| # Copyright 2015 The Android Open Source Project |
| # |
| # Licensed under the Apache License, Version 2.0 (the "License"); |
| # you may not use this file except in compliance with the License. |
| # You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, software |
| # distributed under the License is distributed on an "AS IS" BASIS, |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| # See the License for the specific language governing permissions and |
| # limitations under the License. |
| |
| import its.image |
| import its.caps |
| import its.device |
| import its.objects |
| import its.target |
| import math |
| import matplotlib |
| import matplotlib.pyplot |
| import os.path |
| import pylab |
| |
| def main(): |
| """Test that the android.noiseReduction.mode param is applied when set for |
| reprocessing requests. |
| |
| Capture reprocessed images with the camera dimly lit. Uses a high analog |
| gain to ensure the captured image is noisy. |
| |
| Captures three reprocessed images, for NR off, "fast", and "high quality". |
| Also captures a reprocessed image with low gain and NR off, and uses the |
| variance of this as the baseline. |
| """ |
| |
| NAME = os.path.basename(__file__).split(".")[0] |
| |
| with its.device.ItsSession() as cam: |
| props = cam.get_camera_properties() |
| |
| its.caps.skip_unless(its.caps.compute_target_exposure(props) and |
| its.caps.per_frame_control(props) and |
| (its.caps.yuv_reprocess(props) or |
| its.caps.private_reprocess(props))) |
| |
| reprocess_formats = [] |
| if (its.caps.yuv_reprocess(props)): |
| reprocess_formats.append("yuv") |
| if (its.caps.private_reprocess(props)): |
| reprocess_formats.append("private") |
| |
| for reprocess_format in reprocess_formats: |
| # List of variances for R, G, B. |
| variances = [] |
| nr_modes_reported = [] |
| |
| # NR mode 0 with low gain |
| e, s = its.target.get_target_exposure_combos(cam)["minSensitivity"] |
| req = its.objects.manual_capture_request(s, e) |
| req["android.noiseReduction.mode"] = 0 |
| |
| # Test reprocess_format->JPEG reprocessing |
| # TODO: Switch to reprocess_format->YUV when YUV reprocessing is |
| # supported. |
| size = its.objects.get_available_output_sizes("jpg", props)[0] |
| out_surface = {"width":size[0], "height":size[1], "format":"jpg"} |
| cap = cam.do_capture(req, out_surface, reprocess_format) |
| img = its.image.decompress_jpeg_to_rgb_image(cap["data"]) |
| its.image.write_image(img, "%s_low_gain_fmt=jpg.jpg" % (NAME)) |
| tile = its.image.get_image_patch(img, 0.45, 0.45, 0.1, 0.1) |
| ref_variance = its.image.compute_image_variances(tile) |
| print "Ref variances:", ref_variance |
| |
| for nr_mode in range(3): |
| # NR modes 0, 1, 2 with high gain |
| e, s = its.target.get_target_exposure_combos(cam) \ |
| ["maxSensitivity"] |
| req = its.objects.manual_capture_request(s, e) |
| req["android.noiseReduction.mode"] = nr_mode |
| cap = cam.do_capture(req, out_surface, reprocess_format) |
| nr_modes_reported.append( |
| cap["metadata"]["android.noiseReduction.mode"]) |
| |
| img = its.image.decompress_jpeg_to_rgb_image(cap["data"]) |
| its.image.write_image( |
| img, "%s_high_gain_nr=%d_fmt=jpg.jpg" % (NAME, nr_mode)) |
| tile = its.image.get_image_patch(img, 0.45, 0.45, 0.1, 0.1) |
| # Get the variances for R, G, and B channels |
| variance = its.image.compute_image_variances(tile) |
| variances.append( |
| [variance[chan] / ref_variance[chan] for chan in range(3)]) |
| print "Variances with NR mode [0,1,2]:", variances |
| |
| # Draw a plot. |
| for nr_mode in range(3): |
| pylab.plot(range(3), variances[nr_mode], "rgb"[nr_mode]) |
| matplotlib.pyplot.savefig("%s_plot_%s_variances.png" % |
| (NAME, reprocess_format)) |
| |
| assert(nr_modes_reported == [0,1,2]) |
| |
| # Check that the variance of the NR=0 image is higher than for the |
| # NR=1 and NR=2 images. |
| for j in range(3): |
| for i in range(1,3): |
| assert(variances[i][j] < variances[0][j]) |
| |
| if __name__ == '__main__': |
| main() |
| |