Merge pull request #4 from fcakyon/refactor

refactor codebase to utilize sahi

.github/workflows/ci.yml

@@ -13,7 +13,7 @@ jobs:
     strategy:
       matrix:
         operating-system: [ubuntu-latest, windows-latest, macos-latest]
-        python-version: [3.6, 3.7, 3.8]
+        python-version: [3.7, 3.8, 3.9, "3.10"]
       fail-fast: false
 
     steps:
@@ -60,3 +60,7 @@ jobs:
     - name: Test with unittest
       run: |
         python -m unittest
+    - name: Test CLI
+      run: |
+        pip install .
+        labelme2coco tests/data/labelme_annot --train_split_rate 0.85

.github/workflows/package_testing.yml

@@ -0,0 +1,65 @@
+name: Package Testing
+
+on:
+  schedule:
+    - cron: '0 0 * * *' # Runs at 00:00 UTC every day
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+
+    strategy:
+      matrix:
+        operating-system: [ubuntu-latest, windows-latest, macos-latest]
+        python-version: [3.7, 3.8, 3.9, "3.10"]
+      fail-fast: false
+
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v2
+
+      - name: Set up Python
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Restore Ubuntu cache
+        uses: actions/cache@v1
+        if: matrix.operating-system == 'ubuntu-latest'
+        with:
+          path: ~/.cache/pip
+          key: ${{ matrix.os }}-${{ matrix.python-version }}-${{ hashFiles('**/setup.py')}}
+          restore-keys: ${{ matrix.os }}-${{ matrix.python-version }}-
+
+      - name: Restore MacOS cache
+        uses: actions/cache@v1
+        if: matrix.operating-system == 'macos-latest'
+        with:
+          path: ~/Library/Caches/pip
+          key: ${{ matrix.os }}-${{ matrix.python-version }}-${{ hashFiles('**/setup.py')}}
+          restore-keys: ${{ matrix.os }}-${{ matrix.python-version }}-
+
+      - name: Restore Windows cache
+        uses: actions/cache@v1
+        if: matrix.operating-system == 'windows-latest'
+        with:
+          path: ~\AppData\Local\pip\Cache
+          key: ${{ matrix.os }}-${{ matrix.python-version }}-${{ hashFiles('**/setup.py')}}
+          restore-keys: ${{ matrix.os }}-${{ matrix.python-version }}-
+
+      - name: Update pip
+        run: python -m pip install --upgrade pip
+
+      - name: Install latest labelme2coco package
+        run: >
+          pip install --upgrade --force-reinstall labelme2coco
+
+      - name: Test with unittest
+        run: |
+          pip install pytest
+          python -m unittest
+
+      - name: Test CLI
+        run: |
+          pip install .
+          labelme2coco tests/data/labelme_annot --train_split_rate 0.85

README.md

@@ -1,9 +1,9 @@
-[![Downloads](https://pepy.tech/badge/labelme2coco/month)](https://pepy.tech/project/labelme2coco)
+[![Downloads](https://pepy.tech/badge/labelme2coco)](https://pepy.tech/project/labelme2coco)
 [![PyPI version](https://badge.fury.io/py/labelme2coco.svg)](https://badge.fury.io/py/labelme2coco)
 ![CI](https://github.com/fcakyon/labelme2coco/workflows/CI/badge.svg)
 
 # labelme2coco Python Package for Linux/MacOS/Windows
-Make your own dataset for object detection/instance segmentation using [labelme](https://github.com/wkentaro/labelme) and transform the format to coco json format 
+Make your own dataset for object detection/instance segmentation using [labelme](https://github.com/wkentaro/labelme) and transform the format to coco json format.
 
