104 lines
3.8 KiB
Python
104 lines
3.8 KiB
Python
# 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.caps
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import its.device
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import its.objects
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import its.target
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import numpy
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import math
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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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def main():
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"""Test that device processing can be inverted to linear pixels.
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Captures a sequence of shots with the device pointed at a uniform
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target. Attempts to invert all the ISP processing to get back to
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linear R,G,B pixel data.
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"""
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NAME = os.path.basename(__file__).split(".")[0]
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RESIDUAL_THRESHOLD = 0.0003 # approximately each sample is off by 2/255
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# The HAL3.2 spec requires that curves up to 64 control points in length
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# must be supported.
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L = 64
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LM1 = float(L-1)
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gamma_lut = numpy.array(
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sum([[i/LM1, math.pow(i/LM1, 1/2.2)] for i in xrange(L)], []))
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inv_gamma_lut = numpy.array(
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sum([[i/LM1, math.pow(i/LM1, 2.2)] for i in xrange(L)], []))
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with its.device.ItsSession() as cam:
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props = cam.get_camera_properties()
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its.caps.skip_unless(its.caps.compute_target_exposure(props) and
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its.caps.per_frame_control(props))
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debug = its.caps.debug_mode()
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if debug:
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fmt = its.objects.get_largest_yuv_format(props)
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else:
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fmt = its.objects.get_smallest_yuv_format(props)
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e,s = its.target.get_target_exposure_combos(cam)["midSensitivity"]
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s /= 2
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sens_range = props['android.sensor.info.sensitivityRange']
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sensitivities = [s*1.0/3.0, s*2.0/3.0, s, s*4.0/3.0, s*5.0/3.0]
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sensitivities = [s for s in sensitivities
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if s > sens_range[0] and s < sens_range[1]]
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req = its.objects.manual_capture_request(0, e)
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req["android.blackLevel.lock"] = True
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req["android.tonemap.mode"] = 0
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req["android.tonemap.curveRed"] = gamma_lut.tolist()
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req["android.tonemap.curveGreen"] = gamma_lut.tolist()
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req["android.tonemap.curveBlue"] = gamma_lut.tolist()
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r_means = []
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g_means = []
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b_means = []
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for sens in sensitivities:
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req["android.sensor.sensitivity"] = sens
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cap = cam.do_capture(req, fmt)
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img = its.image.convert_capture_to_rgb_image(cap)
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its.image.write_image(
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img, "%s_sens=%04d.jpg" % (NAME, sens))
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img = its.image.apply_lut_to_image(img, inv_gamma_lut[1::2] * LM1)
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tile = its.image.get_image_patch(img, 0.45, 0.45, 0.1, 0.1)
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rgb_means = its.image.compute_image_means(tile)
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r_means.append(rgb_means[0])
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g_means.append(rgb_means[1])
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b_means.append(rgb_means[2])
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pylab.plot(sensitivities, r_means, 'r')
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pylab.plot(sensitivities, g_means, 'g')
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pylab.plot(sensitivities, b_means, 'b')
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pylab.ylim([0,1])
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matplotlib.pyplot.savefig("%s_plot_means.png" % (NAME))
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# Check that each plot is actually linear.
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for means in [r_means, g_means, b_means]:
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line,residuals,_,_,_ = numpy.polyfit(range(5),means,1,full=True)
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print "Line: m=%f, b=%f, resid=%f"%(line[0], line[1], residuals[0])
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assert(residuals[0] < RESIDUAL_THRESHOLD)
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if __name__ == '__main__':
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main()
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