def adjust_non_voc_csv(csv_file, image_path, image_width, image_height):
"""
Read relative data and return adjusted frame accordingly.
Args:
csv_file: .csv file containing the following columns:
[image, object_name, object_index, bx, by, bw, bh]
image_path: Path prefix to be added.
image_width: image width.
image_height: image height
Returns:
pandas DataFrame with the following columns:
['image_path', 'object_name', 'img_width', 'img_height', 'x_min',
'y_min', 'x_max', 'y_max', 'relative_width', 'relative_height',
'object_id']
"""
image_path = get_abs_path(image_path, verify=True)
coordinates = []
old_frame = pd.read_csv(get_abs_path(csv_file, verify=True))
new_frame = pd.DataFrame()
new_frame['image_path'] = old_frame['image'].apply(
lambda item: get_abs_path(image_path, item))
new_frame['object_name'] = old_frame['object_name']
new_frame['img_width'] = image_width
new_frame['img_height'] = image_height
new_frame['relative_width'] = old_frame['bw']
new_frame['relative_height'] = old_frame['bh']
new_frame['object_id'] = old_frame['object_index'] + 1
for index, row in old_frame.iterrows():
image, object_name, object_index, bx, by, bw, bh = row
co = ratios_to_coordinates(bx, by, bw, bh, image_width, image_height)
coordinates.append(co)
(
new_frame['x_min'],
new_frame['y_min'],
new_frame['x_max'],
new_frame['y_max'],
) = np.array(coordinates).T
new_frame[['x_min', 'y_min', 'x_max',
'y_max']] = new_frame[['x_min', 'y_min', 'x_max',
'y_max']].astype('int64')
print(f'Parsed labels:\n{new_frame["object_name"].value_counts()}')
classes = new_frame['object_name'].drop_duplicates()
LOGGER.info(
f'Adjustment from existing received {len(new_frame)} labels containing '
f'{len(classes)} classes')
LOGGER.info(f'Added prefix to images: {image_path}')
return new_frame[[
'image_path',
'object_name',
'img_width',
'img_height',
'x_min',
'y_min',
'x_max',
'y_max',
'relative_width',
'relative_height',
'object_id',
]]
def relative_to_coordinates(self, out_file=None):
"""
Convert relative coordinates in self.mapping
to coordinates.
Args:
out_file: path to new converted csv.
Returns:
pandas DataFrame with the new coordinates.
"""
items_to_save = []
for index, data in self.mapping.iterrows():
image_name, object_name, object_index, bx, by, bw, bh = data
x1, y1, x2, y2 = ratios_to_coordinates(bx, by, bw, bh,
self.image_width,
self.image_height)
items_to_save.append([
image_name,
x1,
y1,
x2,
y2,
object_name,
object_index,
bx,
by,
bw,
bh,
])
new_data = pd.DataFrame(
items_to_save,
columns=[
'image',
'x1',
'y1',
'x2',
'y2',
'object_type',
'object_id',
'bx',
'by',
'bw',
'bh',
],
)
new_data[['x1', 'y1', 'x2', 'y2']] = new_data[['x1', 'y1', 'x2',
'y2']].astype('int64')
if out_file:
new_data.to_csv(out_file, index=False)
LOGGER.info(f'Converted labels in {self.labels_file} to coordinates')
return new_data