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# Copyright 2013 The Android Open Source Project
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import its.image
import its.caps
import its.device
import its.objects
import its.target
import pylab
import numpy
import os.path
import matplotlib
import matplotlib.pyplot
def main():
"""Test that a constant exposure is seen as ISO and exposure time vary.
Take a series of shots that have ISO and exposure time chosen to balance
each other; result should be the same brightness, but over the sequence
the images should get noisier.
"""
NAME = os.path.basename(__file__).split(".")[0]
THRESHOLD_MAX_OUTLIER_DIFF = 0.1
THRESHOLD_MIN_LEVEL = 0.1
THRESHOLD_MAX_LEVEL = 0.9
THRESHOLD_MAX_ABS_GRAD = 0.001
mults = []
r_means = []
g_means = []
b_means = []
with its.device.ItsSession() as cam:
props = cam.get_camera_properties()
if not its.caps.compute_target_exposure(props):
print "Test skipped"
return
e,s = its.target.get_target_exposure_combos(cam)["minSensitivity"]
expt_range = props['android.sensor.info.exposureTimeRange']
sens_range = props['android.sensor.info.sensitivityRange']
m = 1
while s*m < sens_range[1] and e/m > expt_range[0]:
mults.append(m)
req = its.objects.manual_capture_request(s*m, e/m)
cap = cam.do_capture(req)
img = its.image.convert_capture_to_rgb_image(cap)
its.image.write_image(img, "%s_mult=%02d.jpg" % (NAME, m))
tile = its.image.get_image_patch(img, 0.45, 0.45, 0.1, 0.1)
rgb_means = its.image.compute_image_means(tile)
r_means.append(rgb_means[0])
g_means.append(rgb_means[1])
b_means.append(rgb_means[2])
m = m + 4
# Draw a plot.
pylab.plot(mults, r_means, 'r')
pylab.plot(mults, g_means, 'g')
pylab.plot(mults, b_means, 'b')
pylab.ylim([0,1])
matplotlib.pyplot.savefig("%s_plot_means.png" % (NAME))
# Check for linearity. For each R,G,B channel, fit a line y=mx+b, and
# assert that the gradient is close to 0 (flat) and that there are no
# crazy outliers. Also ensure that the images aren't clamped to 0 or 1
# (which would make them look like flat lines).
for chan in xrange(3):
values = [r_means, g_means, b_means][chan]
m, b = numpy.polyfit(mults, values, 1).tolist()
print "Channel %d line fit (y = mx+b): m = %f, b = %f" % (chan, m, b)
assert(abs(m) < THRESHOLD_MAX_ABS_GRAD)
assert(b > THRESHOLD_MIN_LEVEL and b < THRESHOLD_MAX_LEVEL)
for v in values:
assert(v > THRESHOLD_MIN_LEVEL and v < THRESHOLD_MAX_LEVEL)
assert(abs(v - b) < THRESHOLD_MAX_OUTLIER_DIFF)
if __name__ == '__main__':
main()
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