def main(_):
parser = argparse.ArgumentParser()
parser.add_argument('--output_path',
nargs='?',
const=True,
type=str,
default=os.path.dirname(__file__),
help='Path for saving output images')
flags = parse_cmd(parser)
normalize = True
lidar_grss2013_scale = 5
lidar_grss2018_scale = lidar_grss2013_scale / 2.5
loader_name = "GRSS2013DataLoader"
loader = get_class(loader_name + '.' + loader_name)(flags.path)
grss_2013_data_set = loader.load_data(0, normalize)
loader_name = "GRSS2018DataLoader"
loader = get_class(loader_name + '.' + loader_name)(flags.path)
grss_2018_data_set = loader.load_data(0, normalize)
grss_2013_band = 8
grss_2018_band = 2
match_data(grss_2013_band, grss_2018_band, grss_2013_data_set,
grss_2018_data_set, lidar_grss2013_scale, lidar_grss2018_scale)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--output_path',
nargs='?',
const=True,
type=str,
default=os.path.dirname(__file__),
help='Path for saving output images')
flags = parse_cmd(parser)
loader_name = flags.loader_name
loader = get_class(loader_name + '.' + loader_name)(flags.path)
sample_set = loader.load_samples(0.1, 0.1)
data_set = loader.load_data(0, True)
shadow_map, _ = loader.load_shadow_map(0, data_set)
plt.imshow(shadow_map * 255)
plt.title("figure_name"), plt.xticks([]), plt.yticks([])
plt.show()
non_shadow_test_sample = 0
for point in sample_set.validation_targets:
if shadow_map[point[1], point[0]] == 1:
shadow_map[point[1], point[0]] = 0
else:
non_shadow_test_sample = non_shadow_test_sample + 1
plt.imshow(shadow_map * 255)
plt.title("figure_name"), plt.xticks([]), plt.yticks([])
plt.show()
imwrite("shadow_map.tif", shadow_map, planarconfig='contig')
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--output_path', nargs='?', const=True, type=str,
default=os.path.dirname(__file__),
help='Path for saving output images')
flags = parse_cmd(parser)
loader_name = flags.loader_name
loader = get_class(loader_name + '.' + loader_name)(flags.path)
sample_set = loader.load_samples(0.1, 0.1)
data_set = loader.load_data(0, True)
scene_shape = loader.get_scene_shape(data_set)
target_classes_as_image = create_target_image_via_samples(sample_set, scene_shape)
shadow_map = get_shadow_map(target_classes_as_image)
imwrite("muulf_shadow_map.tif", shadow_map, planarconfig='contig')
create_shadow_corrected_image(data_set.casi, loader.load_data(0, False).casi, shadow_map)
draw_targets(loader.get_target_color_list(), target_classes_as_image, "Targets")
# retval, labels, stats, centroids = cv2.connectedComponentsWithStats(shadow_map)
contours, hierarchy = cv2.findContours(shadow_map, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)
# draw_im = numpy.zeros(shadow_map.shape, dtype=numpy.uint8)
# cv2.drawContours(draw_im, contours, -1, 255, 3)
for contour in contours:
target_map = {}
fill_targets_for_contour(contour, target_classes_as_image, target_map)
if BUILDING_SHADOW_CLASS in target_map:
del target_map[BUILDING_SHADOW_CLASS]
if INVALID_TARGET_VALUE in target_map:
del target_map[INVALID_TARGET_VALUE]
if BUILDING_CLASS in target_map:
del target_map[BUILDING_CLASS]
final_neigh_target = None
final_neigh_count = 0
for neigh_target, neigh_count in target_map.items():
if final_neigh_target is not None:
if neigh_count > final_neigh_count:
final_neigh_target = neigh_target
final_neigh_count = neigh_count
else:
final_neigh_target = neigh_target
final_neigh_count = neigh_count
# print(final_neigh_target)
# print(final_neigh_count)
if final_neigh_target is None:
print("found contour with no proper neighbors")
else:
image = get_contour_image(shadow_map.shape, contour)
target_classes_as_image[image] = final_neigh_target
print("shadow converted to neighboring target %d" % final_neigh_target)
draw_targets(loader.get_target_color_list(), target_classes_as_image, "Targets after shadow correction")
# increase target level as one
target_classes_as_image[target_classes_as_image != INVALID_TARGET_VALUE] = target_classes_as_image[
target_classes_as_image != INVALID_TARGET_VALUE] + 1
imwrite("muulf_gt_shadow_corrected.tif", target_classes_as_image, planarconfig='contig')
def main(_):
flags = parse_cmd(argparse.ArgumentParser())
print('Input information:', flags)
nn_model = get_class(flags.model_name + '.' + flags.model_name)()
if flags.max_evals == 1:
print('Running in single execution training mode')
