def parmap_lists(f, xs_list, j=cpu_count() // 2, chunksize=1, pool=ThreadPool):
""" Map over a list of lists in parallel by flattening then splitting at the end"""
cam_lengths = map_list(len, xs_list)
xs = concat_lists(xs_list)
results = parmap_list(f, xs, j=j, chunksize=chunksize, pool=pool)
return split_list(results, cam_lengths)
def calibrate_intrinsic(args):
paths = setup_paths(args.paths)
setup_logging(args.runtime.log_level, [], log_file=paths.log_file)
info(pformat_struct(args))
image_path = os.path.expanduser(args.paths.image_path)
info(f"Finding images in {image_path}")
camera_images = find_camera_images(image_path,
args.paths.cameras,
args.paths.camera_pattern,
matching=False)
image_counts = {
k: len(files)
for k, files in zip(camera_images.cameras, camera_images.filenames)
}
info("Found camera directories with images {}".format(image_counts))
board_names, boards = split_dict(
find_board_config(image_path, args.paths.boards))
info("Loading images..")
images = image.detect.load_images(camera_images.filenames,
prefix=camera_images.image_path,
j=args.runtime.num_threads)
image_sizes = map_list(common_image_size, images)
info({
k: image_size
for k, image_size in zip(camera_images.cameras, image_sizes)
})
cache_key = struct(boards=boards,
image_sizes=image_sizes,
filenames=camera_images.filenames)
detected_points = detect_boards_cached(boards,
images,
paths.detections,
cache_key,
j=args.runtime.num_threads)
cameras, errs = calibrate_cameras(boards,
detected_points,
image_sizes,
model=args.camera.distortion_model,
fix_aspect=args.camera.fix_aspect,
has_skew=args.camera.allow_skew,
max_images=args.camera.limit_intrinsic)
for name, camera, err in zip(camera_images.cameras, cameras, errs):
info(f"Calibrated {name}, with RMS={err:.2f}")
info(camera)
info("")
info(f"Writing single calibrations to {paths.calibration_file}")
export_single(paths.calibration_file, cameras, camera_images.cameras,
camera_images.filenames)
def map_lists(f, xs_list, j=len(os.sched_getaffinity(0)) // 2, chunksize=1, pool=ThreadPool):
""" Map over a list of lists in parallel by flattening then splitting at the end"""
cam_lengths = map_list(len, xs_list)
flat_files = concat_lists(xs_list)
with pool(processes=j) as pool:
iter = pool.imap(f, flat_files, chunksize=chunksize)
results = list(tqdm(iter, total=len(flat_files)))
return split_list(results, cam_lengths)
def _load_images(self, j=cpu_count()):
assert self.filenames is not None, "_load_images: no filenames set"
info("Loading images..")
self.images = image.detect.load_images(
self.filenames, j=j, prefix=self.image_path)
self.image_size = map_list(common_image_size, self.images)
info(f"Loaded {self.sizes.image * self.sizes.camera} images")
info(
{k: image_size for k, image_size in zip(
self.names.camera, self.image_size)})
def add_camera_images(self, camera_images, j=cpu_count()):
check_camera_images(camera_images)
self.names = self.names._extend(
camera=camera_images.cameras, image=camera_images.image_names)
self.filenames = camera_images.filenames
self.image_path = camera_images.image_path
if 'images' in camera_images:
self.images = camera_images.images
self.image_size = map_list(common_image_size, self.images)
else:
self._load_images(j=j)