gistfile1.txt

import time
import cv2
import shutil
from tqdm import tqdm
import numpy as np
import os

#Add the path of cropped folder in which all the classes folder presents
cropped_folder_path = r"D:\Abu\My Learnings\Classes Validation\DataBox\Classes Validation\cropped_images"

#Add the path of error folder in which the error classes will be stored
error_folder_path = r"D:\Abu\My Learnings\Classes Validation\DataBox\Classes Validation\Error"
folder_dic, master_dic_full, master_dic, Images_to_compare, Title, Original_Title = {}, {}, {},  [], [], []
percentage_of_similarity, Percentage_of_Non_similarity = 0, 0

#By using swift and flann we will find the image similarity
sift = cv2.SIFT_create()
index_params = dict(algorithm=0, trees=1)
search_params = dict()

#In this loop we will be taking the count or no of folders which presents inside the cropped folder
for count, folder in enumerate(os.listdir(cropped_folder_path)):
    folder_dic[folder] = count
    if not os.path.exists(os.path.join(error_folder_path, folder)):
        os.mkdir((os.path.join(error_folder_path, folder)))


#In these loops we will be matching one class with all other classes and move the images to the error folder
for main_folder, index in folder_dic.items():
    time.sleep(1)
    print("Main Folder ->", main_folder)
    for master_images_path in os.listdir(os.path.join(cropped_folder_path, main_folder)):
        master_images = cv2.imread(cropped_folder_path + "/" + main_folder + "/" + master_images_path)
        for check_folder in os.listdir(cropped_folder_path):
            if folder_dic[check_folder] > index:
                time.sleep(2)
                for check_image_path in tqdm(os.listdir(os.path.join(cropped_folder_path, check_folder))):
                    check_image = cv2.imread(cropped_folder_path + "/" + check_folder + "/" + check_image_path)
                    if master_images.shape == check_image.shape:
                        difference = cv2.subtract(master_images, check_image)
                        b, g, r = cv2.split(difference)
                        if cv2.countNonZero(b) == 0 and cv2.countNonZero(g) == 0 and cv2.countNonZero(r) == 0:
                            print("Similarity: 100%")
                    flann = cv2.FlannBasedMatcher()
                    kp_1, desc_1 = sift.detectAndCompute(master_images, None)
                    kp_2, desc_2 = sift.detectAndCompute(check_image, None)
                    matches = flann.knnMatch(desc_1, desc_2, k=2)
                    similar_images, Non_similar_images = [], []
                    for m, n in matches:
                        if m.distance < 0.75 * n.distance:
                            similar_images.append([m])
                        else:
                            Non_similar_images.append([m])
                        number_key_points = 0
                        if len(kp_1) <= len(kp_2):
                            number_key_points = len(kp_1)
                        else:
                            number_key_points = len(kp_2)
                        percentage_of_similarity = len(similar_images) / number_key_points * 100
                        Percentage_of_Non_similarity = len(Non_similar_images) / number_key_points * 100
                    if percentage_of_similarity > Percentage_of_Non_similarity:
                        print("Title: " + os.path.join(cropped_folder_path, master_images_path),
                              file=open("output.txt", "a"))
                        print("Original Title: " + os.path.join(cropped_folder_path, check_folder),
                              file=open(error_folder_path + "/" + check_folder + "/output.txt", "a"))
                        print("similarity:" + str(int(percentage_of_similarity)) + "%\n",
                              file=open(error_folder_path + "/" + check_folder + "/output.txt", "a"))
                        print("Non_similarity:" + str(int(Percentage_of_Non_similarity)) + "%\n",
                              file=open(error_folder_path + "/" + check_folder + "/output.txt", "a"))
                        print("Same classes occurred",
                              file=open(error_folder_path + "/" + check_folder + "/output.txt", "a"))
                        error_save_variable = error_folder_path + "/" + check_folder
                        if os.path.exists(os.path.join(error_save_variable, check_image_path)):
                            os.remove(os.path.join(error_save_variable, check_image_path))
                        else:
                            shutil.move((cropped_folder_path + "/" + check_folder + "/" + check_image_path),
                                        error_save_variable)