Repo aufgeräumt, alles mindestens einmal verschoben, aus Prinzip -

Email Service eingebaut, um bei der Massenverarbeitung Logdateien schicken zu können.
Und ArgumentParser, bisher nur bei sorted_set_facedetector implementiert, um Programm mit Parametern über Konsole zu starten. Biaaaatchesssss <3

.gitignore

@@ -20,10 +20,10 @@ Icon
 .Trashes
 .VolumeIcon.icns
 .com.apple.timemachine.donotpresent
-basis_data/dataset/*
-basis_data/sorted_set/*
-basis_data/source_emotion/*
-basis_data/source_images/*
+projectmood/img_data/dataset/*
+projectmood/img_data/sorted_set/*
+projectmood/img_data/source_emotion/*
+projectmood/img_data/source_images/*
 
 # Directories potentially created on remote AFP share
 .AppleDB

projectmood/archive/FaceDetectionAndLandmark

-# Import required modules
-import cv2
-import dlib
-
-# Set up some required objects
-video_capture = cv2.VideoCapture(0)  # Webcam object
-detector = dlib.get_frontal_face_detector()  # Face detector
-detectors = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
-predictor = dlib.shape_predictor(
-    "shape_predictor_68_face_landmarks.dat")  # Landmark identifier. Set the filename to whatever you named the downloaded file
-
-
-while True:
-    ret, frame = video_capture.read()
-    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
-    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
-    clahe_image = clahe.apply(gray)
-    faces = detectors.detectMultiScale(gray, 1.3, 5)
-
-    detections = detector(clahe_image, 1)  # Detect the faces in the image
-
-    for k, d in enumerate(detections):  # For each detected face
-
-        shape = predictor(clahe_image, d)  # Get coordinates
-        for i in range(1, 68):  # There are 68 landmark points on each face
-            cv2.circle(frame, (shape.part(i).x, shape.part(i).y), 1, (0, 0, 255),
-                       thickness=2)  # For each point, draw a red circle with thickness2 on the original frame
-            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_color = frame[y:y + h, x:x + w]
-
-
-    cv2.imshow("image", frame)  # Display the frame
-
-    if cv2.waitKey(1) & 0xFF == ord('q'):  # Exit program when the user presses 'q'
-        break

projectmood/archive/cvhelper.py

-import cv2
-
-
-def createwindow(name, view, x=70, y=70, w=150, h=150):
-    cv2.namedWindow(name, cv2.WINDOW_NORMAL)
-    cv2.resizeWindow(name, w, h)
-    cv2.moveWindow(name, x, y)
-    cv2.imshow(name, view)
\ No newline at end of file

projectmood/archive/experiments.py

-import cv2
-import numpy as np
-from matplotlib import pyplot as plt
-
-from archive import cvhelper
-
-original = cv2.imread('resources/experiments/trump.jpg')
-cvhelper.createwindow("Original", original, 0, 0)
-
-gray = cv2.cvtColor(original, cv2.COLOR_BGR2GRAY)
-hsv = cv2.cvtColor(original, cv2.COLOR_BGR2HSV)
-ret,thresh1 = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
-
-cvhelper.createwindow("Gray", gray, 100)
-cvhelper.createwindow("HSV", hsv, 200)
-cvhelper.createwindow("Threshold", thresh1, 300)
-
-plt.hist(gray.ravel(), 256, [0,256])
-plt.show()
-
-
-k = cv2.waitKey(0)
-if k == cv2.waitKey(0) & 0xFF:
-    cv2.destroyAllWindows()
\ No newline at end of file