algorithm_params = nn_model.get_default_params(flags.batch_size)
if flags.algorithm_param_path is not None:
algorithm_params = json.load(open(flags.algorithm_param_path, 'r'))
# algorithm_params = namedtuple('GenericDict', algorithm_params_dict.keys())(**algorithm_params_dict)
perform_an_episode(flags, algorithm_params, nn_model,
flags.base_log_path)
# code for dumping the parameters as json
# json.dump(algorithm_params, open('algorithm_param_output_cnnv4.json', 'w'), indent=3)
else:
print('Running in hyper parameter optimization mode')
model_space_fun = nn_model.get_hyper_param_space
global episode_run_index
trial_fileaddress = os.path.join(flags.base_log_path, "trial.p")
while True:
try:
with open(trial_fileaddress, "rb") as read_file:
trials = pickle.load(read_file)
episode_run_index = len(trials.trials)
best = convert_trial_to_dictvalues(
trials.best_trial['misc']['vals'])
except IOError:
print("No trials file found. Starting trials from scratch")
episode_run_index = 0
trials = Trials()
if episode_run_index == flags.max_evals:
break
best = fmin(
fn=lambda params: 1 -
(perform_an_episode(flags, params, nn_model, flags.
base_log_path).validation_accuracy),
space=model_space_fun(),
algo=tpe.suggest,
trials=trials,
max_evals=episode_run_index + 1)
pickle.dump(trials, open(trial_fileaddress, "wb"))
json.dump(trials.results, open('trial_results.json', 'w'), indent=3)
print(space_eval(model_space_fun(), best))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--output_path',
nargs='?',
const=True,
type=str,
default=os.path.dirname(__file__),
help='Path for saving output images')
flags = parse_cmd(parser)
loader_name = flags.loader_name
loader = get_class(loader_name + '.' + loader_name)(flags.path)
sample_set = loader.load_samples(0.1, 0.1)
data_set = loader.load_data(0, False)
scene_shape = loader.get_scene_shape(data_set)
imsave(
os.path.join(flags.output_path, "result_colorized.tif"),
create_colored_image(
create_target_image_via_samples(sample_set, scene_shape),
loader.get_target_color_list()))
def main(_):
parser = argparse.ArgumentParser()
parser.add_argument('--output_path',
nargs='?',
const=True,
type=str,
default=os.path.dirname(__file__),
help='Path for saving output images')
flags = parse_cmd(parser)
model = get_class(flags.model_name + '.' + flags.model_name)()
algorithm_params = model.get_default_params(flags.batch_size)
if flags.algorithm_param_path is not None:
algorithm_params = json.load(open(flags.algorithm_param_path, 'r'))
importer_name = flags.importer_name
data_importer = get_class(importer_name + '.' + importer_name)()
training_data_with_labels, test_data_with_labels, validation_data_with_labels, shadow_dict, class_range, \
scene_shape, color_list = \
data_importer.read_data_set(flags.loader_name, flags.path, flags.test_ratio, flags.neighborhood, True)
validation_data_with_labels = data_importer.create_all_scene_data(
scene_shape, validation_data_with_labels)
testing_tensor, training_tensor, validation_tensor = data_importer.convert_data_to_tensor(
test_data_with_labels, training_data_with_labels,
validation_data_with_labels, class_range)
deep_nn_template = tf.make_template('nn_core',
model.create_tensor_graph,
class_count=class_range.stop)
start_time = time.time()
validation_data_set = validation_tensor.dataset
validation_input_iter = simple_nn_iterator(validation_data_set,
flags.batch_size)
validation_images, validation_labels = validation_input_iter.get_next()
model_input_params = ModelInputParams(x=validation_images,
y=None,
device_id='/gpu:0',
is_training=False)
validation_tensor_outputs = deep_nn_template(
model_input_params, algorithm_params=algorithm_params)
validation_nn_params = NNParams(
input_iterator=validation_input_iter,
data_with_labels=None,
metrics=None,
predict_tensor=validation_tensor_outputs.y_conv)
validation_nn_params.data_with_labels = validation_data_with_labels
prediction = numpy.empty([scene_shape[0] * scene_shape[1]],
dtype=numpy.uint8)
saver = tf.train.Saver(var_list=slim.get_variables_to_restore(
include=["nn_core"], exclude=["image_gen_net_"]))
config = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False)
config.gpu_options.allow_growth = False
config.gpu_options.per_process_gpu_memory_fraction = 1.0
with tf.Session(config=config) as session:
