import sensor, time, image # Rotation. NORMALIZED=False # Keypoint extractor threshold, range from 0 to any number. # This threshold is used when extracting keypoints, the lower # the threshold the higher the number of keypoints extracted. KEYPOINTS_THRESH=32 # Keypoint-level threshold, range from 0 to 100. # This threshold is used when matching two keypoint descriptors, it's the # percentage of the distance between two descriptors to the max distance. # In other words, the minimum matching percentage between 2 keypoints. MATCHING_THRESH=80 # Reset sensor sensor.reset() # Sensor settings sensor.set_contrast(1) sensor.set_gainceiling(16) sensor.set_framesize(sensor.QQVGA) 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, 10): 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: # Expand the ROI by 11 pixels in each direction (half the pattern scale) face = (objects[0][0]-22, objects[0][1]-22,objects[0][2]+22*2, objects[0][3]+22*2) # Extract keypoints using the detect face size as the ROI kpts1 = img.find_keypoints(threshold=KEYPOINTS_THRESH, normalized=NORMALIZED, roi=face) # Draw a rectangle around the first face img.draw_rectangle(objects[0]) # Draw keypoints print(kpts1) img.draw_keypoints(kpts1, size=12) time.sleep(1000) # FPS clock clock = time.clock() while (True): clock.tick() img = sensor.snapshot() # Extract keypoints using the detect face size as the ROI kpts2 = img.find_keypoints(threshold=KEYPOINTS_THRESH, normalized=NORMALIZED) if (kpts2): # Match the first set of keypoints with the second one c=image.match_descriptor(image.FREAK, kpts1, kpts2, threshold=MATCHING_THRESH) # If more than 10% of the keypoints match draw the matching set if (c[2]>25): img.draw_cross(c[0], c[1], size=5) img.draw_string(0, 10, "Match %d%%"%(c[2])) # Draw FPS img.draw_string(0, 0, "FPS:%.2f"%(clock.fps()))