1# Copyright 2013 The Android Open Source Project 2# 3# Licensed under the Apache License, Version 2.0 (the "License"); 4# you may not use this file except in compliance with the License. 5# You may obtain a copy of the License at 6# 7# http://www.apache.org/licenses/LICENSE-2.0 8# 9# Unless required by applicable law or agreed to in writing, software 10# distributed under the License is distributed on an "AS IS" BASIS, 11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12# See the License for the specific language governing permissions and 13# limitations under the License. 14"""Verifies linear behavior in exposure/gain space.""" 15 16 17import logging 18import math 19import os.path 20import matplotlib 21from matplotlib import pylab 22from mobly import test_runner 23import numpy as np 24 25import its_base_test 26import camera_properties_utils 27import capture_request_utils 28import image_processing_utils 29import its_session_utils 30import target_exposure_utils 31 32NAME = os.path.splitext(os.path.basename(__file__))[0] 33NUM_STEPS = 6 34PATCH_H = 0.1 # center 10% patch params 35PATCH_W = 0.1 36PATCH_X = 0.5 - PATCH_W/2 37PATCH_Y = 0.5 - PATCH_H/2 38RESIDUAL_THRESH = 0.0003 # sample error of ~2/255 in np.arange(0, 0.5, 0.1) 39VGA_W, VGA_H = 640, 480 40 41# HAL3.2 spec requires curves up to 64 control points in length be supported 42L = 63 43GAMMA_LUT = np.array( 44 sum([[i/L, math.pow(i/L, 1/2.2)] for i in range(L+1)], [])) 45INV_GAMMA_LUT = np.array( 46 sum([[i/L, math.pow(i/L, 2.2)] for i in range(L+1)], [])) 47 48 49class LinearityTest(its_base_test.ItsBaseTest): 50 """Test that device processing can be inverted to linear pixels. 51 52 Captures a sequence of shots with the device pointed at a uniform 53 target. Attempts to invert all the ISP processing to get back to 54 linear R,G,B pixel data. 55 """ 56 57 def test_linearity(self): 58 logging.debug('Starting %s', NAME) 59 with its_session_utils.ItsSession( 60 device_id=self.dut.serial, 61 camera_id=self.camera_id, 62 hidden_physical_id=self.hidden_physical_id) as cam: 63 props = cam.get_camera_properties() 64 props = cam.override_with_hidden_physical_camera_props(props) 65 camera_properties_utils.skip_unless( 66 camera_properties_utils.compute_target_exposure(props)) 67 sync_latency = camera_properties_utils.sync_latency(props) 68 69 # Load chart for scene 70 its_session_utils.load_scene( 71 cam, props, self.scene, self.tablet, self.chart_distance) 72 73 # Determine sensitivities to test over 74 e_mid, s_mid = target_exposure_utils.get_target_exposure_combos( 75 self.log_path, cam)['midSensitivity'] 76 sens_range = props['android.sensor.info.sensitivityRange'] 77 sensitivities = [s_mid*x/NUM_STEPS for x in range(1, NUM_STEPS)] 78 sensitivities = [s for s in sensitivities 79 if s > sens_range[0] and s < sens_range[1]] 80 81 # Initialize capture request 82 req = capture_request_utils.manual_capture_request(0, e_mid) 83 req['android.blackLevel.lock'] = True 84 req['android.tonemap.mode'] = 0 85 req['android.tonemap.curve'] = {'red': GAMMA_LUT.tolist(), 86 'green': GAMMA_LUT.tolist(), 87 'blue': GAMMA_LUT.tolist()} 88 # Do captures and calculate center patch RGB means 89 r_means = [] 90 g_means = [] 91 b_means = [] 92 fmt = {'format': 'yuv', 'width': VGA_W, 'height': VGA_H} 93 for sens in sensitivities: 94 req['android.sensor.sensitivity'] = sens 95 cap = its_session_utils.do_capture_with_latency( 96 cam, req, sync_latency, fmt) 97 img = image_processing_utils.convert_capture_to_rgb_image(cap) 98 img_name = '%s_sens=%.04d.jpg' % ( 99 os.path.join(self.log_path, NAME), sens) 100 image_processing_utils.write_image(img, img_name) 101 img = image_processing_utils.apply_lut_to_image( 102 img, INV_GAMMA_LUT[1::2] * L) 103 patch = image_processing_utils.get_image_patch( 104 img, PATCH_X, PATCH_Y, PATCH_W, PATCH_H) 105 rgb_means = image_processing_utils.compute_image_means(patch) 106 r_means.append(rgb_means[0]) 107 g_means.append(rgb_means[1]) 108 b_means.append(rgb_means[2]) 109 110 # Plot means 111 pylab.figure(NAME) 112 pylab.plot(sensitivities, r_means, '-ro') 113 pylab.plot(sensitivities, g_means, '-go') 114 pylab.plot(sensitivities, b_means, '-bo') 115 pylab.title(NAME) 116 pylab.xlim([sens_range[0], sens_range[1]/2]) 117 pylab.ylim([0, 1]) 118 pylab.xlabel('sensitivity(ISO)') 119 pylab.ylabel('RGB avg [0, 1]') 120 matplotlib.pyplot.savefig( 121 '%s_plot_means.png' % os.path.join(self.log_path, NAME)) 122 123 # Assert plot curves are linear w/ + slope by examining polyfit residual 124 for means in [r_means, g_means, b_means]: 125 line, residuals, _, _, _ = np.polyfit( 126 range(len(sensitivities)), means, 1, full=True) 127 logging.debug('Line: m=%f, b=%f, resid=%f', 128 line[0], line[1], residuals[0]) 129 if residuals[0] > RESIDUAL_THRESH: 130 raise AssertionError( 131 'residual: {residuals[0]:.5f}, THRESH: {RESIDUAL_THRESH}') 132 if line[0] <= 0: 133 raise AssertionError(f'slope {line[0]:.6f} <= 0!') 134 135if __name__ == '__main__': 136 test_runner.main() 137