107 lines
4.2 KiB
Python
107 lines
4.2 KiB
Python
# Copyright 2023 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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"""Verifies that autoframing can adjust fov to include all faces with different
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skin tones."""
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import logging
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import os.path
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from mobly import test_runner
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import its_base_test
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import camera_properties_utils
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import capture_request_utils
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import image_processing_utils
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import its_session_utils
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import opencv_processing_utils
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_CV2_FACE_SCALE_FACTOR = 1.05 # 5% step for resizing image to find face
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_CV2_FACE_MIN_NEIGHBORS = 4 # recommended 3-6: higher for less faces
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_NUM_TEST_FRAMES = 20
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_NUM_FACES = 3
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_W, _H = 640, 480
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class AutoframingTest(its_base_test.ItsBaseTest):
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"""Test autoframing for faces with different skin tones.
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"""
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def test_autoframing(self):
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"""Test if fov gets adjusted to accommodate all the faces in the frame.
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Do a large zoom on scene2_a using do_3a so that none of that faces are
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visible initially, trigger autoframing, wait for the state to converge and
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make sure all the faces are found.
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"""
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with its_session_utils.ItsSession(
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device_id=self.dut.serial,
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camera_id=self.camera_id,
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hidden_physical_id=self.hidden_physical_id) as cam:
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props = cam.get_camera_properties()
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props = cam.override_with_hidden_physical_camera_props(props)
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# Load chart for scene
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its_session_utils.load_scene(
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cam, props, self.scene, self.tablet, self.chart_distance,
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log_path=self.log_path)
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# Check SKIP conditions
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# Don't run autoframing if face detection or autoframing is not supported
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camera_properties_utils.skip_unless(
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camera_properties_utils.face_detect(props) and
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camera_properties_utils.autoframing(props))
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# Do max-ish zoom with the help of do_3a, keeping all the 'A's off. This
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# zooms into the scene so that none of the faces are in the view
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# initially - which gives room for autoframing to take place.
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max_zoom_ratio = camera_properties_utils.get_max_digital_zoom(props)
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cam.do_3a(do_af=False, zoom_ratio=max_zoom_ratio)
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cam.do_autoframing(zoom_ratio=max_zoom_ratio)
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req = capture_request_utils.auto_capture_request(
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do_autoframing=True, zoom_ratio=max_zoom_ratio)
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req['android.statistics.faceDetectMode'] = 1 # Simple
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fmt = {'format': 'yuv', 'width': _W, 'height': _H}
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caps = cam.do_capture([req]*_NUM_TEST_FRAMES, fmt)
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for i, cap in enumerate(caps):
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faces = cap['metadata']['android.statistics.faces']
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# Face detection and autoframing could take several frames to warm up,
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# but should detect the correct number of faces in last frame
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if i == _NUM_TEST_FRAMES - 1:
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num_faces_found = len(faces)
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if num_faces_found != _NUM_FACES:
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raise AssertionError('Wrong num of faces found! Found: '
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f'{num_faces_found}, expected: {_NUM_FACES}')
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# Also check the faces with open cv to make sure the scene is not
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# distored or anything.
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img = image_processing_utils.convert_capture_to_rgb_image(
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cap, props=props)
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opencv_faces = opencv_processing_utils.find_opencv_faces(
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img, _CV2_FACE_SCALE_FACTOR, _CV2_FACE_MIN_NEIGHBORS)
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num_opencv_faces = len(opencv_faces)
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if num_opencv_faces != _NUM_FACES:
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raise AssertionError('Wrong num of faces found with OpenCV! Found: '
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f'{num_opencv_faces}, expected: {_NUM_FACES}')
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if not faces:
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continue
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logging.debug('Frame %d face metadata:', i)
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logging.debug('Faces: %s', str(faces))
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if __name__ == '__main__':
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test_runner.main()
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