# Copyright 2020 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. """Verify zoom ratio scales circle sizes correctly.""" import logging import math import os.path from mobly import test_runner import numpy as np import cv2 import its_base_test import camera_properties_utils import capture_request_utils import image_processing_utils import its_session_utils import opencv_processing_utils CIRCLE_COLOR = 0 # [0: black, 255: white] CIRCLE_AR_RTOL = 0.15 # contour width vs height (aspect ratio) CIRCLISH_RTOL = 0.05 # contour area vs ideal circle area pi*((w+h)/4)**2 LINE_COLOR = (255, 0, 0) # red LINE_THICKNESS = 5 MIN_AREA_RATIO = 0.00015 # based on 2000/(4000x3000) pixels MIN_CIRCLE_PTS = 25 MIN_FOCUS_DIST_TOL = 0.80 # allow charts a little closer than min NAME = os.path.splitext(os.path.basename(__file__))[0] NUM_STEPS = 10 OFFSET_RTOL = 0.15 RADIUS_RTOL = 0.10 RADIUS_RTOL_MIN_FD = 0.15 ZOOM_MAX_THRESH = 10.0 ZOOM_MIN_THRESH = 2.0 def get_test_tols_and_cap_size(cam, props, chart_distance, debug): """Determine the tolerance per camera based on test rig and camera params. Cameras are pre-filtered to only include supportable cameras. Supportable cameras are: YUV(RGB) Args: cam: camera object props: dict; physical camera properties dictionary chart_distance: float; distance to chart in cm debug: boolean; log additional data Returns: dict of TOLs with camera focal length as key largest common size across all cameras """ ids = camera_properties_utils.logical_multi_camera_physical_ids(props) physical_props = {} physical_ids = [] for i in ids: physical_props[i] = cam.get_camera_properties_by_id(i) # find YUV capable physical cameras if camera_properties_utils.backward_compatible(physical_props[i]): physical_ids.append(i) # find physical camera focal lengths that work well with rig chart_distance_m = abs(chart_distance)/100 # convert CM to M test_tols = {} test_yuv_sizes = [] for i in physical_ids: min_fd = physical_props[i]['android.lens.info.minimumFocusDistance'] focal_l = physical_props[i]['android.lens.info.availableFocalLengths'][0] logging.debug('cam[%s] min_fd: %.3f (diopters), fl: %.2f', i, min_fd, focal_l) yuv_sizes = capture_request_utils.get_available_output_sizes( 'yuv', physical_props[i]) test_yuv_sizes.append(yuv_sizes) if debug: logging.debug('cam[%s] yuv sizes: %s', i, str(yuv_sizes)) # determine if minimum focus distance is less than rig depth if (math.isclose(min_fd, 0.0, rel_tol=1E-6) or # fixed focus 1.0/min_fd < chart_distance_m*MIN_FOCUS_DIST_TOL): test_tols[focal_l] = RADIUS_RTOL else: test_tols[focal_l] = RADIUS_RTOL_MIN_FD logging.debug('loosening RTOL for cam[%s]: ' 'min focus distance too large.', i) # find intersection of formats for max common format common_sizes = list(set.intersection(*[set(list) for list in test_yuv_sizes])) if debug: logging.debug('common_fmt: %s', max(common_sizes)) return test_tols, max(common_sizes) def distance(x, y): return math.sqrt(x**2 + y**2) def circle_cropped(circle, size): """Determine if a circle is cropped by edge of img. Args: circle: list [x, y, radius] of circle size: tuple (x, y) of size of img Returns: Boolean True if selected circle is cropped """ cropped = False circle_x, circle_y = circle[0], circle[1] circle_r = circle[2] x_min, x_max = circle_x - circle_r, circle_x + circle_r y_min, y_max = circle_y - circle_r, circle_y + circle_r if x_min < 0 or y_min < 0 or x_max > size[0] or y_max > size[1]: cropped = True return cropped def