Performance Tweaks vorgenommen

.gitignore

@@ -20,6 +20,7 @@ Icon
 .Trashes
 .VolumeIcon.icns
 .com.apple.timemachine.donotpresent
+basis_data/*
 
 # Directories potentially created on remote AFP share
 .AppleDB

projectmood/Erste_Versuche/experiments.py

-import cv2
-import cvhelper
-import numpy as np
-from matplotlib import pyplot as plt
-
-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/Erste_Versuche/main.py

-import cv2
-import sys
-import dlib
-import numpy as np
-
-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/experiments.py → projectmood/archive/experiments.py

 import cv2
-import cvhelper
 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)
 

projectmood/main.py → projectmood/archive/main.py

 import cv2
-import sys
 import dlib
 import numpy as np
 
-import cvhelper
+from archive import cvhelper
+
 
 def main():
     # Create the haar cascade

projectmood/cvhelper.py

-import cv2
-
-
-def createwindow(name, view, x=70, y=70, w=600, h=600):
-    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/face_detect.py

@@ -3,10 +3,10 @@ This module contains face detections functions.
 """
 import cv2
 
-faceDet = cv2.CascadeClassifier('Haarcascade/haarcascade_frontalface_default.xml')
-faceDet_two = cv2.CascadeClassifier('Haarcascade/haarcascade_frontalface_alt2.xml')
-faceDet_three = cv2.CascadeClassifier('Haarcascade/haarcascade_frontalface_alt.xml')
-faceDet_four = cv2.CascadeClassifier('Haarcascade/haarcascade_frontalface_alt_tree.xml')
+faceDet = 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 find_faces(image):

projectmood/image_sort.py

@@ -2,25 +2,25 @@ import glob
 from shutil import copyfile
 
 emotions = ["neutral", "anger", "contempt", "disgust", "fear", "happy", "sadness", "surprise"]  # Define emotion order
-participants = glob.glob("Basis_data\\source_emotion\\*")  # Returns a list of all folders with participant numbers
+participants = glob.glob("basis_data/source_emotion/*")  # Returns a list of all folders with participant numbers
 
 for x in participants:
     part = "%s" % x[-4:]  # store current participant number
-    for sessions in glob.glob("Basis_data\\%s\\*" % x):  # Store list of sessions for current participant
-        for files in glob.glob("Basis_data\\%s\\*" % sessions):
+    for sessions in glob.glob("basis_data/%s/*" % x):  # Store list of sessions for current participant
+        for files in glob.glob("basis_data/%s/*" % sessions):
             current_session = files[20:-30]
             file = open(files, 'r')
 
             emotion = int(
                 float(file.readline()))  # emotions are encoded as a float, readline as float, then convert to integer.
 
-            sourcefile_emotion = glob.glob("Basis_data\\source_images\\%s\\%s\\*" % (part, current_session))[
+            sourcefile_emotion = glob.glob("basis_data/source_images/%s/%s/*" % (part, current_session))[
                 -1]  # get path for last image in sequence, which contains the emotion
-            sourcefile_neutral = glob.glob("Basis_data\\source_images\\%s\\%s\\*" % (part, current_session))[
+            sourcefile_neutral = glob.glob("basis_data/source_images/%s/%s/*" % (part, current_session))[
                 0]  # do same for neutral image
 
-            dest_neut = "Basis_data\\sorted_set\\neutral\\%s" % sourcefile_neutral[25:]  # Generate path to put neutral image
-            dest_emot = "Basis_data\\sorted_set\\%s\\%s" % (
+            dest_neut = "basis_data/sorted_set/neutral/%s" % sourcefile_neutral[25:]  # Generate path to put neutral image
+            dest_emot = "basis_data/sorted_set/%s/%s" % (
             emotions[emotion], sourcefile_emotion[25:])  # Do same for emotion containing image
 
             copyfile(sourcefile_neutral, dest_neut)  # Copy file

projectmood/process_model.py

@@ -14,7 +14,7 @@ emotions = ["anger", "disgust", "happy", "neutral", "surprise"]  # Emotion list
 fishface = cv2.face.FisherFaceRecognizer_create()
 
 def get_files(emotion):  # Define function to get file list, randomly shuffle it and split 80/20
-    files = glob.glob('Basis_data/dataset/%s/*' % emotion)
+    files = glob.glob('basis_data/dataset/%s/*' % emotion)
     random.shuffle(files)
     training = files[:int(len(files) * 0.8)]  # get first 80% of file list
     prediction = files[-int(len(files) * 0.2):]  # get last 20% of file list
@@ -84,4 +84,4 @@ for idx, meta in enumerate(metascore):
 
 logging.debug("fisherface finished with {}%\n".format(np.mean(metascore)))
 
