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| import cv2 | |
| import os | |
| import random | |
| import numpy as np | |
| from matplotlib import pyplot as plt | |
| from tensorflow.keras.layers import Layer | |
| import tensorflow as tf | |
| import gradio as gr | |
| import uuid | |
| def preprocess(file_path): | |
| # Read in image from file path | |
| byte_img = tf.io.read_file(file_path) | |
| # Load in the image | |
| img = tf.io.decode_jpeg(byte_img) | |
| # Preprocessing steps - resizing the image to be 100x100x3 | |
| img = tf.image.resize(img, (100, 100)) | |
| # Scale image to be between 0 and 1 | |
| img = img / 255.0 | |
| # Return image | |
| return img | |
| # Siamese L1 Distance class | |
| class L1Dist(Layer): | |
| # Init method - inheritance | |
| def __init__(self, **kwargs): | |
| super().__init__() | |
| # Magic happens here - similarity calculation | |
| def call(self, input_embedding, validation_embedding): | |
| return tf.math.abs(input_embedding - validation_embedding) | |
| # Reload model | |
| siamese_model = tf.keras.models.load_model('siamesemodelv2.h5', | |
| custom_objects={'L1Dist': L1Dist, 'BinaryCrossentropy': tf.losses.BinaryCrossentropy}) | |
| def verify(model, detection_threshold, verification_threshold): | |
| # Build results array | |
| results = [] | |
| ver_images= [] | |
| for file in os.listdir(os.path.join('application_data', 'verification_images')): | |
| if file[len(file)-4:] == '.jpg': | |
| ver_images.append(file) | |
| ver_images_len= len(ver_images) | |
| for image in ver_images: | |
| input_img = preprocess(os.path.join('application_data', 'input_image', 'input_image.jpg')) | |
| validation_img = preprocess(os.path.join('application_data', 'verification_images', image)) | |
| # Make Predictions | |
| result = model.predict(list(np.expand_dims([input_img, validation_img], axis=1))) | |
| results.append(result) | |
| # Detection Threshold: Metric above which a prediciton is considered positive | |
| detection = np.sum(np.array(results) > detection_threshold) | |
| print('Detection -- ',detection) | |
| # Verification Threshold: Proportion of positive predictions / total positive samples | |
| verification = detection / len(ver_images) | |
| print('Verification -- ',verification) | |
| verified = verification > verification_threshold | |
| return detection,verification,verified,ver_images_len | |
| def take_ver_images (image): | |
| count=0 | |
| cv2.imwrite(os.path.join('application_data', 'verification_images', '{}.jpg'.format(uuid.uuid1())), image) | |
| count+=1 | |
| return '{} Image Collected!'.format(count) | |
| def verify_identity (image): | |
| cv2.imwrite(os.path.join('application_data', 'input_image', 'input_image.jpg'), image) | |
| detection,verification,verified,ver_images_len = verify(siamese_model, 0.8, 0.7) | |
| # return {'Detections': int(detection),'Confidence': float(verification),'Verified': bool(verified)} | |
| return 'Detected {} out of {} samples'.format(detection,ver_images_len),float(verification),'Verified' if verified else 'Not Verified' | |
| with gr.Blocks() as demo: | |
| with gr.Tab("Setup Identity"): | |
| with gr.Row(): | |
| input_img = gr.inputs.Image(shape=(100,100)) | |
| output_text = gr.outputs.Label() | |
| with gr.Row(): | |
| submit_btn = gr.Button("Add Image") | |
| submit_btn.click(take_ver_images, inputs=input_img,outputs=output_text) | |
| with gr.Tab("Identify"): | |
| with gr.Row(): | |
| input_img1 = gr.inputs.Image(shape=(100,100)) | |
| output_text3 = gr.outputs.Label(label='Verification Result') | |
| with gr.Row(): | |
| output_text1 = gr.outputs.Label(label='Detections') | |
| output_text2 = gr.outputs.Label(label='Confidence') | |
| with gr.Row(): | |
| submit_btn1 = gr.Button("Verify") | |
| submit_btn1.click(verify_identity, inputs=input_img1,outputs=[output_text1,output_text2,output_text3]) | |
| demo.launch(debug=True) | |