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 15import os.path 16 17import its.caps 18import its.device 19import its.image 20import its.objects 21import its.target 22import matplotlib 23from matplotlib import pylab 24import numpy 25 26IMG_STATS_GRID = 9 # find used to find the center 11.11% 27NAME = os.path.basename(__file__).split('.')[0] 28THRESHOLD_MAX_OUTLIER_DIFF = 0.1 29THRESHOLD_MIN_LEVEL = 0.1 30THRESHOLD_MAX_LEVEL = 0.9 31THRESHOLD_MAX_LEVEL_DIFF = 0.045 32THRESHOLD_MAX_LEVEL_DIFF_WIDE_RANGE = 0.06 33THRESH_ROUND_DOWN_GAIN = 0.1 34THRESH_ROUND_DOWN_EXP = 0.03 35THRESH_ROUND_DOWN_EXP0 = 1.00 # tol at 0ms exp; theoretical limit @ 4-line exp 36THRESH_EXP_KNEE = 6E6 # exposures less than knee have relaxed tol 37 38 39def get_raw_active_array_size(props): 40 """Return the active array w, h from props.""" 41 aaw = (props['android.sensor.info.preCorrectionActiveArraySize']['right'] - 42 props['android.sensor.info.preCorrectionActiveArraySize']['left']) 43 aah = (props['android.sensor.info.preCorrectionActiveArraySize']['bottom'] - 44 props['android.sensor.info.preCorrectionActiveArraySize']['top']) 45 return aaw, aah 46 47 48def main(): 49 """Test that a constant exposure is seen as ISO and exposure time vary. 50 51 Take a series of shots that have ISO and exposure time chosen to balance 52 each other; result should be the same brightness, but over the sequence 53 the images should get noisier. 54 """ 55 mults = [] 56 r_means = [] 57 g_means = [] 58 b_means = [] 59 raw_r_means = [] 60 raw_gr_means = [] 61 raw_gb_means = [] 62 raw_b_means = [] 63 threshold_max_level_diff = THRESHOLD_MAX_LEVEL_DIFF 64 65 with its.device.ItsSession() as cam: 66 props = cam.get_camera_properties() 67 props = cam.override_with_hidden_physical_camera_props(props) 68 its.caps.skip_unless(its.caps.compute_target_exposure(props)) 69 sync_latency = its.caps.sync_latency(props) 70 process_raw = its.caps.raw16(props) and its.caps.manual_sensor(props) 71 debug = its.caps.debug_mode() 72 largest_yuv = its.objects.get_largest_yuv_format(props) 73 if debug: 74 fmt = largest_yuv 75 else: 76 match_ar = (largest_yuv['width'], largest_yuv['height']) 77 fmt = its.objects.get_smallest_yuv_format(props, match_ar=match_ar) 78 79 e, s = its.target.get_target_exposure_combos(cam)['minSensitivity'] 80 s_e_product = s*e 81 expt_range = props['android.sensor.info.exposureTimeRange'] 82 sens_range = props['android.sensor.info.sensitivityRange'] 83 84 m = 1.0 85 while s*m < sens_range[1] and e/m > expt_range[0]: 86 mults.append(m) 87 s_test = round(s*m) 88 e_test = s_e_product / s_test 89 print 'Testing s:', s_test, 'e:', e_test 90 req = its.objects.manual_capture_request( 91 s_test, e_test, 0.0, True, props) 92 cap = its.device.do_capture_with_latency( 93 cam, req, sync_latency, fmt) 94 s_res = cap['metadata']['android.sensor.sensitivity'] 95 e_res = cap['metadata']['android.sensor.exposureTime'] 96 # determine exposure tolerance based on exposure time 97 if e_test >= THRESH_EXP_KNEE: 98 thresh_round_down_exp = THRESH_ROUND_DOWN_EXP 99 else: 100 thresh_round_down_exp = ( 101 THRESH_ROUND_DOWN_EXP + 102 (THRESH_ROUND_DOWN_EXP0 - THRESH_ROUND_DOWN_EXP) * 103 (THRESH_EXP_KNEE - e_test) / THRESH_EXP_KNEE) 104 s_msg = 's_write: %d, s_read: %d, TOL=%.f%%' % ( 105 s_test, s_res, THRESH_ROUND_DOWN_GAIN*100) 106 e_msg = 'e_write: %.3fms, e_read: %.3fms, TOL=%.f%%' % ( 107 e_test/1.0E6, e_res/1.0E6, thresh_round_down_exp*100) 108 assert 0 <= s_test - s_res < s_test * THRESH_ROUND_DOWN_GAIN, s_msg 109 assert 0 <= e_test - e_res < e_test * thresh_round_down_exp, e_msg 110 s_e_product_res = s_res * e_res 111 request_result_ratio = float(s_e_product) / s_e_product_res 112 print 'Capture result s:', s_res, 'e:', e_res 113 img = its.image.convert_capture_to_rgb_image(cap) 