projectmood/archive/main.py

-import cv2
-import dlib
-import numpy as np
-
-from archive import cvhelper
-
-
-def main():
-    # Create the haar cascade
-    detector = cv2.CascadeClassifier('resources/haarcascade_frontalface_default.xml')
-
-    # Create the landmark predictor
-    predictor = dlib.shape_predictor("resources/shape_predictor_68_face_landmarks.dat")
-
-    cap = cv2.VideoCapture(0)
-
-    while True:
-
-        # Read the Video
-        ret, img = cap.read()
-
-        # convert the video to gray scale
-        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-
-        # Contrast Limited Adaptive Histogram Equalization
-        clahe = cv2.createCLAHE(clipLimit=4.0, tileGridSize=(8, 8))
-        clahe_image = clahe.apply(gray)
-
-        # Detect faces in the video
-        faces = detector.detectMultiScale(
-            clahe_image,
-            scaleFactor=1.05,
-            minNeighbors=5,
-            minSize=(100, 100),
-        )
-
-        # Draw a rectangle around the faces
-        for (x, y, w, h) in faces:
-            cv2.rectangle(clahe_image, (x, y), (x + w, y + h), (255, 0, 0), 2)
-
-            # Converting the OpenCV rectangle coordinates to Dlib rectangle
-            dlib_rect = dlib.rectangle(int(x), int(y), int(x + w), int(y + h))
-
-            # use that rectangle as the bounding box to detect the face landmarks,
-            # and extract out the coordinates of the landmarks so OpenCV can use them
-            detected_landmarks = predictor(clahe_image, dlib_rect).parts()
-            landmarks = np.matrix([[p.x, p.y] for p in detected_landmarks])
-
-            # enumerate through the landmark coordinates and mark them on the image
-            for idx, point in enumerate(landmarks):
-                pos = (point[0, 0], point[0, 1])
-
-                # annotate the positions
-                cv2.putText(clahe_image, str(idx), pos,
-                            fontFace=cv2.FONT_HERSHEY_SIMPLEX,
-                            fontScale=0.4,
-                            color=(0, 0, 255))
-
-                # draw points on the landmark positions
-                cv2.circle(clahe_image, pos, 3, color=(0, 255, 255))
-
-        # draw the annotated image on an OpenCV window
-        cvhelper.createwindow('Mood', clahe_image)
-
-        if cv2.waitKey(1) & 0xFF == ord('q'):
-            break
-
-    cap.release()
-    cv2.destroyAllWindows()
-
-
-if __name__ == '__main__':
-    main()
\ No newline at end of file

projectmood/classifier/master/classifier.py

-# The Emotion Face detection Scripts
-# You can modify this script as you wish
-import cv2
-import glob as gb
-import random
-import numpy as np
-
-# Emotion list
-emojis = ["neutral", "anger", "contempt", "disgust", "fear", "happy", "sadness", "surprise"]
-# Initialize fisher face classifier
-fisher_face = cv2.createFisherFaceRecognizer()
-data = {}
-
-
-# Function defination to get file list, randomly shuffle it and split 67/33
-def getFiles(emotion):
-    files = gb.glob("final_dataset\\%s\\*" % emotion)
-    random.shuffle(files)
-    training = files[:int(len(files) * 0.67)]  # get first 67% of file list
-    prediction = files[-int(len(files) * 0.33):]  # get last 33% of file list
-    return training, prediction
-
-
-def makeTrainingAndValidationSet():
-    training_data = []
-    training_labels = []
-    prediction_data = []
-    prediction_labels = []
-    for emotion in emojis:
-        training, prediction = getFiles(emotion)
-        # Append data to training and prediction list, and generate labels 0-7
-        for item in training:
-            image = cv2.imread(item)  # open image
-            gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)  # convert to grayscale
-            training_data.append(gray)  # append image array to training data list
-            training_labels.append(emojis.index(emotion))
-
-        for item in prediction:  # repeat above process for prediction set
-            image = cv2.imread(item)
-            gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-            prediction_data.append(gray)
-            prediction_labels.append(emojis.index(emotion))
-    return training_data, training_labels, prediction_data, prediction_labels
-
-
-def runClassifier():
-    training_data, training_labels, prediction_data, prediction_labels = makeTrainingAndValidationSet()
-
-    print("training fisher face classifier suing the training data")
-    print("size of training set is:", len(training_labels), "images")
-    fisher_face.train(training_data, np.asarray(training_labels))
-    print("classification prediction")
-    counter = 0
-    right = 0
-    wrong = 0
-    for image in prediction_data:
-        pred, conf = fisher_face.predict(image)
-        if pred == prediction_labels[counter]:
-            right += 1
-            counter += 1
-        else:
-            wrong += 1
-            counter += 1
-    return (100 * right) / (right + wrong)
-
-
-# Now run the classifier
-metascore = []
-for i in range(0, 10):
-    right = runClassifier()
-    print("got", right, "percent right!")
-    metascore.append(right)
-print("\n\nend score:", np.mean(metascore), "percent right!")
\ No newline at end of file

