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.caps 17 import its.device 18 import its.objects 19 import its.target 20 import numpy 21 import math 22 from matplotlib import pylab 23 import os.path 24 import matplotlib 25 import matplotlib.pyplot 26 27 NAME = os.path.basename(__file__).split('.')[0] 28 RESIDUAL_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. 31 L = 64 32 LM1 = float(L-1) 33 34 35 def 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 108 109 if __name__ == '__main__': 110 main() 111 112
