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 pylab 21 import os.path 22 import matplotlib 23 import matplotlib.pyplot 24 25 def main(): 26 """Test that the android.colorCorrection.* params are applied when set. 27 28 Takes shots with different transform and gains values, and tests that 29 they look correspondingly different. The transform and gains are chosen 30 to make the output go redder or bluer. 31 32 Uses a linear tonemap. 33 """ 34 NAME = os.path.basename(__file__).split(".")[0] 35 36 THRESHOLD_MAX_DIFF = 0.1 37 38 with its.device.ItsSession() as cam: 39 props = cam.get_camera_properties() 40 its.caps.skip_unless(its.caps.compute_target_exposure(props) and 41 its.caps.per_frame_control(props)) 42 43 # Baseline request 44 e, s = its.target.get_target_exposure_combos(cam)["midSensitivity"] 45 req = its.objects.manual_capture_request(s, e, True) 46 req["android.colorCorrection.mode"] = 0 47 48 # Transforms: 49 # 1. Identity 50 # 2. Identity 51 # 3. Boost blue 52 transforms = [its.objects.int_to_rational([1,0,0, 0,1,0, 0,0,1]), 53 its.objects.int_to_rational([1,0,0, 0,1,0, 0,0,1]), 54 its.objects.int_to_rational([1,0,0, 0,1,0, 0,0,2])] 55 56 # Gains: 57 # 1. Unit 58 # 2. Boost red 59 # 3. Unit 60 gains = [[1,1,1,1], [2,1,1,1], [1,1,1,1]] 61 62 r_means = [] 63 g_means = [] 64 b_means = [] 65 66 # Capture requests: 67 # 1. With unit gains, and identity transform. 68 # 2. With a higher red gain, and identity transform. 69 # 3. With unit gains, and a transform that boosts blue. 70 for i in range(len(transforms)): 71 req["android.colorCorrection.transform"] = transforms[i] 72 req["android.colorCorrection.gains"] = gains[i] 73 cap = cam.do_capture(req) 74 img = its.image.convert_capture_to_rgb_image(cap) 75 its.image.write_image(img, "%s_req=%d.jpg" % (NAME, i)) 76 tile = its.image.get_image_patch(img, 0.45, 0.45, 0.1, 0.1) 77 rgb_means = its.image.compute_image_means(tile) 78 r_means.append(rgb_means[0]) 79 g_means.append(rgb_means[1]) 80 b_means.append(rgb_means[2]) 81 ratios = [rgb_means[0] / rgb_means[1], rgb_means[2] / rgb_means[1]] 82 print "Means = ", rgb_means, " Ratios =", ratios 83 84 # Draw a plot. 85 domain = range(len(transforms)) 86 pylab.plot(domain, r_means, 'r') 87 pylab.plot(domain, g_means, 'g') 88 pylab.plot(domain, b_means, 'b') 89 pylab.ylim([0,1]) 90 matplotlib.pyplot.savefig("%s_plot_means.png" % (NAME)) 91 92 # Expect G0 == G1 == G2, R0 == 0.5*R1 == R2, B0 == B1 == 0.5*B2 93 # Also need to ensure that the image is not clamped to white/black. 94 assert(all(g_means[i] > 0.2 and g_means[i] < 0.8 for i in xrange(3))) 95 assert(abs(g_means[1] - g_means[0]) < THRESHOLD_MAX_DIFF) 96 assert(abs(g_means[2] - g_means[1]) < THRESHOLD_MAX_DIFF) 97 assert(abs(r_means[2] - r_means[0]) < THRESHOLD_MAX_DIFF) 98 assert(abs(r_means[1] - 2.0 * r_means[0]) < THRESHOLD_MAX_DIFF) 99 assert(abs(b_means[1] - b_means[0]) < THRESHOLD_MAX_DIFF) 100 assert(abs(b_means[2] - 2.0 * b_means[0]) < THRESHOLD_MAX_DIFF) 101 102 if __name__ == '__main__': 103 main() 104 105
