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A simple script to perform webcam visual anomaly detection with autoencoders built with Keras
#!/usr/bin/python3
from __future__ import absolute_import, print_function
import cv2
import numpy as np
from time import sleep, time
from collections import deque
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import RMSprop
from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D, ZeroPadding2D
from tensorflow.keras.callbacks import History
def build_model(img_size=32):
input_img = Input(shape=(img_size, img_size, 1))
x = Conv2D(32, (5, 5), activation='relu', padding='same')(input_img)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
encoded = MaxPooling2D((2, 2), padding='same', name='encoder')(x)
x = Conv2D(8, (3, 3), activation='relu', padding='same')(encoded)
x = UpSampling2D((2, 2))(x)
x = Conv2D(32, (3, 3), activation='relu', padding='same')(x)
x = UpSampling2D((2, 2))(x)
decoded = Conv2D(1, (5, 5), activation='sigmoid', padding='same')(x)
autoencoder = Model(input_img, decoded)
autoencoder.compile(optimizer=RMSprop(lr=lr), loss='binary_crossentropy')
return autoencoder
def load_run_render(frame):
im = cv2.resize(frame, (img_size, img_size))
im = cv2.cvtColor(im, cv2.COLOR_RGB2GRAY)
im = np.expand_dims(np.expand_dims(im, axis=0).astype(np.float32), axis=-1) / 255
history = History()
start_time = time()
autoencoder.fit(im, im,
epochs=1,
batch_size=1,
shuffle=True,
callbacks=[history])
end_time = time()
return history.history['loss'][-1]
def main():
# Begin Video Capture
cam = cv2.VideoCapture(0)
idx = 0
while True:
try:
ret, frame = cam.read()
ts = int(time() * 1000)
print(idx)
if ret:
loss = load_run_render(frame)
q.append(loss)
if len(q) > 50:
prev_hist = list(q)[:-1]
threshold = 5 * np.std(prev_hist)
if np.abs(loss - np.mean(prev_hist)) > threshold:
print(loss, 'anomaly')
else:
print(loss, 'normal')
else:
pass
except Exception as e:
print(e)
pass
sleep(DELAY)
idx += 1
if __name__ == '__main__':
# Declare Parameters
lr = 1e-4
DELAY = 0.2
img_size=128
q = deque(maxlen=300)
# Build the Autoencoder
autoencoder = build_model(img_size=img_size)
# Run the Training/Inference Loop
main()
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