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| # Copyright 2021 Asuhariet Ygvar | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express | |
| # or implied. See the License for the specific language governing | |
| # permissions and limitations under the License. | |
| import sys | |
| import onnxruntime | |
| import numpy as np | |
| from PIL import Image | |
| import gradio as gr | |
| import torch | |
| import os | |
| os.system('wget https://www.dropbox.com/s/ggf6ok63u7hywhc/neuralhash_128x96_seed1.dat') | |
| os.system('wget https://www.dropbox.com/s/1jug4wtevz1rol0/model.onnx') | |
| torch.hub.download_url_to_file('https://cdn.pixabay.com/photo/2017/09/11/15/58/sunset-2739472_1280.jpg', 'sunset.jpg') | |
| torch.hub.download_url_to_file('https://i.imgur.com/W8aXbd2.png', 'rotate.png') | |
| torch.hub.download_url_to_file('https://user-images.githubusercontent.com/1328/129860794-e7eb0132-d929-4c9d-b92e-4e4faba9e849.png', 'dog.png') | |
| torch.hub.download_url_to_file('https://user-images.githubusercontent.com/1328/129860810-f414259a-3253-43e3-9e8e-a0ef78372233.png', 'same.png') | |
| torch.hub.download_url_to_file('https://cdn.pixabay.com/photo/2021/08/23/17/53/cat-6568422_1280.jpg', 'cat1.jpg') | |
| torch.hub.download_url_to_file('https://i.imgur.com/fMoVhSz.png', 'cat2.png') | |
| torch.hub.download_url_to_file('https://cdn.pixabay.com/photo/2021/08/10/09/41/lesser-sand-plover-6535531_1280.jpg', 'bird1.jpg') | |
| torch.hub.download_url_to_file('https://i.imgur.com/jDgKAC7.png', 'bird2.png') | |
| # Load ONNX model | |
| session = onnxruntime.InferenceSession('model.onnx') | |
| # Load output hash matrix | |
| seed1 = open('neuralhash_128x96_seed1.dat', 'rb').read()[128:] | |
| seed1 = np.frombuffer(seed1, dtype=np.float32) | |
| seed1 = seed1.reshape([96, 128]) | |
| pre_text = "<p style='display: flex; flex-grow: 1; align-items: center; justify-content: center; padding: 2rem 1rem; font-size: 1.5rem; line-height: 2rem; font-weight: 400;'>{}</p>" | |
| # Preprocess image | |
| def inference(img, img2): | |
| image = Image.open(img.name).convert('RGB') | |
| image = image.resize([360, 360]) | |
| arr = np.array(image).astype(np.float32) / 255.0 | |
| arr = arr * 2.0 - 1.0 | |
| arr = arr.transpose(2, 0, 1).reshape([1, 3, 360, 360]) | |
| # Run model | |
| inputs = {session.get_inputs()[0].name: arr} | |
| outs = session.run(None, inputs) | |
| # Convert model output to hex hash | |
| hash_output = seed1.dot(outs[0].flatten()) | |
| hash_bits = ''.join(['1' if it >= 0 else '0' for it in hash_output]) | |
| hash_hex = '{:0{}x}'.format(int(hash_bits, 2), len(hash_bits) // 4) | |
| image2 = Image.open(img2.name).convert('RGB') | |
| image2 = image2.resize([360, 360]) | |
| arr2 = np.array(image2).astype(np.float32) / 255.0 | |
| arr2 = arr2 * 2.0 - 1.0 | |
| arr2 = arr2.transpose(2, 0, 1).reshape([1, 3, 360, 360]) | |
| # Run model | |
| inputs2 = {session.get_inputs()[0].name: arr2} | |
| outs2 = session.run(None, inputs2) | |
| # Convert model output to hex hash | |
| hash_output2 = seed1.dot(outs2[0].flatten()) | |
| hash_bits2 = ''.join(['1' if it >= 0 else '0' for it in hash_output2]) | |
| hash_hex2 = '{:0{}x}'.format(int(hash_bits2, 2), len(hash_bits2) // 4) | |
| if hash_hex == hash_hex2: | |
| return pre_text.format("Same Hash"), pre_text.format(hash_hex), pre_text.format(hash_hex2) | |
| return pre_text.format("Different Hash"), pre_text.format(hash_hex), pre_text.format(hash_hex2) | |
| title = "AppleNeuralHash" | |
| description = "Gradio demo for Apple NeuralHash, a perceptual hashing method for images based on neural networks. It can tolerate image resize and compression. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below." | |
| article = "<p style='text-align: center'><a href='https://www.apple.com/child-safety/pdf/CSAM_Detection_Technical_Summary.pdf'>CSAM Detection Technical Summary</a> | <a href='https://github.com/AsuharietYgvar/AppleNeuralHash2ONNX'>Github Repo</a> | <a href='https://github.com/AsuharietYgvar/AppleNeuralHash2ONNX/issues/1'>Working Collision example images from github issue</a></p> " | |
| examples = [['sunset.jpg','rotate.png'],['dog.png','same.png'],['cat1.jpg','cat2.png'],['bird1.jpg','bird2.png']] | |
| gr.Interface( | |
| inference, | |
| [gr.inputs.Image(type="file", label="Input Image"),gr.inputs.Image(type="file", label="Input Image")], | |
| [gr.outputs.HTML(label="Comparison.."), gr.outputs.HTML(label="First Hash"), gr.outputs.HTML(label="Second Hash")], | |
| title=title, | |
| description=description, | |
| article=article, | |
| examples=examples, | |
| allow_flagging=False | |
| ).launch(debug=True) |