diff --git a/usr/examples/07-Face-Detection/face_tracking.py b/usr/examples/07-Face-Detection/face_tracking.py index 44afb1bb1..3a8372330 100644 --- a/usr/examples/07-Face-Detection/face_tracking.py +++ b/usr/examples/07-Face-Detection/face_tracking.py @@ -5,23 +5,18 @@ # script finds a face in the image using the frontalface Haar Cascade. # After which the script uses the keypoints feature to automatically learn your # face and track it. Keypoints can be used to automatically track anything. - import sensor, time, image # Reset sensor sensor.reset() - -# Sensor settings -sensor.set_contrast(1) +sensor.set_contrast(3) sensor.set_gainceiling(16) -sensor.set_framesize(sensor.QQVGA) +sensor.set_framesize(sensor.VGA) +sensor.set_windowing((320, 240)) 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...") +sensor.skip_frames(60) # Load Haar Cascade # By default this will use all stages, lower satges is faster but less accurate. @@ -36,19 +31,20 @@ 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) + objects = img.find_features(face_cascade, threshold=0.5, scale=1.25) if objects: # Expand the ROI by 31 pixels in every direction face = (objects[0][0]-31, objects[0][1]-31,objects[0][2]+31*2, objects[0][3]+31*2) # Extract keypoints using the detect face size as the ROI - kpts1 = img.find_keypoints(scale_factor=1.2, max_keypoints=100, roi=face) + kpts1 = img.find_keypoints(threshold=10, scale_factor=1.1, max_keypoints=100, 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) +img.draw_keypoints(kpts1, size=24) +img = sensor.snapshot() +time.sleep(2000) # FPS clock clock = time.clock() @@ -56,12 +52,12 @@ clock = time.clock() while (True): clock.tick() img = sensor.snapshot() - # Extract keypoints using the detect face size as the ROI - kpts2 = img.find_keypoints(scale_factor=1.2, max_keypoints=100) + # Extract keypoints from the whole frame + kpts2 = img.find_keypoints(threshold=10, scale_factor=1.1, max_keypoints=100, normalized=True) if (kpts2): # Match the first set of keypoints with the second one - c=image.match_descriptor(kpts1, kpts2) + c=image.match_descriptor(kpts1, kpts2, threshold=85) match = c[6] # C[6] contains the number of matches. if (match>5): img.draw_rectangle(c[2:6])