# 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 # # 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. # --------------------------------------------------------------------------- # # The Google Python style guide should be used for scripts: # # http://google-styleguide.googlecode.com/svn/trunk/pyguide.html # # --------------------------------------------------------------------------- # # 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: TestBeds: - Name: TEST_BED_TUTORIAL Controllers: AndroidDevice: - serial: 03281FDD40008Y label: dut TestParams: camera: "1" scene: "0" A sample script call: python tests/tutorial.py --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( device_id=self.dut.serial, camera_id=self.camera_id, 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. # logging.info will be printed to screen & test_log.INFO # logging.debug to test_log.DEBUG in /tmp/logs/mobly/... directory logging.info('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) logging.info('AE: sensitivity %d, exposure %dms', sens, exp/1000000.0) logging.info('AWB: gains %s', str(gains)) logging.info('AWB: transform %s', str(xform)) logging.info('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. logging.info('RGB image dimensions: %s', str(rgbimg.shape)) logging.info('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) logging.info('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) image_processing_utils.write_image( 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') matplotlib.pyplot.savefig(f'{file_name}_histogram.png') # 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__': test_runner.main()