def decode_dataset(data):
data = to_structs(data)
config = data.config
classes = [struct(id=int(k), **v) for k, v in config.classes.items()]
images = {i.image_file: decode_image(i, config) for i in data.images}
return config, DetectionDataset(classes=classes, images=images)
def decode_dataset(data):
data = to_structs(data)
config = data.config
classes = [struct(id=int(k), **v) for k, v in config.classes.items()]
images = filter_none([decode_image(i, config) for i in data.images])
images.sort(key=lambda image: image.start)
return struct(classes=classes, images=images, config=config)
def read_log(file):
entries = [to_structs(json.loads(line)) for line in open(file, mode="r")]
steps = {}
tags = {}
for entry in entries:
step = steps.get(entry.step) or {}
step[entry.tag] = struct(value=entry.value, time=entry.time)
tags[entry.tag] = True
steps[entry.step] = step
return struct (tags=tags.keys(), steps={i : Struct(step) for i, step in steps.items()})
def load_dataset(filename):
return to_structs(import_json(filename))
def process_command(str):
nonlocal env
if str is None:
print("Server disconnected.")
raise UserCommand('pause')
try:
tag, data = split_tagged(to_structs(json.loads(str)))
if tag == 'command':
raise UserCommand(data)
elif tag == 'init':
config, dataset = init_dataset(data)
env = initialise(config, dataset, args)
args.no_load = False # For subsequent initialisations
if not args.paused:
raise UserCommand('resume')
elif tag == 'import':
file, image_data = data
image = decode_image(image_data, env.config)
env.dataset.update_image(image)
elif tag == 'update':
file, method, image_data = data
image = decode_image(image_data, env.config)
env.dataset.update_image(image)
if image.category == 'validate':
env.best.score = 0
if env.pause_time == 0:
env.pause_time = env.args.pause_epochs
raise UserCommand('resume')
else:
env.pause_time = env.args.pause_epochs
elif tag == 'detect':
reqId, file, annotations, nms_params = data
review = decode_object_map(
annotations, env.config) if len(annotations) > 0 else None
if env is not None:
results = detect_request(env,
file,
nms_params,
review=review)
send_command('detect_request', (reqId, file, results))
else:
send_command('req_error',
[reqId, image, "model not available yet"])
else:
send_command('error', "unknown command: " + tag)
print("unknown command: " + tag)
except (JSONDecodeError) as err:
send_command('error', repr(err))
return None
plt.plot(x, density(x), label=labels[k], color=dataset_colors[k])
plt.xlabel('object size as percent of image size')
plt.ylabel('density')
plt.xlim(xmin=0.0, xmax=100.0)
plt.legend()
return fig, ax
if __name__ == '__main__':
figure_path = "/home/oliver/sync/figures/summaries"
loaded = load_all(datasets._subset("penguins", "branches", "scallops"),
base_path)
voc = to_structs(import_voc())
coco = to_structs(import_coco(subsets=[('val2017', 'validate')]))
loaded = loaded._extend(pascal_voc=voc, coco=coco)
keys = list(sorted(loaded.keys()))
fig, ax = plot_sizes_density(loaded,
keys,
labels=dataset_labels._extend(
pascal_voc="pascal vOC", coco="coco"))
fig.savefig(path.join(figure_path, "sizes_density.pdf"),
bbox_inches='tight')