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 math 16import os.path 17import its.caps 18import its.device 19import its.image 20import its.objects 21import numpy as np 22 23NAME = os.path.basename(__file__).split(".")[0] 24 25 26def main(): 27 """Capture auto and manual shots that should look the same. 28 29 Manual shots taken with just manual WB, and also with manual WB+tonemap. 30 31 In all cases, the general color/look of the shots should be the same, 32 however there can be variations in brightness/contrast due to different 33 "auto" ISP blocks that may be disabled in the manual flows. 34 """ 35 36 with its.device.ItsSession() as cam: 37 props = cam.get_camera_properties() 38 its.caps.skip_unless(its.caps.read_3a(props) and 39 its.caps.per_frame_control(props)) 40 mono_camera = its.caps.mono_camera(props) 41 42 # Converge 3A and get the estimates. 43 debug = its.caps.debug_mode() 44 largest_yuv = its.objects.get_largest_yuv_format(props) 45 if debug: 46 fmt = largest_yuv 47 else: 48 match_ar = (largest_yuv["width"], largest_yuv["height"]) 49 fmt = its.objects.get_smallest_yuv_format(props, match_ar=match_ar) 50 sens, exp, gains, xform, focus = cam.do_3a(get_results=True, 51 mono_camera=mono_camera) 52 xform_rat = its.objects.float_to_rational(xform) 53 print "AE sensitivity %d, exposure %dms" % (sens, exp/1000000.0) 54 print "AWB gains", gains 55 print "AWB transform", xform 56 print "AF distance", focus 57 58 # Auto capture. 59 req = its.objects.auto_capture_request() 60 cap_auto = cam.do_capture(req, fmt) 61 img_auto = its.image.convert_capture_to_rgb_image(cap_auto) 62 its.image.write_image(img_auto, "%s_auto.jpg" % (NAME)) 63 xform_a = its.objects.rational_to_float( 64 cap_auto["metadata"]["android.colorCorrection.transform"]) 65 gains_a = cap_auto["metadata"]["android.colorCorrection.gains"] 66 print "Auto gains:", gains_a 67 print "Auto transform:", xform_a 68 69 # Manual capture 1: WB 70 req = its.objects.manual_capture_request(sens, exp, focus) 71 req["android.colorCorrection.transform"] = xform_rat 72 req["android.colorCorrection.gains"] = gains 73 cap_man1 = cam.do_capture(req, fmt) 74 img_man1 = its.image.convert_capture_to_rgb_image(cap_man1) 75 its.image.write_image(img_man1, "%s_manual_wb.jpg" % (NAME)) 76 xform_m1 = its.objects.rational_to_float( 77 cap_man1["metadata"]["android.colorCorrection.transform"]) 78 gains_m1 = cap_man1["metadata"]["android.colorCorrection.gains"] 79 print "Manual wb gains:", gains_m1 80 print "Manual wb transform:", xform_m1 81 82 # Manual capture 2: WB + tonemap 83 gamma = sum([[i/63.0, math.pow(i/63.0, 1/2.2)] for i in xrange(64)], []) 84 req["android.tonemap.mode"] = 0 85 req["android.tonemap.curve"] = { 86 "red": gamma, "green": gamma, "blue": gamma} 87 cap_man2 = cam.do_capture(req, fmt) 88 img_man2 = its.image.convert_capture_to_rgb_image(cap_man2) 89 its.image.write_image(img_man2, "%s_manual_wb_tm.jpg" % (NAME)) 90 xform_m2 = its.objects.rational_to_float( 91 cap_man2["metadata"]["android.colorCorrection.transform"]) 92 gains_m2 = cap_man2["metadata"]["android.colorCorrection.gains"] 93 print "Manual wb+tm gains:", gains_m2 94 print "Manual wb+tm transform:", xform_m2 95 96 # Check that the WB gains and transform reported in each capture 97 # result match with the original AWB estimate from do_3a. 98 for g, x in [(gains_m1, xform_m1), (gains_m2, xform_m2)]: 99 assert all([abs(xform[i] - x[i]) < 0.05 for i in range(9)]) 100 assert all([abs(gains[i] - g[i]) < 0.05 for i in range(4)]) 101 102 # Check that auto AWB settings are close 103 assert all([np.isclose(xform_a[i], xform[i], rtol=0.25, atol=0.1) for i in range(9)]) 104 assert all([np.isclose(gains_a[i], gains[i], rtol=0.25, atol=0.1) for i in range(4)]) 105 106if __name__ == "__main__": 107 main() 108 109