Update app.py

app.py

@@ -1,9 +1,55 @@
 import gradio as gr
 import cv2
+import numpy as np
+from keras.preprocessing.image import img_to_array
+from keras.models import model_from_json
 
-def process_image(img):
-  blur = cv2.blur(img,(5,5))
+# Facial expression recognizer initialization
+face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
+model = model_from_json(open('facial_expression_model_structure.json', 'r').read())
+model.load_weights('facial_expression_model_weights.h5')
+
+emotions = ('angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral')
+
+def blur_image(img):
+  blur = cv2.blur(img,(10, 10))
   return blur
+  
+def process_image(img):
+  # Resize the frame
+  frame = cv2.resize(img, None, fx=scaling_factor, fy=scaling_factor, interpolation=cv2.INTER_AREA)
+  
+  # Convert to grayscale
+  gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+
+  # Run the face detector on the grayscale image
+  face_rects = face_cascade.detectMultiScale(gray, 1.3, 5)
+  
+  # Draw a rectangle around the face
+  for (x,y,w,h) in face_rects:
+    #cv2.rectangle(frame, (x,y), (x+w,y+h), (0,255,0), 3)
+    cv2.rectangle(frame,(x,y),(x+w,y+h),(255,0,0),2) #draw rectangle to main image
+
+    detected_face = frame[int(y):int(y+h), int(x):int(x+w)] #crop detected face
+    detected_face = cv2.cvtColor(detected_face, cv2.COLOR_BGR2GRAY) #transform to gray scale
+    detected_face = cv2.resize(detected_face, (48, 48)) #resize to 48x48
+
+    img_pixels = img_to_array(detected_face)
+    img_pixels = np.expand_dims(img_pixels, axis = 0)
+
+    img_pixels /= 255 #pixels are in scale of [0, 255]. normalize all pixels in scale of [0, 1]
+
+    predictions = model.predict(img_pixels) #store probabilities of 7 expressions
+
+    #find max indexed array 0: angry, 1:disgust, 2:fear, 3:happy, 4:sad, 5:surprise, 6:neutral
+    max_index = np.argmax(predictions[0])
+    emotion = emotions[max_index]
+
+    #write emotion text above rectangle
+    cv2.putText(frame, emotion, (int(x), int(y)), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255), 2)
+    
+  return frame
+
 
 interface = gr.Interface(
   fn = process_image,