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 22import pylab 23import os.path 24import matplotlib 25import matplotlib.pyplot 26 27def main(): 28 """Test that device processing can be inverted to linear pixels. 29 30 Captures a sequence of shots with the device pointed at a uniform 31 target. Attempts to invert all the ISP processing to get back to 32 linear R,G,B pixel data. 33 """ 34 NAME = os.path.basename(__file__).split(".")[0] 35 36 RESIDUAL_THRESHOLD = 0.0003 # approximately each sample is off by 2/255 37 38 # The HAL3.2 spec requires that curves up to 64 control points in length 39 # must be supported. 40 L = 64 41 LM1 = float(L-1) 42 43 gamma_lut = numpy.array( 44 sum([[i/LM1, math.pow(i/LM1, 1/2.2)] for i in xrange(L)], [])) 45 inv_gamma_lut = numpy.array( 46 sum([[i/LM1, math.pow(i/LM1, 2.2)] for i in xrange(L)], [])) 47 48 with its.device.ItsSession() as cam: 49 props = cam.get_camera_properties() 50 its.caps.skip_unless(its.caps.compute_target_exposure(props) and 51 its.caps.per_frame_control(props)) 52 53 debug = its.caps.debug_mode() 54 if debug: 55 fmt = its.objects.get_largest_yuv_format(props) 56 else: 57 fmt = its.objects.get_smallest_yuv_format(props) 58 59 e,s = its.target.get_target_exposure_combos(cam)["midSensitivity"] 60 s /= 2 61 sens_range = props['android.sensor.info.sensitivityRange'] 62 sensitivities = [s*1.0/3.0, s*2.0/3.0, s, s*4.0/3.0, s*5.0/3.0] 63 sensitivities = [s for s in sensitivities 64 if s > sens_range[0] and s < sens_range[1]] 65 66 req = its.objects.manual_capture_request(0, e) 67 req["android.blackLevel.lock"] = True 68 req["android.tonemap.mode"] = 0 69 req["android.tonemap.curveRed"] = gamma_lut.tolist() 70 req["android.tonemap.curveGreen"] = gamma_lut.tolist() 71 req["android.tonemap.curveBlue"] = gamma_lut.tolist() 72 73 r_means = [] 74 g_means = [] 75 b_means = [] 76 77 for sens in sensitivities: 78 req["android.sensor.sensitivity"] = sens 79 cap = cam.do_capture(req, fmt) 80 img = its.image.convert_capture_to_rgb_image(cap) 81 its.image.write_image( 82 img, "%s_sens=%04d.jpg" % (NAME, sens)) 83 img = its.image.apply_lut_to_image(img, inv_gamma_lut[1::2] * LM1) 84 tile = its.image.get_image_patch(img, 0.45, 0.45, 0.1, 0.1) 85 rgb_means = its.image.compute_image_means(tile) 86 r_means.append(rgb_means[0]) 87 g_means.append(rgb_means[1]) 88 b_means.append(rgb_means[2]) 89 90 pylab.plot(sensitivities, r_means, 'r') 91 pylab.plot(sensitivities, g_means, 'g') 92 pylab.plot(sensitivities, b_means, 'b') 93 pylab.ylim([0,1]) 94 matplotlib.pyplot.savefig("%s_plot_means.png" % (NAME)) 95 96 # Check that each plot is actually linear. 97 for means in [r_means, g_means, b_means]: 98 line,residuals,_,_,_ = numpy.polyfit(range(5),means,1,full=True) 99 print "Line: m=%f, b=%f, resid=%f"%(line[0], line[1], residuals[0]) 100 assert(residuals[0] < RESIDUAL_THRESHOLD) 101 102if __name__ == '__main__': 103 main() 104 105