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50 lines
1.6 KiB
Python
50 lines
1.6 KiB
Python
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.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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eyes_cascade = image.HaarCascade("eye", stages=24)
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print(face_cascade, eyes_cascade)
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# FPS clock
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clock = time.clock()
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def draw_cross(img, x, y, l):
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img.draw_line((x-l, y, x+l, y))
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img.draw_line((x, y-l, x, y+l))
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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 a face !
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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=0.5, scale=1.5)
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# Draw faces
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for face in objects:
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img.draw_rectangle(face)
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# Now find eyes within each face.
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# Note: Use a higher threshold here (more detections) and lower scale (to find small objects)
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eyes = img.find_features(eyes_cascade, threshold=0.65, scale=1.25, roi=face)
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for e in eyes:
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e = [face[0]+e[0], face[1]+e[1], e[2], e[3]] # Add face offset
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img.draw_rectangle(e)
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# Draw crosshair, add width/2 and height/2
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draw_cross(img, e[0]+int(e[2]/2), e[1]+int(e[3]/2), 5)
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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()) |