| # 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 os.path |
| |
| import its.caps |
| import its.device |
| import its.image |
| import its.objects |
| import its.target |
| |
| import matplotlib |
| from matplotlib import pylab |
| |
| NAME = os.path.basename(__file__).split('.')[0] |
| NUM_STEPS = 5 |
| |
| |
| def main(): |
| """Test that the android.sensor.sensitivity parameter is applied.""" |
| |
| sensitivities = None |
| r_means = [] |
| g_means = [] |
| b_means = [] |
| |
| with its.device.ItsSession() as cam: |
| props = cam.get_camera_properties() |
| its.caps.skip_unless(its.caps.compute_target_exposure(props)) |
| sync_latency = its.caps.sync_latency(props) |
| |
| debug = its.caps.debug_mode() |
| largest_yuv = its.objects.get_largest_yuv_format(props) |
| if debug: |
| fmt = largest_yuv |
| else: |
| match_ar = (largest_yuv['width'], largest_yuv['height']) |
| fmt = its.objects.get_smallest_yuv_format(props, match_ar=match_ar) |
| |
| expt, _ = its.target.get_target_exposure_combos(cam)['midSensitivity'] |
| sens_range = props['android.sensor.info.sensitivityRange'] |
| sens_step = (sens_range[1] - sens_range[0]) / float(NUM_STEPS-1) |
| sensitivities = [ |
| sens_range[0] + i * sens_step for i in range(NUM_STEPS)] |
| |
| for s in sensitivities: |
| req = its.objects.manual_capture_request(s, expt) |
| cap = its.device.do_capture_with_latency( |
| cam, req, sync_latency, fmt) |
| img = its.image.convert_capture_to_rgb_image(cap) |
| its.image.write_image(img, '%s_iso=%04d.jpg' % (NAME, s)) |
| 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]) |
| |
| # Draw a plot. |
| pylab.plot(sensitivities, r_means, '-ro') |
| pylab.plot(sensitivities, g_means, '-go') |
| pylab.plot(sensitivities, b_means, '-bo') |
| pylab.ylim([0, 1]) |
| pylab.title(NAME) |
| pylab.xlabel('Gain (ISO)') |
| pylab.ylabel('RGB means') |
| matplotlib.pyplot.savefig('%s_plot_means.png' % (NAME)) |
| |
| # Test for pass/fail: check that each shot is brighter than the previous. |
| for means in [r_means, g_means, b_means]: |
| for i in range(len(means)-1): |
| assert means[i+1] > means[i] |
| |
| if __name__ == '__main__': |
| main() |
| |