Änderungen

.gitignore

@@ -98,6 +98,9 @@ instance/
 # Sphinx documentation
 docs/_build/
 resources/csv
+resources/img_data/models/
+
+
 
 # PyBuilder
 target/

projectmood/process_model.py

@@ -29,9 +29,9 @@ parser.add_argument('-5', action='append_const', dest='emotions', const='disgust
 parser.add_argument('-6', action='append_const', dest='emotions', const='anger', help='anger')
 parser.add_argument('-d', '--dataset', action='store', dest='dataset', default='resources/img_data/dataset/',
                     help='path to dataset')
-parser.add_argument('-i' '--iterations', action='store', dest='iterations', type=int, default=30,
+parser.add_argument('-i' '--iterations', action='store', dest='iterations', type=int, default=1,
                     help='number of iterations')
-parser.add_argument('-p', '--properties', nargs='+', dest='properties', help='pre-processing steps for logging')
+parser.add_argument('-p', '--properties', nargs='*', dest='properties', help='pre-processing steps for logging')
 parser.add_argument('-t', '--test', action='store_true', dest='test', help='prevent writing new model to classifier')
 parser.add_argument('-c', '--csv', action='store_true', dest='csv', help='activate csv output')
 parser.add_argument('-x', '--email', action='store_true', dest='email', help='activate email notifications')
@@ -44,9 +44,6 @@ if not arguments.emotions:
 
 logging.info('Fisherface training started')
 
-if arguments.email:
-    sendMail('Fisherface training started')
-
 def _get_faces_from_emotion(emotion):
     """
     Get all the files to an emotion from the dataset, mix them, and split them into a training and test set.
@@ -70,9 +67,9 @@ def image_preprocessing(image):
     """
     img = cv2.imread(image)  # open image
     gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)  # convert to grayscale
-    clahe = cv2.createCLAHE(2.0, (8, 8))
-    norm = clahe.apply(gray)
-    return norm
+    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
+    face = clahe.apply(gray)
+    return face
 
 
 def make_sets():
@@ -128,9 +125,6 @@ for i in range(1, arguments.iterations + 1):
     logging.info("{} : {}%".format(i, int(correct)))
     metascore.append(correct)
 
-    if arguments.email and i % (int(arguments.iterations / 4)) == 0:
-        sendMail(str(i) + ' iterations done', body='up-to-date average: {}%'.format(np.mean(metascore)))
-
 if arguments.csv:
     file = open("resources/csv/{}.csv".format('_'.join(arguments.properties).lower()), "w")
     for entry in metascore:
@@ -142,7 +136,6 @@ logging.info("Fisherface training finished - {}% average\n".format(np.mean(metas
 
 if not arguments.test:
     fishface.write('resources/models/detection_model.xml')
-    logging.info('saved trained classifier')
 
 if arguments.email:
-    sendMail('Fisherface training finished', filepath='resources/models/detection_model.xml')
+    sendMail('Fisherface training finished', body=str(arguments), filepath='resources/models/detection_model.xml')

projectmood/webcam.py

@@ -11,9 +11,17 @@ from face_detect import extract_faces
 from image_commons import nparray_as_image, draw_with_alpha, draw_img
 
 parser = argparse.ArgumentParser(description='ProjectMood Emotion Detection')
+parser.add_argument('-0', action='append_const', dest='emotions', const='neutral', help='neutral')
+parser.add_argument('-1', action='append_const', dest='emotions', const='happy', help='happy')
+parser.add_argument('-2', action='append_const', dest='emotions', const='sadness', help='sadness')
+parser.add_argument('-3', action='append_const', dest='emotions', const='surprise', help='surprise')
+parser.add_argument('-4', action='append_const', dest='emotions', const='fear', help='fear')
+parser.add_argument('-5', action='append_const', dest='emotions', const='disgust', help='disgust')
+parser.add_argument('-6', action='append_const', dest='emotions', const='anger', help='anger')
 parser.add_argument('-b', '--buffer', action='store', dest='buffer', default=12, type=int, help='size of ringbuffer')
 parser.add_argument('-m', '--model', action='store', dest='model', default='resources/models/detection_model.xml',
                     help='path to model')
+parser.add_argument('-r', '--resize', action='store', type=int, dest='resizefactor')
 arguments = parser.parse_args()
 
 
@@ -26,7 +34,6 @@ def _load_emoticons(emotions):
     return [nparray_as_image(cv2.imread('resources/emojis/{}.png'.format(emotion), -1), mode=None) for emotion in
             emotions]
 
-
 def show_webcam_and_run(model, emoticons, window_size=(600, 600), window_name=parser.description, update_time=1):
     """
     Shows a webcam image, recognizes faces and emotions in real time and draws emoticons next to the faces.
@@ -42,9 +49,12 @@ def show_webcam_and_run(model, emoticons, window_size=(600, 600), window_name=pa
 
     vc = WebcamVideoStream().start()
 
-    # a random image from the dataset to determine the image format (important for Fisherface)
-    random = cv2.imread('resources/img_data/dataset/{}/0.jpg'.format(emotions[0]))
-    resizefactor = np.size(random, 0)
+    if not arguments.resizefactor:
+        # a random image from the dataset to determine the image format (important for Fisherface)
+        random = cv2.imread('resources/img_data/dataset/{}/0.jpg'.format(arguments.emotions[0]))
+        resizefactor = np.size(random, 0)
+    else:
+        resizefactor = arguments.resizefactor
 
     # The RingBuffer stores the last x Predictions
     buffer = RingBuffer(arguments.buffer)
@@ -61,6 +71,8 @@ def show_webcam_and_run(model, emoticons, window_size=(600, 600), window_name=pa
             # Get the entries as an array
             predictions = buffer.get()
 
+            print(predictions)
+
             # Read the processed input image
             processed_image = nparray_as_image(normalized_face[:, :], mode='L')
             w, h = vc.size()
@@ -89,10 +101,7 @@ def show_webcam_and_run(model, emoticons, window_size=(600, 600), window_name=pa
 
 
 if __name__ == '__main__':
-    # The emotions in the Dataset folder should also be loaded in the application
-    _, emotions, _ = next(os.walk('resources/img_data/dataset'), (None, [], None))
-
-    emoticons = _load_emoticons(emotions)
+    emoticons = _load_emoticons(arguments.emotions)
 
     fisher_face = cv2.face.FisherFaceRecognizer_create()