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Feature/sg 953 add XML/VOC datasets (#117)
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* add yolo

* improve doc and name

* first draft

* support VOC

* wip

* rename

* add more explicit default extensions

* upadte extension list

* update strings

* rename annotation to label

* add lena nd iter

* add s

* minor fix

* add len and iter

* inherit

* replace image_set with split

* add doc

* add staticmethod

* fix naming

* improve doc

* improve config_path doc
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@Louis-Dupont
Louis-Dupont authored Jul 4, 2023
1 parent 310acde commit 8940293
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194 changes: 191 additions & 3 deletions documentation/datasets.md
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Expand Up @@ -6,7 +6,7 @@ These datasets contain only the very basic functionalities and are not recommend
## Object Detection


### Yolo Format Dataset
### Yolo Format Detection Dataset

The Yolo format Detection Dataset supports any dataset stored in the YOLO format.

Expand Down Expand Up @@ -79,6 +79,194 @@ dataset_root/
```python
from data_gradients.datasets.detection import YoloFormatDetectionDataset

train_loader = YoloFormatDetectionDataset(root_dir="<path/to/dataset_root>", images_dir="images/train", labels_dir="labels/train")
val_loader = YoloFormatDetectionDataset(root_dir="<path/to/dataset_root>", images_dir="images/validation", labels_dir="labels/validation")
train_set = YoloFormatDetectionDataset(root_dir="<path/to/dataset_root>", images_dir="images/train", labels_dir="labels/train")
val_set = YoloFormatDetectionDataset(root_dir="<path/to/dataset_root>", images_dir="images/validation", labels_dir="labels/validation")
```

### VOC Format Detection Dataset

The VOC format Detection Dataset supports datasets where labels are stored in XML following according to VOC standard.

#### Expected folder structure
Any structure including at least one sub-directory for images and one for xml labels. They can be the same.

Example 1: Separate directories for images and labels
```
dataset_root/
├── images/
│ ├── train/
│ │ ├── 1.jpg
│ │ ├── 2.jpg
│ │ └── ...
│ ├── test/
│ │ ├── ...
│ └── validation/
│ ├── ...
└── labels/
├── train/
│ ├── 1.xml
│ ├── 2.xml
│ └── ...
├── test/
│ ├── ...
└── validation/
├── ...
```

Example 2: Same directory for images and labels
```
dataset_root/
├── train/
│ ├── 1.jpg
│ ├── 1.xml
│ ├── 2.jpg
│ ├── 2.xml
│ └── ...
└── validation/
├── ...
```

**Note**: The label file need to be stored in XML format, but the file extension can be different.

#### Expected label files structure
The label files must be structured in XML format, like in the following example:

``` xml
<annotation>
<object>
<name>chair</name>
<bndbox>
<xmin>1</xmin>
<ymin>213</ymin>
<xmax>263</xmax>
<ymax>375</ymax>
</bndbox>
</object>
<object>
<name>sofa</name>
<bndbox>
<xmin>104</xmin>
<ymin>151</ymin>
<xmax>334</xmax>
<ymax>287</ymax>
</bndbox>
</object>
</annotation>
```


#### Instantiation
```
dataset_root/
├── images/
│ ├── train/
│ │ ├── 1.jpg
│ │ ├── 2.jpg
│ │ └── ...
│ ├── test/
│ │ ├── ...
│ └── validation/
│ ├── ...
└── labels/
├── train/
│ ├── 1.txt
│ ├── 2.txt
│ └── ...
├── test/
│ ├── ...
└── validation/
├── ...
```

```python
from data_gradients.datasets.detection import VOCFormatDetectionDataset

train_set = VOCFormatDetectionDataset(root_dir="<path/to/dataset_root>", images_dir="images/train", labels_dir="labels/train")
val_set = VOCFormatDetectionDataset(root_dir="<path/to/dataset_root>", images_dir="images/validation", labels_dir="labels/validation")
```


### VOC Detection Dataset
VOC Detection Dataset is a sub-class of the [VOC Format Detection Dataset](#voc_format_detection_dataset),
where the folders are structured exactly similarly to the original PascalVOC.

#### Expected folder structure
Any structure including at least one sub-directory for images and one for xml labels. They can be the same.

