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Update keypoints.py script.
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@ -10,7 +10,8 @@ sensor.reset()
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# Sensor settings
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# Sensor settings
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sensor.set_contrast(1)
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sensor.set_contrast(1)
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sensor.set_gainceiling(16)
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sensor.set_gainceiling(16)
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sensor.set_framesize(sensor.QCIF)
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sensor.set_framesize(sensor.VGA)
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sensor.set_windowing((240, 240))
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sensor.set_pixformat(sensor.GRAYSCALE)
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sensor.set_pixformat(sensor.GRAYSCALE)
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sensor.set_auto_gain(False, value=100)
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sensor.set_auto_gain(False, value=100)
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@ -32,14 +33,16 @@ clock = time.clock()
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while (True):
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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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# NOTE: See the docs for other arguments
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if (kpts1 == None):
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kpts2 = img.find_keypoints(max_keypoints=100, scale_factor=1.2)
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# NOTE: By default find_keypoints returns multi-scale keypoints extracted from an image pyramid.
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kpts1 = img.find_keypoints(max_keypoints=150, threshold=20, scale_factor=1.1)
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if (kpts2 and kpts1 == None):
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kpts1 = kpts2
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draw_keypoints(img, kpts1)
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draw_keypoints(img, kpts1)
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elif kpts2:
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else:
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c = image.match_descriptor(kpts1, kpts2, threshold=85)
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# NOTE: When extracting keypoints to match the first descriptor, we use normalized=True to extract
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# keypoints from the first scale only, which will match one of the scales in the first descriptor.
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kpts2 = img.find_keypoints(max_keypoints=150, threshold=20, normalized=True)
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if (kpts2):
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c = image.match_descriptor(kpts1, kpts2, threshold=80)
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match = c[6] # C[6] contains the number of matches.
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match = c[6] # C[6] contains the number of matches.
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if (match>5):
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if (match>5):
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img.draw_rectangle(c[2:6])
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img.draw_rectangle(c[2:6])
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