Spaces:
Runtime error
Runtime error
File size: 3,788 Bytes
20be593 576ab32 20be593 576ab32 20be593 576ab32 20be593 1528743 576ab32 20be593 576ab32 20be593 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 |
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)
|