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Tried to emulate Arduino's 11 folders... I'd perfer to have all the shield scripts in one folder... but, that might not make sense. I don't really want one script per folder however. So, I might merge some more stuff in the future. I have a grand idea here that will become evident as I work though the examples. Anyway, the current structure is not final. It will be in flux for a little while. As for Git History, folder history is the best we're going to get. Git and GitHub don't seem to deal with moves too well.
76 lines
2.5 KiB
Python
76 lines
2.5 KiB
Python
import sensor, time, image
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# Rotation.
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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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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, 10):
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img = sensor.snapshot()
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img.draw_string(0, 0, "Please wait...")
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# Load Haar Cascade
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# By default this will use all stages, lower satges is faster but less accurate.
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face_cascade = image.HaarCascade("frontalface", stages=25)
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print(face_cascade)
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# First set of keypoints
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kpts1 = None
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# Find a face!
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while (kpts1 == None):
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img = sensor.snapshot()
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img.draw_string(0, 0, "Looking for a face...")
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# Find faces
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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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# Expand the ROI by 11 pixels in each direction (half the pattern scale)
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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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# 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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# Draw a rectangle around the first face
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img.draw_rectangle(objects[0])
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# Draw keypoints
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print(kpts1)
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img.draw_keypoints(kpts1, size=12)
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time.sleep(1000)
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# FPS clock
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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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# 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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if (kpts2):
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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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# If more than 10% of the keypoints match draw the matching set
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if (c[2]>25):
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img.draw_cross(c[0], c[1], size=5)
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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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