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projectmood/datasets/affectnet.py

@@ -25,64 +25,78 @@ parser.add_argument('-a', '--arousal', action='store', default='-2', dest='arous
 arguments = parser.parse_args()
 logging.debug(arguments)
 
-emotidict = {0: 'neutral', 1: 'happy', 2: 'sadness', 3: 'surprise', 4: 'fear', 5: 'disgust', 6: 'anger', 7: 'contempt',
-             8: 'none', 9: 'uncertain', 10: 'noface'}
+# the numbers define the particular emotions in the .csv file
+emotiondict = {0: 'neutral', 1: 'happy', 2: 'sadness', 3: 'surprise', 4: 'fear', 5: 'disgust', 6: 'anger', 7: 'contempt',
+               8: 'none', 9: 'uncertain', 10: 'noface'}
 
-emoticounter = {}
-for e in arguments.emotions:
-    emoticounter[e] = 0
-
-curdir = os.path.abspath(os.path.dirname(__file__))
-csv = os.path.join(curdir, arguments.csv)
-source = os.path.join(curdir, arguments.source)
-destination = os.path.join(curdir, arguments.destination)
+# count emotions
+emotioncounter = {}
+for emotion in arguments.emotions:
+    emotioncounter[emotion] = 0
 
+# define some useful paths
+currentdir = os.path.abspath(os.path.dirname(__file__))
+csv = os.path.join(currentdir, arguments.csv)
+source = os.path.join(currentdir, arguments.source)
+destination = os.path.join(currentdir, arguments.destination)
 
 def generate_records(csvfile):
+    # read the .csv file
     data = pd.read_csv(csvfile, delimiter=',', dtype='a')
 
+    # get the necessary columns
     imagepaths = pd.np.array(data['subDirectory_filePath'], pd.np.str)
     labels = pd.np.array(data['expression'], pd.np.int)
     valence = pd.np.array(data['valence'], pd.np.float)
     arousal = pd.np.array(data['arousal'], pd.np.float)
 
-    for e in arguments.emotions:
-        dest = os.path.join(destination, e)
-        if not os.path.exists(dest):
-            os.makedirs(dest)
+    for emotion in arguments.emotions:
+        # join the destination path with the emotion identifier and create it if it isn't..
+        dest_subfolder = os.path.join(destination, emotion)
+        if not os.path.exists(dest_subfolder):
+            os.makedirs(dest_subfolder)
         else:
-            emoticounter[e] = len(os.listdir(dest))
+            # .. otherwise, count images
+            emotioncounter[emotion] = len(os.listdir(dest_subfolder))
 
-    data = zip(labels, imagepaths, valence, arousal)
+    # wrap tupels for iterating
+    csvrows = zip(labels, imagepaths, valence, arousal)
 
-    for d in data:
-        emotion = emotidict.get(d[0])
+    for c in csvrows:
+        # get emotion
+        emotion = emotiondict.get(c[0])
 
-        if emotion not in arguments.emotions or d[2] < arguments.valence or d[3] < arguments.arousal or emoticounter[
+        # check if the row fits the argument conditions
+        if emotion not in arguments.emotions or c[2] < arguments.valence or c[3] < arguments.arousal or emotioncounter[
             emotion] >= arguments.limit:
             continue
 
-        img = d[1]
+        # the image path
+        img = c[1]
         pathfrom = os.path.join(source, img)
 
+        # check if the respective file exists
         if not os.path.exists(pathfrom):
             logging.info(img + ' not found')
             continue
 
-        folder = os.path.join(destination, emotion)
-        filePath = os.path.join(folder, 'V' + str(round(d[2], 1)) + 'A' + str(round(d[3], 1)) + '_' + img.split('/')[1])
+        dest_subfolder = os.path.join(destination, emotion)
+        # join destination path with filename
+        filePath = os.path.join(dest_subfolder, img.split('/')[1])
 
+        # if not exists, copy file to destination
         if not os.path.isfile(filePath):
             copyfile(pathfrom, filePath)
             logging.info('wrote file ' + filePath)
-            emoticounter[emotion] += 1
+            emotioncounter[emotion] += 1
 
-        if all(count >= arguments.limit for count in emoticounter.values()):
+        # break if enough files were copied
+        if all(count >= arguments.limit for count in emotioncounter.values()):
             break
 
 
 if __name__ == '__main__':
     generate_records(csv)
 
-    logging.info('finish')
-    logging.info(emoticounter)
+    print('finish')
+    print(emotioncounter)

projectmood/datasets/cohn_kanade.py

 """
-Diese Klasse sortiert die source_images anhand der source_emotions in die Ordner des sorted_set ein
+This script maps the source_emotions to the source_images and creates a sorted set
 """
 
 import glob
 from shutil import copyfile
 
-"""
-Array der Emotionen
-Gibt die Reihenfolge der Emotionen an (Labels von 0-7)
-"""
 emotions = ["neutral", "anger", "contempt", "disgust", "fear", "happy", "sadness", "surprise"]
 
-"""
-Liste mit den Dateiordnern aller Teilnehmer 
-"""
+# returns a list with each subfolder representing a participant number
 participants = glob.glob("../resources/img_data/source_emotion/*")
 
 for x in participants:
 
