Landmarks aus dlip und Haar cascade aus OpenCV

projectmood/Haarcascade

-
 import cv2
+import dlib
+import numpy as np
 
+# Create the haar cascade
 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):
+
+    # Read the Video
     ret, img = cap.read()
+    # convert the video to grayscale
     gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-    faces = detector.detectMultiScale(gray, 1.3, 5)
+
+    # Detect faces in the video
+    faces = detector.detectMultiScale(
+        gray,
+        scaleFactor=1.05,
+        minNeighbors=5,
+        minSize=(100, 100),
+        flags=cv2.CASCADE_SCALE_IMAGE
+    )
+
+    # Draw a rectangle around the faces
     for (x, y, w, h) in faces:
-        cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)
+        cv2.rectangle(gray, (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()
+        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(gray, 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))
+
+    # draw the annotated image on an OpenCV window
+    cv2.imshow('Window', gray)
 
-    cv2.imshow('frame', img)
     if cv2.waitKey(1) & 0xFF == ord('q'):
         break