diff --git a/usr/examples/07-Face-Detection/face_tracking.py b/usr/examples/07-Face-Detection/face_tracking.py index 339feafaf..2563dd0f4 100644 --- a/usr/examples/07-Face-Detection/face_tracking.py +++ b/usr/examples/07-Face-Detection/face_tracking.py @@ -5,23 +5,9 @@ # 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. -# -# NOTE: LOTS OF KEYPOINTS MAY CAUSE THE SYSTEM TO RUN OUT OF MEMORY! import sensor, time, image -# Normalized keypoints are not rotation invariant... -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() @@ -50,12 +36,12 @@ 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_factor=1.5) + 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) + # 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(threshold=KEYPOINTS_THRESH, normalized=NORMALIZED, roi=face) + kpts1 = img.find_keypoints(scale_factor=1.2, max_keypoints=100, roi=face) # Draw a rectangle around the first face img.draw_rectangle(objects[0]) @@ -71,15 +57,16 @@ 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) + kpts2 = img.find_keypoints(scale_factor=1.2, max_keypoints=100) 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])) + c=image.match_descriptor(image.ORB, kpts1, kpts2) + match = c[6] # C[6] contains the number of matches. + if (match>2): + img.draw_rectangle(c[2:6]) + img.draw_cross(c[0], c[1], size=10) + print(kpts2, "matched:%d dt:%d"%(match, c[7])) # Draw FPS img.draw_string(0, 0, "FPS:%.2f"%(clock.fps()))