Completed Computer Vision toolbox

face_detect.py

@@ -1,4 +1,45 @@
 """ Experiment with face detection and image filtering using OpenCV """
 
 import cv2
-import numpy as np
+import numpy as np
+
+cap = cv2.VideoCapture(0)
+kernel = np.ones((21,21),'uint8')
+radius = 10
+black = (0,0,0)
+white = (255,255,255)
+
+while(True):
+    # Capture frame-by-frame
+    ret, frame = cap.read()
+    face_cascade = cv2.CascadeClassifier('/usr/share/opencv/haarcascades/haarcascade_frontalface_alt.xml')
+    faces = face_cascade.detectMultiScale(frame, scaleFactor=1.2, minSize=(20,20))
+
+    for (x,y,w,h) in faces:
+    	frame[y:y+h,x:x+w,:] = cv2.dilate(frame[y:y+h,x:x+w,:],kernel)
+    	radius = int(h/15)      # eye radius
+    	radius2 = int(.6*radius)# pupil/smile radius
+    	xpos1 = x+int(.33*w)    # left x position
+    	xpos2 = x+int(.66*w)    # right x position
+    	ypos = y+int(.4*h)		# height for eyes
+
+    	# drawing the eyes
+    	cv2.circle(frame, (xpos1,ypos), radius, white, thickness = -1)
+    	cv2.circle(frame, (xpos2,ypos), radius, white, thickness = -1)
+
+    	# drawing the pupils
+    	cv2.circle(frame, (xpos1,ypos), radius2, black, thickness = -1)
+    	cv2.circle(frame, (xpos2,ypos), radius2, black, thickness = -1)
+
+    	# drawing the smile
+    	cv2.ellipse(frame, (x+int(.5*w),y+int(.65*h)), (int(w/4), int(h/5)), 0, 0, 180, black, radius2)
+    	#cv2.rectangle(frame,(x,y),(x+w,y+h),(0,0,255))
+
+    # 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()