-fishface.write('Basis_data/models/detection_model.xml')
\ No newline at end of file
+fishface.write('basis_data/models/detection_model.xml')
\ No newline at end of file

projectmood/sorted_set_facedetector.py

@@ -2,10 +2,10 @@ import cv2
 import glob
 import logging
 
-faceDet = cv2.CascadeClassifier('Haarcascade/haarcascade_frontalface_default.xml')
-faceDet_two = cv2.CascadeClassifier('Haarcascade/haarcascade_frontalface_alt2.xml')
-faceDet_three = cv2.CascadeClassifier('Haarcascade/haarcascade_frontalface_alt.xml')
-faceDet_four = cv2.CascadeClassifier('Haarcascade/haarcascade_frontalface_alt_tree.xml')
+faceDet = 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')
 
 logging.basicConfig(level=logging.DEBUG, format='%(asctime)s %(name)-12s %(levelname)-8s %(message)s',
                     datefmt='%m-%d %H:%M',
@@ -20,7 +20,7 @@ undetected: list = []
 
 
 def detect_faces(emotion):
-    files = glob.glob('Basis_data/sorted_set/%s/*' % emotion)  # Get list of all images with emotion
+    files = glob.glob('basis_data/sorted_set/%s/*' % emotion)  # Get list of all images with emotion
 
     global undetected
     global totalFaces
@@ -33,13 +33,13 @@ def detect_faces(emotion):
         frame = cv2.imread(f)  # Open image
 
         # Detect face using 4 different classifiers
-        face = faceDet.detectMultiScale(frame, scaleFactor=1.05, minNeighbors=6, minSize=(10, 10),
+        face = faceDet.detectMultiScale(frame, scaleFactor=1.05, minNeighbors=10, minSize=(10, 10),
                                         flags=cv2.CASCADE_SCALE_IMAGE)
-        face_two = faceDet_two.detectMultiScale(frame, scaleFactor=1.05, minNeighbors=6, minSize=(10, 10),
+        face_two = faceDet_two.detectMultiScale(frame, scaleFactor=1.05, minNeighbors=10, minSize=(10, 10),
                                                 flags=cv2.CASCADE_SCALE_IMAGE)
-        face_three = faceDet_three.detectMultiScale(frame, scaleFactor=1.05, minNeighbors=6, minSize=(10, 10),
+        face_three = faceDet_three.detectMultiScale(frame, scaleFactor=1.05, minNeighbors=10, minSize=(10, 10),
                                                     flags=cv2.CASCADE_SCALE_IMAGE)
-        face_four = faceDet_four.detectMultiScale(frame, scaleFactor=1.05, minNeighbors=6, minSize=(10, 10),
+        face_four = faceDet_four.detectMultiScale(frame, scaleFactor=1.05, minNeighbors=10, minSize=(10, 10),
                                                   flags=cv2.CASCADE_SCALE_IMAGE)
 
         # Go over detected faces, stop at first detected face, return empty if no face.
@@ -63,19 +63,20 @@ def detect_faces(emotion):
 
             try:
                 out = cv2.resize(frame, (250, 250))  # Resize face so all images have same size
-                cv2.imwrite('Basis_data/dataset/%s/%s.jpg' % (emotion, fileNumber), out)  # Write image
+                cv2.imwrite('basis_data/dataset/%s/%s.jpg' % (emotion, fileNumber), out)  # Write image
             except:
                 pass  # If error, pass file
 
         totalFiles += 1  # Increment image number
         fileNumber += 1
+    logging.debug("end of set\n")
 
 
 for emotion in emotions:
     detect_faces(emotion)  # Call functional
-    logging.debug("\n")
 
 logging.debug("{} Gesichter in {} Dateien gefunden.".format(totalFaces, totalFiles))
-logging.debug("Kein Gesicht wurde gefunden in:")
+logging.debug("In {} Dateien wurde kein Gesicht gefunden:".format(totalFiles - totalFaces))
+
 for f in undetected:
     logging.debug(f)

projectmood/webcam.py

@@ -19,7 +19,7 @@ def _load_emoticons(emotions):
     :param emotions: Array of emotions names.
     :return: Array of emotions graphics.
     """
-    return [nparray_as_image(cv2.imread('Basis_data/graphics/%s.png' % emotion, -1), mode=None) for emotion in emotions]
+    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=None, window_name='webcam', update_time=10):
@@ -69,7 +69,7 @@ if __name__ == '__main__':
 
     # load mode
     fisher_face = cv2.face.FisherFaceRecognizer_create()
-    fisher_face.read('Basis_data/models/detection_model.xml')
+    fisher_face.read('basis_data/models/detection_model.xml')
     # use learnt model
     window_name = 'WEBCAM (press ESC to exit)'
 show_webcam_and_run(fisher_face, emoticons, window_size=(800, 800), window_name=window_name, update_time=1)