1# Copyright 2016 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 its.device 16import its.caps 17import its.image 18import its.objects 19import its.target 20import os.path 21import pylab 22import matplotlib 23import matplotlib.pyplot 24 25def main(): 26 """Capture a set of raw/yuv images with different 27 sensitivity/post Raw sensitivity boost combination 28 and check if the output pixel mean matches request settings 29 """ 30 NAME = os.path.basename(__file__).split(".")[0] 31 32 # Each raw image 33 RATIO_THRESHOLD = 0.1 34 # Waive the check if raw pixel value is below this level (signal too small 35 # that small black level error converts to huge error in percentage) 36 RAW_PIXEL_VAL_THRESHOLD = 0.03 37 38 with its.device.ItsSession() as cam: 39 props = cam.get_camera_properties() 40 its.caps.skip_unless(its.caps.raw_output(props) and 41 its.caps.post_raw_sensitivity_boost(props) and 42 its.caps.compute_target_exposure(props) and 43 its.caps.per_frame_control(props)) 44 45 w,h = its.objects.get_available_output_sizes( 46 "yuv", props, (1920, 1080))[0] 47 48 if its.caps.raw16(props): 49 raw_format = 'raw' 50 elif its.caps.raw10(props): 51 raw_format = 'raw10' 52 elif its.caps.raw12(props): 53 raw_format = 'raw12' 54 else: # should not reach here 55 raise its.error.Error('Cannot find available RAW output format') 56 57 out_surfaces = [{"format": raw_format}, 58 {"format": "yuv", "width": w, "height": h}] 59 60 sens_min, sens_max = props['android.sensor.info.sensitivityRange'] 61 sens_boost_min, sens_boost_max = \ 62 props['android.control.postRawSensitivityBoostRange'] 63 64 65 e_target, s_target = \ 66 its.target.get_target_exposure_combos(cam)["midSensitivity"] 67 68 reqs = [] 69 settings = [] 70 s_boost = sens_boost_min 71 while s_boost <= sens_boost_max: 72 s_raw = int(round(s_target * 100.0 / s_boost)) 73 if s_raw < sens_min or s_raw > sens_max: 74 break 75 req = its.objects.manual_capture_request(s_raw, e_target) 76 req['android.control.postRawSensitivityBoost'] = s_boost 77 reqs.append(req) 78 settings.append((s_raw, s_boost)) 79 if s_boost == sens_boost_max: 80 break 81 s_boost *= 2 82 # Always try to test maximum sensitivity boost value 83 if s_boost > sens_boost_max: 84 s_boost = sens_boost_max 85 86 caps = cam.do_capture(reqs, out_surfaces) 87 88 raw_rgb_means = [] 89 yuv_rgb_means = [] 90 raw_caps, yuv_caps = caps 91 if not isinstance(raw_caps, list): 92 raw_caps = [raw_caps] 93 if not isinstance(yuv_caps, list): 94 yuv_caps = [yuv_caps] 95 for i in xrange(len(reqs)): 96 (s, s_boost) = settings[i] 97 raw_cap = raw_caps[i] 98 yuv_cap = yuv_caps[i] 99 raw_rgb = its.image.convert_capture_to_rgb_image(raw_cap, props=props) 100 yuv_rgb = its.image.convert_capture_to_rgb_image(yuv_cap) 101 raw_tile = its.image.get_image_patch(raw_rgb, 0.45,0.45,0.1,0.1) 102 yuv_tile = its.image.get_image_patch(yuv_rgb, 0.45,0.45,0.1,0.1) 103 raw_rgb_means.append(its.image.compute_image_means(raw_tile)) 104 yuv_rgb_means.append(its.image.compute_image_means(yuv_tile)) 105 its.image.write_image(raw_tile, 106 "%s_raw_s=%04d_boost=%04d.jpg" % (NAME,s,s_boost)) 107 its.image.write_image(yuv_tile, 108 "%s_yuv_s=%04d_boost=%04d.jpg" % (NAME,s,s_boost)) 109 print "s=%d, s_boost=%d: raw_means %s, yuv_means %s"%( 110 s,s_boost,raw_rgb_means[-1], yuv_rgb_means[-1]) 111 112 xs = range(len(reqs)) 113 pylab.plot(xs, [rgb[0] for rgb in raw_rgb_means], 'r') 114 pylab.plot(xs, [rgb[1] for rgb in raw_rgb_means], 'g') 115 pylab.plot(xs, [rgb[2] for rgb in raw_rgb_means], 'b') 116 pylab.ylim([0,1]) 117 matplotlib.pyplot.savefig("%s_raw_plot_means.png" % (NAME)) 118 pylab.clf() 119 pylab.plot(xs, [rgb[0] for rgb in yuv_rgb_means], 'r') 120 pylab.plot(xs, [rgb[1] for rgb in yuv_rgb_means], 'g') 121 pylab.plot(xs, [rgb[2] for rgb in yuv_rgb_means], 'b') 122 pylab.ylim([0,1]) 123 matplotlib.pyplot.savefig("%s_yuv_plot_means.png" % (NAME)) 124 125 rgb_str = ["R", "G", "B"] 126 # Test that raw means is about 2x brighter than next step 127 for step in range(1, len(reqs)): 128 (s_prev, s_boost_prev) = settings[step - 1] 129 (s, s_boost) = settings[step] 130 expect_raw_ratio = s_prev / float(s) 131 raw_thres_min = expect_raw_ratio * (1 - RATIO_THRESHOLD) 132 raw_thres_max = expect_raw_ratio * (1 + RATIO_THRESHOLD) 133 for rgb in range(3): 134 ratio = raw_rgb_means[step - 1][rgb] / raw_rgb_means[step][rgb] 135 print ("Step (%d,%d) %s channel: %f, %f, ratio %f," + 136 " threshold_min %f, threshold_max %f") % ( 137 step-1, step, rgb_str[rgb], 138 raw_rgb_means[step - 1][rgb], 139 raw_rgb_means[step][rgb], 140 ratio, raw_thres_min, raw_thres_max) 141 if (raw_rgb_means[step][rgb] <= RAW_PIXEL_VAL_THRESHOLD): 142 continue 143 assert(raw_thres_min < ratio < raw_thres_max) 144 145 # Test that each yuv step is about the same bright as their mean 146 yuv_thres_min = 1 - RATIO_THRESHOLD 147 yuv_thres_max = 1 + RATIO_THRESHOLD 148 for rgb in range(3): 149 vals = [val[rgb] for val in yuv_rgb_means] 150 for step in range(len(reqs)): 151 if (raw_rgb_means[step][rgb] <= RAW_PIXEL_VAL_THRESHOLD): 152 vals = vals[:step] 153 mean = sum(vals) / len(vals) 154 print "%s channel vals %s mean %f"%(rgb_str[rgb], vals, mean) 155 for step in range(len(vals)): 156 ratio = vals[step] / mean 157 assert(yuv_thres_min < ratio < yuv_thres_max) 158 159if __name__ == '__main__': 160 main() 161