| # 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 manual burst capture consistency.""" |
| |
| |
| import logging |
| import os.path |
| from matplotlib import pylab |
| import matplotlib.pyplot |
| 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 |
| import target_exposure_utils |
| |
| API_LEVEL_30 = 30 |
| BURST_LEN = 50 |
| COLORS = ['R', 'G', 'B'] |
| NAME = os.path.splitext(os.path.basename(__file__))[0] |
| NUM_BURSTS = 5 |
| PATCH_H = 0.1 # center 10% |
| PATCH_W = 0.1 |
| PATCH_X = 0.5 - PATCH_W/2 |
| PATCH_Y = 0.5 - PATCH_H/2 |
| SPREAD_THRESH = 0.03 |
| SPREAD_THRESH_API_LEVEL_30 = 0.02 |
| |
| NUM_FRAMES = BURST_LEN * NUM_BURSTS |
| |
| |
| class BurstSamenessManualTest(its_base_test.ItsBaseTest): |
| """Take long bursts of images and check that they're all identical. |
| |
| Assumes a static scene. Can be used to idenfity if there are sporadic |
| frames that are processed differently or have artifacts. Uses manual |
| capture settings. |
| """ |
| |
| def test_burst_sameness_manual(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.compute_target_exposure(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) |
| |
| # Capture at the smallest resolution |
| _, fmt = capture_request_utils.get_fastest_manual_capture_settings(props) |
| e, s = target_exposure_utils.get_target_exposure_combos( |
| log_path, cam)['minSensitivity'] |
| req = capture_request_utils.manual_capture_request(s, e) |
| w, h = fmt['width'], fmt['height'] |
| |
| # Capture bursts of YUV shots. |
| # Get the mean values of a center patch for each. |
| # Also build a 4D array, imgs, which is an array of all RGB images. |
| r_means = [] |
| g_means = [] |
| b_means = [] |
| imgs = np.empty([NUM_FRAMES, h, w, 3]) |
| for j in range(NUM_BURSTS): |
| caps = cam.do_capture([req]*BURST_LEN, [fmt]) |
| for i, cap in enumerate(caps): |
| n = j*BURST_LEN + i |
| imgs[n] = image_processing_utils.convert_capture_to_rgb_image(cap) |
| patch = image_processing_utils.get_image_patch( |
| imgs[n], PATCH_X, PATCH_Y, PATCH_W, PATCH_H) |
| means = image_processing_utils.compute_image_means(patch) |
| r_means.append(means[0]) |
| g_means.append(means[1]) |
| b_means.append(means[2]) |
| |
| # Save first frame for setup debug |
| image_processing_utils.write_image( |
| imgs[0], '%s_frame000.jpg' % os.path.join(log_path, NAME)) |
| |
| # Save all frames if debug |
| if self.debug_mode: |
| logging.debug('Dumping all images') |
| for i in range(1, NUM_FRAMES): |
| image_processing_utils.write_image( |
| imgs[i], '%s_frame%03d.jpg'%(os.path.join(log_path, NAME), i)) |
| |
| # Plot RGB means vs frames |
| frames = range(NUM_FRAMES) |
| pylab.figure(NAME) |
| pylab.title(NAME) |
| pylab.plot(frames, r_means, '-ro') |
| pylab.plot(frames, g_means, '-go') |
| pylab.plot(frames, b_means, '-bo') |
| pylab.ylim([0, 1]) |
| pylab.xlabel('frame number') |
| pylab.ylabel('RGB avg [0, 1]') |
| matplotlib.pyplot.savefig( |
| '%s_plot_means.png' % os.path.join(log_path, NAME)) |
| |
| # determine spread_thresh |
| spread_thresh = SPREAD_THRESH |
| if its_session_utils.get_first_api_level(self.dut.serial) >= API_LEVEL_30: |
| spread_thresh = SPREAD_THRESH_API_LEVEL_30 |
| |
| # PASS/FAIL based on center patch similarity. |
| for plane, means in enumerate([r_means, g_means, b_means]): |
| spread = max(means) - min(means) |
| msg = '%s spread: %.5f, spread_thresh: %.2f' % ( |
| COLORS[plane], spread, spread_thresh) |
| logging.debug('%s', msg) |
| assert spread < spread_thresh, msg |
| |
| if __name__ == '__main__': |
| test_runner.main() |
| |