Finished Toolbox

face_detect.py

@@ -1,4 +1,43 @@
 """ Experiment with face detection and image filtering using OpenCV """
 
+import numpy as np
 import cv2
-import numpy as np
+
+def draw_eye(img, center):
+    cv2.circle(img, center, int(20), (int(255),int(255),int(255)), -1)
+    cv2.circle(img, center, int(7), (int(255),int(0),int(0)), -1)
+    cv2.circle(img, center, int(3), (int(0),int(0),int(0)), -1)
+
+def draw_face(x,y,w,h,img):
+    center_eye1 = (int(x+(.3*h)), int(y+(.34*w)))
+    center_eye2 = (int(x+(.7*h)), int(y+(.34*w)))
+    center_sad = (int(x+(.5*h)), int(y+(.8*w)))
+    draw_eye(img, center_eye1)
+    draw_eye(img, center_eye2)
+    cv2.ellipse(img, center_sad, (int(40), int(20)), 0, 190, 350, (int(0),int(0),int(0)), thickness=4)
+
+cap = cv2.VideoCapture(0)
+face_cascade = cv2.CascadeClassifier('/Users/ewesterhoff/Downloads/haarcascade_frontalface_alt.xml')
+kernel = np.ones((50,50), 'uint8')
+
+
+while True:
+    # Capture frame-by-frame
+    ret, frame = cap.read()
+    faces = face_cascade.detectMultiScale(frame, scaleFactor=1.2, minSize=(20, 20))
+    #for (x, y, w, h) in faces:
+    #    cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 0, 255))
+
+    for (x, y, w, h) in faces:
+        frame[y:y+h, x:x+w, :] = cv2.dilate(frame[y:y+h, x:x+w, :], kernel)
+        cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 0, 255))
+        draw_face(x,y,w,h, 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()