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Description
Add support for YOLO format bounding box annotations.
We share this snippet to unblock current users and will include a thoroughly tested version in an upcoming release.
YOLO annotation format
The YOLO annotation contains directories with both image and annotation files,
with corresponding names and different suffixes (e.g., 100.jpg and 100.txt).
Each txt file contains one line for each object, formatted as:
[class, x_center, y_center, width, height] with coordinates given
in the range of [0,1] relative to image dimensions.
E.g.:
2 0.7378676470588236 0.5125 0.030147058823529412 0.055
2 0.3044117647058823 0.65375 0.041176470588235294 0.0725
3 0.736764705882353 0.453125 0.04264705882352941 0.06875
Classes are given using integers of class strings.
Snippet for parsing a YOLO format data directory:
import os
import fastdup
import pandas as pd
from PIL import Image
def parse_object(obj_str, img_w, img_h):
item_list = obj_str.split(' ')
class_id = item_list[0]
cx_rel, cy_rel, w_rel, h_rel = [float(o) for o in item_list[1:]]
x = round(img_w * (cx_rel - w_rel / 2))
y = round(img_h * (cy_rel - h_rel / 2))
w = round(img_w * w_rel)
h = round(img_h * h_rel)
return [class_id, x,y,w,h]
data_dir = '/path/to/your/yolo/data/'
img_file_list = [f for f in os.listdir(data_dir) if f.endswith('.jpg')]
annotation_list = []
for img_fn in img_file_list:
img_full_path = os.path.join(data_dir, img_fn)
img_w, img_h = Image.open(img_full_path).size
anot_full_path = img_full_path.replace('jpg', 'txt')
with open(anot_full_path, 'r') as f:
for o in f.readlines():
bbox_field_list = parse_object(o, img_w, img_h)
annotation_list.append([img_fn, img_w, img_h] + bbox_field_list)
columns=['img_filename', 'img_w', 'img_h', 'label',
'bbox_x', 'bbox_y', 'bbox_w', 'bbox_h']
annotation_df = pd.DataFrame(annotation_list, columns=columns)
fd = fastdup.create(work_dir, input_dir=data_dir)
fd.run(annotations=annotation_df)
This is still under development. Please let us know if there are any further requests or issues.
Thanks