Example 1: Separate directories for images and labels
```
dataset_root/
├── VOC2007/
│ ├── JPEGImages/
│ │ ├── 1.jpg
│ │ ├── 2.jpg
│ │ └── ...
│ ├── Annotations/
│ │ ├── 1.xml
│ │ ├── 2.xml
│ │ └── ...
│ └── ImageSets/
│ └── Main
│ ├── train.txt
│ ├── val.txt
│ ├── train_val.txt
│ └── ...
└── VOC2012/
└── ...
```


**Note**: The label file need to be stored in XML format, but the file extension can be different.

#### Expected label files structure
The label files must be structured in XML format, like in the following example:

``` xml
<annotation>
<object>
<name>chair</name>
<bndbox>
<xmin>1</xmin>
<ymin>213</ymin>
<xmax>263</xmax>
<ymax>375</ymax>
</bndbox>
</object>
<object>
<name>sofa</name>
<bndbox>
<xmin>104</xmin>
<ymin>151</ymin>
<xmax>334</xmax>
<ymax>287</ymax>
</bndbox>
</object>
</annotation>
```


#### Instantiation
Let's take an example where we only have VOC2012
```
dataset_root/
└── VOC2012/
├── JPEGImages/
│ ├── 1.jpg
│ ├── 2.jpg
│ └── ...
├── Annotations/
│ ├── 1.xml
│ ├── 2.xml
│ └── ...
└── ImageSets/
└── Main
├── train.txt
└── val.txt
```

```python
from data_gradients.datasets.detection import VOCDetectionDataset

train_set = VOCDetectionDataset(root_dir="<path/to/dataset_root>", year=2012, image_set="train")
val_set = VOCDetectionDataset(root_dir="<path/to/dataset_root>", year=2012, image_set="val")
```
109 changes: 100 additions & 9 deletions src/data_gradients/datasets/FolderProcessor.py
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Expand Up @@ -41,11 +41,11 @@ def __init__(
verbose: bool = True,
):
"""
:param images_dir: The directory containing the images.
:param labels_dir: The directory containing the labels.
:param label_extensions: The extensions of the labels. Only the labels with these extensions will be considered.
:param image_extensions: The extensions of the images. Only the images with these extensions will be considered.
:param verbose: Whether to print extra messages.
:param images_dir: The directory containing the images.
:param labels_dir: The directory containing the labels.
:param label_extensions: The extensions of the labels. Only the labels with these extensions will be considered.
:param image_extensions: The extensions of the images. Only the images with these extensions will be considered.
:param verbose: Whether to print extra messages.
"""

self.images_dir = images_dir
Expand All @@ -60,7 +60,11 @@ def _normalize_extension(self, extensions: List[str]) -> List[str]:
return [ext.replace(".", "").lower() for ext in extensions]

def get_image_and_label_file_names(self, images_dir: str, labels_dir: str) -> List[Tuple[str, str]]:
"""Gather all image and label files from the provided sub_dirs."""
"""Gather all image and label files that are in the directories.
:param images_dir: The directory containing the images.
:param labels_dir: The directory containing the labels.
:return: A list of tuple(<path-to-image>, <path-to-label>).
"""
images_with_labels_files = []

if not os.path.exists(images_dir):
Expand All @@ -83,10 +87,9 @@ def _get_file_names_in_folder(self, images_dir: str, labels_dir: str) -> Tuple[L

def _match_file_names(self, all_images_file_names: List[str], all_labels_file_names: List[str]) -> List[Tuple[str, str]]:
"""Matches the names of image and label files."""
base_name = lambda file_name: os.path.splitext(os.path.basename(file_name))[0]

image_file_base_names = {base_name(file_name): file_name for file_name in all_images_file_names}
label_file_base_names = {base_name(file_name): file_name for file_name in all_labels_file_names}
image_file_base_names = {self.get_filename(file_name): file_name for file_name in all_images_file_names}
label_file_base_names = {self.get_filename(file_name): file_name for file_name in all_labels_file_names}

common_base_names = set(image_file_base_names.keys()) & set(label_file_base_names.keys())
unmatched_image_files = set(image_file_base_names.keys()) - set(label_file_base_names.keys())
Expand All @@ -108,6 +111,10 @@ def is_label(self, filename: str) -> bool:
"""Check if the given file name refers to image."""
return filename.split(".")[-1].lower() in self.label_extensions

@staticmethod
def get_filename(file_name: str) -> str:
return os.path.splitext(os.path.basename(file_name))[0]

def __len__(self):
return len(self.images_with_labels_files)