-    """
-    Teilnehmernummer 
-    """
+    # participant number
     number = "%s" % x[-4:]
     for sessions in glob.glob("%s/*" % x):
         for files in glob.glob("%s/*" % sessions):
-            """
-            Sessionnummer
-            """
+
+            # session number
             current_session = files[39:-30]
 
-            """
-            Öffnet die zur aktuellen Emotion korrelierende .txt Datei
-            """
+            # opens the correlating .txt file
             file = open(files, 'r')
 
-            """
-            In der Datei steht die aktuell betrachtete Emotion, kodiert als float-Wert 
-            """
+            # the respective emotion is encoded as a float value
             emotion = int(float(file.readline()))
 
-            source_emotions = glob.glob("../resources/img_data/source_images/%s/%s/*.png" % (number, current_session))
-            source_emotions.sort()
+            # get a sorted list with the image sequence from the considered session
+            source_images = glob.glob("../resources/img_data/source_images/%s/%s/*.png" % (number, current_session))
+            source_images.sort()
 
-            """
-            Das letzte Bild einer Sequenz ist eine ausgeprägte Emotion
-            """
-            sourcefile_emotion = source_emotions[-1]
+            # the last image is the distinct emotion
+            sourcefile_emotion = source_images[-1]
 
-            """
-            Das erste Bild ist ein neutraler Ausdruck
-            """
-            sourcefile_neutral = source_emotions[0]
+            # the first image is the neutral expression
+            sourcefile_neutral = source_images[0]
 
-            """
-            Erstellt neue Pfade zum einsortieren
-            
-            Für den neutralen Ausdruck 
-            Für die Emotion 
-            """
-            dest_neut = "../resources/img_data/sorted_set/neutral/%s" % sourcefile_neutral[44:]
-            dest_emot = "../resources/img_data/sorted_set/%s/%s" % (emotions[emotion], sourcefile_emotion[44:])
+            # create new paths
+            destination_neutral = "../resources/img_data/sorted_set/neutral/%s" % sourcefile_neutral[44:]
+            destination_emotion = "../resources/img_data/sorted_set/%s/%s" % (emotions[emotion], sourcefile_emotion[44:])
 
-            """Kopiert Dateien"""
-            copyfile(sourcefile_neutral, dest_neut)
-            copyfile(sourcefile_emotion, dest_emot)
+            # copy files
+            copyfile(sourcefile_neutral, destination_neutral)
+            copyfile(sourcefile_emotion, destination_emotion)

projectmood/datasets/fer2013.py

@@ -5,29 +5,36 @@ import cv2
 import numpy as np
 import pandas as pd
 
-curdir = os.path.abspath(os.path.dirname(__file__))
+currentdir = os.path.abspath(os.path.dirname(__file__))
 
 
-def gen_record(csvfile, channel):
+def gen_record(csvfile):
+    # read the .csv file
     data = pd.read_csv(csvfile, delimiter=',', dtype='a')
+    # the emotion labels encoded as floats
     labels = np.array(data['emotion'], np.float)
-    # print(labels,'\n',data['emotion'])
 
+    # the images are stored as pixel values
     imagebuffer = np.array(data['pixels'])
+    # convert them to images
     images = np.array([np.fromstring(image, np.uint8, sep=' ') for image in imagebuffer])
-    del imagebuffer
     num_shape = int(np.sqrt(images.shape[-1]))
     images.shape = (images.shape[0], num_shape, num_shape)
-    # img=images[0];cv2.imshow('test',img);cv2.waitKey(0);cv2.destroyAllWindow();exit()
+
+    # creates a set from a total of three usage types
     dirs = set(data['Usage'])
     subdirs = set(labels)
+
+
     class_dir = {}
     for dr in dirs:
-        dest = os.path.join(curdir, dr)
+        # create destination directories
+        dest = os.path.join(currentdir, dr)
         class_dir[dr] = dest
         if not os.path.exists(dest):
             os.mkdir(dest)
 
+    # wrap data for iterating
     data = zip(labels, images, data['Usage'])
 
     for d in data:
@@ -35,8 +42,8 @@ def gen_record(csvfile, channel):
         if not os.path.exists(destdir):
             os.mkdir(destdir)
         img = d[1]
+        # create a unique name
         filepath = unique_name(destdir, d[-1])
-        print('[^_^] Write image to %s' % filepath)
         if not filepath:
             continue
         sig = cv2.imwrite(filepath, img)
@@ -55,9 +62,5 @@ def unique_name(pardir, prefix, suffix='jpg'):
 
 if __name__ == '__main__':
     filename = 'fer2013.csv'
-    filename = os.path.join(curdir, filename)
-    gen_record(filename, 1)
-
-    # ##################### test
-    # tmp = unique_name('./Training','Training')
-    # print(tmp)
+    filename = os.path.join(currentdir, filename)
+    gen_record(filename)

projectmood/process_model.py

@@ -142,6 +142,7 @@ 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')
+    sendMail('Fisherface training finished', filepath='resources/models/detection_model.xml')