# 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,
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# See the License for the specific language governing permissions and
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"""Verifies correct exposure control."""
import logging
import os.path
import matplotlib
from matplotlib import pylab
from mobly import test_runner
import numpy as np
import its_base_test
import camera_properties_utils
import capture_request_utils
import image_processing_utils
import its_session_utils
import target_exposure_utils
_EXP_CORRECTION_FACTOR = 2 # mult or div factor to correct brightness
_NAME = os.path.splitext(os.path.basename(__file__))[0]
_NUM_PTS_2X_GAIN = 3 # 3 points every 2x increase in gain
_PATCH_H = 0.1 # center 10% patch params
_PATCH_W = 0.1
_PATCH_X = 0.5 - _PATCH_W/2
_PATCH_Y = 0.5 - _PATCH_H/2
_RAW_STATS_GRID = 9 # define 9x9 (11.11%) spacing grid for rawStats processing
_RAW_STATS_XY = _RAW_STATS_GRID//2 # define X, Y location for center rawStats
_THRESH_MIN_LEVEL = 0.1
_THRESH_MAX_LEVEL = 0.9
_THRESH_MAX_LEVEL_DIFF = 0.045
_THRESH_MAX_LEVEL_DIFF_WIDE_RANGE = 0.06
_THRESH_MAX_OUTLIER_DIFF = 0.1
_THRESH_ROUND_DOWN_ISO = 0.04
_THRESH_ROUND_DOWN_EXP = 0.03
_THRESH_ROUND_DOWN_EXP0 = 1.00 # RTOL @0ms exp; theoretical limit @ 4-line exp
_THRESH_EXP_KNEE = 6E6 # exposures less than knee have relaxed tol
_WIDE_EXP_RANGE_THRESH = 64.0 # threshold for 'wide' range sensor
def adjust_exp_for_brightness(
cam, props, fmt, exp, iso, sync_latency, test_name_with_path):
"""Take an image and adjust exposure and sensitivity.
Args:
cam: camera object
props: camera properties dict
fmt: capture format
exp: exposure time (ns)
iso: sensitivity
sync_latency: number for sync latency
test_name_with_path: path for saved files
Returns:
adjusted exposure
"""
req = capture_request_utils.manual_capture_request(
iso, exp, 0.0, True, props)
cap = its_session_utils.do_capture_with_latency(
cam, req, sync_latency, fmt)
img = image_processing_utils.convert_capture_to_rgb_image(cap)
image_processing_utils.write_image(
img, f'{test_name_with_path}.jpg')
patch = image_processing_utils.get_image_patch(
img, _PATCH_X, _PATCH_Y, _PATCH_W, _PATCH_H)
r, g, b = image_processing_utils.compute_image_means(patch)
logging.debug('Sample RGB values: %.3f, %.3f, %.3f', r, g, b)
if g < _THRESH_MIN_LEVEL:
exp *= _EXP_CORRECTION_FACTOR
logging.debug('exp increased by %dx: %d', _EXP_CORRECTION_FACTOR, exp)
elif g > _THRESH_MAX_LEVEL:
exp //= _EXP_CORRECTION_FACTOR
logging.debug('exp decreased to 1/%dx: %d', _EXP_CORRECTION_FACTOR, exp)
return exp
def plot_rgb_means(title, x, r, g, b, test_name_with_path):
"""Plot the RGB mean data.
Args:
title: string for figure title
x: x values for plot, gain multiplier
r: r plane means
g: g plane means
b: b plane menas
test_name_with_path: path for saved files
"""
pylab.figure(title)
pylab.semilogx(x, r, 'ro-')
pylab.semilogx(x, g, 'go-')
pylab.semilogx(x, b, 'bo-')
pylab.title(f'{_NAME} {title}')
pylab.xlabel('Gain Multiplier')
pylab.ylabel('Normalized RGB Plane Avg')
pylab.minorticks_off()
pylab.xticks(x[0::_NUM_PTS_2X_GAIN], x[0::_NUM_PTS_2X_GAIN])
pylab.ylim([0, 1])
plot_name = f'{test_name_with_path}_plot_rgb_means.png'
matplotlib.pyplot.savefig(plot_name)
def plot_raw_means(title, x, r, gr, gb, b, test_name_with_path):
"""Plot the RAW mean data.
