| # 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 numpy |
| 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] |
| |
| NUM_SAMPLES_PER_MODE = 4 |
| SNR_TOLERANCE = 3 # unit in db |
| |
| 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.noise_reduction_mode(props, 0) and |
| (its.caps.yuv_reprocess(props) or |
| its.caps.private_reprocess(props))) |
| |
| # If reprocessing is supported, ZSL NR mode must be avaiable. |
| assert(its.caps.noise_reduction_mode(props, 4)) |
| |
| 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. |
| snrs = [[], [], []] |
| 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_snr = its.image.compute_image_snrs(tile) |
| print "Ref SNRs:", ref_snr |
| |
| e, s = its.target.get_target_exposure_combos(cam)["maxSensitivity"] |
| for nr_mode in range(5): |
| # Skip unavailable modes |
| if not its.caps.noise_reduction_mode(props, nr_mode): |
| nr_modes_reported.append(nr_mode) |
| for channel in range(3): |
| snrs[channel].append(0) |
| continue |
| |
| rgb_snr_list = [] |
| # Capture several images to account for per frame noise |
| # variations |
| for n in range(NUM_SAMPLES_PER_MODE): |
| req = its.objects.manual_capture_request(s, e) |
| req["android.noiseReduction.mode"] = nr_mode |
| cap = cam.do_capture(req, out_surface, reprocess_format) |
| |
| img = its.image.decompress_jpeg_to_rgb_image(cap["data"]) |
| if n == 0: |
| its.image.write_image( |
| img, |
| "%s_high_gain_nr=%d_fmt=jpg.jpg" |
| %(NAME, nr_mode)) |
| nr_modes_reported.append( |
| cap["metadata"]["android.noiseReduction.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 |
| rgb_snrs = its.image.compute_image_snrs(tile) |
| rgb_snr_list.append(rgb_snrs) |
| |
| r_snrs = [rgb[0] for rgb in rgb_snr_list] |
| g_snrs = [rgb[1] for rgb in rgb_snr_list] |
| b_snrs = [rgb[2] for rgb in rgb_snr_list] |
| rgb_snrs = [numpy.mean(r_snrs), |
| numpy.mean(g_snrs), |
| numpy.mean(b_snrs)] |
| print "NR mode", nr_mode, "SNRs:" |
| print " R SNR:", rgb_snrs[0],\ |
| "Min:", min(r_snrs), "Max:", max(r_snrs) |
| print " G SNR:", rgb_snrs[1],\ |
| "Min:", min(g_snrs), "Max:", max(g_snrs) |
| print " B SNR:", rgb_snrs[2],\ |
| "Min:", min(b_snrs), "Max:", max(b_snrs) |
| |
| for chan in range(3): |
| snrs[chan].append(rgb_snrs[chan]) |
| |
| # Draw a plot. |
| for channel in range(3): |
| pylab.plot(range(5), snrs[channel], "rgb"[channel]) |
| |
| matplotlib.pyplot.savefig("%s_plot_%s_SNRs.png" % |
| (NAME, reprocess_format)) |
| |
| assert(nr_modes_reported == [0,1,2,3,4]) |
| |
| for j in range(3): |
| # Larger is better |
| # Verify OFF(0) is not better than FAST(1) |
| assert(snrs[j][0] < |
| snrs[j][1] + SNR_TOLERANCE) |
| # Verify FAST(1) is not better than HQ(2) |
| assert(snrs[j][1] < |
| snrs[j][2] + SNR_TOLERANCE) |
| # Verify HQ(2) is better than OFF(0) |
| assert(snrs[j][0] < snrs[j][2]) |
| if its.caps.noise_reduction_mode(props, 3): |
| # Verify OFF(0) is not better than MINIMAL(3) |
| assert(snrs[j][0] < |
| snrs[j][3] + SNR_TOLERANCE) |
| # Verify MINIMAL(3) is not better than HQ(2) |
| assert(snrs[j][3] < |
| snrs[j][2] + SNR_TOLERANCE) |
| # Verify ZSL(4) is close to MINIMAL(3) |
| assert(numpy.isclose(snrs[j][4], snrs[j][3], |
| atol=SNR_TOLERANCE)) |
| else: |
| # Verify ZSL(4) is close to OFF(0) |
| assert(numpy.isclose(snrs[j][4], snrs[j][0], |
| atol=SNR_TOLERANCE)) |
| |
| if __name__ == '__main__': |
| main() |
| |
