| # Copyright 2013 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 matplotlib |
| import matplotlib.pyplot |
| import numpy |
| import os.path |
| import pylab |
| |
| def main(): |
| """Test that the android.noiseReduction.mode param is applied when set. |
| |
| Capture images with the camera dimly lit. Uses a high analog gain to |
| ensure the captured image is noisy. |
| |
| Captures three images, for NR off, "fast", and "high quality". |
| Also captures an 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 |
| # List of SNRs for R,G,B. |
| snrs = [[], [], []] |
| |
| # Reference (baseline) SNR for each of R,G,B. |
| ref_snr = [] |
| |
| nr_modes_reported = [] |
| |
| 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)) |
| |
| # 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 |
| cap = cam.do_capture(req) |
| rgb_image = its.image.convert_capture_to_rgb_image(cap) |
| its.image.write_image( |
| rgb_image, |
| "%s_low_gain.jpg" % (NAME)) |
| rgb_tile = its.image.get_image_patch(rgb_image, 0.45, 0.45, 0.1, 0.1) |
| ref_snr = its.image.compute_image_snrs(rgb_tile) |
| print "Ref SNRs:", ref_snr |
| |
| e, s = its.target.get_target_exposure_combos(cam)["maxSensitivity"] |
| # NR modes 0, 1, 2, 3, 4 with high gain |
| for mode in range(5): |
| # Skip unavailable modes |
| if not its.caps.noise_reduction_mode(props, mode): |
| nr_modes_reported.append(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"] = mode |
| cap = cam.do_capture(req) |
| rgb_image = its.image.convert_capture_to_rgb_image(cap) |
| if n == 0: |
| nr_modes_reported.append( |
| cap["metadata"]["android.noiseReduction.mode"]) |
| its.image.write_image( |
| rgb_image, |
| "%s_high_gain_nr=%d.jpg" % (NAME, mode)) |
| rgb_tile = its.image.get_image_patch( |
| rgb_image, 0.45, 0.45, 0.1, 0.1) |
| rgb_snrs = its.image.compute_image_snrs(rgb_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", 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 j in range(3): |
| pylab.plot(range(5), snrs[j], "rgb"[j]) |
| matplotlib.pyplot.savefig("%s_plot_SNRs.png" % (NAME)) |
| |
| assert(nr_modes_reported == [0,1,2,3,4]) |
| |
| for j in range(3): |
| # Larger SNR 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) |
| if its.caps.noise_reduction_mode(props, 4): |
| # Verify ZSL(4) is close to MINIMAL(3) |
| assert(numpy.isclose(snrs[j][4], snrs[j][3], |
| atol=SNR_TOLERANCE)) |
| elif its.caps.noise_reduction_mode(props, 4): |
| # Verify ZSL(4) is close to OFF(0) |
| assert(numpy.isclose(snrs[j][4], snrs[j][0], |
| atol=SNR_TOLERANCE)) |
| |
| if __name__ == '__main__': |
| main() |
| |