Args:
title: string for figure title
x: x values for plot, gain multiplier
r: R plane means
gr: Gr plane means
gb: Gb plane means
b: B plane menas
test_name_with_path: path for saved files
"""
pylab.figure(title)
pylab.semilogx(x, r, 'ro-', label='R')
pylab.semilogx(x, gr, 'go-', label='Gr')
pylab.semilogx(x, gb, 'ko-', label='Gb')
pylab.semilogx(x, b, 'bo-', label='B')
pylab.title(f'{_NAME} {title}')
pylab.xlabel('Gain Multiplier')
pylab.ylabel('Normalized RAW Plane Avg')
pylab.minorticks_off()
pylab.xticks(x[0::_NUM_PTS_2X_GAIN], x[0::_NUM_PTS_2X_GAIN])
pylab.ylim([0, 1])
pylab.legend(numpoints=1)
plot_name = f'{test_name_with_path}_plot_raw_means.png'
matplotlib.pyplot.savefig(plot_name)
def check_line_fit(color, mults, values, thresh_max_level_diff):
"""Find line fit and check values.
Check for linearity. Verify sample pixel mean values are close to each
other. Also ensure that the images aren't clamped to 0 or 1
(which would also make them look like flat lines).
Args:
color: string to define RGB or RAW channel
mults: list of multiplication values for gain*m, exp/m
values: mean values for chan
thresh_max_level_diff: threshold for max difference
"""
m, b = np.polyfit(mults, values, 1).tolist()
min_val = min(values)
max_val = max(values)
max_diff = max_val - min_val
logging.debug('Channel %s line fit (y = mx+b): m = %f, b = %f', color, m, b)
logging.debug('Channel min %f max %f diff %f', min_val, max_val, max_diff)
if max_diff >= thresh_max_level_diff:
raise AssertionError(f'max_diff: {max_diff:.4f}, '
f'THRESH: {thresh_max_level_diff:.3f}')
if not _THRESH_MAX_LEVEL > b > _THRESH_MIN_LEVEL:
raise AssertionError(f'b: {b:.2f}, THRESH_MIN: {_THRESH_MIN_LEVEL}, '
f'THRESH_MAX: {_THRESH_MAX_LEVEL}')
for v in values:
if not _THRESH_MAX_LEVEL > v > _THRESH_MIN_LEVEL:
raise AssertionError(f'v: {v:.2f}, THRESH_MIN: {_THRESH_MIN_LEVEL}, '
f'THRESH_MAX: {_THRESH_MAX_LEVEL}')
if abs(v - b) >= _THRESH_MAX_OUTLIER_DIFF:
raise AssertionError(f'v: {v:.2f}, b: {b:.2f}, '
f'THRESH_DIFF: {_THRESH_MAX_OUTLIER_DIFF}')
def get_raw_active_array_size(props):
"""Return the active array w, h from props."""
aaw = (props['android.sensor.info.preCorrectionActiveArraySize']['right'] -
props['android.sensor.info.preCorrectionActiveArraySize']['left'])
aah = (props['android.sensor.info.preCorrectionActiveArraySize']['bottom'] -
props['android.sensor.info.preCorrectionActiveArraySize']['top'])
return aaw, aah
class ExposureXIsoTest(its_base_test.ItsBaseTest):
"""Test that a constant brightness is seen as ISO and exposure time vary.
Take a series of shots that have ISO and exposure time chosen to balance
each other; result should be the same brightness, but over the sequence
the images should get noisier.
"""
def test_exposure_x_iso(self):
mults = []
r_means = []
g_means = []
b_means = []
raw_r_means = []
raw_gr_means = []
raw_gb_means = []
raw_b_means = []
thresh_max_level_diff = _THRESH_MAX_LEVEL_DIFF
with its_session_utils.ItsSession(
device_id=self.dut.serial,
camera_id=self.camera_id,
hidden_physical_id=self.hidden_physical_id) as cam:
props = cam.get_camera_properties()
props = cam.override_with_hidden_physical_camera_props(props)
test_name_with_path = os.path.join(self.log_path, _NAME)
# Check SKIP conditions
camera_properties_utils.skip_unless(
camera_properties_utils.compute_target_exposure(props))
# Load chart for scene
its_session_utils.load_scene(
cam, props, self.scene, self.tablet,
its_session_utils.CHART_DISTANCE_NO_SCALING)
# Initialize params for requests
debug = self.debug_mode
raw_avlb = (camera_properties_utils.raw16(props) and
camera_properties_utils.manual_sensor(props))
sync_latency = camera_properties_utils.sync_latency(props)
logging.debug('sync latency: %d frames', sync_latency)
largest_yuv = capture_request_utils.get_largest_yuv_format(props)
