# Copyright 2016 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 import its.caps import its.cv2image import its.device import its.image import its.objects import numpy as np NUM_IMGS = 12 FRAME_TIME_TOL = 10 # ms SHARPNESS_TOL = 0.10 # percentage POSITION_TOL = 0.10 # percentage VGA_WIDTH = 640 VGA_HEIGHT = 480 NAME = os.path.basename(__file__).split('.')[0] CHART_FILE = os.path.join(os.environ['CAMERA_ITS_TOP'], 'pymodules', 'its', 'test_images', 'ISO12233.png') CHART_HEIGHT = 13.5 # cm CHART_DISTANCE = 30.0 # cm CHART_SCALE_START = 0.65 CHART_SCALE_STOP = 1.35 CHART_SCALE_STEP = 0.025 def test_lens_movement_reporting(cam, props, fmt, sensitivity, exp, af_fd): """Return fd, sharpness, lens state of the output images. Args: cam: An open device session. props: Properties of cam fmt: dict; capture format sensitivity: Sensitivity for the 3A request as defined in android.sensor.sensitivity exp: Exposure time for the 3A request as defined in android.sensor.exposureTime af_fd: Focus distance for the 3A request as defined in android.lens.focusDistance Returns: Object containing reported sharpness of the output image, keyed by the following string: 'sharpness' """ # initialize chart class chart = its.cv2image.Chart(CHART_FILE, CHART_HEIGHT, CHART_DISTANCE, CHART_SCALE_START, CHART_SCALE_STOP, CHART_SCALE_STEP) # find chart location xnorm, ynorm, wnorm, hnorm = chart.locate(cam, props, fmt, sensitivity, exp, af_fd) # initialize variables and take data sets data_set = {} white_level = int(props['android.sensor.info.whiteLevel']) min_fd = props['android.lens.info.minimumFocusDistance'] fds = [af_fd, min_fd] fds = sorted(fds * NUM_IMGS) reqs = [] for i, fd in enumerate(fds): reqs.append(its.objects.manual_capture_request(sensitivity, exp)) reqs[i]['android.lens.focusDistance'] = fd caps = cam.do_capture(reqs, fmt) for i, cap in enumerate(caps): data = {'fd': fds[i]} data['loc'] = cap['metadata']['android.lens.focusDistance'] data['lens_moving'] = (cap['metadata']['android.lens.state'] == 1) timestamp = cap['metadata']['android.sensor.timestamp'] if i == 0: timestamp_init = timestamp timestamp -= timestamp_init timestamp *= 1E-6 data['timestamp'] = timestamp print ' focus distance (diopters): %.3f' % data['fd'] print ' current lens location (diopters): %.3f' % data['loc'] print ' lens moving %r' % data['lens_moving'] y, _, _ = its.image.convert_capture_to_planes(cap, props) y = its.image.flip_mirror_img_per_argv(y) chart = its.image.normalize_img(its.image.get_image_patch(y, xnorm, ynorm, wnorm, hnorm)) its.image.write_image(chart, '%s_i=%d_chart.jpg' % (NAME, i)) data['sharpness'] = white_level*its.image.compute_image_sharpness(chart) print 'Chart sharpness: %.1f\n' % data['sharpness'] data_set[i] = data return data_set def main(): """Test if focus distance is properly reported. Capture images at a variety of focus locations. """ print '\nStarting test_lens_movement_reporting.py' with its.device.ItsSession() as cam: props = cam.get_camera_properties() its.caps.skip_unless(not its.caps.fixed_focus(props)) its.caps.skip_unless(its.caps.lens_approx_calibrated(props)) min_fd = props['android.lens.info.minimumFocusDistance'] fmt = {'format': 'yuv', 'width': VGA_WIDTH, 'height': VGA_HEIGHT} # Get proper sensitivity, exposure time, and focus distance with 3A. s, e, _, _, fd = cam.do_3a(get_results=True) # Get sharpness for each focal distance d = test_lens_movement_reporting(cam, props, fmt, s, e, fd) for k in sorted(d): print ('i: %d\tfd: %.3f\tlens location (diopters): %.3f \t' 'sharpness: %.1f \tlens_moving: %r \t' 'timestamp: %.1fms' % (k, d[k]['fd'], d[k]['loc'], d[k]['sharpness'], d[k]['lens_moving'], d[k]['timestamp'])) # assert frames are consecutive print 'Asserting frames are consecutive' times = [v['timestamp'] for v in d.itervalues()] diffs = np.gradient(times) assert np.isclose(np.amax(diffs)-np.amax(diffs), 0, atol=FRAME_TIME_TOL) # remove data when lens is moving for k in sorted(d): if d[k]['lens_moving']: del d[k] # split data into min_fd and af data for processing d_min_fd = {} d_af_fd = {} for k in sorted(d): if d[k]['fd'] == min_fd: d_min_fd[k] = d[k] if d[k]['fd'] == fd: d_af_fd[k] = d[k] # assert reported locations are close at af_fd print 'Asserting lens location of af_fd data' min_loc = min([v['loc'] for v in d_af_fd.itervalues()]) max_loc = max([v['loc'] for v in d_af_fd.itervalues()]) assert np.isclose(min_loc, max_loc, rtol=POSITION_TOL) # assert reported sharpness is close at af_fd print 'Asserting sharpness of af_fd data' min_sharp = min([v['sharpness'] for v in d_af_fd.itervalues()]) max_sharp = max([v['sharpness'] for v in d_af_fd.itervalues()]) assert np.isclose(min_sharp, max_sharp, rtol=SHARPNESS_TOL) # assert reported location is close to assign location for af_fd print 'Asserting lens location close to assigned fd for af_fd data' assert np.isclose(d_af_fd[0]['loc'], d_af_fd[0]['fd'], rtol=POSITION_TOL) # assert reported location is close for min_fd captures print 'Asserting lens location similar min_fd data' min_loc = min([v['loc'] for v in d_min_fd.itervalues()]) max_loc = max([v['loc'] for v in d_min_fd.itervalues()]) assert np.isclose(min_loc, max_loc, rtol=POSITION_TOL) # assert reported sharpness is close at min_fd print 'Asserting sharpness of min_fd data' min_sharp = min([v['sharpness'] for v in d_min_fd.itervalues()]) max_sharp = max([v['sharpness'] for v in d_min_fd.itervalues()]) assert np.isclose(min_sharp, max_sharp, rtol=SHARPNESS_TOL) # assert reported location is close to assign location for min_fd print 'Asserting lens location close to assigned fd for min_fd data' assert np.isclose(d_min_fd[NUM_IMGS*2-1]['loc'], d_min_fd[NUM_IMGS*2-1]['fd'], rtol=POSITION_TOL) if __name__ == '__main__': main()