| # 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 math |
| import os.path |
| import its.caps |
| import its.device |
| import its.image |
| import its.objects |
| import matplotlib |
| from matplotlib import pylab |
| |
| NAME = os.path.basename(__file__).split('.')[0] |
| BAYER_LIST = ['R', 'GR', 'GB', 'B'] |
| DIFF_THRESH = 0.0012 # absolute variance delta threshold |
| FRAC_THRESH = 0.2 # relative variance delta threshold |
| NUM_STEPS = 4 |
| SENS_TOL = 0.97 # specification is <= 3% |
| |
| |
| def main(): |
| """Verify that the DNG raw model parameters are correct.""" |
| |
| # Pass if the difference between expected and computed variances is small, |
| # defined as being within an absolute variance delta or relative variance |
| # delta of the expected variance, whichever is larger. This is to allow the |
| # test to pass in the presence of some randomness (since this test is |
| # measuring noise of a small patch) and some imperfect scene conditions |
| # (since ITS doesn't require a perfectly uniformly lit scene). |
| |
| with its.device.ItsSession() as cam: |
| props = cam.get_camera_properties() |
| props = cam.override_with_hidden_physical_camera_props(props) |
| its.caps.skip_unless( |
| its.caps.raw(props) and |
| its.caps.raw16(props) and |
| its.caps.manual_sensor(props) and |
| its.caps.read_3a(props) and |
| its.caps.per_frame_control(props) and |
| not its.caps.mono_camera(props)) |
| |
| white_level = float(props['android.sensor.info.whiteLevel']) |
| cfa_idxs = its.image.get_canonical_cfa_order(props) |
| |
| # Expose for the scene with min sensitivity |
| sens_min, _ = props['android.sensor.info.sensitivityRange'] |
| sens_max_ana = props['android.sensor.maxAnalogSensitivity'] |
| sens_step = (sens_max_ana - sens_min) / NUM_STEPS |
| s_ae, e_ae, _, _, f_dist = cam.do_3a(get_results=True) |
| s_e_prod = s_ae * e_ae |
| sensitivities = range(sens_min, sens_max_ana+1, sens_step) |
| |
| var_expected = [[], [], [], []] |
| var_measured = [[], [], [], []] |
| sens_valid = [] |
| for sens in sensitivities: |
| # Capture a raw frame with the desired sensitivity |
| exp = int(s_e_prod / float(sens)) |
| req = its.objects.manual_capture_request(sens, exp, f_dist) |
| cap = cam.do_capture(req, cam.CAP_RAW) |
| planes = its.image.convert_capture_to_planes(cap, props) |
| s_read = cap['metadata']['android.sensor.sensitivity'] |
| print 'iso_write: %d, iso_read: %d' % (sens, s_read) |
| |
| # Test each raw color channel (R, GR, GB, B) |
| noise_profile = cap['metadata']['android.sensor.noiseProfile'] |
| assert len(noise_profile) == len(BAYER_LIST) |
| for i in range(len(BAYER_LIST)): |
| print BAYER_LIST[i], |
| # Get the noise model parameters for this channel of this shot. |
| ch = cfa_idxs[i] |
| s, o = noise_profile[ch] |
| |
| # Use a very small patch to ensure gross uniformity (i.e. so |
| # non-uniform lighting or vignetting doesn't affect the variance |
| # calculation) |
| black_level = its.image.get_black_level(i, props, |
| cap['metadata']) |
| level_range = white_level - black_level |
| plane = its.image.get_image_patch(planes[i], 0.49, 0.49, |
| 0.02, 0.02) |
| tile_raw = plane * white_level |
| tile_norm = ((tile_raw - black_level) / level_range) |
| |
| # exit if distribution is clipped at 0, otherwise continue |
| mean_img_ch = tile_norm.mean() |
| var_model = s * mean_img_ch + o |
| # This computation is a suspicious because if the data were |
| # clipped, the mean and standard deviation could be affected |
| # in a way that affects this check. However, empirically, |
| # the mean and standard deviation change more slowly than the |
| # clipping point itself does, so the check remains correct |
| # even after the signal starts to clip. |
| mean_minus_3sigma = mean_img_ch - math.sqrt(var_model) * 3 |
| if mean_minus_3sigma < 0: |
| e_msg = '\nPixel distribution crosses 0.\n' |
| e_msg += 'Likely black level over-clips.\n' |
| e_msg += 'Linear model is not valid.\n' |
| e_msg += 'mean: %.3e, var: %.3e, u-3s: %.3e' % ( |
| mean_img_ch, var_model, mean_minus_3sigma) |
| assert 0, e_msg |
| else: |
| print 'mean:', mean_img_ch, |
| var_measured[i].append( |
| its.image.compute_image_variances(tile_norm)[0]) |
| print 'var:', var_measured[i][-1], |
| var_expected[i].append(var_model) |
| print 'var_model:', var_expected[i][-1] |
| print '' |
| sens_valid.append(sens) |
| |
| # plot data and models |
| for i, ch in enumerate(BAYER_LIST): |
| pylab.plot(sens_valid, var_expected[i], 'rgkb'[i], |
| label=ch+' expected') |
| pylab.plot(sens_valid, var_measured[i], 'rgkb'[i]+'.--', |
| label=ch+' measured') |
| pylab.xlabel('Sensitivity') |
| pylab.ylabel('Center patch variance') |
| pylab.legend(loc=2) |
| matplotlib.pyplot.savefig('%s_plot.png' % NAME) |
| |
| # PASS/FAIL check |
| for i, ch in enumerate(BAYER_LIST): |
| diffs = [abs(var_measured[i][j] - var_expected[i][j]) |
| for j in range(len(sens_valid))] |
| print 'Diffs (%s):'%(ch), diffs |
| for j, diff in enumerate(diffs): |
| thresh = max(DIFF_THRESH, FRAC_THRESH*var_expected[i][j]) |
| assert diff <= thresh, 'diff: %.5f, thresh: %.4f' % (diff, thresh) |
| |
| if __name__ == '__main__': |
| main() |