import sensor, time, image NORMALIZED=False MATCHING_THRESH=70 KEYPOINTS_THRESH=32 # Reset sensor sensor.reset() # Sensor settings sensor.set_contrast(1) sensor.set_gainceiling(16) sensor.set_framesize(sensor.HQVGA) sensor.set_pixformat(sensor.GRAYSCALE) # Skip a few frames to allow the sensor settle down # Note: This takes more time when exec from the IDE. for i in range(0, 30): img = sensor.snapshot() img.draw_string(0, 0, "Please wait...") # Load Haar Cascade # By default this will use all stages, lower satges is faster but less accurate. face_cascade = image.HaarCascade("frontalface", stages=25) print(face_cascade) # First set of keypoints kpts1 = None # Find a face! while (kpts1 == None): img = sensor.snapshot() img.draw_string(0, 0, "Looking for a face...") # Find faces objects = img.find_features(face_cascade, threshold=0.5, scale=1.5) if objects: # Draw a rectangle around the first face img.draw_rectangle(objects[0]) # Extract keypoints using the detect face size as the ROI kpts1 = img.find_keypoints(threshold=KEYPOINTS_THRESH, normalized=NORMALIZED, roi=objects[0]) if kpts1: img.draw_keypoints(kpts1) time.sleep(1000) # FPS clock clock = time.clock() while (True): clock.tick() img = sensor.snapshot() # Extract keypoints using the whole image. kpts2 = img.find_keypoints(threshold=KEYPOINTS_THRESH, normalized=NORMALIZED) # Match the first set of keypoints with the second one if (kpts2): c=img.match_keypoints(kpts1, kpts2, MATCHING_THRESH) # If a match was found, draw the matching keypoints if (c): img.draw_cross(c[0], c[1], size=5) img.draw_string(0, 0, "Tracking face...") print (clock.fps())