# Copyright 2016 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] RATIO_THRESHOLD = 0.1 # Each raw image # Waive the check if raw pixel value is below this level (signal too small # that small black level error converts to huge error in percentage) RAW_PIXEL_VAL_THRESHOLD = 0.03 def main(): """Check post RAW sensitivity boost. Capture a set of raw/yuv images with different sensitivity/post RAW sensitivity boost combination and check if the output pixel mean matches request settings """ with its.device.ItsSession() as cam: props = cam.get_camera_properties() its.caps.skip_unless(its.caps.raw_output(props) and its.caps.post_raw_sensitivity_boost(props) and its.caps.compute_target_exposure(props) and its.caps.per_frame_control(props) and not its.caps.mono_camera(props)) w, h = its.objects.get_available_output_sizes( 'yuv', props, (1920, 1080))[0] if its.caps.raw16(props): raw_format = 'raw' elif its.caps.raw10(props): raw_format = 'raw10' elif its.caps.raw12(props): raw_format = 'raw12' else: # should not reach here raise its.error.Error('Cannot find available RAW output format') out_surfaces = [{'format': raw_format}, {'format': 'yuv', 'width': w, 'height': h}] sens_min, sens_max = props['android.sensor.info.sensitivityRange'] sens_boost_min, sens_boost_max = \ props['android.control.postRawSensitivityBoostRange'] e_target, s_target = \ its.target.get_target_exposure_combos(cam)['midSensitivity'] reqs = [] settings = [] s_boost = sens_boost_min while s_boost <= sens_boost_max: s_raw = int(round(s_target * 100.0 / s_boost)) if s_raw < sens_min or s_raw > sens_max: break req = its.objects.manual_capture_request(s_raw, e_target) req['android.control.postRawSensitivityBoost'] = s_boost reqs.append(req) settings.append((s_raw, s_boost)) if s_boost == sens_boost_max: break s_boost *= 2 # Always try to test maximum sensitivity boost value if s_boost > sens_boost_max: s_boost = sens_boost_max caps = cam.do_capture(reqs, out_surfaces) raw_rgb_means = [] yuv_rgb_means = [] raw_caps, yuv_caps = caps if not isinstance(raw_caps, list): raw_caps = [raw_caps] if not isinstance(yuv_caps, list): yuv_caps = [yuv_caps] for i in xrange(len(reqs)): (s, s_boost) = settings[i] raw_cap = raw_caps[i] yuv_cap = yuv_caps[i] raw_rgb = its.image.convert_capture_to_rgb_image( raw_cap, props=props) yuv_rgb = its.image.convert_capture_to_rgb_image(yuv_cap) raw_tile = its.image.get_image_patch(raw_rgb, 0.45, 0.45, 0.1, 0.1) yuv_tile = its.image.get_image_patch(yuv_rgb, 0.45, 0.45, 0.1, 0.1) raw_rgb_means.append(its.image.compute_image_means(raw_tile)) yuv_rgb_means.append(its.image.compute_image_means(yuv_tile)) its.image.write_image(raw_tile, '%s_raw_s=%04d_boost=%04d.jpg' % ( NAME, s, s_boost)) its.image.write_image(yuv_tile, '%s_yuv_s=%04d_boost=%04d.jpg' % ( NAME, s, s_boost)) print 's=%d, s_boost=%d: raw_means %s, yuv_means %s'%( s, s_boost, raw_rgb_means[-1], yuv_rgb_means[-1]) xs = range(len(reqs)) pylab.plot(xs, [rgb[0] for rgb in raw_rgb_means], '-ro') pylab.plot(xs, [rgb[1] for rgb in raw_rgb_means], '-go') pylab.plot(xs, [rgb[2] for rgb in raw_rgb_means], '-bo') pylab.ylim([0, 1]) name = '%s_raw_plot_means' % NAME pylab.title(name) pylab.xlabel('requests') pylab.ylabel('RGB means') matplotlib.pyplot.savefig('%s.png' % name) pylab.clf() pylab.plot(xs, [rgb[0] for rgb in yuv_rgb_means], '-ro') pylab.plot(xs, [rgb[1] for rgb in yuv_rgb_means], '-go') pylab.plot(xs, [rgb[2] for rgb in yuv_rgb_means], '-bo') pylab.ylim([0, 1]) name = '%s_yuv_plot_means' % NAME pylab.title(name) pylab.xlabel('requests') pylab.ylabel('RGB means') matplotlib.pyplot.savefig('%s.png' % name) rgb_str = ['R', 'G', 'B'] # Test that raw means is about 2x brighter than next step for step in range(1, len(reqs)): (s_prev, _) = settings[step - 1] (s, s_boost) = settings[step] expect_raw_ratio = s_prev / float(s) raw_thres_min = expect_raw_ratio * (1 - RATIO_THRESHOLD) raw_thres_max = expect_raw_ratio * (1 + RATIO_THRESHOLD) for rgb in range(3): ratio = raw_rgb_means[step - 1][rgb] / raw_rgb_means[step][rgb] print 'Step (%d,%d) %s channel: %f, %f, ratio %f,' % ( step-1, step, rgb_str[rgb], raw_rgb_means[step - 1][rgb], raw_rgb_means[step][rgb], ratio), print 'threshold_min %f, threshold_max %f' % ( raw_thres_min, raw_thres_max) if raw_rgb_means[step][rgb] <= RAW_PIXEL_VAL_THRESHOLD: continue assert raw_thres_min < ratio < raw_thres_max # Test that each yuv step is about the same bright as their mean yuv_thres_min = 1 - RATIO_THRESHOLD yuv_thres_max = 1 + RATIO_THRESHOLD for rgb in range(3): vals = [val[rgb] for val in yuv_rgb_means] for step in range(len(reqs)): if raw_rgb_means[step][rgb] <= RAW_PIXEL_VAL_THRESHOLD: vals = vals[:step] mean = sum(vals) / len(vals) print '%s channel vals %s mean %f'%(rgb_str[rgb], vals, mean) for step in range(len(vals)): ratio = vals[step] / mean assert yuv_thres_min < ratio < yuv_thres_max if __name__ == '__main__': main()