N bissl Aufgeräumt

projectmood/cvhelper.py

 import cv2
 
-def createwindow(name, view, x=70, y=70, width=700, height=700):
+
+def createwindow(name: str, view, x=70, y=70, width=700, height=700):
     cv2.namedWindow(name, cv2.WINDOW_NORMAL)
     cv2.resizeWindow(name, width, height)
     cv2.moveWindow(name, x, y)
-    cv2.imshow(name, view)
+    return cv2.imshow(name, view)
\ No newline at end of file

projectmood/main.py

 import cv2
 import sys
 import dlib
+import numpy as np
 
-from projectmood import cvhelper
-
+import cvhelper
 
 def main():
-    # Set up some required objects
-    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
-
-    if len(sys.argv) > 1:
-        cap = cv2.VideoCapture(str(sys.argv[1]))
-    else:
+    # 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):
-        # Capture frame-by-frame
-        ret, frame = cap.read()
+    while True:
 
-        # Our operations on the frame come here
-        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+        # Read the Video
+        ret, img = cap.read()
 
-        clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
-        clahe_image = clahe.apply(gray)
-        faces = detectors.detectMultiScale(gray, 1.3, 5)
+        # convert the video to gray scale
+        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
 
-        detections = detector(clahe_image, 1)  # Detect the faces in the image
+        # Contrast Limited Adaptive Histogram Equalization
+        clahe = cv2.createCLAHE(clipLimit=4.0, tileGridSize=(8, 8))
+        clahe_image = clahe.apply(gray)
 
-        for k, d in enumerate(detections):  # For each detected face
+        # Detect faces in the video
+        faces = detector.detectMultiScale(
+            clahe_image,
+            scaleFactor=1.05,
+            minNeighbors=5,
+            minSize=(100, 100),
+        )
 
-            shape = predictor(clahe_image, d)  # Get coordinates
-            for i in range(1, 68):  # There are 68 landmark points on each face
-                cv2.circle(gray, (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
+        # Draw a rectangle around the faces
         for (x, y, w, h) in faces:
-                    cv2.rectangle(gray, (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.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))
 
-        cvhelper.createwindow('Grayscale', gray)
+            # 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
-    # When everything done, release the capture
+
     cap.release()
     cv2.destroyAllWindows()
 
+
 if __name__ == '__main__':
     main()
\ No newline at end of file

setup.py

@@ -8,8 +8,6 @@ setup(
     version='0.1',
     packages=['projectmood'],
     license='Carl-von-Ossietzky Universitaet',
-    author='tjadoihmels',
-    author_email='tjado.ihmels@uni-oldenburg.de',
     long_description=long_description,
     platforms='any',
     description='Christina Tsiroglou, Arne Gerdes, Tjado Ihmels',