unwanted files removed

Emotion_Video/main.py

-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
-
-face_classifier = cv2.CascadeClassifier(r'F:\SLIIT\Research Project\New folder\Emotion Classification\haarcascade_frontalface_default.xml')
-classifier =load_model(r'F:\SLIIT\Research Project\New folder\Emotion Classification\model.h5')
-
-emotion_labels = ['Angry','Disgust','Fear','Happy','Neutral', 'Sad', 'Surprise']
-
-cap = cv2.VideoCapture(0)
-
-
-
-while True:
-    _, frame = cap.read()
-    labels = []
-    gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
-    faces = face_classifier.detectMultiScale(gray)
-
-    for (x,y,w,h) in faces:
-        cv2.rectangle(frame,(x,y),(x+w,y+h),(0,255,255),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
-            roi = img_to_array(roi)
-            roi = np.expand_dims(roi,axis=0)
-
-            prediction = classifier.predict(roi)[0]
-            label=emotion_labels[prediction.argmax()]
-            label_position = (x,y-10)
-            cv2.putText(frame,label,label_position,cv2.FONT_HERSHEY_SIMPLEX,1,(0,255,0),2)
-        else:
-            cv2.putText(frame,'No Faces',(30,80),cv2.FONT_HERSHEY_SIMPLEX,1,(0,255,0),2)
-    cv2.imshow('Emotion Detector',frame)
-    if cv2.waitKey(1) & 0xFF == ord('q'):
-        break
-
-cap.release()
-cv2.destroyAllWindows()
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