projectmood/classifier/master/face_detection.py

-import cv2
-import glob as gb
-
-face_detector1 = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
-face_detector2 = cv2.CascadeClassifier("haarcascade_frontalface_alt2.xml")
-face_detector3 = cv2.CascadeClassifier("haarcascade_frontalface_alt.xml")
-face_detector4 = cv2.CascadeClassifier("haarcascade_frontalface_alt_tree.xml")
-emotion_list = ["neutral", "anger", "contempt", "disgust", "fear", "happy", "sadness", "surprise"]
-
-
-def faceDetection(emotion):
-    files = gb.glob("selected_set\\%s\\*" % emotion)  # Get list of all images with emotion
-    filenumber = 0
-    for f in files:
-        frame = cv2.imread(f)  # Open image
-        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)  # Convert image to grayscale
-
-        # Detect face using 4 different classifiers
-        face1 = face_detector1.detectMultiScale(gray, scaleFactor=1.1,
-                                                minNeighbors=10, minSize=(5, 5), flags=cv2.CASCADE_SCALE_IMAGE)
-        face2 = face_detector2.detectMultiScale(gray, scaleFactor=1.1,
-                                                minNeighbors=10, minSize=(5, 5), flags=cv2.CASCADE_SCALE_IMAGE)
-        face3 = face_detector3.detectMultiScale(gray, scaleFactor=1.1,
-                                                minNeighbors=10, minSize=(5, 5), flags=cv2.CASCADE_SCALE_IMAGE)
-        face4 = face_detector4.detectMultiScale(gray, scaleFactor=1.1,
-                                                minNeighbors=10, minSize=(5, 5), flags=cv2.CASCADE_SCALE_IMAGE)
-        # Go over detected faces, stop at first detected face, return empty if no face.
-        if len(face1) == 1:
-            facefeatures = face1
-        elif len(face2) == 1:
-            facefeatures == face2
-        elif len(face3) == 1:
-            facefeatures = face3
-        elif len(face4) == 1:
-            facefeatures = face4
-        else:
-            facefeatures = ""
-
-        # Cut and save face
-        for (x, y, w, h) in facefeatures:  # get coordinates and size of rectangle containing face
-            print("face found in file: %s" % f)
-            gray = gray[y:y + h, x:x + w]  # Cut the frame to size
-
-            try:
-                out = cv2.resize(gray, (350, 350))  # Resize face so all images have same size
-                cv2.imwrite("final_dataset\\%s\\%s.jpg" % (emotion, filenumber), out)  # Write image
-            except:
-                pass  # pass the file on error
-        filenumber += 1  # Increment image number
-
-
-if __name__ == '__main__':
-    for emotion in emotion_list:
-        faceDetection(emotion)  # Call our face detection module

