# 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. import os.path import cv2 import its.caps import its.device import its.image import its.objects NAME = os.path.basename(__file__).split('.')[0] NUM_TEST_FRAMES = 20 NUM_FACES = 3 FD_MODE_OFF = 0 FD_MODE_SIMPLE = 1 FD_MODE_FULL = 2 W, H = 640, 480 def main(): """Test face detection.""" with its.device.ItsSession() as cam: props = cam.get_camera_properties() props = cam.override_with_hidden_physical_camera_props(props) its.caps.skip_unless(its.caps.face_detect(props)) mono_camera = its.caps.mono_camera(props) fd_modes = props['android.statistics.info.availableFaceDetectModes'] a = props['android.sensor.info.activeArraySize'] aw, ah = a['right'] - a['left'], a['bottom'] - a['top'] if its.caps.read_3a(props): _, _, _, _, _ = cam.do_3a(get_results=True, mono_camera=mono_camera) for fd_mode in fd_modes: assert FD_MODE_OFF <= fd_mode <= FD_MODE_FULL req = its.objects.auto_capture_request() req['android.statistics.faceDetectMode'] = fd_mode fmt = {'format': 'yuv', 'width': W, 'height': H} caps = cam.do_capture([req]*NUM_TEST_FRAMES, fmt) for i, cap in enumerate(caps): md = cap['metadata'] assert md['android.statistics.faceDetectMode'] == fd_mode faces = md['android.statistics.faces'] # 0 faces should be returned for OFF mode if fd_mode == FD_MODE_OFF: assert not faces continue # Face detection could take several frames to warm up, # but should detect the correct number of faces in last frame if i == NUM_TEST_FRAMES - 1: img = its.image.convert_capture_to_rgb_image(cap, props=props) fnd_faces = len(faces) print 'Found %d face(s), expected %d.' % (fnd_faces, NUM_FACES) # draw boxes around faces for rect in [face['bounds'] for face in faces]: top_left = (int(round(rect['left']*W/aw)), int(round(rect['top']*H/ah))) bot_rght = (int(round(rect['right']*W/aw)), int(round(rect['bottom']*H/ah))) cv2.rectangle(img, top_left, bot_rght, (0, 1, 0), 2) img_name = '%s_fd_mode_%s.jpg' % (NAME, fd_mode) its.image.write_image(img, img_name) assert fnd_faces == NUM_FACES if not faces: continue print 'Frame %d face metadata:' % i print ' Faces:', faces print '' # Reasonable scores for faces face_scores = [face['score'] for face in faces] for score in face_scores: assert score >= 1 and score <= 100 # Face bounds should be within active array face_rectangles = [face['bounds'] for face in faces] for rect in face_rectangles: assert rect['top'] < rect['bottom'] assert rect['left'] < rect['right'] assert 0 <= rect['top'] <= ah assert 0 <= rect['bottom'] <= ah assert 0 <= rect['left'] <= aw assert 0 <= rect['right'] <= aw if __name__ == '__main__': main()