# Restore variables from disk.
saver.restore(session, flags.base_log_path)
# Init for imaging the results
validation_tensor.importer.perform_tensor_initialize(
session, validation_tensor, validation_nn_params)
perform_prediction(session, validation_nn_params, prediction)
scene_as_image = numpy.reshape(prediction, scene_shape)
imsave(os.path.join(flags.output_path, "result_raw.tif"),
scene_as_image)
imsave(os.path.join(flags.output_path, "result_colorized.tif"),
create_colored_image(scene_as_image, color_list))
# Init for accuracy
# validation_tensor.importer.perform_tensor_initialize(session, validation_tensor, validation_nn_params)
# validation_accuracy = calculate_accuracy(session, validation_nn_params)
# print('Validation accuracy=%g' % validation_accuracy)
print('Done evaluation(%.3f sec)' % (time.time() - start_time))
def main():
# parse arguments
parser = argparse.ArgumentParser()
parser.add_argument('-c', '--cmds', help='commandlist filename')
parser.add_argument('-d', '--delay', help='delay between commands [s]')
parser.add_argument("-l",
help='start in live modus',
action="store_true",
default=False)
args = parser.parse_args()
if args.l == False:
global cmd_delay
try:
commandlist = cmd_parser.parse_cmds(args.cmds)
print commandlist, cmd_delay
except ValueError as e:
print e
except IOError as e:
print "I/O error({0}): {1}".format(e.errno,
e.strerror) + ":", args.cmds
if args.delay != None:
cmd_delay = args.delay
try:
i2ctool = I2C.I2CTool()
pool = ThreadPool(processes=1)
for command in commandlist:
if command.isif:
res = execute_command(pool, i2ctool, command)
if res == config.I2C_TRUE:
execute_command(
pool, i2ctool,
cmd_parser.parse_cmd(command.getThen()))
elif res == config.I2C_FALSE:
execute_command(
pool, i2ctool,
cmd_parser.parse_cmd(command.getElse()))
else:
res = execute_command(pool, i2ctool, command)
if res == config.I2C_ERR:
print "ERROR: command", command
print "exit programm"
break
finally:
exit_program()
else:
print "To exit enter 'quit'"
i2ctool = I2C.I2CTool()
pool = ThreadPool(processes=1)
while True:
try:
print "Enter command: ",
line = sys.stdin.readline()
if line.startswith("quit"):
exit_program()
command = cmd_parser.parse_cmd(line)
if command.isif:
res = execute_command(pool, i2ctool, command)
if res == config.I2C_TRUE:
execute_command(
pool, i2ctool,
cmd_parser.parse_cmd(command.getThen()))
elif res == config.I2C_FALSE:
execute_command(
pool, i2ctool,
cmd_parser.parse_cmd(command.getElse()))
else:
execute_command(pool, i2ctool, command)
except ValueError as e:
print e
def main(_):
parser = argparse.ArgumentParser()
parser.add_argument('--output_path',
nargs='?',
const=True,
type=str,
default=os.path.dirname(__file__),
help='Path for saving output images')
flags = parse_cmd(parser)
model = get_class(flags.model_name + '.' + flags.model_name)()
algorithm_params = model.get_default_params(flags.batch_size)
if flags.algorithm_param_path is not None:
algorithm_params = json.load(open(flags.algorithm_param_path, 'r'))
importer_name = flags.importer_name
# data_importer = get_class(importer_name + '.' + importer_name)()
data_importer = ControlledDataImporter()
training_data_with_labels, test_data_with_labels, validation_data_with_labels, shadow_dict, class_range, \
scene_shape, color_list = \
data_importer.read_data_set(flags.loader_name, flags.path, flags.test_ratio, flags.neighborhood, True)
validation_data_with_labels = data_importer.create_all_scene_data(