find_center_circle(img, img_name, color, min_area, debug): """Find the circle closest to the center of the image. Finds all contours in the image. Rejects those too small and not enough points to qualify as a circle. The remaining contours must have center point of color=color and are sorted based on distance from the center of the image. The contour closest to the center of the image is returned. Note: hierarchy is not used as the hierarchy for black circles changes as the zoom level changes. Args: img: numpy img array with pixel values in [0,255]. img_name: str file name for saved image color: int 0 --> black, 255 --> white min_area: int minimum area of circles to screen out debug: bool to save extra data Returns: circle: [center_x, center_y, radius] """ # gray scale & otsu threshold to binarize the image gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) _, img_bw = cv2.threshold( np.uint8(gray), 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) # use OpenCV to find contours (connected components) contours = opencv_processing_utils.find_all_contours(255-img_bw) # check contours and find the best circle candidates circles = [] img_ctr = [gray.shape[1] // 2, gray.shape[0] // 2] for contour in contours: area = cv2.contourArea(contour) if area > min_area and len(contour) >= MIN_CIRCLE_PTS: shape = opencv_processing_utils.component_shape(contour) radius = (shape['width'] + shape['height']) / 4 colour = img_bw[shape['cty']][shape['ctx']] circlish = round((math.pi * radius**2) / area, 4) if (colour == color and (1 - CIRCLISH_RTOL <= circlish <= 1 + CIRCLISH_RTOL) and math.isclose(shape['width'], shape['height'], rel_tol=CIRCLE_AR_RTOL)): circles.append([shape['ctx'], shape['cty'], radius, circlish, area]) if not circles: raise AssertionError('No circle was detected. Please take pictures ' 'according to instructions carefully!') if debug: logging.debug('circles [x, y, r, pi*r**2/area, area]: %s', str(circles)) # find circle closest to center circles.sort(key=lambda x: distance(x[0] - img_ctr[0], x[1] - img_ctr[1])) circle = circles[0] # mark image center size = gray.shape m_x, m_y = size[1] // 2, size[0] // 2 marker_size = LINE_THICKNESS * 10 cv2.drawMarker(img, (m_x, m_y), LINE_COLOR, markerType=cv2.MARKER_CROSS, markerSize=marker_size, thickness=LINE_THICKNESS) # add circle to saved image center_i = (int(round(circle[0], 0)), int(round(circle[1], 0))) radius_i = int(round(circle[2], 0)) cv2.circle(img, center_i, radius_i, LINE_COLOR, LINE_THICKNESS) image_processing_utils.write_image(img / 255.0, img_name) return [circle[0], circle[1], circle[2]] class ZoomTest(its_base_test.ItsBaseTest): """Test the camera zoom behavior. """ def test_zoom(self): test_data = {} with its_session_utils.ItsSession( device_id=self.dut.serial, camera_id=self.camera_id, hidden_physical_id=self.hidden_physical_id) as cam: props = cam.get_camera_properties() props = cam.override_with_hidden_physical_camera_props(props) camera_properties_utils.skip_unless( camera_properties_utils.zoom_ratio_range(props)) # Load chart for scene its_session_utils.load_scene( cam, props, self.scene, self.tablet, self.chart_distance) z_range = props['android.control.zoomRatioRange'] logging.debug('testing zoomRatioRange: %s', str(z_range)) debug = self.debug_mode z_min, z_max = float(z_range[0]), float(z_range[1]) camera_properties_utils.skip_unless(z_max >= z_min * ZOOM_MIN_THRESH) z_list = np.arange(z_min, z_max, float(z_max - z_min) / (NUM_STEPS - 1)) z_list = np.append(z_list, z_max) # set