def store_value(filepath, key, value):
if not os.path.exists(filepath):
data = {}
else:
data = io.read_json(filepath)
data[key] = str(value)
with open(filepath, "w") as f:
json.dump(data, f)
return True
def write_from_json(dataset_path, label_json_fp, label_name, **kwargs):
data = io.read_json(label_json_fp)
fps = []
for c in (0, 1):
fps.extend(glob.glob(os.path.join(dataset_path, "*", str(c), "*ID*.TXT")))
for fp in fps:
prefix = get_prefix(fp)
header = HTPA32x32d.tools.read_txt_header(fp)
label = None
for chunk in header.split(","):
if label_name in chunk:
label = int(chunk.split(label_name)[-1])
if label:
print("Label for {} exists, ignoring!".format(fp))
if not label:
new_label = data[get_prefix(fp)]
assert type(new_label) == int
new_header = header+","+label_name+str(new_label)
HTPA32x32d.tools.modify_txt_header(fp, new_header)
K = tf.keras.backend
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('config_json_name', type=str)
args = parser.parse_args()
config_json_name = args.config_json_name.split(".json")[0]
if not os.path.exists(TRAIN_LOG_FP):
with open(TRAIN_LOG_FP, "w") as f:
dt = datetime.datetime.now()
f.write("Log started by {} on {} at {}. \n".format(
config_json_name, dt.date(), dt.time()))
# 1.
# Read model
model_config = io.read_json(os.path.join(
"settings", config_json_name+".json"))
for key in MODEL_CONFIG_KEYS:
try:
model_config[key]
except KeyError:
raise Exception(
"Key {} not found in model configuration.".format(key))
try:
Setup = SETUP_DIC[model_config["setup"]]
except KeyError:
raise ValueError("Specified setup {} doesn't exist. Implemented setups: {}".format(
model_config["setup"], SETUP_DIC))
dataset_path, destination_parent_path = model_config[
'dataset_intermediate_path'], model_config['dataset_processed_parent_path']
processed_develop_path = os.path.join(
destination_parent_path, os.path.split(dataset_path)[1])
def label_gui(dataset_path, label_json_fp, label_name, label_batch_size, **kwargs):
global global_curr_pos
global global_max_pos
global global_timesteps
global global_img_fp_dict
global global_label_pos
data = io.read_json(label_json_fp)
fps = []
for c in (0, 1):
fps.extend(glob.glob(os.path.join(dataset_path, "*", str(c), "*ID*.TXT")))
loop_idx = 0
for fp in fps:
prefix = get_prefix(fp)
if data[prefix]:
continue
absolute_prefix = get_absolute_prefix(fp)
rgb_dir = absolute_prefix+"IDRGB"
img_fp_list = glob.glob(os.path.join(rgb_dir, "*.jpg"))
global_img_fp_dict = {}
for img_fp in img_fp_list:
timestep = float(os.path.split(img_fp)[-1].split(".jpg")[0].replace("-","."))
global_img_fp_dict[timestep] = img_fp
global_timesteps = list(global_img_fp_dict.keys())
global_timesteps.sort()
global_max_pos = len(global_timesteps)-1
global_curr_pos = global_max_pos
global_label_pos = None
def key_event(e):
global global_curr_pos
global global_max_pos
global global_timesteps
global global_img_fp_dict
global global_label_pos
if e.key == " ":
global_label_pos = global_curr_pos
plt.close()
return
if e.key == "right":
global_curr_pos = global_curr_pos + 1
if global_curr_pos > global_max_pos:
global_curr_pos = 0
elif e.key == "left":
global_curr_pos = global_curr_pos - 1
if global_curr_pos < 0:
global_curr_pos = global_max_pos
ax.cla()
fig.canvas.set_window_title(str(global_curr_pos))
implot = ax.imshow(cv2.imread(global_img_fp_dict[global_timesteps[global_curr_pos]])[:,:,::-1])
fig.canvas.draw()
ax = plt.gca()
fig = plt.gcf()
plt.xticks([]), plt.yticks([])
fig.canvas.set_window_title(str(global_curr_pos))
implot = ax.imshow(cv2.imread(global_img_fp_dict[global_timesteps[global_curr_pos]])[:,:,::-1])
cid = implot.figure.canvas.mpl_connect('key_press_event', key_event)
plt.show()
with open(os.path.join(rgb_dir, "timesteps.pkl"), "rb") as handle:
timesteps_pkl = pickle.load(handle)
real_label = timesteps_pkl.index(("{0:.2f}".format(global_timesteps[global_label_pos])).replace(".", "-")+".jpg")
print("Labeling {} as {} at {}!".format(absolute_prefix, real_label, timesteps_pkl[real_label]))
data[prefix] = real_label
loop_idx += 1
if loop_idx >= label_batch_size:
break
with open(label_json_fp, "w") as f:
json.dump(data, f)