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100 lines
3.8 KiB
100 lines
3.8 KiB
# Copyright 2013 The Android Open Source Project
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import its.image
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import its.device
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import its.objects
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from matplotlib import pylab
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import os.path
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import matplotlib
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import matplotlib.pyplot
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import numpy
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def main():
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"""Black level consistence test.
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Test: capture dark frames and check if black level correction is done
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correctly.
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1. Black level should be roughly consistent for repeating shots.
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2. Noise distribution should be roughly centered at black level.
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Shoot with the camera covered (i.e.) dark/black. The test varies the
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sensitivity parameter.
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"""
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NAME = os.path.basename(__file__).split(".")[0]
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NUM_REPEAT = 3
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NUM_STEPS = 3
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# Only check the center part where LSC has little effects.
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R = 200
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# The most frequent pixel value in each image; assume this is the black
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# level, since the images are all dark (shot with the lens covered).
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ymodes = []
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umodes = []
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vmodes = []
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with its.device.ItsSession() as cam:
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props = cam.get_camera_properties()
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sens_range = props['android.sensor.info.sensitivityRange']
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sens_step = (sens_range[1] - sens_range[0]) / float(NUM_STEPS-1)
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sensitivities = [sens_range[0] + i*sens_step for i in range(NUM_STEPS)]
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print "Sensitivities:", sensitivities
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for si, s in enumerate(sensitivities):
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for rep in xrange(NUM_REPEAT):
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req = its.objects.manual_capture_request(100, 1*1000*1000)
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req["android.blackLevel.lock"] = True
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req["android.sensor.sensitivity"] = s
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cap = cam.do_capture(req)
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yimg,uimg,vimg = its.image.convert_capture_to_planes(cap)
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w = cap["width"]
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h = cap["height"]
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# Magnify the noise in saved images to help visualize.
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its.image.write_image(yimg * 2,
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"%s_s=%05d_y.jpg" % (NAME, s), True)
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its.image.write_image(numpy.absolute(uimg - 0.5) * 2,
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"%s_s=%05d_u.jpg" % (NAME, s), True)
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yimg = yimg[w/2-R:w/2+R, h/2-R:h/2+R]
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uimg = uimg[w/4-R/2:w/4+R/2, w/4-R/2:w/4+R/2]
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vimg = vimg[w/4-R/2:w/4+R/2, w/4-R/2:w/4+R/2]
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yhist,_ = numpy.histogram(yimg*255, 256, (0,256))
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ymodes.append(numpy.argmax(yhist))
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uhist,_ = numpy.histogram(uimg*255, 256, (0,256))
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umodes.append(numpy.argmax(uhist))
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vhist,_ = numpy.histogram(vimg*255, 256, (0,256))
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vmodes.append(numpy.argmax(vhist))
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# Take 32 bins from Y, U, and V.
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# Histograms of U and V are cropped at the center of 128.
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pylab.plot(range(32), yhist.tolist()[0:32], 'rgb'[si])
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pylab.plot(range(32), uhist.tolist()[112:144], 'rgb'[si]+'--')
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pylab.plot(range(32), vhist.tolist()[112:144], 'rgb'[si]+'--')
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pylab.xlabel("DN: Y[0:32], U[112:144], V[112:144]")
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pylab.ylabel("Pixel count")
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pylab.title("Histograms for different sensitivities")
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matplotlib.pyplot.savefig("%s_plot_histograms.png" % (NAME))
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print "Y black levels:", ymodes
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print "U black levels:", umodes
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print "V black levels:", vmodes
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if __name__ == '__main__':
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main()
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