Expand All @@ -117,3 +124,87 @@ def __getitem__(self, index):
def __iter__(self):
for image_label_file in self.images_with_labels_files:
yield image_label_file


class ImageLabelConfigIterator(ImageLabelFilesIterator):
"""Iterate over all image and label files in the provided directories."""

def __init__(
self,
images_dir: str,
labels_dir: str,
config_path: str,
label_extensions: Sequence[str],
image_extensions: Sequence[str] = DEFAULT_IMG_EXTENSIONS,
verbose: bool = True,
):
"""
:param images_dir: The directory containing the images.
:param labels_dir: The directory containing the labels.
:param config_path: Path to the config file. This config file should contain the list of file ids to include.
:param label_extensions: The extensions of the labels. Only the labels with this extensions will be considered.
:param image_extensions: The extensions of the images. Only the images with this extensions will be considered.
:param verbose: Whether to print extra messages.
"""
self.config_path = config_path
super().__init__(
images_dir=images_dir,
labels_dir=labels_dir,
label_extensions=label_extensions,
image_extensions=image_extensions,
verbose=verbose,
)

def get_image_and_label_file_names(self, images_dir: str, labels_dir: str) -> List[Tuple[str, str]]:
"""Gather all image and label files that are both listed in the config_path and in the directories.
:param images_dir: The directory containing the images.
:param labels_dir: The directory containing the labels.
:return: A list of tuple(<path-to-image>, <path-to-label>).
"""
images_with_labels_files = super().get_image_and_label_file_names(images_dir=images_dir, labels_dir=labels_dir)
file_ids = self._load_file_ids(config_path=self.config_path)
filename_to_images_with_labels_files = {
self.get_filename(image_path): (image_path, label_path) for (image_path, label_path) in images_with_labels_files
}

images_with_labels_files = []
for file_id in file_ids:
if file_id in filename_to_images_with_labels_files:
images_with_labels_files.append(filename_to_images_with_labels_files[file_id])
elif self.verbose:
logger.warning(
f"No file with `file_id={file_id}` found in `images_dir={images_dir}` and/or `labels_dir={labels_dir}`. "
f"Hide this message by setting `verbose=False`."
)

if images_with_labels_files == []:
error_msg = (
f"Out of {len(file_ids)} file ids found in `config_path={self.config_path}`, "
f"no matching file found in `images_dir={images_dir}` and/or `labels_dir={labels_dir}`."
)
if not self.verbose:
error_msg += "\nSet `verbose=True` for more information."
raise RuntimeError(error_msg)
elif len(images_with_labels_files) != len(file_ids):
logger.warning(
f"Out of {len(file_ids)} file ids found in `config_path={self.config_path}`, "
f"{len(images_with_labels_files)} were found in both `images_dir={images_dir}` and `labels_dir={labels_dir}`. "
f"Hide this message by setting `verbose=False`."
)

return images_with_labels_files

def _load_file_ids(self, config_path: str) -> List[str]:
if not os.path.exists(config_path):
raise FileNotFoundError(f"The config file `{config_path}` does not exist.")

with open(config_path, "r") as f:
try:
file_ids = f.read().split()
except Exception as e:
raise e(f"Could not properly parse `config_path={config_path}`") from e

if file_ids == []:
raise RuntimeError(f"`config_path={config_path}` is empty and contains no file IDs.")

return file_ids
4 changes: 2 additions & 2 deletions src/data_gradients/datasets/__init__.py
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@@ -1,4 +1,4 @@
from data_gradients.datasets.detection import YoloFormatDetectionDataset
from data_gradients.datasets.detection import VOCDetectionDataset, VOCFormatDetectionDataset, YoloFormatDetectionDataset
from data_gradients.datasets.bdd_dataset import BDDDataset

__all__ = ["YoloFormatDetectionDataset", "BDDDataset"]
__all__ = ["VOCDetectionDataset", "VOCFormatDetectionDataset", "YoloFormatDetectionDataset", "BDDDataset"]
4 changes: 3 additions & 1 deletion src/data_gradients/datasets/detection/__init__.py
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@@ -1,3 +1,5 @@
from data_gradients.datasets.detection.voc_detection_dataset import VOCDetectionDataset
from data_gradients.datasets.detection.voc_format_detection_dataset import VOCFormatDetectionDataset
from data_gradients.datasets.detection.yolo_format_detection_dataset import YoloFormatDetectionDataset

__all__ = ["YoloFormatDetectionDataset"]
__all__ = ["VOCDetectionDataset", "VOCFormatDetectionDataset", "YoloFormatDetectionDataset"]
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