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# Copyright 2014 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.
# --------------------------------------------------------------------------- #
# The Google Python style guide should be used for scripts: #
# #
# --------------------------------------------------------------------------- #
# The ITS modules that are in the utils directory. To see formatted
# docs, use the "pydoc" command:
# > pydoc image_processing_utils
"""Tutorial script for CameraITS tests."""
import capture_request_utils
import image_processing_utils
import its_base_test
import its_session_utils
# Standard Python modules.
import logging
import os.path
# Modules from the numpy, scipy, and matplotlib libraries. These are used for
# the image processing code, and images are represented as numpy arrays.
from matplotlib import pylab
import numpy
import matplotlib
import matplotlib.pyplot
# Module for Mobly
from mobly import test_runner
# A convention in each script is to use the filename (without the extension)
# as the name of the test, when printing results to the screen or dumping files.
_NAME = os.path.basename(__file__).split('.')[0]
# Each script has a class definition
class TutorialTest(its_base_test.ItsBaseTest):
"""Test the validity of some metadata entries.
Looks at the capture results and at the camera characteristics objects.
Script uses a config.yml file in the CameraITS directory.
A sample config.yml file:
- serial: 03281FDD40008Y
label: dut
camera: "1"
scene: "0"
A sample script call:
python tests/ --config config.yml
def test_tutorial(self):
# Each script has a string description of what it does. This is the first
# entry inside the main function.
"""Tutorial script to show how to use the ITS infrastructure."""
# The standard way to open a session with a connected camera device. This
# creates a cam object which encapsulates the session and which is active
# within the scope of the 'with' block; when the block exits, the camera
# session is closed. The device and camera are defined in the config.yml
# file.
with its_session_utils.ItsSession(
hidden_physical_id=self.hidden_physical_id) as cam:
# Append the log_path to store images in the proper location.
# Images will be stored in the test output folder:
# /tmp/logs/mobly/$TEST_BED_NAME/$DATE/TutorialTest
file_name = os.path.join(self.log_path, _NAME)
# Get the static properties of the camera device. Returns a Python
# associative array object; print it to the console.
props = cam.get_camera_properties()
logging.debug('props\n%s', str(props))
# Grab a YUV frame with manual exposure of sensitivity = 200, exposure
# duration = 50ms.
req = capture_request_utils.manual_capture_request(200, 50*1000*1000)
cap = cam.do_capture(req)
# Print the properties of the captured frame; width and height are
# integers, and the metadata is a Python associative array object.
# will be printed to screen & test_log.INFO
# logging.debug to test_log.DEBUG in /tmp/logs/mobly/... directory'Captured image width: %d, height: %d',
cap['width'], cap['height'])
logging.debug('metadata\n%s', str(cap['metadata']))
# The captured image is YUV420. Convert to RGB, and save as a file.
rgbimg = image_processing_utils.convert_capture_to_rgb_image(cap)
image_processing_utils.write_image(rgbimg, f'{file_name}_rgb.jpg')
# Can also get the Y,U,V planes separately; save these to greyscale
# files.
yimg, uimg, vimg = image_processing_utils.convert_capture_to_planes(cap)
image_processing_utils.write_image(yimg, f'{file_name}_y_plane.jpg')
image_processing_utils.write_image(uimg, f'{file_name}_u_plane.jpg')
image_processing_utils.write_image(vimg, f'{file_name}_v_plane.jpg')
# Run 3A on the device. In this case, just use the entire image as the
# 3A region, and run each of AWB,AE,AF. Can also change the region and
# specify independently for each of AE,AWB,AF whether it should run.
# NOTE: This may fail, if the camera isn't pointed at a reasonable
# target scene. If it fails, the script will end. The logcat messages
# can be inspected to see the status of 3A running on the device.
# If this keeps on failing, try also rebooting the device before
# running the test.
sens, exp, gains, xform, focus = cam.do_3a(get_results=True)'AE: sensitivity %d, exposure %dms', sens, exp/1000000.0)'AWB: gains %s', str(gains))'AWB: transform %s', str(xform))'AF: distance %.4f', focus)
# Grab a new manual frame, using the 3A values, and convert it to RGB
# and save it to a file too. Note that the 'req' object is just a
# Python dictionary that is pre-populated by the capture_request_utils
# functions (in this case a default manual capture), and the key/value
# pairs in the object can be used to set any field of the capture
# request. Here, the AWB gains and transform (CCM) are being used.
# Note that the CCM transform is in a rational format in capture
# requests, meaning it is an object with integer numerators and
# denominators. The 3A routine returns simple floats instead, however,
# so a conversion from float to rational must be performed.
req = capture_request_utils.manual_capture_request(sens, exp)
xform_rat = capture_request_utils.float_to_rational(xform)
req['android.colorCorrection.transform'] = xform_rat
req['android.colorCorrection.gains'] = gains
cap = cam.do_capture(req)
rgbimg = image_processing_utils.convert_capture_to_rgb_image(cap)
image_processing_utils.write_image(rgbimg, f'{file_name}_rgb_2.jpg')
# log the actual capture request object that was used.
logging.debug('req: %s', str(req))
# Images are numpy arrays. The dimensions are (h,w,3) when indexing,
# in the case of RGB images. Greyscale images are (h,w,1). Pixels are
# generally float32 values in the [0,1] range, however some of the
# helper functions in image_processing_utils deal with the packed YUV420
# and other formats of images that come from the device (and convert
# them to float32).
# Print the dimensions of the image, and the top-left pixel value,
# which is an array of 3 floats.'RGB image dimensions: %s', str(rgbimg.shape))'RGB image top-left pixel: %s', str(rgbimg[0, 0]))
# Grab a center tile from the image; this returns a new image. Save
# this tile image. In this case, the tile is the middle 10% x 10%
# rectangle.
tile = image_processing_utils.get_image_patch(
rgbimg, 0.45, 0.45, 0.1, 0.1)
image_processing_utils.write_image(tile, f'{file_name}_rgb_2_tile.jpg')
# Compute the mean values of the center tile image.
rgb_means = image_processing_utils.compute_image_means(tile)'RGB means: %s', str(rgb_means))
# Apply a lookup table to the image, and save the new version. The LUT
# is basically a tonemap, and can be used to implement a gamma curve.
# In this case, the LUT is used to double the value of each pixel.
lut = numpy.array([2*i for i in range(65536)])
rgbimg_lut = image_processing_utils.apply_lut_to_image(rgbimg, lut)
rgbimg_lut, f'{file_name}_rgb_2_lut.jpg')
# Compute a histogram of the luma image, in 256 buckets.
yimg, _, _ = image_processing_utils.convert_capture_to_planes(cap)
hist, _ = numpy.histogram(yimg*255, 256, (0, 256))
# Plot the histogram using matplotlib, and save as a PNG image.
pylab.plot(range(256), hist.tolist())
pylab.xlabel('Luma DN')
pylab.ylabel('Pixel count')
pylab.title('Histogram of luma channel of captured image')
# Capture a frame to be returned as a JPEG. Load it as an RGB image,
# then save it back as a JPEG.
cap = cam.do_capture(req, cam.CAP_JPEG)
rgbimg = image_processing_utils.convert_capture_to_rgb_image(cap)
image_processing_utils.write_image(rgbimg, f'{file_name}_jpg.jpg')
r, _, _ = image_processing_utils.convert_capture_to_planes(cap)
image_processing_utils.write_image(r, f'{file_name}_r.jpg')
# This is the standard boilerplate in each test that allows the script to both
# be executed directly and imported as a module.
if __name__ == '__main__':