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Moved feature detection scripts into their own folders and added explict frame_skip value per Ibrahim's request. Finished working on snapshot and video recording scripts for next release. ... From CMUcam4 work I learned that people will just want examples that do "X" thing. So, in general, our examples should include a simple script showing off a feature and then a more complex script that does "X" where "X" is some app that a person would want. For example, we'll get reuqests for face tracking with servos, and movement detection with servos. So, instead of answering this question a million times with an example script we'll just have examples for all kinds of things people will want. Gotta automate dealing with help support at the end of the day...
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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