# 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()