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.noiseReduction.mode param is applied when set. 27 28 Capture images with the camera dimly lit. Uses a high analog gain to 29 ensure the captured image is noisy. 30 31 Captures three images, for NR off, "fast", and "high quality". 32 Also captures an image with low gain and NR off, and uses the variance 33 of this as the baseline. 34 """ 35 NAME = os.path.basename(__file__).split(".")[0] 36 37 # List of variances for Y,U,V. 38 variances = [[],[],[]] 39 40 # Reference (baseline) variance for each of Y,U,V. 41 ref_variance = [] 42 43 nr_modes_reported = [] 44 45 with its.device.ItsSession() as cam: 46 props = cam.get_camera_properties() 47 its.caps.skip_unless(its.caps.compute_target_exposure(props) and 48 its.caps.per_frame_control(props)) 49 50 # NR mode 0 with low gain 51 e, s = its.target.get_target_exposure_combos(cam)["minSensitivity"] 52 req = its.objects.manual_capture_request(s, e) 53 req["android.noiseReduction.mode"] = 0 54 cap = cam.do_capture(req) 55 its.image.write_image( 56 its.image.convert_capture_to_rgb_image(cap), 57 "%s_low_gain.jpg" % (NAME)) 58 planes = its.image.convert_capture_to_planes(cap) 59 for j in range(3): 60 img = planes[j] 61 tile = its.image.get_image_patch(img, 0.45, 0.45, 0.1, 0.1) 62 ref_variance.append(its.image.compute_image_variances(tile)[0]) 63 print "Ref variances:", ref_variance 64 65 for i in range(3): 66 # NR modes 0, 1, 2 with high gain 67 e, s = its.target.get_target_exposure_combos(cam)["maxSensitivity"] 68 req = its.objects.manual_capture_request(s, e) 69 req["android.noiseReduction.mode"] = i 70 cap = cam.do_capture(req) 71 nr_modes_reported.append( 72 cap["metadata"]["android.noiseReduction.mode"]) 73 its.image.write_image( 74 its.image.convert_capture_to_rgb_image(cap), 75 "%s_high_gain_nr=%d.jpg" % (NAME, i)) 76 planes = its.image.convert_capture_to_planes(cap) 77 for j in range(3): 78 img = planes[j] 79 tile = its.image.get_image_patch(img, 0.45, 0.45, 0.1, 0.1) 80 variance = its.image.compute_image_variances(tile)[0] 81 variances[j].append(variance / ref_variance[j]) 82 print "Variances with NR mode [0,1,2]:", variances 83 84 # Draw a plot. 85 for j in range(3): 86 pylab.plot(range(3), variances[j], "rgb"[j]) 87 matplotlib.pyplot.savefig("%s_plot_variances.png" % (NAME)) 88 89 assert(nr_modes_reported == [0,1,2]) 90 91 # Check that the variance of the NR=0 image is higher than for the 92 # NR=1 and NR=2 images. 93 for j in range(3): 94 for i in range(1,3): 95 assert(variances[j][i] < variances[j][0]) 96 97 if __name__ == '__main__': 98 main() 99 100