match_ar = (largest_yuv['width'], largest_yuv['height'])
fmt = capture_request_utils.get_near_vga_yuv_format(
props, match_ar=match_ar)
e, s = target_exposure_utils.get_target_exposure_combos(
self.log_path, cam)['minSensitivity']
# Take a shot and adjust parameters for brightness
logging.debug('Target exposure combo values. exp: %d, iso: %d',
e, s)
e = adjust_exp_for_brightness(
cam, props, fmt, e, s, sync_latency, test_name_with_path)
# Initialize values to define test range
s_e_product = s * e
expt_range = props['android.sensor.info.exposureTimeRange']
sens_range = props['android.sensor.info.sensitivityRange']
m = 1.0
# Do captures with a range of exposures, but constant s*e
while s*m < sens_range[1] and e/m > expt_range[0]:
mults.append(m)
s_req = round(s * m)
e_req = s_e_product // s_req
logging.debug('Testing s: %d, e: %dns', s_req, e_req)
req = capture_request_utils.manual_capture_request(
s_req, e_req, 0.0, True, props)
cap = its_session_utils.do_capture_with_latency(
cam, req, sync_latency, fmt)
s_res = cap['metadata']['android.sensor.sensitivity']
e_res = cap['metadata']['android.sensor.exposureTime']
# determine exposure tolerance based on exposure time
if e_req >= _THRESH_EXP_KNEE:
thresh_round_down_exp = _THRESH_ROUND_DOWN_EXP
else:
thresh_round_down_exp = (
_THRESH_ROUND_DOWN_EXP +
(_THRESH_ROUND_DOWN_EXP0 - _THRESH_ROUND_DOWN_EXP) *
(_THRESH_EXP_KNEE - e_req) / _THRESH_EXP_KNEE)
if not 0 <= s_req - s_res < s_req * _THRESH_ROUND_DOWN_ISO:
raise AssertionError(f's_req: {s_req}, s_res: {s_res}, '
f'RTOL=-{_THRESH_ROUND_DOWN_ISO*100}%')
if not 0 <= e_req - e_res < e_req * thresh_round_down_exp:
raise AssertionError(f'e_req: {e_req}ns, e_res: {e_res}ns, '
f'RTOL=-{thresh_round_down_exp*100}%')
s_e_product_res = s_res * e_res
req_res_ratio = s_e_product / s_e_product_res
logging.debug('Capture result s: %d, e: %dns', s_res, e_res)
img = image_processing_utils.convert_capture_to_rgb_image(cap)
image_processing_utils.write_image(
img, f'{test_name_with_path}_mult={m:.2f}.jpg')
patch = image_processing_utils.get_image_patch(
img, _PATCH_X, _PATCH_Y, _PATCH_W, _PATCH_H)
rgb_means = image_processing_utils.compute_image_means(patch)
# Adjust for the difference between request and result
r_means.append(rgb_means[0] * req_res_ratio)
g_means.append(rgb_means[1] * req_res_ratio)
b_means.append(rgb_means[2] * req_res_ratio)
# Do with RAW_STATS space if debug
if raw_avlb and debug:
aaw, aah = get_raw_active_array_size(props)
fmt_raw = {'format': 'rawStats',
'gridWidth': aaw//_RAW_STATS_GRID,
'gridHeight': aah//_RAW_STATS_GRID}
raw_cap = its_session_utils.do_capture_with_latency(
cam, req, sync_latency, fmt_raw)
r, gr, gb, b = image_processing_utils.convert_capture_to_planes(
raw_cap, props)
raw_r_means.append(r[_RAW_STATS_XY, _RAW_STATS_XY] * req_res_ratio)
raw_gr_means.append(gr[_RAW_STATS_XY, _RAW_STATS_XY] * req_res_ratio)
raw_gb_means.append(gb[_RAW_STATS_XY, _RAW_STATS_XY] * req_res_ratio)
raw_b_means.append(b[_RAW_STATS_XY, _RAW_STATS_XY] * req_res_ratio)
# Test number of points per 2x gain
m *= pow(2, 1.0/_NUM_PTS_2X_GAIN)
# Loosen threshold for devices with wider exposure range
if m >= _WIDE_EXP_RANGE_THRESH:
thresh_max_level_diff = _THRESH_MAX_LEVEL_DIFF_WIDE_RANGE
# Draw plots and check data
if raw_avlb and debug:
plot_raw_means('RAW data', mults, raw_r_means, raw_gr_means, raw_gb_means,
raw_b_means, test_name_with_path)
for ch, color in enumerate(['R', 'Gr', 'Gb', 'B']):
values = [raw_r_means, raw_gr_means, raw_gb_means, raw_b_means][ch]
check_line_fit(color, mults, values, thresh_max_level_diff)
plot_rgb_means(f'RGB (1x: iso={s}, exp={e})', mults,
r_means, g_means, b_means, test_name_with_path)
for ch, color in enumerate(['R', 'G', 'B']):
values = [r_means, g_means, b_means][ch]
check_line_fit(color, mults, values, thresh_max_level_diff)
if __name__ == '__main__':
test_runner.main()