| # Copyright 2013 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 linear behavior in exposure/gain space.""" |
| |
| |
| import logging |
| import math |
| 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 |
| import target_exposure_utils |
| |
| NAME = os.path.splitext(os.path.basename(__file__))[0] |
| NUM_STEPS = 6 |
| PATCH_H = 0.1 # center 10% patch params |
| PATCH_W = 0.1 |
| PATCH_X = 0.5 - PATCH_W/2 |
| PATCH_Y = 0.5 - PATCH_H/2 |
| RESIDUAL_THRESH = 0.0003 # sample error of ~2/255 in np.arange(0, 0.5, 0.1) |
| VGA_W, VGA_H = 640, 480 |
| |
| # HAL3.2 spec requires curves up to 64 control points in length be supported |
| L = 63 |
| GAMMA_LUT = np.array( |
| sum([[i/L, math.pow(i/L, 1/2.2)] for i in range(L+1)], [])) |
| INV_GAMMA_LUT = np.array( |
| sum([[i/L, math.pow(i/L, 2.2)] for i in range(L+1)], [])) |
| |
| |
| class LinearityTest(its_base_test.ItsBaseTest): |
| """Test that device processing can be inverted to linear pixels. |
| |
| Captures a sequence of shots with the device pointed at a uniform |
| target. Attempts to invert all the ISP processing to get back to |
| linear R,G,B pixel data. |
| """ |
| |
| def test_linearity(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) |
| camera_properties_utils.skip_unless( |
| camera_properties_utils.compute_target_exposure(props)) |
| sync_latency = camera_properties_utils.sync_latency(props) |
| |
| # Load chart for scene |
| its_session_utils.load_scene( |
| cam, props, self.scene, self.tablet, self.chart_distance) |
| |
| # Determine sensitivities to test over |
| e_mid, s_mid = target_exposure_utils.get_target_exposure_combos( |
| self.log_path, cam)['midSensitivity'] |
| sens_range = props['android.sensor.info.sensitivityRange'] |
| sensitivities = [s_mid*x/NUM_STEPS for x in range(1, NUM_STEPS)] |
| sensitivities = [s for s in sensitivities |
| if s > sens_range[0] and s < sens_range[1]] |
| |
| # Initialize capture request |
| req = capture_request_utils.manual_capture_request(0, e_mid) |
| req['android.blackLevel.lock'] = True |
| req['android.tonemap.mode'] = 0 |
| req['android.tonemap.curve'] = {'red': GAMMA_LUT.tolist(), |
| 'green': GAMMA_LUT.tolist(), |
| 'blue': GAMMA_LUT.tolist()} |
| # Do captures and calculate center patch RGB means |
| r_means = [] |
| g_means = [] |
| b_means = [] |
| fmt = {'format': 'yuv', 'width': VGA_W, 'height': VGA_H} |
| for sens in sensitivities: |
| req['android.sensor.sensitivity'] = sens |
| cap = its_session_utils.do_capture_with_latency( |
| cam, req, sync_latency, fmt) |
| img = image_processing_utils.convert_capture_to_rgb_image(cap) |
| img_name = '%s_sens=%.04d.jpg' % ( |
| os.path.join(self.log_path, NAME), sens) |
| image_processing_utils.write_image(img, img_name) |
| img = image_processing_utils.apply_lut_to_image( |
| img, INV_GAMMA_LUT[1::2] * L) |
| patch = image_processing_utils.get_image_patch( |
| img, PATCH_X, PATCH_Y, PATCH_W, PATCH_H) |
| rgb_means = image_processing_utils.compute_image_means(patch) |
| r_means.append(rgb_means[0]) |
| g_means.append(rgb_means[1]) |
| b_means.append(rgb_means[2]) |
| |
| # Plot means |
| pylab.figure(NAME) |
| pylab.plot(sensitivities, r_means, '-ro') |
| pylab.plot(sensitivities, g_means, '-go') |
| pylab.plot(sensitivities, b_means, '-bo') |
| pylab.title(NAME) |
| pylab.xlim([sens_range[0], sens_range[1]/2]) |
| pylab.ylim([0, 1]) |
| pylab.xlabel('sensitivity(ISO)') |
| pylab.ylabel('RGB avg [0, 1]') |
| matplotlib.pyplot.savefig( |
| '%s_plot_means.png' % os.path.join(self.log_path, NAME)) |
| |
| # Assert plot curves are linear w/ + slope by examining polyfit residual |
| for means in [r_means, g_means, b_means]: |
| line, residuals, _, _, _ = np.polyfit( |
| range(len(sensitivities)), means, 1, full=True) |
| logging.debug('Line: m=%f, b=%f, resid=%f', |
| line[0], line[1], residuals[0]) |
| msg = 'residual: %.5f, THRESH: %.4f' % (residuals[0], RESIDUAL_THRESH) |
| assert residuals[0] < RESIDUAL_THRESH, msg |
| assert line[0] > 0, 'slope %.6f less than 0!' % line[0] |
| |
| if __name__ == '__main__': |
| test_runner.main() |