| # 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. |
| """Verifies EV compensation is applied.""" |
| |
| |
| import logging |
| import os.path |
| import matplotlib |
| from matplotlib import pylab |
| from mobly import test_runner |
| import numpy as np |
| |
| import its_base_test |
| import camera_properties_utils |
| import capture_request_utils |
| import image_processing_utils |
| import its_session_utils |
| |
| LINEAR_TONEMAP_CURVE = [0.0, 0.0, 1.0, 1.0] |
| LOCKED = 3 |
| LUMA_DELTA_THRESH = 0.05 |
| LUMA_LOCKED_TOL = 0.05 |
| NAME = os.path.splitext(os.path.basename(__file__))[0] |
| PATCH_H = 0.1 # center 10% |
| PATCH_W = 0.1 |
| PATCH_X = 0.5 - PATCH_W/2 |
| PATCH_Y = 0.5 - PATCH_H/2 |
| THRESH_CONVERGE_FOR_EV = 8 # AE must converge within this num auto reqs for EV |
| YUV_FULL_SCALE = 255.0 |
| YUV_SAT_MIN = 250.0 |
| YUV_SAT_TOL = 3.0 |
| |
| |
| def create_request_with_ev(ev): |
| req = capture_request_utils.auto_capture_request() |
| req['android.control.aeExposureCompensation'] = ev |
| req['android.control.aeLock'] = True |
| # Use linear tonemap to avoid brightness being impacted by tone curves. |
| req['android.tonemap.mode'] = 0 |
| req['android.tonemap.curve'] = {'red': LINEAR_TONEMAP_CURVE, |
| 'green': LINEAR_TONEMAP_CURVE, |
| 'blue': LINEAR_TONEMAP_CURVE} |
| return req |
| |
| |
| def extract_luma_from_capture(cap): |
| """Extract luma from capture.""" |
| y = image_processing_utils.convert_capture_to_planes(cap)[0] |
| patch = image_processing_utils.get_image_patch( |
| y, PATCH_X, PATCH_Y, PATCH_W, PATCH_H) |
| luma = image_processing_utils.compute_image_means(patch)[0] |
| return luma |
| |
| |
| def create_ev_comp_changes(props): |
| """Create the ev compensation steps and shifts from control params.""" |
| ev_compensation_range = props['android.control.aeCompensationRange'] |
| range_min = ev_compensation_range[0] |
| range_max = ev_compensation_range[1] |
| ev_per_step = capture_request_utils.rational_to_float( |
| props['android.control.aeCompensationStep']) |
| logging.debug('ev_step_size_in_stops: %d', ev_per_step) |
| steps_per_ev = int(round(1.0 / ev_per_step)) |
| ev_steps = range(range_min, range_max + 1, steps_per_ev) |
| ev_shifts = [pow(2, step * ev_per_step) for step in ev_steps] |
| return ev_steps, ev_shifts |
| |
| |
| class EvCompensationAdvancedTest(its_base_test.ItsBaseTest): |
| """Tests that EV compensation is applied.""" |
| |
| def test_ev_compensation_advanced(self): |
| logging.debug('Starting %s', NAME) |
| with its_session_utils.ItsSession( |
| device_id=self.dut.serial, |
| camera_id=self.camera_id, |
| hidden_physical_id=self.hidden_physical_id) as cam: |
| props = cam.get_camera_properties() |
| props = cam.override_with_hidden_physical_camera_props(props) |
| log_path = self.log_path |
| |
| # check SKIP conditions |
| camera_properties_utils.skip_unless( |
| camera_properties_utils.ev_compensation(props) and |
| camera_properties_utils.manual_sensor(props) and |
| camera_properties_utils.manual_post_proc(props) and |
| camera_properties_utils.per_frame_control(props)) |
| |
| # Load chart for scene |
| its_session_utils.load_scene( |
| cam, props, self.scene, self.tablet, self.chart_distance) |
| |
| # Create ev compensation changes |
| ev_steps, ev_shifts = create_ev_comp_changes(props) |
| |
| # 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. |
| mono_camera = camera_properties_utils.mono_camera(props) |
| cam.do_3a(ev_comp=0, lock_ae=True, do_af=False, mono_camera=mono_camera) |
| |
| # Create requests and capture |
| largest_yuv = capture_request_utils.get_largest_yuv_format(props) |
| match_ar = (largest_yuv['width'], largest_yuv['height']) |
| fmt = capture_request_utils.get_smallest_yuv_format( |
| props, match_ar=match_ar) |
| lumas = [] |
| for ev in ev_steps: |
| # Capture a single shot with the same EV comp and locked AE. |
| req = create_request_with_ev(ev) |
| caps = cam.do_capture([req]*THRESH_CONVERGE_FOR_EV, fmt) |
| for cap in caps: |
| if cap['metadata']['android.control.aeState'] == LOCKED: |
| lumas.append(extract_luma_from_capture(cap)) |
| break |
| assert cap['metadata']['android.control.aeState'] == LOCKED |
| logging.debug('lumas in AE locked captures: %s', str(lumas)) |
| |
| i_mid = len(ev_steps) // 2 |
| luma_normal = lumas[i_mid] / ev_shifts[i_mid] |
| expected_lumas = [min(1.0, luma_normal*shift) for shift in ev_shifts] |
| |
| # Create plot |
| pylab.figure(NAME) |
| pylab.plot(ev_steps, lumas, '-ro', label='measured', alpha=0.7) |
| pylab.plot(ev_steps, expected_lumas, '-bo', label='expected', alpha=0.7) |
| pylab.title(NAME) |
| pylab.xlabel('EV Compensation') |
| pylab.ylabel('Mean Luma (Normalized)') |
| pylab.legend(loc='lower right', numpoints=1, fancybox=True) |
| matplotlib.pyplot.savefig( |
| '%s_plot_means.png' % os.path.join(log_path, NAME)) |
| |
| luma_diffs = [expected_lumas[i]-lumas[i] for i in range(len(ev_steps))] |
| max_diff = max(abs(i) for i in luma_diffs) |
| avg_diff = abs(np.array(luma_diffs)).mean() |
| logging.debug( |
| 'Max delta between modeled and measured lumas: %.4f', max_diff) |
| logging.debug( |
| 'Avg delta between modeled and measured lumas: %.4f', avg_diff) |
| assert max_diff < LUMA_DELTA_THRESH, 'diff: %.3f, THRESH: %.2f' % ( |
| max_diff, LUMA_DELTA_THRESH) |
| |
| if __name__ == '__main__': |
| test_runner.main() |