├── .gitignore ├── .idea └── .gitignore ├── LICENSE ├── README.md ├── assets ├── 2.jpg ├── 33.jpg ├── hashed_harold.gif ├── move_similar.gif └── search_similar.gif ├── image_hash.py └── images └── input_images ├── bus3504.jpg ├── bus3531.jpg ├── bus3559.jpg ├── bus3586.jpg ├── bus3617.jpg ├── bus3641.jpg ├── bus3669.jpg ├── bus3693.jpg ├── bus3715.jpg ├── bus3740.jpg ├── bus3763.jpg ├── car2744.jpg ├── car2798.jpg ├── car2821.jpg ├── car2847.jpg ├── car2871.jpg ├── car2880.jpg ├── car2903.jpg ├── car2926.jpg ├── car2951.jpg ├── car2976.jpg ├── car3001.jpg ├── car3027.jpg ├── car3053.jpg ├── car3080.jpg ├── car3107.jpg ├── car3132.jpg ├── car3159.jpg ├── car3160.jpg ├── car3185.jpg ├── car3186.jpg ├── car3211.jpg ├── car3237.jpg ├── car3264.jpg ├── car3290.jpg ├── car3423.jpg ├── car3450.jpg ├── car3476.jpg ├── car3505.jpg ├── car3532.jpg ├── car3560.jpg ├── car3642.jpg ├── car3685.jpg ├── person0.jpg ├── person1.jpg ├── person1005.jpg ├── person1016.jpg ├── person1018.jpg ├── person1027.jpg ├── person1038.jpg ├── person105.jpg ├── person1050.jpg ├── person1055.jpg ├── person117.jpg ├── person13.jpg ├── person130.jpg ├── person143.jpg ├── person157.jpg ├── person170.jpg ├── person18.jpg ├── person183.jpg ├── person196.jpg ├── person2.jpg ├── person208.jpg ├── person221.jpg ├── person233.jpg ├── person247.jpg ├── person25.jpg ├── person260.jpg ├── person274.jpg ├── person286.jpg ├── person300.jpg ├── person315.jpg ├── person328.jpg ├── person33.jpg ├── person341.jpg ├── person354.jpg ├── person367.jpg ├── person381.jpg ├── person3822.jpg ├── person3823.jpg ├── person3824.jpg ├── person3825.jpg ├── person3826.jpg ├── person3827.jpg ├── person3828.jpg ├── person3829.jpg ├── person3830.jpg ├── person3831.jpg ├── person3832.jpg ├── person3833.jpg ├── person3834.jpg ├── person3835.jpg ├── person3836.jpg ├── person3837.jpg ├── person3838.jpg ├── person3839.jpg ├── person3840.jpg ├── person3841.jpg ├── person3842.jpg ├── person3843.jpg ├── person3846.jpg ├── person3847.jpg ├── person3848.jpg ├── person3849.jpg ├── person3850.jpg ├── person3851.jpg ├── person3852.jpg ├── person3856.jpg ├── person3857.jpg ├── person3858.jpg ├── person393.jpg ├── person407.jpg ├── person41.jpg ├── person420.jpg ├── person434.jpg ├── person447.jpg ├── person459.jpg ├── person471.jpg ├── person483.jpg ├── person495.jpg ├── person496.jpg ├── person5.jpg ├── person50.jpg ├── person507.jpg ├── person519.jpg ├── person530.jpg ├── person542.jpg ├── person552.jpg ├── person563.jpg ├── person572.jpg ├── person582.jpg ├── person592.jpg ├── person593.jpg ├── person60.jpg ├── person603.jpg ├── person604.jpg ├── person612.jpg ├── person621.jpg ├── person631.jpg ├── person641.jpg ├── person643.jpg ├── person652.jpg ├── person654.jpg ├── person663.jpg ├── person675.jpg ├── person686.jpg ├── person687.jpg ├── person696.jpg ├── person697.jpg ├── person708.jpg ├── person709.jpg ├── person71.jpg ├── person719.jpg ├── person721.jpg ├── person731.jpg ├── person742.jpg ├── person743.jpg ├── person754.jpg ├── person765.jpg ├── person777.jpg ├── person790.jpg ├── person791.jpg ├── person8.jpg ├── person805.jpg ├── person819.jpg ├── person82.jpg ├── person820.jpg ├── person831.jpg ├── person844.jpg ├── person857.jpg ├── person869.jpg ├── person881.jpg ├── person882.jpg ├── person893.jpg ├── person894.jpg ├── person906.jpg ├── person919.jpg ├── person932.jpg ├── person94.jpg ├── person942.jpg ├── person947.jpg ├── person960.jpg ├── person972.jpg ├── person983.jpg └── person995.jpg /.gitignore: -------------------------------------------------------------------------------- 1 | 2 | .idea/ImageCleaner.iml 3 | .idea/inspectionProfiles/profiles_settings.xml 4 | .idea/misc.xml 5 | .idea/modules.xml 6 | .idea/vcs.xml 7 | -------------------------------------------------------------------------------- /.idea/.gitignore: -------------------------------------------------------------------------------- 1 | # Default ignored files 2 | /shelf/ 3 | /workspace.xml 4 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2020 Aleksander Fedotov 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # ImageCleaner 2 | Cleans similar or duplicate images from folder