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Update autosplit() with annotated_only option (#2466)
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* Be able to create dataset from annotated images only

Add the ability to create a dataset/splits only with images that have an annotation file, i.e a .txt file, associated to it. As we talked about this, the absence of a txt file could mean two things:

* either the image wasn't yet labelled by someone,
* either there is no object to detect.

When it's easy to create small datasets, when you have to create datasets with thousands of images (and more coming), it's hard to track where you at and you don't want to wait to have all of them annotated before starting to train. Which means some images would lack txt files and annotations, resulting in label inconsistency as you say in #2313. By adding the annotated_only argument to the function, people could create, if they want to, datasets/splits only with images that were labelled, for sure.

* Cleanup and update print()

Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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kinoute and glenn-jocher authored Mar 15, 2021
1 parent 6f718ce commit 38ff499
Showing 1 changed file with 11 additions and 7 deletions.
18 changes: 11 additions & 7 deletions utils/datasets.py
Original file line number Diff line number Diff line change
Expand Up @@ -1032,20 +1032,24 @@ def extract_boxes(path='../coco128/'): # from utils.datasets import *; extract_
b[[1, 3]] = np.clip(b[[1, 3]], 0, h)
assert cv2.imwrite(str(f), im[b[1]:b[3], b[0]:b[2]]), f'box failure in {f}'


def autosplit(path='../coco128', weights=(0.9, 0.1, 0.0)): # from utils.datasets import *; autosplit('../coco128')
def autosplit(path='../coco128', weights=(0.9, 0.1, 0.0), annotated_only=False):
""" Autosplit a dataset into train/val/test splits and save path/autosplit_*.txt files
# Arguments
path: Path to images directory
weights: Train, val, test weights (list)
Usage: from utils.datasets import *; autosplit('../coco128')
Arguments
path: Path to images directory
weights: Train, val, test weights (list)
annotated_only: Only use images with an annotated txt file
"""
path = Path(path) # images dir
files = list(path.rglob('*.*'))
files = sum([list(path.rglob(f"*.{img_ext}")) for img_ext in img_formats], []) # image files only
n = len(files) # number of files
indices = random.choices([0, 1, 2], weights=weights, k=n) # assign each image to a split

txt = ['autosplit_train.txt', 'autosplit_val.txt', 'autosplit_test.txt'] # 3 txt files
[(path / x).unlink() for x in txt if (path / x).exists()] # remove existing

print(f'Autosplitting images from {path}' + ', using *.txt labeled images only' * annotated_only)
for i, img in tqdm(zip(indices, files), total=n):
if img.suffix[1:] in img_formats:
if not annotated_only or Path(img2label_paths([str(img)])[0]).exists(): # check label
with open(path / txt[i], 'a') as f:
f.write(str(img) + '\n') # add image to txt file

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