Histogram hinzugefügt

projectmood/Haarcascade

@@ -8,19 +8,23 @@ detector = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
 # create the landmark predictor
 predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
 
-
 cap = cv2.VideoCapture(0)
 
-while (True):
+while True:
 
     # Read the Video
     ret, img = cap.read()
-    # convert the video to grayscale
+
+    # 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(
-        gray,
+        clahe_image,
         scaleFactor=1.05,
         minNeighbors=5,
         minSize=(100, 100),
@@ -29,16 +33,14 @@ while (True):
 
     # 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)
+        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))
-        print
-        dlib_rect
 
         # 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(gray, dlib_rect).parts()
+        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
@@ -46,16 +48,16 @@ while (True):
             pos = (point[0, 0], point[0, 1])
 
             # annotate the positions
-            cv2.putText(gray, str(idx), pos,
+            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(gray, pos, 3, color=(0, 255, 255))
+            cv2.circle(clahe_image, pos, 3, color=(0, 255, 255))
 
     # draw the annotated image on an OpenCV window
-    cv2.imshow('Window', gray)
+    cv2.imshow('Window', clahe_image)
 
     if cv2.waitKey(1) & 0xFF == ord('q'):
         break