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Merge pull request #77 from fastlabel/develop
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‎README.md‎

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@@ -22,6 +22,7 @@ _If you are using FastLabel prototype, please install version 0.2.2._
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- [Pascal VOC](#pascal-voc)
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- [labelme](#labelme)
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- [Segmentation](#segmentation)
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- [Converter to FastLabel format](#converter-to-fastlabel-format)
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## Installation
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@@ -942,6 +943,303 @@ tasks = client.get_image_tasks(project="YOUR_PROJECT_SLUG")
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client.export_semantic_segmentation(tasks)
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```
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## Converter to FastLabel format
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### Response
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Example of a converted annotations
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```python
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{
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'sample1.jpg': [
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{
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'points': [
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100,
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100,
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200,
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200
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],
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'type': 'bbox',
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'value': 'cat'
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}
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],
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'sample2.jpg': [
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{
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'points': [
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100,
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100,
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200,
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200
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],
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'type': 'bbox',
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'value': 'cat'
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}
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]
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}
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```
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In the case of YOLO, Pascal VOC, and labelme, the key is the tree structure if the tree structure is multi-level.
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```
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dataset
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├── sample1.jpg
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├── sample1.txt
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└── sample_dir
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├── sample2.jpg
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└── sample2.txt
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```
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```python
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{
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'sample1.jpg': [
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{
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'points': [
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100,
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100,
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200,
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200
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],
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'type': 'bbox',
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'value': 'cat'
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}
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],
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'sample_dir/sample2.jpg': [
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{
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'points': [
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100,
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100,
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200,
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200
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],
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'type': 'bbox',
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'value': 'cat'
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}
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]
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}
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```
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### COCO
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Supported bbox or polygon annotation type.
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Convert annotation file of [COCO format](https://cocodataset.org/#format-data) as a Fastlabel format and create task.
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file_path: COCO annotation json file path
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```python
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annotations_map = client.convert_coco_to_fastlabel(file_path="./sample.json")
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task_id = client.create_image_task(
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project="YOUR_PROJECT_SLUG",
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name="sample.jpg",
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file_path="./sample.jpg",
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annotations=annotations_map.get("sample.jpg")
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)
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```
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Example of converting annotations to create multiple tasks.
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In the case of the following tree structure.
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```
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dataset
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├── annotation.json
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├── sample1.jpg
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└── sample2.jpg
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```
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Example source code.
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```python
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import fastlabel
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project = "YOUR_PROJECT_SLUG"
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input_file_path = "./dataset/annotation.json"
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input_dataset_path = "./dataset/"
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annotations_map = client.convert_coco_to_fastlabel(file_path=input_file_path)
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for image_file_path in glob.iglob(os.path.join(input_dataset_path, "**/**.jpg"), recursive=True):
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time.sleep(1)
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name = image_file_path.replace(os.path.join(*[input_dataset_path, ""]), "")
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file_path = image_file_path
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annotations = annotations_map.get(name) if annotations_map.get(name) is not None else []
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task_id = client.create_image_task(
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project=project,
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name=name,
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file_path=file_path,
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annotations=annotations
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)
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```
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### YOLO
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Supported bbox annotation type.
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Convert annotation file of YOLO format as a Fastlabel format and create task.
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classes_file_path: YOLO classes text file path
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dataset_folder_path: Folder path containing YOLO Images and annotation
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```python
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annotations_map = client.convert_yolo_to_fastlabel(
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classes_file_path="./classes.txt",
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dataset_folder_path="./dataset/"
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)
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task_id = client.create_image_task(
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project="YOUR_PROJECT_SLUG",
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name="sample.jpg",
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file_path="./dataset/sample.jpg",
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annotations=annotations_map.get("sample.jpg")
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)
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```
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Example of converting annotations to create multiple tasks.
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In the case of the following tree structure.