projectmood/classifier/master/img_seq.py

-import glob as gb
-from shutil import copyfile
-
-emotions_list = ["neutral", "anger", "contempt", "disgust", "fear", "happy", "sadness", "surprise"]
-emotions_folders = gb.glob("emotions\\*")  # Returns a list of all folders with participant numbers
-
-
-def imageWithEmotionEtraction():
-    for x in emotions_folders:
-        participant = "%s" % x[-4:]  # store current participant number
-        for sessions in gb.glob("%s\\*" % x):
-            for files in gb.glob("%s\\*" % sessions):
-                current_session = files[20:-30]
-                file = open(files, 'r')
-
-                emotion = int(float(file.readline()))
-                # get path for last image in sequence, which contains the emotion
-                sourcefile_emotion = gb.glob("images\\%s\\%s\\*" % (participant, current_session))[-1]
-                # do same for neutral image
-                sourcefile_neutral = gb.glob("images\\%s\\%s\\*" % (participant, current_session))[0]
-                # Generate path to put neutral image
-                dest_neut = "selected_set\\neutral\\%s" % sourcefile_neutral[25:]
-                # Do same for emotion containing image
-                dest_emot = "selected_set\\%s\\%s" % (emotions_list[emotion], sourcefile_emotion[25:])
-
-                copyfile(sourcefile_neutral, dest_neut)  # Copy file
-                copyfile(sourcefile_emotion, dest_emot)  # Copy file
-
-
-if __name__ == '__main__':
-    imageWithEmotionEtraction()
\ No newline at end of file

projectmood/email_service.py

+import smtplib
+from email.mime.multipart import MIMEMultipart
+from email.mime.text import MIMEText
+from email.mime.base import MIMEBase
+from email import encoders
+import os
+
+def sendMail(subject, to = 'tjado.ihmels@uni-oldenburg.de', body = '', filepath=''):
+    fromaddr = "projectmood18@gmail.com"
+
+    msg = MIMEMultipart()
+
+    msg['From'] = fromaddr
+    msg['To'] = to
+    msg['Subject'] = subject
+
+    msg.attach(MIMEText(body, 'plain'))
+
+    if filepath:
+        filename = os.path.basename(filepath)
+        attachment = open(filepath, "rb")
+        part = MIMEBase('application', 'octet-stream')
+        part.set_payload((attachment).read())
+        encoders.encode_base64(part)
+        part.add_header('Content-Disposition', "attachment; filename= %s" % filename)
+
+        msg.attach(part)
+
+    server = smtplib.SMTP('smtp.gmail.com', 587)
+    server.starttls()
+    server.login(fromaddr, "kR2-zgY-BTf-gRT")
+    text = msg.as_string()
+    server.sendmail(fromaddr, to, text)
+    server.quit()
+
+
+sendMail('logs/sorted_set_facedetector.log', body='TESTX')
\ No newline at end of file

projectmood/face_detect.py

-"""
-This module contains face detections functions.
-"""
 import cv2
 
-faceDet_one = cv2.CascadeClassifier('resources/Haarcascade/haarcascade_frontalface_default.xml')
-faceDet_two = cv2.CascadeClassifier('resources/Haarcascade/haarcascade_frontalface_alt2.xml')
-faceDet_three = cv2.CascadeClassifier('resources/Haarcascade/haarcascade_frontalface_alt.xml')
-faceDet_four = cv2.CascadeClassifier('resources/Haarcascade/haarcascade_frontalface_alt_tree.xml')
+faceDet_one = cv2.CascadeClassifier('resources/haarcascade/haarcascade_frontalface_default.xml')
+faceDet_two = cv2.CascadeClassifier('resources/haarcascade/haarcascade_frontalface_alt2.xml')
+faceDet_three = cv2.CascadeClassifier('resources/haarcascade/haarcascade_frontalface_alt.xml')
+faceDet_four = cv2.CascadeClassifier('resources/haarcascade/haarcascade_frontalface_alt_tree.xml')
 
 
+def extract_faces(image):
+    faces = find_faces(image)
+    normalized_faces = [preprocess_face(face) for face in faces]
+    return normalized_faces
+
 def find_faces(image):
     faces_coordinates = locate_faces(image)
     cutted_faces = [image[y:y + h, x:x + w] for (x, y, w, h) in faces_coordinates]
-    normalized_faces = [normalize_face(face) for face in cutted_faces]
-    return zip(normalized_faces, faces_coordinates)
+    return cutted_faces
 
