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Update face tracking example
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@ -5,23 +5,9 @@
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# script finds a face in the image using the frontalface Haar Cascade.
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# script finds a face in the image using the frontalface Haar Cascade.
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# After which the script uses the keypoints feature to automatically learn your
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# After which the script uses the keypoints feature to automatically learn your
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# face and track it. Keypoints can be used to automatically track anything.
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# face and track it. Keypoints can be used to automatically track anything.
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#
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# NOTE: LOTS OF KEYPOINTS MAY CAUSE THE SYSTEM TO RUN OUT OF MEMORY!
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import sensor, time, image
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import sensor, time, image
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# Normalized keypoints are not rotation invariant...
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NORMALIZED=False
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# Keypoint extractor threshold, range from 0 to any number.
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# This threshold is used when extracting keypoints, the lower
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# the threshold the higher the number of keypoints extracted.
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KEYPOINTS_THRESH=32
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# Keypoint-level threshold, range from 0 to 100.
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# This threshold is used when matching two keypoint descriptors, it's the
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# percentage of the distance between two descriptors to the max distance.
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# In other words, the minimum matching percentage between 2 keypoints.
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MATCHING_THRESH=80
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# Reset sensor
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# Reset sensor
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sensor.reset()
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sensor.reset()
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@ -50,12 +36,12 @@ while (kpts1 == None):
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img = sensor.snapshot()
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img = sensor.snapshot()
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img.draw_string(0, 0, "Looking for a face...")
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img.draw_string(0, 0, "Looking for a face...")
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# Find faces
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# Find faces
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objects = img.find_features(face_cascade, threshold=0.5, scale_factor=1.5)
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objects = img.find_features(face_cascade, threshold=0.5, scale=1.5)
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if objects:
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if objects:
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# Expand the ROI by 11 pixels in each direction (half the pattern scale)
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# Expand the ROI by 31 pixels in every direction
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face = (objects[0][0]-22, objects[0][1]-22,objects[0][2]+22*2, objects[0][3]+22*2)
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face = (objects[0][0]-31, objects[0][1]-31,objects[0][2]+31*2, objects[0][3]+31*2)
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# Extract keypoints using the detect face size as the ROI
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# Extract keypoints using the detect face size as the ROI
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kpts1 = img.find_keypoints(threshold=KEYPOINTS_THRESH, normalized=NORMALIZED, roi=face)
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kpts1 = img.find_keypoints(scale_factor=1.2, max_keypoints=100, roi=face)
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# Draw a rectangle around the first face
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# Draw a rectangle around the first face
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img.draw_rectangle(objects[0])
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img.draw_rectangle(objects[0])
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@ -71,15 +57,16 @@ while (True):
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clock.tick()
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clock.tick()
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img = sensor.snapshot()
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img = sensor.snapshot()
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# Extract keypoints using the detect face size as the ROI
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# Extract keypoints using the detect face size as the ROI
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kpts2 = img.find_keypoints(threshold=KEYPOINTS_THRESH, normalized=NORMALIZED)
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kpts2 = img.find_keypoints(scale_factor=1.2, max_keypoints=100)
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if (kpts2):
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if (kpts2):
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# Match the first set of keypoints with the second one
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# Match the first set of keypoints with the second one
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c=image.match_descriptor(image.FREAK, kpts1, kpts2, threshold=MATCHING_THRESH)
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c=image.match_descriptor(image.ORB, kpts1, kpts2)
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# If more than 10% of the keypoints match draw the matching set
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match = c[6] # C[6] contains the number of matches.
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if (c[2]>25):
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if (match>2):
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img.draw_cross(c[0], c[1], size=5)
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img.draw_rectangle(c[2:6])
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img.draw_string(0, 10, "Match %d%%"%(c[2]))
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img.draw_cross(c[0], c[1], size=10)
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print(kpts2, "matched:%d dt:%d"%(match, c[7]))
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# Draw FPS
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# Draw FPS
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img.draw_string(0, 0, "FPS:%.2f"%(clock.fps()))
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img.draw_string(0, 0, "FPS:%.2f"%(clock.fps()))
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