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```
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yolo
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├── classes.txt
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└── dataset
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├── sample1.jpg
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├── sample1.txt
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├── sample2.jpg
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└── sample2.txt
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```
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Example source code.
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```python
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import fastlabel
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project = "YOUR_PROJECT_SLUG"
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input_file_path = "./classes.txt"
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input_dataset_path = "./dataset/"
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annotations_map = client.convert_yolo_to_fastlabel(
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classes_file_path=input_file_path,
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dataset_folder_path=input_dataset_path
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)
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for image_file_path in glob.iglob(os.path.join(input_dataset_path, "**/**.jpg"), recursive=True):
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time.sleep(1)
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name = image_file_path.replace(os.path.join(*[input_dataset_path, ""]), "")
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file_path = image_file_path
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annotations = annotations_map.get(name) if annotations_map.get(name) is not None else []
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task_id = client.create_image_task(
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project=project,
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name=name,
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file_path=file_path,
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annotations=annotations
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)
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```
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### Pascal VOC
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Supported bbox annotation type.
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Convert annotation file of Pascal VOC format as a Fastlabel format and create task.
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folder_path: Folder path including pascal VOC format annotation files
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```python
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annotations_map = client.convert_pascalvoc_to_fastlabel(folder_path="./dataset/")
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task_id = client.create_image_task(
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project="YOUR_PROJECT_SLUG",
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name="sample.jpg",
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file_path="./dataset/sample.jpg",
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annotations=annotations_map.get("sample.jpg")
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)
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```
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Example of converting annotations to create multiple tasks.
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In the case of the following tree structure.
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```
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dataset
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├── sample1.jpg
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├── sample1.xml
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├── sample2.jpg
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└── sample2.xml
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```
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Example source code.
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```python
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import fastlabel
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project = "YOUR_PROJECT_SLUG"
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input_dataset_path = "./dataset/"
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annotations_map = client.convert_pascalvoc_to_fastlabel(folder_path=input_dataset_path)
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for image_file_path in glob.iglob(os.path.join(input_dataset_path, "**/**.jpg"), recursive=True):
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time.sleep(1)
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name = image_file_path.replace(os.path.join(*[input_dataset_path, ""]), "")
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file_path = image_file_path
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annotations = annotations_map.get(name) if annotations_map.get(name) is not None else []
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task_id = client.create_image_task(
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project=project,
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name=name,
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file_path=file_path,
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annotations=annotations
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)
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```
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### labelme
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support the following annotation types.
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- bbox
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- polygon
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- points
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- line
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Convert annotation file of labelme format as a Fastlabel format and create task.
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folder_path: Folder path including labelme format annotation files
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```python
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annotations_map = client.convert_labelme_to_fastlabel(folder_path="./dataset/")
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task_id = client.create_image_task(
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project="YOUR_PROJECT_SLUG",
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name="sample.jpg",
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file_path="./sample.jpg",
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annotations=annotations_map.get("sample.jpg")
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)
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```
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Example of converting annotations to create multiple tasks.
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In the case of the following tree structure.
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```
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dataset
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├── sample1.jpg
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├── sample1.json
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├── sample2.jpg
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└── sample2.json
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```
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Example source code.
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```python
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import fastlabel
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project = "YOUR_PROJECT_SLUG"
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input_dataset_path = "./dataset/"
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annotations_map = client.convert_labelme_to_fastlabel(folder_path=input_dataset_path)
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for image_file_path in glob.iglob(os.path.join(input_dataset_path, "**/**.jpg"), recursive=True):
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time.sleep(1)
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name = image_file_path.replace(os.path.join(*[input_dataset_path, ""]), "")
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file_path = image_file_path
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annotations = annotations_map.get(name) if annotations_map.get(name) is not None else []
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task_id = client.create_image_task(
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project=project,
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name=name,
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file_path=file_path,
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annotations=annotations
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)
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```
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> Please check const.COLOR_PALLETE for index colors.
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## API Docs

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