Testing using KNN

Testing for the data with live camera is written in optimized but beginner friendly code.

Testing.py

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+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'C:\Users\Synergiz\PycharmProjects\Emotion_Rec\haarcascade.xml')
+classifier = load_model(
+    r'C:\Users\Synergiz\PycharmProjects\Emotion_Rec\Emo_little-h5.h5')
+
+class_labels = ['Angry', 'Happy', 'Neutral', 'Sad', 'Surprise']
+
+cap = cv2.VideoCapture(0)
+
+while True:
+    # Grab a single frame of video
+    ret, frame = cap.read()
+    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)
+        # rect,face,image = face_detector(frame)
+
+        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 ROI, then 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)
+        else:
+            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
+
+cap.release()
+cv2.destroyAllWindows()