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64 lines
1.7 KiB
Python
64 lines
1.7 KiB
Python
import sensor, time, image
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NORMALIZED=False
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MATCHING_THRESH=70
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KEYPOINTS_THRESH=32
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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.HQVGA)
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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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# 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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# Draw a rectangle around the first face
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img.draw_rectangle(objects[0])
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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=objects[0])
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if kpts1:
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img.draw_keypoints(kpts1)
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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 whole image.
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kpts2 = img.find_keypoints(threshold=KEYPOINTS_THRESH, normalized=NORMALIZED)
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# Match the first set of keypoints with the second one
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if (kpts2):
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c=img.match_keypoints(kpts1, kpts2, MATCHING_THRESH)
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# If a match was found, draw the matching keypoints
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if (c):
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img.draw_cross(c[0], c[1], size=5)
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img.draw_string(0, 0, "Tracking face...")
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print (clock.fps())
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