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import tensorflow as tf
import json
import numpy as np
from matplotlib import pyplot as plt
import cv2
from tensorflow.keras.models import load_model
#Load the model
facetracker = load_model('facetracker.h5')
#Load the video
cap = cv2.VideoCapture(0)
while cap.isOpened():
# Capture frame-by-frame
_ , frame = cap.read()
frame = frame[50:500, 50:500,:]
rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
resized = tf.image.resize(rgb, (120,120))
yhat = facetracker.predict(np.expand_dims(resized/255,0))
sample_coords = yhat[1][0]
if yhat[0] > 0.5:
# Controls the main rectangle
cv2.rectangle(frame,
tuple(np.multiply(sample_coords[:2], [450,450]).astype(int)),
tuple(np.multiply(sample_coords[2:], [450,450]).astype(int)),
(255,0,0), 2)
# Controls the label rectangle
cv2.rectangle(frame,
tuple(np.add(np.multiply(sample_coords[:2], [450,450]).astype(int),
[0,-30])),
tuple(np.add(np.multiply(sample_coords[:2], [450,450]).astype(int),
[80,0])),
(255,0,0), -1)
# Controls the text rendered
cv2.putText(frame, 'Muka', tuple(np.add(np.multiply(sample_coords[:2], [450,450]).astype(int),
[0,-5])),
cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255), 2, cv2.LINE_AA)
# Display the resulting frame
cv2.imshow('Face Detection', frame)
# Press Q on keyboard to exit
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
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