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Add ORB keypoints example scripts.
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# Freak Example
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#
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# This script shows off keypoint tracking by itself. Put an object in front of
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# your OpenMV Cam without anything else in the image (i.e. camera should be
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# facing a smooth wall) and the camera will learn the keypoints for an track
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# whatever object is in the image. You can save keypoints to disk either via
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# the OpenMV IDE or from in your script.
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#
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# Matching keypoints works by first extracting keypoints from an ROI. Once those
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# are extracted then the OpenMV Cam compares the extracted keypoints against all
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# the keypoints in an image. It tries to find the center matching point between
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# the two sets of keypoints.
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#
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# Keep in mind that keypoint matching with just one training example isn't very
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# robust. If you want professional quality results then stick with getting
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# professionally generated Haar Cascades like the frontalface or eye cascade.
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# That said, if you're in a very controlled enviroment then keypoint tracking
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# allows your OpenMV Cam to learn objects on the fly.
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#
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# If... you want really good keypoint matching results we suggest you gather
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# keypoints from all faces of an object and with multiple rotations and scales.
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# Comparing against all theses sets of keypoints helps versus just one.
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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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# 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=30
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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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sensor.reset()
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# Sensor settings
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sensor.set_contrast(1)
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sensor.set_gainceiling(16)
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sensor.set_framesize(sensor.QQVGA)
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sensor.set_pixformat(sensor.GRAYSCALE)
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# Skip a few frames to allow the sensor settle down
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# Note: This takes more time when exec from the IDE.
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for i in range(0, 30):
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img = sensor.snapshot()
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img.draw_string(0, 0, "Please wait...")
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kpts1 = None
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# Uncomment to load keypoints from file
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#kpts1 = image.load_descriptor(image.FREAK, "/desc.freak")
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clock = time.clock()
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while (True):
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clock.tick()
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img = sensor.snapshot()
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kpts2 = img.find_keypoints(threshold=KEYPOINTS_THRESH, normalized=NORMALIZED)
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if (kpts1==None):
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kpts1 = kpts2
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print(kpts1)
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elif kpts2:
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c = image.match_descriptor(image.FREAK, kpts1, kpts2, threshold=MATCHING_THRESH)
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# C[3] contains the percentage of matching keypoints.
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# If more than 25% of the keypoints match, draw stuff.
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if (c[2]>25):
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img.draw_cross(c[0], c[1], size=15)
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img.draw_string(0, 10, "Match %d%%"%(c[2]))
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# Draw FPS
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img.draw_string(0, 0, "FPS:%.2f"%(clock.fps()))
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53
usr/examples/09-Feature-Detection/keypoints.py
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53
usr/examples/09-Feature-Detection/keypoints.py
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# Object tracking with keypoints example.
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# Show the camera an object and then run the script. A set of keypoints will be extracted
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# once and then tracked in the following frames. If you want a new set of keypoints re-run
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# the script. NOTE: see the docs for arguments to tune find_keypoints and match_keypoints.
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import sensor, time, image
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# Reset sensor
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sensor.reset()
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# Sensor settings
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sensor.set_contrast(1)
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sensor.set_gainceiling(16)
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sensor.set_framesize(sensor.QCIF)
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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.skip_frames(30)
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def draw_keypoints(img, kpts):
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print(kpts)
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img.draw_keypoints(kpts)
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img = sensor.snapshot()
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time.sleep(1000)
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kpts1 = None
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# NOTE: uncomment to load a keypoints descriptor from file
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#kpts1 = image.load_descriptor(image.ORB, "/desc.orb")
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#img = sensor.snapshot()
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#draw_keypoints(img, kpts1)
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clock = time.clock()
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while (True):
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clock.tick()
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img = sensor.snapshot()
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# NOTE: See the docs for other arguments
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kpts2 = img.find_keypoints(max_keypoints=100, scale_factor=1.2)
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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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elif kpts2:
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c = image.match_descriptor(image.ORB, kpts1, kpts2, threshold=70)
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match = c[6] # C[6] contains the number of matches.
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if (match>2):
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img.draw_rectangle(c[2:6])
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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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# NOTE: uncomment if you want to draw the keypoints
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#img.draw_keypoints(kpts2, size=KEYPOINTS_SIZE, matched=True)
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# Draw FPS
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img.draw_string(0, 0, "FPS:%.2f"%(clock.fps()))
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31
usr/examples/09-Feature-Detection/keypoints_save.py
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31
usr/examples/09-Feature-Detection/keypoints_save.py
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# Keypoints descriptor example.
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# This example shows how to save a keypoints descriptor to file. Show the camera an object
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# and then run the script. The script will extract and save a keypoints descriptor and the image.
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# You can use the keypoints_editor.py util to remove unwanted keypoints.
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#
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# NOTE: Please reset the camera after running this script to see the new file.
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import sensor, time, image
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# Reset sensor
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sensor.reset()
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# Sensor settings
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sensor.set_contrast(1)
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sensor.set_gainceiling(16)
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sensor.set_framesize(sensor.QCIF)
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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.skip_frames(30)
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FILE_NAME = "desc"
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img = sensor.snapshot()
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# NOTE: See the docs for other arguments
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kpts = img.find_keypoints(scale_factor=1.2)
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image.save_descriptor(image.ORB, "/%s.orb"%(FILE_NAME), kpts)
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img.save("/%s.pgm"%(FILE_NAME))
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img.draw_keypoints(kpts)
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sensor.snapshot()
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time.sleep(1000)
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