parser.add_argument(
"--no_object_folder",
type=str,
default=no_object_Folder,
help=
"Absolute path to a folder containing images that have no annotations. Default is "
+ no_object_Folder)
FLAGS = parser.parse_args()
#Prepare the dataset for YOLO
multi_df = pd.read_csv(FLAGS.VoTT_csv)
labels = multi_df['label'].unique()
labeldict = dict(zip(labels, range(len(labels))))
multi_df.drop_duplicates(subset=None, keep='first', inplace=True)
train_path = FLAGS.VoTT_Folder
convert_vott_csv_to_yolo(multi_df,
labeldict,
path=train_path,
target_name=FLAGS.YOLO_filename,
zeroAnnotationPath=FLAGS.no_object_folder)
# Make classes file
file = open(classes_filename, "w")
#Sort Dict by Values
SortedLabelDict = sorted(labeldict.items(), key=lambda x: x[1])
for elem in SortedLabelDict:
file.write(elem[0] + '\n')
file.close()
parser.add_argument(
"--YOLO_filename",
type=str,
default=YOLO_filename,
help=
"Absolute path to the file where the labels in YOLO format should be saved. Default is "
+ YOLO_filename,
)
FLAGS = parser.parse_args()
# Prepare the dataset for YOLO
multi_df = pd.read_csv(FLAGS.VoTT_csv)
labels = multi_df["label"].unique()
labeldict = dict(zip(labels, range(len(labels))))
multi_df.drop_duplicates(subset=None, keep="first", inplace=True)
train_path = FLAGS.VoTT_Folder
convert_vott_csv_to_yolo(multi_df,
labeldict,
path=train_path,
target_name=FLAGS.YOLO_filename)
# Make classes file
file = open(classes_filename, "w")
# Sort Dict by Values
SortedLabelDict = sorted(labeldict.items(), key=lambda x: x[1])
for elem in SortedLabelDict:
file.write(elem[0] + "\n")
file.close()
df_csv["code"].astype(int).values)
if FLAGS.AWS:
df_csv["image_path"] = ChangeToOtherMachine(
df_csv["image_path"].values, remote_machine=AWS_path)
df_csv.to_csv(CSV_filename, index=False)
# Get label names and sort
if FLAGS.drop_classes:
df_csv = df_csv[df_csv["label"].isin(["door", "window", "shop"])]
codes = df_csv["code"].unique()
df_csv["code"] = df_csv["code"].apply(
lambda x: np.where(codes == x)[0][0])
if FLAGS.single_class:
df_csv["code"] = 0
df_csv["label"] = "opening"
df_csv.to_csv("est.csv")
sorted_names = ((df_csv.drop_duplicates(subset=["code", "label"])[[
"code", "label"
]].sort_values(by=["code"]))["label"]).values
# Write sorted names to file to make classes file
with open(classes_filename, "w") as f:
for name in sorted_names[:-1]:
f.write("%s\n" % name)
f.write("%s" % sorted_names[-1])
# Convert Vott csv format to YOLO format
convert_vott_csv_to_yolo(df_csv,
abs_path=True,
target_name=FLAGS.YOLO_filename)
