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 its.image 16import its.caps 17import its.device 18import its.objects 19import its.target 20import numpy 21import math 22from matplotlib import pylab 23import os.path 24import matplotlib 25import matplotlib.pyplot 26 27NAME = os.path.basename(__file__).split('.')[0] 28RESIDUAL_THRESHOLD = 0.0003 # approximately each sample is off by 2/255 29# The HAL3.2 spec requires that curves up to 64 control points in length 30# must be supported. 31L = 64 32LM1 = float(L-1) 33 34 35def main(): 36 """Test that device processing can be inverted to linear pixels. 37 38 Captures a sequence of shots with the device pointed at a uniform 39 target. Attempts to invert all the ISP processing to get back to 40 linear R,G,B pixel data. 41 """ 42 gamma_lut = numpy.array( 43 sum([[i/LM1, math.pow(i/LM1, 1/2.2)] for i in xrange(L)], [])) 44 inv_gamma_lut = numpy.array( 45 sum([[i/LM1, math.pow(i/LM1, 2.2)] for i in xrange(L)], [])) 46 47 with its.device.ItsSession() as cam: 48 props = cam.get_camera_properties() 49 its.caps.skip_unless(its.caps.compute_target_exposure(props) and 50 its.caps.per_frame_control(props)) 51 52 debug = its.caps.debug_mode() 53 largest_yuv = its.objects.get_largest_yuv_format(props) 54 if debug: 55 fmt = largest_yuv 56 else: 57 match_ar = (largest_yuv['width'], largest_yuv['height']) 58 fmt = its.objects.get_smallest_yuv_format(props, match_ar=match_ar) 59 60 e,s = its.target.get_target_exposure_combos(cam)["midSensitivity"] 61 s /= 2 62 sens_range = props['android.sensor.info.sensitivityRange'] 63 sensitivities = [s*1.0/3.0, s*2.0/3.0, s, s*4.0/3.0, s*5.0/3.0] 64 sensitivities = [s for s in sensitivities 65 if s > sens_range[0] and s < sens_range[1]] 66 67 req = its.objects.manual_capture_request(0, e) 68 req['android.blackLevel.lock'] = True 69 req['android.tonemap.mode'] = 0 70 req['android.tonemap.curve'] = { 71 'red': gamma_lut.tolist(), 72 'green': gamma_lut.tolist(), 73 'blue': gamma_lut.tolist()} 74 75 r_means = [] 76 g_means = [] 77 b_means = [] 78 79 for sens in sensitivities: 80 req["android.sensor.sensitivity"] = sens 81 cap = cam.do_capture(req, fmt) 82 img = its.image.convert_capture_to_rgb_image(cap) 83 its.image.write_image( 84 img, '%s_sens=%04d.jpg' % (NAME, sens)) 85 img = its.image.apply_lut_to_image(img, inv_gamma_lut[1::2] * LM1) 86 tile = its.image.get_image_patch(img, 0.45, 0.45, 0.1, 0.1) 87 rgb_means = its.image.compute_image_means(tile) 88 r_means.append(rgb_means[0]) 89 g_means.append(rgb_means[1]) 90 b_means.append(rgb_means[2]) 91 92 pylab.title(NAME) 93 pylab.plot(sensitivities, r_means, '-ro') 94 pylab.plot(sensitivities, g_means, '-go') 95 pylab.plot(sensitivities, b_means, '-bo') 96 pylab.xlim([sens_range[0], sens_range[1]/2]) 97 pylab.ylim([0, 1]) 98 pylab.xlabel('sensitivity(ISO)') 99 pylab.ylabel('RGB avg [0, 1]') 100 matplotlib.pyplot.savefig('%s_plot_means.png' % (NAME)) 101 102 # Check that each plot is actually linear. 103 for means in [r_means, g_means, b_means]: 104 line, residuals, _, _, _ = numpy.polyfit(range(len(sensitivities)), 105 means, 1, full=True) 106 print 'Line: m=%f, b=%f, resid=%f'%(line[0], line[1], residuals[0]) 107 assert residuals[0] < RESIDUAL_THRESHOLD 108 109if __name__ == '__main__': 110 main() 111 112