1# Copyright 2014 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 16import its.caps 17import its.device 18import its.image 19import its.objects 20import matplotlib 21from matplotlib import pylab 22 23NAME = os.path.basename(__file__).split('.')[0] 24BAYER_LIST = ['R', 'GR', 'GB', 'B'] 25DIFF_THRESH = 0.0005 # absolute variance delta threshold 26FRAC_THRESH = 0.2 # relative variance delta threshold 27NUM_STEPS = 4 28STATS_GRID = 49 # center 2.04% of image for calculations 29 30 31def main(): 32 """Verify that the DNG raw model parameters are correct.""" 33 34 # Pass if the difference between expected and computed variances is small, 35 # defined as being within an absolute variance delta or relative variance 36 # delta of the expected variance, whichever is larger. This is to allow the 37 # test to pass in the presence of some randomness (since this test is 38 # measuring noise of a small patch) and some imperfect scene conditions 39 # (since ITS doesn't require a perfectly uniformly lit scene). 40 41 with its.device.ItsSession() as cam: 42 43 props = cam.get_camera_properties() 44 its.caps.skip_unless(its.caps.raw(props) and 45 its.caps.raw16(props) and 46 its.caps.manual_sensor(props) and 47 its.caps.read_3a(props) and 48 its.caps.per_frame_control(props)) 49 debug = its.caps.debug_mode() 50 51 white_level = float(props['android.sensor.info.whiteLevel']) 52 cfa_idxs = its.image.get_canonical_cfa_order(props) 53 aax = props['android.sensor.info.activeArraySize']['left'] 54 aay = props['android.sensor.info.activeArraySize']['top'] 55 aaw = props['android.sensor.info.activeArraySize']['right']-aax 56 aah = props['android.sensor.info.activeArraySize']['bottom']-aay 57 58 # Expose for the scene with min sensitivity 59 sens_min, sens_max = props['android.sensor.info.sensitivityRange'] 60 sens_step = (sens_max - sens_min) / NUM_STEPS 61 s_ae, e_ae, _, _, f_dist = cam.do_3a(get_results=True) 62 s_e_prod = s_ae * e_ae 63 sensitivities = range(sens_min, sens_max, sens_step) 64 65 var_expected = [[], [], [], []] 66 var_measured = [[], [], [], []] 67 x = STATS_GRID/2 # center in H of STATS_GRID 68 y = STATS_GRID/2 # center in W of STATS_GRID 69 for sens in sensitivities: 70 71 # Capture a raw frame with the desired sensitivity 72 exp = int(s_e_prod / float(sens)) 73 req = its.objects.manual_capture_request(sens, exp, f_dist) 74 if debug: 75 cap = cam.do_capture(req, cam.CAP_RAW) 76 planes = its.image.convert_capture_to_planes(cap, props) 77 else: 78 cap = cam.do_capture(req, {'format': 'rawStats', 79 'gridWidth': aaw/STATS_GRID, 80 'gridHeight': aah/STATS_GRID}) 81 mean_img, var_img = its.image.unpack_rawstats_capture(cap) 82 83 # Test each raw color channel (R, GR, GB, B) 84 noise_profile = cap['metadata']['android.sensor.noiseProfile'] 85 assert len(noise_profile) == len(BAYER_LIST) 86 for i in range(len(BAYER_LIST)): 87 # Get the noise model parameters for this channel of this shot. 88 ch = cfa_idxs[i] 89 s, o = noise_profile[ch] 90 91 # Use a very small patch to ensure gross uniformity (i.e. so 92 # non-uniform lighting or vignetting doesn't affect the variance 93 # calculation) 94 black_level = its.image.get_black_level(i, props, 95 cap['metadata']) 96 level_range = white_level - black_level 97 if debug: 98 plane = ((planes[i] * white_level - black_level) / 99 level_range) 100 tile = its.image.get_image_patch(plane, 0.49, 0.49, 101 0.02, 0.02) 102 mean_img_ch = tile.mean() 103 var_measured[i].append( 104 its.image.compute_image_variances(tile)[0]) 105 else: 106 mean_img_ch = (mean_img[x, y, ch]-black_level)/level_range 107 var_measured[i].append(var_img[x, y, ch]/level_range**2) 108 var_expected[i].append(s * mean_img_ch + o) 109 110 for i, ch in enumerate(BAYER_LIST): 111 pylab.plot(sensitivities, var_expected[i], 'rgkb'[i], 112 label=ch+' expected') 113 pylab.plot(sensitivities, var_measured[i], 'rgkb'[i]+'--', 114 label=ch+' measured') 115 pylab.xlabel('Sensitivity') 116 pylab.ylabel('Center patch variance') 117 pylab.legend(loc=2) 118 matplotlib.pyplot.savefig('%s_plot.png' % NAME) 119 120 # PASS/FAIL check 121 for i, ch in enumerate(BAYER_LIST): 122 diffs = [var_measured[i][j] - var_expected[i][j] 123 for j in range(NUM_STEPS)] 124 print 'Diffs (%s):'%(ch), diffs 125 for j, diff in enumerate(diffs): 126 thresh = max(DIFF_THRESH, FRAC_THRESH*var_expected[i][j]) 127 assert diff <= thresh 128 129if __name__ == '__main__': 130 main() 131 132