 ## Convert LabelMe annotations to COCO format in one step
 [labelme](https://github.com/wkentaro/labelme) is a widely used is a graphical image annotation tool that supports classification, segmentation, instance segmentation and object detection formats.
@@ -14,21 +14,60 @@ You can use this package to convert labelme annotations to COCO format.
 ## Getting started
 ### Installation
 ```
-pip install labelme2coco
+pip install -U labelme2coco
 ```
 
-### Usage
+### Basic Usage
+
+```python
+labelme2coco path/to/labelme/dir
+```
+
+```python
+labelme2coco path/to/labelme/dir --train_split_rate 0.85
+```
+
+### Advanced Usage
+
 ```python
 # import package
 import labelme2coco
 
 # set directory that contains labelme annotations and image files
 labelme_folder = "tests/data/labelme_annot"
 
-# set path for coco json to be saved
-save_json_path = "tests/data/test_coco.json"
+# set export dir
+export_dir = "tests/data/"
+
+# set train split rate
+train_split_rate = 0.85
 
 # convert labelme annotations to coco
-labelme2coco.convert(labelme_folder, save_json_path)
+labelme2coco.convert(labelme_folder, export_dir, train_split_rate)
 ```
 
+```python
+# import functions
+from labelme2coco import get_coco_from_labelme_folder, save_json
+
+# set labelme training data directory
+labelme_train_folder = "tests/data/labelme_annot"
+
+# set labelme validation data directory
+labelme_val_folder = "tests/data/labelme_annot"
+
+# set path for coco json to be saved
+export_dir = "tests/data/"
+
+# create train coco object
+train_coco = get_coco_from_labelme_folder(labelme_train_folder)
+
+# export train coco json
+save_json(train_coco.json, export_dir+"train.json")
+
+# create val coco object
+val_coco = get_coco_from_labelme_folder(labelme_val_folder, coco_category_list=train_coco.json_categories)
+
+# export val coco json
+save_json(val_coco.json, export_dir+"val.json")
+```

labelme2coco/__init__.py

@@ -1,14 +1,46 @@
 from __future__ import absolute_import
 
-__version__ = "0.1.2"
+__version__ = "0.2.0"
 
-from labelme2coco.labelme2coco import labelme2coco
+import logging
+import os
+from pathlib import Path
 
+from sahi.utils.file import save_json
 
-def convert(labelme_folder: str, save_json_path: str):
+from labelme2coco.labelme2coco import get_coco_from_labelme_folder
+
+logger = logging.getLogger(__name__)
+logging.basicConfig(
+    format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
+    datefmt="%m/%d/%Y %H:%M:%S",
+    level=os.environ.get("LOGLEVEL", "INFO").upper(),
+)
+
+
+def convert(
+    labelme_folder: str,
+    export_dir: str = "runs/labelme2coco/",
+    train_split_rate: float = 1,
+):
     """
     Args:
         labelme_folder: folder that contains labelme annotations and image files
-        save_json_path: oath for coco json to be saved
+        export_dir: path for coco jsons to be exported
+        train_split_rate: ration fo train split
     """
-    labelme2coco(labelme_folder, save_json_path)
+    coco = get_coco_from_labelme_folder(labelme_folder)
+    if train_split_rate < 1:
+        result = coco.split_coco_as_train_val(train_split_rate)
+        # export train split
+        save_path = str(Path(export_dir) / "train.json")
+        save_json(result["train_coco"].json, save_path)
+        logger.info(f"Training split in COCO format is exported to {save_path}")
+        # export val split
+        save_path = str(Path(export_dir) / "val.json")
+        save_json(result["val_coco"].json, save_path)
+        logger.info(f"Validation split in COCO format is exported to {save_path}")
+    else:
+        save_path = str(Path(export_dir) / "dataset.json")
+        save_json(coco.json, save_path)
+        logger.info(f"Converted annotations in COCO format is exported to {save_path}")

labelme2coco/cli.py

@@ -0,0 +1,12 @@
+import fire
+
+from labelme2coco import convert
+
+
+def app() -> None:
+    """Cli app."""
+    fire.Fire(convert)
+
+
+if __name__ == "__main__":
+    app()