| # 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 os.path |
| |
| import its.caps |
| import its.device |
| import its.image |
| import its.objects |
| import matplotlib |
| from matplotlib import pylab |
| import numpy as np |
| |
| NAME = os.path.basename(__file__).split('.')[0] |
| LOCKED = 3 |
| LUMA_LOCKED_TOL = 0.05 |
| THRESH_CONVERGE_FOR_EV = 8 # AE must converge within this num |
| YUV_FULL_SCALE = 255.0 |
| YUV_SAT_MIN = 250.0 |
| YUV_SAT_TOL = 3.0 |
| |
| |
| def main(): |
| """Tests that EV compensation is applied.""" |
| |
| with its.device.ItsSession() as cam: |
| props = cam.get_camera_properties() |
| its.caps.skip_unless(its.caps.ev_compensation(props) and |
| its.caps.ae_lock(props)) |
| |
| debug = its.caps.debug_mode() |
| mono_camera = its.caps.mono_camera(props) |
| 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) |
| |
| ev_per_step = its.objects.rational_to_float( |
| props['android.control.aeCompensationStep']) |
| steps_per_ev = int(1.0 / ev_per_step) |
| evs = range(-2 * steps_per_ev, 2 * steps_per_ev + 1, steps_per_ev) |
| lumas = [] |
| |
| # Converge 3A, and lock AE once converged. skip AF trigger as |
| # dark/bright scene could make AF convergence fail and this test |
| # doesn't care the image sharpness. |
| cam.do_3a(ev_comp=0, lock_ae=True, do_af=False, mono_camera=mono_camera) |
| |
| for ev in evs: |
| # Capture a single shot with the same EV comp and locked AE. |
| req = its.objects.auto_capture_request() |
| req['android.control.aeExposureCompensation'] = ev |
| req['android.control.aeLock'] = True |
| caps = cam.do_capture([req]*THRESH_CONVERGE_FOR_EV, fmt) |
| luma_locked = [] |
| for i, cap in enumerate(caps): |
| if cap['metadata']['android.control.aeState'] == LOCKED: |
| y = its.image.convert_capture_to_planes(cap)[0] |
| tile = its.image.get_image_patch(y, 0.45, 0.45, 0.1, 0.1) |
| luma = its.image.compute_image_means(tile)[0] |
| luma_locked.append(luma) |
| if i == THRESH_CONVERGE_FOR_EV-1: |
| lumas.append(luma) |
| print 'lumas in AE locked captures: ', luma_locked |
| msg = 'AE locked lumas: %s, RTOL: %.2f' % ( |
| str(luma_locked), LUMA_LOCKED_TOL) |
| assert np.isclose(min(luma_locked), max(luma_locked), |
| rtol=LUMA_LOCKED_TOL), msg |
| assert caps[THRESH_CONVERGE_FOR_EV-1]['metadata']['android.control.aeState'] == LOCKED |
| |
| pylab.plot(evs, lumas, '-ro') |
| pylab.title(NAME) |
| pylab.xlabel('EV Compensation') |
| pylab.ylabel('Mean Luma (Normalized)') |
| matplotlib.pyplot.savefig('%s_plot_means.png' % (NAME)) |
| |
| # Trim extra saturated images |
| while (lumas[-2] >= YUV_SAT_MIN/YUV_FULL_SCALE and |
| lumas[-1] >= YUV_SAT_MIN/YUV_FULL_SCALE and |
| len(lumas) > 2): |
| lumas.pop(-1) |
| print 'Removed saturated image.' |
| |
| # Only allow positive EVs to give saturated image |
| assert len(lumas) > 2, '3 or more unsaturated images needed' |
| min_luma_diffs = min(np.diff(lumas)) |
| print 'Min of the luma value difference between adjacent ev comp: ', |
| print min_luma_diffs |
| # All luma brightness should be increasing with increasing ev comp. |
| assert min_luma_diffs > 0, 'Luma is not increasing!' |
| |
| if __name__ == '__main__': |
| main() |