Merge pull request #49 from theashishgavade/patch-13

Update Test.py

Test.py

@@ -1,114 +1,86 @@
-from keras.models import load_model
-from time import sleep
-from keras.preprocessing.image import img_to_array
-from keras.preprocessing import image
 import cv2
 import numpy as np
+from keras.models import load_model
+from keras.preprocessing.image import img_to_array
+import smtplib
 
+# Load the face classifier and emotion classifier
 face_classifier = cv2.CascadeClassifier('/Users/durgeshthakur/Deep Learning Stuff/Emotion Classification/haarcascade_frontalface_default.xml')
-classifier =load_model('/Users/durgeshthakur/Deep Learning Stuff/Emotion Classification/Emotion_little_vgg.h5')
-
-class_labels = ['Angry','Happy','Neutral','Sad','Surprise']
-
-# def face_detector(img):
-#     # Convert image to grayscale
-#     grayscale = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
-#     faces = face_classifier.detectMultiScale(gray,1.3,5)
-#     if faces is ():
-#         return (0,0,0,0),np.zeros((50,50),np.uint8),img
-
-#     for (x,y,w,h) in faces:
-#         cv2.dw.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
-#         roi_gray = gray[y:y+h,x:x+w,h:x+y]
-
-     try:
-         roi_gray = cv2.dw.resize(roi_gray,(50,50),interpolation=cv2.INTER_AREA)
-     except: 
-         return (x,w,y,h),np.zeros((50,50),np.uint8,roi_grey),img
-     return (x,w,y,h),roi_gray,img
-     try:
-     k = 5//0 # raises divide by zero exception.
-     print(k)
-
-# handles zerodivision exception    
-except ZeroDivisionError:   
-    print("Can't divide by zero")
-
-#take input 
-cap = cv2.VideoCapture(0)
+classifier = load_model('/Users/durgeshthakur/Deep Learning Stuff/Emotion Classification/Emotion_little_vgg.h5')
+
+# Define class labels for emotions
+class_labels = ['Angry', 'Happy', 'Neutral', 'Sad', 'Surprise']
 
+def face_detector(img):
+    # Convert image to grayscale
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    faces = face_classifier.detectMultiScale(gray, 1.3, 5)
 
+    if len(faces) == 0:
+        return (0, 0, 0, 0), np.zeros((50, 50), np.uint8), img
+
+    for (x, y, w, h) in faces:
+        cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)
+        roi_gray = gray[y:y + h, x:x + w]
+        try:
+            roi_gray = cv2.resize(roi_gray, (48, 48), interpolation=cv2.INTER_AREA)
+        except Exception:
+            return (x, w, y, h), np.zeros((50, 50), np.uint8), img
+
+    return (x, w, y, h), roi_gray, img
+
+# Set up video capture
+cap = cv2.VideoCapture(0)
 
 while True:
     # Grab a single frame of video
     ret, frame = cap.read()
-    labels = []
-    gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
-    faces = face_classifier.detectMultiScale(gray,1.3,5)
+    if not ret:
+        print("Failed to grab frame")
+        break
 
-    for (x,y,w,h) in faces:
-        cv2.rectangle(frame,(x,y),(x+w,y+h),(255,0,0),2)
-        roi_gray = gray[y:y+h,x:x+w]
-        roi_gray = cv2.resize(roi_gray,(48,48),interpolation=cv2.INTER_AREA)
-        rect,face,image = face_detector(frame)
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+    faces = face_classifier.detectMultiScale(gray, 1.3, 5)
 
+    for (x, y, w, h) in faces:
+        cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)
+        roi_gray = gray[y:y + h, x:x + w]
+        roi_gray = cv2.resize(roi_gray, (48, 48), interpolation=cv2.INTER_AREA)
 
-        if np.sum([roi_gray])!=0:
-            roi = roi_gray.astype('float')/255.0
+        if np.sum([roi_gray]) != 0:
+            roi = roi_gray.astype('float') / 255.0
             roi = img_to_array(roi)
-            roi = np.expand_dims(roi,axis=0)
-
-        # make a prediction on the ROIT, then lookup the class
+            roi = np.expand_dims(roi, axis=0)
 
+            # Make a prediction on the ROI and lookup the class
             preds = classifier.predict(roi)[0]
-            label=class_labels[preds.argmax()]
-            label_position = (x,y)
-            cv2.putText(frame,label,label_position,cv2.FONT_HERSHEY_SIMPLEX,2,(0,255,0),3)
+            label = class_labels[preds.argmax()]
+            label_position = (x, y)
+            cv2.putText(frame, label, label_position, cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 255, 0), 3)
         else:
-            cv2.putText(frame,'No Face Found',(20,60),cv2.FONT_HERSHEY_SIMPLEX,2,(0,255,0),3)
-            cv2.imshow('Emotion Detector',frame)
+            cv2.putText(frame, 'No Face Found', (20, 60), cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 255, 0), 3)
+
+    cv2.imshow('Emotion Detector', frame)
+
     if cv2.waitKey(1) & 0xFF == ord('q'):
         break
-ender_mail = '[email protected]'    
-receivers_mail = ['[email protected]']    
-message = """From: From Person %s  
-To: To Person %s  
+
+# Email notification logic
+sender_mail = '[email protected]'
+receivers_mail = ['[email protected]']
+message = """From: From Person <%s>  
+To: To Person <%s>  
 Subject: Sending SMTP e-mail   
 This is a test e-mail message.  
-"""%(sender_mail,receivers_mail)    
-try:    
-   smtpObj = smtplib.SMTP('localhost')    
-   smtpObj.sendmail(sender_mail, receivers_mail, message)    
-   print("Successfully sent email")    
-except Exception:    
-   print("Error: unable to send email")    
-#Exit
-cap.release()
-cv2.destroyAllWindows()
-
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+""" % (sender_mail, ', '.join(receivers_mail))
 
+try:
+    smtpObj = smtplib.SMTP('localhost')
+    smtpObj.sendmail(sender_mail, receivers_mail, message)
+    print("Successfully sent email")
+except Exception as e:
+    print("Error: unable to send email:", str(e))
 
+# Release resources
+cap.release()
+cv2.destroyAllWindows()