114 its.image.write_image(img, '%s_mult=%3.2f.jpg' % (NAME, m)) 115 tile = its.image.get_image_patch(img, 0.45, 0.45, 0.1, 0.1) 116 rgb_means = its.image.compute_image_means(tile) 117 # Adjust for the difference between request and result 118 r_means.append(rgb_means[0] * request_result_ratio) 119 g_means.append(rgb_means[1] * request_result_ratio) 120 b_means.append(rgb_means[2] * request_result_ratio) 121 # do same in RAW space if possible 122 if process_raw and debug: 123 aaw, aah = get_raw_active_array_size(props) 124 fmt_raw = {'format': 'rawStats', 125 'gridWidth': aaw/IMG_STATS_GRID, 126 'gridHeight': aah/IMG_STATS_GRID} 127 raw_cap = its.device.do_capture_with_latency( 128 cam, req, sync_latency, fmt_raw) 129 r, gr, gb, b = its.image.convert_capture_to_planes( 130 raw_cap, props) 131 raw_r_means.append(r[IMG_STATS_GRID/2, IMG_STATS_GRID/2] 132 * request_result_ratio) 133 raw_gr_means.append(gr[IMG_STATS_GRID/2, IMG_STATS_GRID/2] 134 * request_result_ratio) 135 raw_gb_means.append(gb[IMG_STATS_GRID/2, IMG_STATS_GRID/2] 136 * request_result_ratio) 137 raw_b_means.append(b[IMG_STATS_GRID/2, IMG_STATS_GRID/2] 138 * request_result_ratio) 139 # Test 3 steps per 2x gain 140 m *= pow(2, 1.0 / 3) 141 142 # Allow more threshold for devices with wider exposure range 143 if m >= 64.0: 144 threshold_max_level_diff = THRESHOLD_MAX_LEVEL_DIFF_WIDE_RANGE 145 146 # Draw plots 147 pylab.figure('rgb data') 148 pylab.plot(mults, r_means, 'ro-') 149 pylab.plot(mults, g_means, 'go-') 150 pylab.plot(mults, b_means, 'bo-') 151 pylab.title(NAME + 'RGB Data') 152 pylab.xlabel('Gain Multiplier') 153 pylab.ylabel('Normalized RGB Plane Avg') 154 pylab.ylim([0, 1]) 155 matplotlib.pyplot.savefig('%s_plot_means.png' % (NAME)) 156 157 if process_raw and debug: 158 pylab.figure('raw data') 159 pylab.plot(mults, raw_r_means, 'ro-', label='R') 160 pylab.plot(mults, raw_gr_means, 'go-', label='GR') 161 pylab.plot(mults, raw_gb_means, 'ko-', label='GB') 162 pylab.plot(mults, raw_b_means, 'bo-', label='B') 163 pylab.title(NAME + 'RAW Data') 164 pylab.xlabel('Gain Multiplier') 165 pylab.ylabel('Normalized RAW Plane Avg') 166 pylab.ylim([0, 1]) 167 pylab.legend(numpoints=1) 168 matplotlib.pyplot.savefig('%s_plot_raw_means.png' % (NAME)) 169 170 # Check for linearity. Verify sample pixel mean values are close to each 171 # other. Also ensure that the images aren't clamped to 0 or 1 172 # (which would make them look like flat lines). 173 for chan in xrange(3): 174 values = [r_means, g_means, b_means][chan] 175 m, b = numpy.polyfit(mults, values, 1).tolist() 176 max_val = max(values) 177 min_val = min(values) 178 max_diff = max_val - min_val 179 print 'Channel %d line fit (y = mx+b): m = %f, b = %f' % (chan, m, b) 180 print 'Channel max %f min %f diff %f' % (max_val, min_val, max_diff) 181 assert max_diff < threshold_max_level_diff 182 assert b > THRESHOLD_MIN_LEVEL and b < THRESHOLD_MAX_LEVEL 183 for v in values: 184 assert v > THRESHOLD_MIN_LEVEL and v < THRESHOLD_MAX_LEVEL 185 assert abs(v - b) < THRESHOLD_MAX_OUTLIER_DIFF 186 if process_raw and debug: 187 for chan in xrange(4): 188 values = [raw_r_means, raw_gr_means, raw_gb_means, 189 raw_b_means][chan] 190 m, b = numpy.polyfit(mults, values, 1).tolist() 191 max_val = max(values) 192 min_val = min(values) 193 max_diff = max_val - min_val 194 print 'Channel %d line fit (y = mx+b): m = %f, b = %f' % (chan, 195 m, b) 196 print 'Channel max %f min %f diff %f' % (max_val, min_val, max_diff) 197 assert max_diff < threshold_max_level_diff 198 assert b > THRESHOLD_MIN_LEVEL and b < THRESHOLD_MAX_LEVEL 199 for v in values: 200 assert v > THRESHOLD_MIN_LEVEL and v < THRESHOLD_MAX_LEVEL 201 assert abs(v - b) < THRESHOLD_MAX_OUTLIER_DIFF 202 203if __name__ == '__main__': 204 main() 205