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 15 import its.image 16 import its.device 17 import its.objects 18 import os.path 19 import pprint 20 import math 21 import numpy 22 import matplotlib.pyplot 23 import mpl_toolkits.mplot3d 24 25 def main(): 26 """Test that valid data comes back in CaptureResult objects. 27 """ 28 NAME = os.path.basename(__file__).split(".")[0] 29 30 def r2f(r): 31 return float(r["numerator"]) / float(r["denominator"]) 32 33 if not its.device.reboot_device_on_argv(): 34 its.device.reboot_device() 35 36 # Run a first pass, which starts with a 3A convergence step. 37 with its.device.ItsSession() as cam: 38 # Get 3A lock first, so the auto values in the capture result are 39 # populated properly. 40 r = [0,0,1,1] 41 sens,exp,awb_gains,awb_transform,_ = cam.do_3a(r,r,r,True,True,False) 42 43 # Capture an auto shot using the converged 3A. 44 req = its.objects.auto_capture_request() 45 fname, w, h, cap_res = cam.do_capture(req) 46 img = its.image.load_yuv420_to_rgb_image(fname, w, h) 47 its.image.write_image(img, "%s_n=1_pass=1_auto.jpg" % (NAME)) 48 auto_gains = cap_res["android.colorCorrection.gains"] 49 auto_transform = cap_res["android.colorCorrection.transform"] 50 51 # Capture a request using default (unit/identify) gains, and get the 52 # predicted gains and transform. 53 req = its.objects.manual_capture_request(sens, exp/(1000.0*1000.0)) 54 fname, w, h, cap_res = cam.do_capture(req) 55 img = its.image.load_yuv420_to_rgb_image(fname, w, h) 56 its.image.write_image(img, "%s_n=2_pass=1_identity.jpg" % (NAME)) 57 pred_gains_1 = cap_res["android.statistics.predictedColorGains"] 58 pred_transform_1 = cap_res["android.statistics.predictedColorTransform"] 59 60 # Capture a request using the predicted gains/transform. 61 req = its.objects.manual_capture_request(sens, exp/(1000.0*1000.0)) 62 req["android.colorCorrection.transform"] = pred_transform_1 63 req["android.colorCorrection.gains"] = pred_gains_1 64 fname, w, h, md_obj = cam.do_capture(req) 65 img = its.image.load_yuv420_to_rgb_image(fname, w, h) 66 its.image.write_image(img, "%s_n=3_pass=1_predicted.jpg" % (NAME)) 67 68 print "Pass 1 metering gains:", awb_gains 69 print "Pass 1 metering transform:", awb_transform 70 print "Pass 1 auto shot gains:", auto_gains 71 print "Pass 1 auto shot transform:", [r2f(t) for t in auto_transform] 72 print "Pass 1 predicted gains:", pred_gains_1 73 print "Pass 1 predicted transform:", [r2f(t) for t in pred_transform_1] 74 75 if not its.device.reboot_device_on_argv(): 76 its.device.reboot_device() 77 78 # Run a second pass after rebooting that doesn't start with 3A convergence. 79 with its.device.ItsSession() as cam: 80 # Capture a request using default (unit/identify) gains, and get the 81 # predicted gains and transform. 82 req = its.objects.manual_capture_request(sens, exp/(1000.0*1000.0)) 83 fname, w, h, cap_res = cam.do_capture(req) 84 img = its.image.load_yuv420_to_rgb_image(fname, w, h) 85 its.image.write_image(img, "%s_n=4_pass=2_identity.jpg" % (NAME)) 86 pred_gains_2 = cap_res["android.statistics.predictedColorGains"] 87 pred_transform_2 = cap_res["android.statistics.predictedColorTransform"] 88 89 # Capture a request using the predicted gains/transform. 90 req = its.objects.manual_capture_request(sens, exp/(1000.0*1000.0)) 91 req["android.colorCorrection.transform"] = pred_transform_2 92 req["android.colorCorrection.gains"] = pred_gains_2 93 fname, w, h, md_obj = cam.do_capture(req) 94 img = its.image.load_yuv420_to_rgb_image(fname, w, h) 95 its.image.write_image(img, "%s_n=5_pass=2_predicted.jpg" % (NAME)) 96 97 print "Pass 2 predicted gains:", pred_gains_2 98 print "Pass 2 predicted transform:", [r2f(t) for t in pred_transform_2] 99 100 if __name__ == '__main__': 101 main() 102 103
