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58 lines
1.8 KiB
Python
58 lines
1.8 KiB
Python
# This work is licensed under the MIT license.
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# Copyright (c) 2013-2023 OpenMV LLC. All rights reserved.
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# https://github.com/openmv/openmv/blob/master/LICENSE
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#
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# Face Eye Detection Example
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#
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# This script uses the built-in frontalface detector to find a face and then
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# the eyes within the face. If you want to determine the eye gaze please see the
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# iris_detection script for an example on how to do that.
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import sensor
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import time
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import 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("/rom/haarcascade_frontalface.cascade", stages=25)
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eyes_cascade = image.HaarCascade("/rom/haarcascade_eye.cascade", 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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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_factor=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(
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eyes_cascade, threshold=0.5, scale_factor=1.2, roi=face
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)
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for e in eyes:
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img.draw_rectangle(e)
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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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