)
date = ' '.join(dates)
ts_check["product"] = txt
txt+= ' '+ date
ts_check["all"] = txt
print(dates[-2] + ' ' + dates[-1])
ts_check["cooked"] = datetime.datetime.strptime(dates[0] + ' ' + times[1], '%d.%m.%y, %H:%M')
ts_check["write-off"] = datetime.datetime.strptime(dates[-1] + ' ' + times[-1], '%d.%m.%y, %H:%M')
ts_check["status"] = 'Добавлено'
return ts_check
return False
def spell_ch(word, dict):
# print(word.lower())
# print("Словарь ", dict)
spell = SpellChecker()
spell.word_frequency.load_words(dict)
cr = spell.correction(word)
if cr not in dict:
cr = None
return cr