 
-def normalize_face(face):
+def preprocess_face(face):
     face = cv2.cvtColor(face, cv2.COLOR_BGR2GRAY)
     face = cv2.resize(face, (250, 250))
+    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
+    face = clahe.apply(face)
     return face
 
 
-def locate_faces(image, scaleFactor=1.4, minNeighbors=3, minSize=(20, 20)):
-    minx, miny = minSize
+def locate_faces(image, scaleFactor=1.2, minNeighbors=6, minSize=(100, 100)):
 
-    face = faceDet_one.detectMultiScale(image, scaleFactor, minNeighbors, minSize=(minx, miny),
+    face = faceDet_one.detectMultiScale(image, scaleFactor, minNeighbors, minSize=minSize,
                                         flags=cv2.CASCADE_SCALE_IMAGE)
 
     if len(face) == 1:
         return face
 
-    face = faceDet_two.detectMultiScale(image, scaleFactor, minNeighbors, minSize=(minx, miny),
+    face = faceDet_two.detectMultiScale(image, scaleFactor, minNeighbors, minSize=minSize,
                                             flags=cv2.CASCADE_SCALE_IMAGE)
 
     if len(face) == 1:
         return face
 
-    face = faceDet_three.detectMultiScale(image, scaleFactor, minNeighbors, minSize=(minx, miny),
+    face = faceDet_three.detectMultiScale(image, scaleFactor, minNeighbors, minSize=minSize,
                                                 flags=cv2.CASCADE_SCALE_IMAGE)
 
     if len(face) == 1:
         return face
 
-    face = faceDet_four.detectMultiScale(image, scaleFactor, minNeighbors, minSize=(minx, miny),
+    face = faceDet_four.detectMultiScale(image, scaleFactor, minNeighbors, minSize=minSize,
                                               flags=cv2.CASCADE_SCALE_IMAGE)
 
     if len(face) == 1:

projectmood/image_sort.py

@@ -14,7 +14,7 @@ emotions = ["neutral", "anger", "contempt", "disgust", "fear", "happy", "sadness
 """
 Liste mit den Dateiordnern aller Teilnehmer 
 """
-participants = glob.glob("basis_data/source_emotion/*")
+participants = glob.glob("img_data/source_emotion/*")
 
 for x in participants:
 
@@ -39,7 +39,7 @@ for x in participants:
             """
             emotion = int(float(file.readline()))
 
-            source_emotions = glob.glob("basis_data/source_images/%s/%s/*.png" % (number, current_session))
+            source_emotions = glob.glob("img_data/source_images/%s/%s/*.png" % (number, current_session))
             source_emotions.sort()
 
             """
@@ -58,8 +58,8 @@ for x in participants:
             Für den neutralen Ausdruck 
             Für die Emotion 
             """
-            dest_neut = "basis_data/sorted_set/neutral/%s" % sourcefile_neutral[36:]
-            dest_emot = "basis_data/sorted_set/%s/%s" % (emotions[emotion], sourcefile_emotion[36:])
+            dest_neut = "img_data/sorted_set/neutral/%s" % sourcefile_neutral[36:]
+            dest_emot = "img_data/sorted_set/%s/%s" % (emotions[emotion], sourcefile_emotion[36:])
 
             """Kopiert Dateien"""
             copyfile(sourcefile_neutral, dest_neut)

projectmood/process_model.py

@@ -31,7 +31,7 @@ def _get_faces_from_emotion(emotion):
     :param emotion: Die Emotion
     :return: training, prediction
     """
-    files = glob.glob('basis_data/dataset/{}/*'.format(emotion))
+    files = glob.glob('img_data/dataset/{}/*'.format(emotion))
     random.shuffle(files)
 
