121 lines
4.7 KiB
Python
121 lines
4.7 KiB
Python
# Copyright 2013 The Android Open Source Project
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import its.image
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import its.caps
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import its.device
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import its.objects
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import its.target
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import pylab
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import numpy
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import os.path
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import matplotlib
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import matplotlib.pyplot
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def main():
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"""Test that a constant exposure is seen as ISO and exposure time vary.
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Take a series of shots that have ISO and exposure time chosen to balance
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each other; result should be the same brightness, but over the sequence
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the images should get noisier.
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"""
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NAME = os.path.basename(__file__).split(".")[0]
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THRESHOLD_MAX_OUTLIER_DIFF = 0.1
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THRESHOLD_MIN_LEVEL = 0.1
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THRESHOLD_MAX_LEVEL = 0.9
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THRESHOLD_MAX_LEVEL_DIFF = 0.045
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THRESHOLD_MAX_LEVEL_DIFF_WIDE_RANGE = 0.06
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THRESHOLD_ROUND_DOWN_GAIN = 0.1
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THRESHOLD_ROUND_DOWN_EXP = 0.05
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mults = []
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r_means = []
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g_means = []
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b_means = []
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threshold_max_level_diff = THRESHOLD_MAX_LEVEL_DIFF
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with its.device.ItsSession() as cam:
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props = cam.get_camera_properties()
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its.caps.skip_unless(its.caps.compute_target_exposure(props) and
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its.caps.per_frame_control(props))
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debug = its.caps.debug_mode()
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if debug:
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fmt = its.objects.get_largest_yuv_format(props)
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else:
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fmt = its.objects.get_smallest_yuv_format(props)
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e,s = its.target.get_target_exposure_combos(cam)["minSensitivity"]
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s_e_product = s*e
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expt_range = props['android.sensor.info.exposureTimeRange']
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sens_range = props['android.sensor.info.sensitivityRange']
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m = 1.0
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while s*m < sens_range[1] and e/m > expt_range[0]:
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mults.append(m)
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s_test = round(s*m)
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e_test = s_e_product / s_test
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print "Testing s:", s_test, "e:", e_test
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req = its.objects.manual_capture_request(
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s_test, e_test, 0.0, True, props)
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cap = cam.do_capture(req, fmt)
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s_res = cap["metadata"]["android.sensor.sensitivity"]
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e_res = cap["metadata"]["android.sensor.exposureTime"]
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assert(0 <= s_test - s_res < s_test * THRESHOLD_ROUND_DOWN_GAIN)
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assert(0 <= e_test - e_res < e_test * THRESHOLD_ROUND_DOWN_EXP)
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s_e_product_res = s_res * e_res
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request_result_ratio = s_e_product / s_e_product_res
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print "Capture result s:", s_test, "e:", e_test
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img = its.image.convert_capture_to_rgb_image(cap)
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its.image.write_image(img, "%s_mult=%3.2f.jpg" % (NAME, m))
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tile = its.image.get_image_patch(img, 0.45, 0.45, 0.1, 0.1)
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rgb_means = its.image.compute_image_means(tile)
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# Adjust for the difference between request and result
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r_means.append(rgb_means[0] * request_result_ratio)
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g_means.append(rgb_means[1] * request_result_ratio)
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b_means.append(rgb_means[2] * request_result_ratio)
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# Test 3 steps per 2x gain
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m = m * pow(2, 1.0 / 3)
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# Allow more threshold for devices with wider exposure range
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if m >= 64.0:
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threshold_max_level_diff = THRESHOLD_MAX_LEVEL_DIFF_WIDE_RANGE
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# Draw a plot.
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pylab.plot(mults, r_means, 'r.-')
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pylab.plot(mults, g_means, 'g.-')
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pylab.plot(mults, b_means, 'b.-')
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pylab.ylim([0,1])
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matplotlib.pyplot.savefig("%s_plot_means.png" % (NAME))
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# Check for linearity. Verify sample pixel mean values are close to each
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# other. Also ensure that the images aren't clamped to 0 or 1
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# (which would make them look like flat lines).
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for chan in xrange(3):
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values = [r_means, g_means, b_means][chan]
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m, b = numpy.polyfit(mults, values, 1).tolist()
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max_val = max(values)
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min_val = min(values)
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max_diff = max_val - min_val
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print "Channel %d line fit (y = mx+b): m = %f, b = %f" % (chan, m, b)
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print "Channel max %f min %f diff %f" % (max_val, min_val, max_diff)
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assert(max_diff < threshold_max_level_diff)
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assert(b > THRESHOLD_MIN_LEVEL and b < THRESHOLD_MAX_LEVEL)
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for v in values:
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assert(v > THRESHOLD_MIN_LEVEL and v < THRESHOLD_MAX_LEVEL)
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assert(abs(v - b) < THRESHOLD_MAX_OUTLIER_DIFF)
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if __name__ == '__main__':
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main()
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