Commit 36ac2db8 authored by Arne Gerdes's avatar Arne Gerdes
Browse files

Kommentare hinzugefügt

parent 7ab441c1
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This module is the main module in this package. It loads emotion recognition model from a file,
shows a webcam image, recognizes face and it's emotion and draw emotion on the image.
Dieses Modul ist das Main-Modul. Es lädt das Modell aus models, zeigt ein Webcam-Bild,
erkennt das Gesicht und seine Emotionen und zeichnet ein Emoticon in das Bild.
import cv2
......@@ -14,21 +14,21 @@ import numpy as np
def _load_emoticons(emotions):
Loads emotions images from graphics folder.
:param emotions: Array of emotions names.
:return: Array of emotions graphics.
Lädt die Emoticons aus dem graphics Ordner.
:param emotions: Array von Emotionen.
:return: Array von Emotions Grafiken.
return [nparray_as_image(cv2.imread('resources/graphics/%s.png' % emotion, -1), mode=None) for emotion in emotions]
def show_webcam_and_run(model, emoticons, window_size=(600, 600), window_name='Mood Expression', update_time=1):
Shows webcam image, detects faces and its emotions in real time and draw emoticons over those faces.
:param model: Learnt emotion detection model.
:param emoticons: List of emotions images.
:param window_size: Size of webcam image window.
:param window_name: Name of webcam image window.
:param update_time: Image update time interval.
Zeigt ein Webcam-Bild, erkennt Gesichter und Emotionen in Echtzeit und zeichnet Emoticons neben die Gesichter.
:param model: Trainiertes Model
:param emoticons: Liste von Emoticons.
:param window_size: Grösse des Webcam-Fensters.
:param window_name: Name des Webcam-Fensters.
:param update_time: Bildaktualisierungzeit.
cv2.namedWindow(window_name, cv2.WINDOW_NORMAL)
if window_size:
......@@ -37,19 +37,39 @@ def show_webcam_and_run(model, emoticons, window_size=(600, 600), window_name='M
vc = WebcamVideoStream().start()
frame =
puffer = RingBuffer(7) # Der RingBuffer speichert die letzten Predictions
Der RingBuffer speichert die letzten 7 Predictions
puffer = RingBuffer(7)
while True:
for normalized_face, (x, y, w, h) in find_faces(frame):
prediction = model.predict(normalized_face) # do prediction
puffer.append(prediction[0]) # Speichere letzte Prediction
preds = puffer.get() # Hole Einträge als Array
Seichert die Predictions
Holt die Einträge als Array
preds = puffer.get()
Kein Eintrag im RingBuffer ist None
if not (any(x is None for x in preds)):
Vorkommen der Predictions zählen
unique, counts = np.unique(preds, return_counts=True)
if not (any(x is None for x in preds)): # Kein Eintrag im RingBuffer ist None
unique, counts = np.unique(preds, return_counts=True) # Vorkommen der Predictions zählen
image_to_draw = emoticons[unique[0]] # häufigster Wert wird dargestellt
Häufigster Wert wird dargestellt
image_to_draw = emoticons[unique[0]]
draw_with_alpha(frame, image_to_draw, (40, 40, 200, 200))
cv2.imshow(window_name, frame)
......@@ -67,8 +87,9 @@ if __name__ == '__main__':
emotions = ['happy', 'neutral', 'surprise']
emoticons = _load_emoticons(emotions)
# load mode
fisher_face = cv2.face.FisherFaceRecognizer_create()
"""Läadt das trainierte Model"""'basis_data/models/detection_model.xml')
# use learnt model
show_webcam_and_run(fisher_face, emoticons)
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