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# Copyright 2014 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.device
import its.caps
import its.objects
import its.image
import os.path
import pylab
import matplotlib
import matplotlib.pyplot
def main():
"""Capture a set of raw images with increasing gains and measure the noise.
Capture raw-only, in a burst.
"""
NAME = os.path.basename(__file__).split(".")[0]
# Each shot must be 1% noisier (by the variance metric) than the previous
# one.
VAR_THRESH = 1.01
NUM_STEPS = 5
with its.device.ItsSession() as cam:
props = cam.get_camera_properties()
if not its.caps.raw16(props) or \
not its.caps.manual_sensor(props) or \
not its.caps.read_3a(props):
print "Test skipped"
return
# Expose for the scene with min sensitivity
sens_min, sens_max = props['android.sensor.info.sensitivityRange']
sens_step = (sens_max - sens_min) / NUM_STEPS
s_ae,e_ae,_,_,_ = cam.do_3a()
s_e_prod = s_ae * e_ae
reqs = []
settings = []
for s in range(sens_min, sens_max, sens_step):
e = int(s_e_prod / float(s))
req = its.objects.manual_capture_request(s, e)
reqs.append(req)
settings.append((s,e))
caps = cam.do_capture(reqs, cam.CAP_RAW)
variances = []
for i,cap in enumerate(caps):
(s,e) = settings[i]
# Measure the variance. Each shot should be noisier than the
# previous shot (as the gain is increasing).
plane = its.image.convert_capture_to_planes(cap, props)[1]
tile = its.image.get_image_patch(plane, 0.45,0.45,0.1,0.1)
var = its.image.compute_image_variances(tile)[0]
variances.append(var)
img = its.image.convert_capture_to_rgb_image(cap, props=props)
its.image.write_image(img, "%s_s=%05d_var=%f.jpg" % (NAME,s,var))
print "s=%d, e=%d, var=%e"%(s,e,var)
pylab.plot(range(len(variances)), variances)
matplotlib.pyplot.savefig("%s_variances.png" % (NAME))
# Test that each shot is noisier than the previous one.
for i in range(len(variances) - 1):
assert(variances[i] < variances[i+1] / VAR_THRESH)
if __name__ == '__main__':
main()
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