using perceptual image hashing with live preview 3 | 4 | ## How it works 5 | [Perceptual hashing](https://en.wikipedia.org/wiki/Perceptual_hashing#:~:text=Perceptual%20hashing%20is%20the%20use,drastic%20change%20in%20output%20value) 6 | 7 | #### The script detects similar images based on a simple algorithm: 8 | - Reducing size of each image to NxN pixels (set by user) 9 | - Converting to grayscale 10 | - Calculating the mean of all color values 11 | - Setting the color of each pixel to black if the NxN matrix value is above average, and to white otherwise 12 | - [Hamming distance](https://en.wikipedia.org/wiki/Hamming_distance): comparing hashes with each other and count the number of different pixels 13 | - The image is considered similar and moved from the folder if hamming distance is less than a threshold set by user 14 | 15 | ## How to run 16 | 17 | Tested with Python 3.8.2, OpenCV 4.3.0, Ubuntu 20.04 LTS 18 | 19 | Folder `input_images` contains 11 objects in 186 image variations created by YOLO neural detector from video 20 | 21 | This example will leave all unique 11 objects in the folder, the rest will be moved to `similar_images`: 22 | 23 | `python image_hash.py -i images/input_images -o images/similar_images -s 16 -t 33` 24 | 25 | Args: 26 | 27 | `-i`: input path to scan 28 | 29 | `-o`: output path to move similar images 30 | 31 | `-s`: hash size (default=16) 32 | 33 | `-t`: threshold percent value for detection from 0 to 100, where 0 is absolute similarity of hashes (only nearly identical images will be moved) 34 | 35 | ### Similar images search: 36 | ![](assets/search_similar.gif) 37 | 38 | ### Moving similar images to another folder (right column) leaving only unique (left column): 39 | ![](assets/move_similar.gif) 40 | 41 | ### Hash generation: 42 | ![](assets/hashed_harold.gif) 43 | 44 | ### Similarity threshold (t = 2%) (moved 7 nearly identical images) 45 | ![](assets/2.jpg) 46 | 47 | ### Similarity threshold (t = 33%) (only unique objects remained, 175 similar images moved) 48 | ![](assets/33.jpg) 49 | 50 | 51 | -------------------------------------------------------------------------------- /assets/2.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/alexfcoding/ImageCleaner/6f8af04223d30ae6a77880e280b1ef53f71f8a4e/assets/2.jpg -------------------------------------------------------------------------------- /assets/33.jpg: 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-------------------------------------------------------------------------------- https://raw.githubusercontent.com/alexfcoding/ImageCleaner/6f8af04223d30ae6a77880e280b1ef53f71f8a4e/assets/search_similar.gif -------------------------------------------------------------------------------- /image_hash.py: -------------------------------------------------------------------------------- 1 | # Example: python image_hash.py -i images/input_images -o images/similar_images -s 16 -t 33 2 | 3 | import cv2 4 | import os 5 | from pathlib import Path 6 | import argparse 7 | 8 | ap = argparse.ArgumentParser() 9 | 10 | ap.add_argument("-i", "--input_folder", type=str, required=True, 11 | help="folder to clean similar images") 12 | ap.add_argument("-o", "--similar_images", type=str, required=True, 13 | help="folder to move similar images") 14 | ap.add_argument("-s", "--hash_size", type=int, default=16, 15 | help="hash size") 16 | ap.add_argument("-t", "--threshold", type=int, required=True, 17 | help="threshold for detecting similar images") 18 | 19 | args = vars(ap.parse_args()) 20 | 21 | 22 | def calculate_mean(pixels_list): 23 | mean = 0 24 | total_pixels = len(pixels_list) 25 | for i in range(total_pixels): 26 | mean += pixels_list[i] / total_pixels 27 | return mean 28 | 29 | 30 | def grab_pixels(squeezed_frame): 31 | pixels_list = [] 32 | for x in range(0, squeezed_frame.shape[1], 1): 33 | for y in range(0, squeezed_frame.shape[0], 1): 34 | pixel_color = squeezed_frame[x, y] 35 | pixels_list.append(pixel_color) 36 | return pixels_list 37 | 38 | 39 | def make_bits_list(mean, pixels_list): 40 | bits_list = [] 41 | for i in range(len(pixels_list)): 42 | if pixels_list[i] >= mean: 43 | bits_list.append(255) 44 | else: 45 | bits_list.append(0) 46 | return bits_list 47 | 48 | 49 | def hashify(squeezed_frame, bits_list): 50 | bit_index = 0 51 | hashed_frame = squeezed_frame 52 | for x in range(0, squeezed_frame.shape[1], 1): 53 | for y in range(0, squeezed_frame.shape[0], 1): 54 | hashed_frame[x, y] = bits_list[bit_index] 55 | bit_index += 1 56 | return hashed_frame 57 | 58 | 59 | def hash_generator_animation(frame, hash_size, iterations): 60 | fourcc = cv2.VideoWriter_fourcc(*"MJPG") 61 | writer = cv2.VideoWriter(f"static/test.avi", fourcc, 25, (frame.shape[1] * 2, frame.shape[0]), True) 62 | 63 | for i in range(iterations): 64 | if hash_size >= 16: 65 | frame_squeezed = cv2.resize(frame, (hash_size, hash_size)) 66 | frame_squeezed = cv2.cvtColor(frame_squeezed, cv2.COLOR_BGR2GRAY) 67 | pixels_list = grab_pixels(frame_squeezed) 68 | mean_color = calculate_mean(pixels_list) 69 | bits_list = make_bits_list(mean_color, pixels_list) 70 | hashed_frame = hashify(frame_squeezed, bits_list) 71 | hashed_frame = cv2.cvtColor(hashed_frame, cv2.COLOR_GRAY2BGR) 72 | cv2.putText(hashed_frame, f"hash_size: {hash_size}", (40, 70), cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 0, 255), 4, 73 | lineType=cv2.LINE_AA) 74 | im_v = cv2.hconcat([frame, hashed_frame]) 75 | cv2.imshow("Hash", im_v) 76 | cv2.waitKey(1) 77 | writer.write(im_v) 78 | hash_size -= 1 79 | 80 | 81 | def generate_hash(frame, hash_size): 82 | frame_squeezed = cv2.resize(frame, (hash_size, hash_size)) 83 | frame_squeezed = cv2.cvtColor(frame_squeezed, cv2.COLOR_BGR2GRAY) 84 | pixels_list = grab_pixels(frame_squeezed) 85 | mean_color = calculate_mean(pixels_list) 86 | bits_list = make_bits_list(mean_color, pixels_list) 87 | hashed_frame = hashify(frame_squeezed, bits_list) 88 | hashed_frame = cv2.cvtColor(hashed_frame, cv2.COLOR_GRAY2BGR) 89 | return bits_list, hashed_frame 90 | 91 | 92 | def clean_folder(input_folder, similar_images, hash_size, threshold): 93 | files = (os.listdir(input_folder)) 94 | list_length = len(files) 95 | i = 0 96 | k = 1 97 | frame = None 98 | hashed_frame = None 99 | duplicate_count = 0 100 | bits_list = [] 101 | 102 | while i < len(files): 103 | sum_diff = 0 104 | 105 | if files[i] is not None: 106 | frame = cv2.imread(f"{input_folder}/{files[i]}") 107 | bits_list, hashed_frame = generate_hash(frame, hash_size) 108 | 109 | while k < len(files): 110 | if (i != k) and (files[k] is not None): 111 | new_frame = cv2.imread(f"{input_folder}/{files[k]}") 112 | new_bits_list, hashed_second_frame = generate_hash(new_frame, hash_size) 113 | 114 | for j in range(len(bits_list)): 115 | if bits_list[j] != new_bits_list[j]: 116 | sum_diff += 1 117 | 118 | print(f"{files[i]} -> {files[k]} sum_diff = {sum_diff}") 119 | 120 | im_h = cv2.hconcat([cv2.resize(frame, (450, 450)), cv2.resize(new_frame, (450, 450))]) 121 | im_h2 = cv2.hconcat([cv2.resize(hashed_frame, (450, 450)), cv2.resize(hashed_second_frame, (450, 450))]) 122 | im_v = cv2.vconcat([im_h, im_h2]) 123 | 124 | if sum_diff <= hash_size * hash_size * threshold / 100: 125 | Path(f"{input_folder}/{files[k]}").rename(f"{similar_images}/{files[k]}") 126 | print(f"Deleted {k} element ({files[k]}) of {list_length}") 127 | del files[k] 128 | duplicate_count += 1 129 | else: 130 | k += 1 131 | 132 | cv2.putText(im_v, f"SIMILAR: {duplicate_count}", (5, 35), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2, 133 | lineType=cv2.LINE_AA) 134 | cv2.imshow("Seeking for similar images...", im_v) 135 | cv2.waitKey(1) 136 | sum_diff = 0 137 | i += 1 138 | k = i + 1 139 | 140 | 141 | clean_folder(args['input_folder'], args['similar_images'], args['hash_size'], args['threshold']) 142 | -------------------------------------------------------------------------------- /images/input_images/bus3504.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/alexfcoding/ImageCleaner/6f8af04223d30ae6a77880e280b1ef53f71f8a4e/images/input_images/bus3504.jpg -------------------------------------------------------------------------------- /images/input_images/bus3531.jpg: -------------------------------------------------------------------------------- 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