     """
@@ -51,8 +51,9 @@ def image_preprocessing(image):
     """
     img = cv2.imread(image)  # open image
     gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)  # convert to grayscale
-    blur = cv2.GaussianBlur(gray, (5, 5), 0)
-    return blur
+    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
+    face = clahe.apply(gray)
+    return face
 
 
 def make_sets():
@@ -108,7 +109,7 @@ fishface = cv2.face.FisherFaceRecognizer_create()
 
 metascore = []
 
-for i in range(0, 10):
+for i in range(0, 40):
     correct = run_recognizer()
     file.write("{}\n".format(int(correct)))
     logging.debug("{} : {}%".format(i, int(correct)))
@@ -117,4 +118,4 @@ for i in range(0, 10):
 file.close()
 
 logging.debug("{} iterations - {}% average\n".format(len(metascore), np.mean(metascore)))
-fishface.write('basis_data/models/detection_model.xml')
+fishface.write('img_data/models/detection_model.xml')

projectmood/sorted_set_facedetector.py

 import cv2
 import glob
 import logging
+import argparse
+import shutil
+import sys
+
+import os
+
 from face_detect import locate_faces
 
 logging.basicConfig(level=logging.DEBUG, format='%(asctime)s %(name)-12s %(levelname)-8s %(message)s',
@@ -9,60 +15,91 @@ logging.basicConfig(level=logging.DEBUG, format='%(asctime)s %(name)-12s %(level
                     filemode='w')
 
 """
-Emotionen die verarbeitet werden sollen 
+Argument Parser erlaubt Parameter für die Verarbeitung anzugeben.
 """
-emotions = ["happy", "neutral", "surprise"]
+
+parser = argparse.ArgumentParser(description='Sorted Set Face Creator Application')
+parser.add_argument('-s', action='store', dest='img_source', default='resources/img_data/sorted_set/',
+                    help='Pfad zu den Bilddateien')
+parser.add_argument('-b', action='store', dest='dataset', default='resources/img_data/dataset/', help='Pfad zum Dataset')
+parser.add_argument('-e', action='append', dest='emotions', default=['happy', 'neutral', 'surprised'],
+                    help='Emotionen die verarbeitet werden sollen')
+parser.add_argument('-r', action='store', dest='resize', default=150, type=int, help='Resize Factor')
+parser.add_argument('-f', action='store', dest='scaleFactor', default=1.1, type=float,
+                    help='Skalierungsfaktor für Haarcascade')
+parser.add_argument('-n', action='store', dest='minNeighbors', default=6, type=int, help='MinNeighbors für Haarcascade')
+parser.add_argument('-m', action='store', dest='minSize', default=40, type=int, help='MinSize für Haarcascade')
+arguments = parser.parse_args()
+logging.debug(arguments)
+
+datasetPath = arguments.dataset
+
+if len(glob.glob(datasetPath + '*')) > 0:
+    deleteDataset = input(
+        'Im Dataset befinden sich Dateien. Durch diesen Prozess werden die existierenden Daten gelöscht. Fortfahren (y/n): ')
+
+    if deleteDataset == 'y':
+        for file in glob.glob(datasetPath + '*'):
+            shutil.rmtree(file)
+    else:
+        sys.exit()
 
 totalFiles: int = 0
 totalFaces: int = 0
 undetected: list = []
+img_source = arguments.img_source
 
 
 def detect_faces(emotion):
     """
     Holt alle Dateien zu einer Emotion aus dem sorted_set
     """
-    files = glob.glob('basis_data/sorted_set/{}/*'.format(emotion))
+    files = glob.glob(img_source + '{}/*'.format(emotion))
 
     global undetected
     global totalFaces
     global totalFiles
 
-    logging.debug("{} files in folder {}".format(len(files), emotion))
+    logging.debug("found {} {} files".format(len(files), emotion))
 
     fileNumber = 0
     for f in files:
         frame = cv2.imread(f)  # Open image
         gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)  # Convert image to grayscale