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52 lines
1.7 KiB
Python
52 lines
1.7 KiB
Python
# Face Detection Example
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#
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# This example shows off the built-in face detection feature of the OpenMV Cam.
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#
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# Face detection works by using the Haar Cascade feature detector on an image. A
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# Haar Cascade is a series of simple area contrasts checks. For the built-in
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# frontalface detector there are 25 stages of checks with each stage having
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# hundreds of checks a piece. Haar Cascades run fast because later stages are
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# only evaluated if previous stages pass. Additionally, your OpenMV Cam uses
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# a data structure called the integral image to quickly execute each area
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# contrast check in constant time (the reason for feature detection being
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# grayscale only is because of the space requirment for the integral image).
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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(3)
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sensor.set_gainceiling(16)
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# HQVGA and GRAYSCALE are the best for face tracking.
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sensor.set_framesize(sensor.HQVGA)
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sensor.set_pixformat(sensor.GRAYSCALE)
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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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# 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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# Capture snapshot
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img = sensor.snapshot()
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# Find objects.
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# Note: Lower scale factor scales-down the image more and detects smaller objects.
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# Higher threshold results in a higher detection rate, with more false positives.
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objects = img.find_features(face_cascade, threshold=0.75, scale_factor=1.25)
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# Draw objects
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for r in objects:
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img.draw_rectangle(r)
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# Print FPS.
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# Note: Actual FPS is higher, streaming the FB makes it slower.
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print(clock.fps())
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