predict.py added

predict.py

+import cv2
+import numpy as np
+from tensorflow.keras.models import model_from_json  
+from tensorflow.keras.preprocessing import image  
+  
+
+#load model  
+model = model_from_json(open("model.json", "r").read())  
+
+#load weights  
+model.load_weights('model.h5')  
+
+
+face_haar_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')  
+  
+  
+cap=cv2.VideoCapture(0)  
+  
+while True:  
+    ret,test_img=cap.read()# captures frame and returns boolean value and captured image  
+    if not ret:  
+        continue  
+    gray_img= cv2.cvtColor(test_img, cv2.COLOR_BGR2GRAY)  
+  
+    faces_detected = face_haar_cascade.detectMultiScale(gray_img, 1.32, 5)  
+    
+  
+    for (x,y,w,h) in faces_detected:
+        print('WORKING')
+        cv2.rectangle(test_img,(x,y),(x+w,y+h),(255,0,0),thickness=7)  
+        roi_gray=gray_img[y:y+w,x:x+h]#cropping region of interest i.e. face area from  image  
+        roi_gray=cv2.resize(roi_gray,(48,48))  
+        img_pixels = image.img_to_array(roi_gray)  
+        img_pixels = np.expand_dims(img_pixels, axis = 0)  
+        img_pixels /= 255  
+  
+        predictions = model.predict(img_pixels)  
+  
+        #find max indexed array  
+        max_index = np.argmax(predictions[0])  
+  
+        emotions = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral']  
+        predicted_emotion = emotions[max_index]  
+        print(predicted_emotion)
+        cv2.putText(test_img, predicted_emotion, (int(x), int(y)), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,0,255), 2)  
+  
+    resized_img = cv2.resize(test_img, (1000, 700))  
+    cv2.imshow('Facial emotion analysis ',resized_img)  
+  
+  
+  
+    if cv2.waitKey(10) == ord('q'):#wait until 'q' key is pressed  
+        break  
+  
+cap.release()  
+cv2.destroyAllWindows