completed

face_detect.py

@@ -1,4 +1,43 @@
 """ Experiment with face detection and image filtering using OpenCV """
 
 import cv2
-import numpy as np
+import numpy as np
+
+
+black = (0, 0, 0)
+white = (255, 255, 255)
+
+cap = cv2.VideoCapture(0)
+
+while True:
+
+    # detect face
+    face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_alt.xml')
+    ret, frame = cap.read()
+    faces = face_cascade.detectMultiScale(frame, scaleFactor=1.2, minSize=(20, 20))
+    # create a NumPy matrix, which controls the degree of blurring
+    kernel = np.ones((40, 40), 'uint8')
+
+    for (x, y, w, h) in faces:
+        # blurring the face
+        frame[y:y+h, x:x+w, :] = cv2.dilate(frame[y:y+h, x:x+w, :], kernel)
+        # draw a face
+        # mouth
+        cv2.line(frame, (int(x+w*.3), int(y+h*.75)), (int(x+w*.7), int(y+h*.75)), black, 10)
+        # Nose
+        cv2.line(frame, (int(x+w*.5), int(y+h*.55)), (int(x+w*.5), int(y+h*.4)), black, 10)
+        # eyes
+        cv2.circle(frame, (int(x+w*.7), int(y+h*.35)), 20, white, -1)
+        cv2.circle(frame, (int(x+w*.3), int(y+h*.35)), 20, white, -1)
+        cv2.circle(frame, (int(x+w*.7), int(y+h*.35)), 10, black, -1)
+        cv2.circle(frame, (int(x+w*.3), int(y+h*.35)), 10, black, -1)
+        # Display the resulting frame
+        cv2.imshow('frame', frame)
+        # Display the resulting frame
+        cv2.imshow('frame', frame)
+        if cv2.waitKey(1) & 0xFF == ord('q'):
+            break
+
+# When everything done, release the capture
+cap.release()
+cv2.destroyAllWindows()