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facedet.py

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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()

facetracker.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:997421d95d0cd7c5a574872f2cc473914c4570464663a1a3650c21727358b067
+size 67363272