scene_shape, validation_data_with_labels)
testing_tensor, training_tensor, validation_tensor = data_importer.convert_data_to_tensor(
test_data_with_labels, training_data_with_labels,
validation_data_with_labels, class_range)
deep_nn_template = tf.make_template('nn_core',
model.create_tensor_graph,
class_count=class_range.stop)
start_time = time.time()
validation_data_set = validation_tensor.dataset
validation_input_iter = simple_nn_iterator(validation_data_set,
flags.batch_size)
validation_images, validation_labels = validation_input_iter.get_next()
model_input_params = ModelInputParams(x=validation_images,
y=None,
device_id='/gpu:0',
is_training=False)
validation_tensor_outputs = deep_nn_template(
model_input_params, algorithm_params=algorithm_params)
validation_nn_params = NNParams(
input_iterator=validation_input_iter,
data_with_labels=None,
metrics=None,
predict_tensor=validation_tensor_outputs.y_conv)
validation_nn_params.data_with_labels = validation_data_with_labels
saver = tf.train.Saver(var_list=slim.get_variables_to_restore(
include=["nn_core"], exclude=["image_gen_net_"]))
config = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False)
config.gpu_options.allow_growth = False
config.gpu_options.per_process_gpu_memory_fraction = 1.0
with tf.Session(config=config) as session:
# Restore variables from disk.
saver.restore(session, flags.base_log_path)
# Init for imaging the results
validation_tensor.importer.perform_tensor_initialize(
session, validation_tensor, validation_nn_params)
histogram_tensors = []
histogram_tensor_names = []
result_map = {}
bin_map = {}
base_bin = 480 / calculate_tensor_size(
validation_tensor_outputs.histogram_tensors[0].tensor)
for histogram_tensor_instance in validation_tensor_outputs.histogram_tensors:
histogram_tensors.append(histogram_tensor_instance.tensor)
histogram_tensor_names.append(histogram_tensor_instance.name)
result_map[histogram_tensor_instance.name] = []
bin_map[histogram_tensor_instance.name] = int(
base_bin *
calculate_tensor_size(histogram_tensor_instance.tensor))
sample_idx = 0
while True:
try:
# prediction, current_prediction = session.run([
# tf.argmax(validation_nn_params.predict_tensor, 1), histogram_tensors])
current_prediction = session.run(histogram_tensors)
if sample_idx > 2000:
for tensor_result, tensor_name in zip(
current_prediction, histogram_tensor_names):
result_map[tensor_name].append(tensor_result)
if sample_idx == 5000:
break
sample_idx = sample_idx + 1
except tf.errors.OutOfRangeError:
break
for tensor_name in histogram_tensor_names:
range_min = sys.float_info.max
range_max = sys.float_info.min
for result in result_map[tensor_name]:
range_min = min(range_min, result.min())
range_max = max(range_max, result.max())
all_hists = numpy.zeros(
[len(result_map[tensor_name]), bin_map[tensor_name]],
dtype=numpy.int)
bin_edges = None
for idx, result in enumerate(result_map[tensor_name]):
hist, bin_edges = numpy.histogram(result,
range=(range_min, range_max),
bins=bin_map[tensor_name])
all_hists[idx] = hist
mean = numpy.mean(all_hists, axis=0)
plt.plot(bin_edges[:-1], mean, label=tensor_name)
plt.xlim(min(bin_edges), max(bin_edges))
plt.xlabel("Value")
plt.ylabel("Counts")
plt.fill_between(bin_edges[:-1], mean)
plt.title(tensor_name)
plt.savefig(
os.path.join(flags.output_path, tensor_name + "_fig.jpg"))
plt.clf()
print('Done evaluation(%.3f sec)' % (time.time() - start_time))