TOLs based on camera and test rig params if camera_properties_utils.logical_multi_camera(props): test_tols, size = get_test_tols_and_cap_size( cam, props, self.chart_distance, debug) else: fl = props['android.lens.info.availableFocalLengths'][0] test_tols = {fl: RADIUS_RTOL} yuv_size = capture_request_utils.get_largest_yuv_format(props) size = [yuv_size['width'], yuv_size['height']] logging.debug('capture size: %s', str(size)) logging.debug('test TOLs: %s', str(test_tols)) # do captures over zoom range and find circles with cv2 cam.do_3a() req = capture_request_utils.auto_capture_request() for i, z in enumerate(z_list): logging.debug('zoom ratio: %.2f', z) req['android.control.zoomRatio'] = z cap = cam.do_capture( req, {'format': 'yuv', 'width': size[0], 'height': size[1]}) img = image_processing_utils.convert_capture_to_rgb_image( cap, props=props) img_name = '%s_%s.jpg' % (os.path.join(self.log_path, NAME), round(z, 2)) image_processing_utils.write_image(img, img_name) # determine radius tolerance of capture cap_fl = cap['metadata']['android.lens.focalLength'] radius_tol = test_tols[cap_fl] # convert to [0, 255] images with unsigned integer img *= 255 img = img.astype(np.uint8) # Find the center circle in img circle = find_center_circle( img, img_name, CIRCLE_COLOR, min_area=MIN_AREA_RATIO * size[0] * size[1] * z * z, debug=debug) if circle_cropped(circle, size): logging.debug('zoom %.2f is too large! Skip further captures', z) break test_data[i] = {'z': z, 'circle': circle, 'r_tol': radius_tol, 'fl': cap_fl} # assert some range is tested before circles get too big zoom_max_thresh = ZOOM_MAX_THRESH z_max_ratio = z_max / z_min if z_max_ratio < ZOOM_MAX_THRESH: zoom_max_thresh = z_max_ratio test_data_max_z = (test_data[max(test_data.keys())]['z'] / test_data[min(test_data.keys())]['z']) logging.debug('test zoom ratio max: %.2f', test_data_max_z) if test_data_max_z < zoom_max_thresh: raise AssertionError(f'Max zoom ratio tested: {test_data_max_z:.4f}, ' f'range advertised min: {z_min}, max: {z_max} ' f'THRESH: {zoom_max_thresh}') # initialize relative size w/ zoom[0] for diff zoom ratio checks radius_0 = float(test_data[0]['circle'][2]) z_0 = float(test_data[0]['z']) for i, data in test_data.items(): logging.debug('Zoom: %.2f, fl: %.2f', data['z'], data['fl']) offset_abs = [(data['circle'][0] - size[0] // 2), (data['circle'][1] - size[1] // 2)] logging.debug('Circle r: %.1f, center offset x, y: %d, %d', data['circle'][2], offset_abs[0], offset_abs[1]) z_ratio = data['z'] / z_0 # check relative size against zoom[0] radius_ratio = data['circle'][2] / radius_0 logging.debug('r ratio req: %.3f, measured: %.3f', z_ratio, radius_ratio) if not math.isclose(z_ratio, radius_ratio, rel_tol=data['r_tol']): raise AssertionError(f'zoom: {z_ratio:.2f}, radius ratio: ' f"{radius_ratio:.2f}, RTOL: {data['r_tol']}") # check relative offset against init vals w/ no focal length change if i == 0 or test_data[i-1]['fl'] != data['fl']: # set init values z_init = float(data['z']) offset_init = [data['circle'][0] - size[0]//2, data['circle'][1] - size[1]//2] else: # check z_ratio = data['z'] / z_init offset_rel = (distance(offset_abs[0], offset_abs[1]) / z_ratio / distance(offset_init[0], offset_init[1])) logging.debug('offset_rel: %.3f', offset_rel) if not math.isclose(offset_rel, 1.0, rel_tol=OFFSET_RTOL): raise AssertionError(f"zoom: {data['z']:.2f}, offset(rel): " f'{offset_rel:.4f}, RTOL: {OFFSET_RTOL}') if __name__ == '__main__': test_runner.main()