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135 lines
5.1 KiB
135 lines
5.1 KiB
# Copyright 2013 The Android Open Source Project
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Verifies linear behavior in exposure/gain space."""
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import logging
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import math
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import os.path
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import matplotlib
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from matplotlib import pylab
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from mobly import test_runner
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import numpy as np
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import its_base_test
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import camera_properties_utils
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import capture_request_utils
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import image_processing_utils
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import its_session_utils
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import target_exposure_utils
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NAME = os.path.splitext(os.path.basename(__file__))[0]
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NUM_STEPS = 6
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PATCH_H = 0.1 # center 10% patch params
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PATCH_W = 0.1
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PATCH_X = 0.5 - PATCH_W/2
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PATCH_Y = 0.5 - PATCH_H/2
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RESIDUAL_THRESH = 0.0003 # sample error of ~2/255 in np.arange(0, 0.5, 0.1)
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VGA_W, VGA_H = 640, 480
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# HAL3.2 spec requires curves up to 64 control points in length be supported
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L = 63
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GAMMA_LUT = np.array(
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sum([[i/L, math.pow(i/L, 1/2.2)] for i in range(L+1)], []))
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INV_GAMMA_LUT = np.array(
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sum([[i/L, math.pow(i/L, 2.2)] for i in range(L+1)], []))
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class LinearityTest(its_base_test.ItsBaseTest):
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"""Test that device processing can be inverted to linear pixels.
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Captures a sequence of shots with the device pointed at a uniform
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target. Attempts to invert all the ISP processing to get back to
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linear R,G,B pixel data.
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"""
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def test_linearity(self):
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logging.debug('Starting %s', NAME)
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with its_session_utils.ItsSession(
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device_id=self.dut.serial,
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camera_id=self.camera_id,
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hidden_physical_id=self.hidden_physical_id) as cam:
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props = cam.get_camera_properties()
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props = cam.override_with_hidden_physical_camera_props(props)
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camera_properties_utils.skip_unless(
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camera_properties_utils.compute_target_exposure(props))
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sync_latency = camera_properties_utils.sync_latency(props)
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# Load chart for scene
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its_session_utils.load_scene(
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cam, props, self.scene, self.tablet, self.chart_distance)
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# Determine sensitivities to test over
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e_mid, s_mid = target_exposure_utils.get_target_exposure_combos(
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self.log_path, cam)['midSensitivity']
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sens_range = props['android.sensor.info.sensitivityRange']
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sensitivities = [s_mid*x/NUM_STEPS for x in range(1, NUM_STEPS)]
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sensitivities = [s for s in sensitivities
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if s > sens_range[0] and s < sens_range[1]]
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# Initialize capture request
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req = capture_request_utils.manual_capture_request(0, e_mid)
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req['android.blackLevel.lock'] = True
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req['android.tonemap.mode'] = 0
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req['android.tonemap.curve'] = {'red': GAMMA_LUT.tolist(),
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'green': GAMMA_LUT.tolist(),
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'blue': GAMMA_LUT.tolist()}
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# Do captures and calculate center patch RGB means
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r_means = []
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g_means = []
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b_means = []
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fmt = {'format': 'yuv', 'width': VGA_W, 'height': VGA_H}
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for sens in sensitivities:
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req['android.sensor.sensitivity'] = sens
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cap = its_session_utils.do_capture_with_latency(
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cam, req, sync_latency, fmt)
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img = image_processing_utils.convert_capture_to_rgb_image(cap)
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img_name = '%s_sens=%.04d.jpg' % (
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os.path.join(self.log_path, NAME), sens)
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image_processing_utils.write_image(img, img_name)
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img = image_processing_utils.apply_lut_to_image(
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img, INV_GAMMA_LUT[1::2] * L)
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patch = image_processing_utils.get_image_patch(
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img, PATCH_X, PATCH_Y, PATCH_W, PATCH_H)
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rgb_means = image_processing_utils.compute_image_means(patch)
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r_means.append(rgb_means[0])
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g_means.append(rgb_means[1])
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b_means.append(rgb_means[2])
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# Plot means
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pylab.figure(NAME)
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pylab.plot(sensitivities, r_means, '-ro')
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pylab.plot(sensitivities, g_means, '-go')
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pylab.plot(sensitivities, b_means, '-bo')
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pylab.title(NAME)
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pylab.xlim([sens_range[0], sens_range[1]/2])
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pylab.ylim([0, 1])
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pylab.xlabel('sensitivity(ISO)')
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pylab.ylabel('RGB avg [0, 1]')
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matplotlib.pyplot.savefig(
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'%s_plot_means.png' % os.path.join(self.log_path, NAME))
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# Assert plot curves are linear w/ + slope by examining polyfit residual
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for means in [r_means, g_means, b_means]:
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line, residuals, _, _, _ = np.polyfit(
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range(len(sensitivities)), means, 1, full=True)
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logging.debug('Line: m=%f, b=%f, resid=%f',
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line[0], line[1], residuals[0])
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msg = 'residual: %.5f, THRESH: %.4f' % (residuals[0], RESIDUAL_THRESH)
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assert residuals[0] < RESIDUAL_THRESH, msg
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assert line[0] > 0, 'slope %.6f less than 0!' % line[0]
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if __name__ == '__main__':
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test_runner.main()
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