text: # Proof-of-concept
  import cv2
  import sys
  from constants import *
  from emotion_recognition import EmotionRecognition
  import numpy as np
  
  cascade_classifier = cv2.CascadeClassifier(CASC_PATH)
  
  
  def brighten(data, b):
      datab = data * b
      return datab
  
  
  def format_image(image):
      if len(image.shape) > 2 and image.shape[2] == 3:
          image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
      else:
          image = cv2.imdecode(image, cv2.CV_LOAD_IMAGE_GRAYSCALE)
      faces = cascade_classifier.detectMultiScale(
          image,
          scaleFactor=1.3,
          minNeighbors=5
      )
      # None is we don't found an image
      if not len(faces) > 0:
          return None
      max_area_face = faces[0]
      for face in faces:
          if face[2] * face[3] > max_area_face[2] * max_area_face[3]:
              max_area_face = face
      # Chop image to face
      face = max_area_face
      image = image[face[1]:(face[1] + face[2]), face[0]:(face[0] + face[3])]
      # Resize image to network size
      try:
          image = cv2.resize(image, (SIZE_FACE, SIZE_FACE),
                             interpolation=cv2.INTER_CUBIC) / 255.
      except Exception:
          print("[+] Problem during resize")
          return None
      # cv2.imshow("Lol", image)
      # cv2.waitKey(0)
      return image
  
  
  # Load Model
  network = EmotionRecognition()
  network.build_network()
  
  video_capture = cv2.VideoCapture(0)
  font = cv2.FONT_HERSHEY_SIMPLEX
  
  feelings_faces = []
  for index, emotion in enumerate(EMOTIONS):
      feelings_faces.append(cv2.imread('./emojis/' + emotion + '.png', -1))
  
  while True:
      # Capture frame-by-frame
      ret, frame = video_capture.read()
  
      # Predict result with network
      result = network.predict(format_image(frame))
  
      # Draw face in frame
      # for (x,y,w,h) in feelings_faces:
      #   cv2.rectangle(frame, (x,y), (x+w,y+h), (255,0,0), 2)
  
      # Write results in frame
      if result is not None:
          for index, emotion in enumerate(EMOTIONS):
              cv2.putText(frame, emotion, (10, index * 20 + 20),
                          cv2.FONT_HERSHEY_PLAIN, 0.5, (0, 255, 0), 1)
              cv2.rectangle(frame, (130, index * 20 + 10), (130 +
                                                            int(result[0][index] * 100), (index + 1) * 20 + 4), (255, 0, 0), -1)
  
          face_image = feelings_faces[np.argmax(result[0])]
  
          # Ugly transparent fix
          for c in range(0, 3):
              frame[200:320, 10:130, c] = face_image[:, :, c] * \
                  (face_image[:, :, 3] / 255.0) + frame[200:320,
                                                        10:130, c] * (1.0 - face_image[:, :, 3] / 255.0)
      
      # Display the resulting frame
      cv2.imshow('Video', frame)
      
  
      if cv2.waitKey(1) & 0xFF == ord('q'):
          break
  
  # When everything is done, release the capture
  video_capture.release()
  cv2.destroyAllWindows()
  