diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 80a10d494..5066f4954 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -1,132 +1,66 @@ -name: CI (PyTorch 1.12.1, TorchVision 0.13.1) +name: CI with uv on: push: branches: [main] paths-ignore: - - '**.md' - - '**.ipynb' - - '**.cff' + - "**.md" + - "**.ipynb" + - "**.cff" pull_request: branches: [main] paths-ignore: - - '**.md' - - '**.ipynb' - - '**.cff' + - "**.md" + - "**.ipynb" + - "**.cff" + + schedule: + - cron: "0 0 * * *" # Runs at 00:00 UTC every day + + workflow_dispatch: # allow running sync via github ui button jobs: - build: + ci: runs-on: ubuntu-latest strategy: matrix: - operating-system: [ubuntu-latest, windows-latest, macos-latest] - python-version: [3.8, 3.9, '3.10'] - fail-fast: false - + python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"] steps: - - name: Checkout - uses: actions/checkout@v4 - - - name: Set up Python - uses: actions/setup-python@v5 + - uses: actions/checkout@v4 + - name: Setup uv python package manager + uses: astral-sh/setup-uv@v5 with: python-version: ${{ matrix.python-version }} - - - name: Restore Ubuntu cache - uses: actions/cache@v4 - 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@v4 - 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 + enable-cache: true + prune-cache: false + # For python 3.8 and 3.9, it does no suffice to install opencv-python-headless + # https://itsmycode.com/importerror-libgl-so-1-cannot-open-shared-object-file-no-such-file-or-directory/ + - name: Cache apt packages uses: actions/cache@v4 - if: matrix.operating-system == 'windows-latest' + if: ${{ env.ACT }} 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: Lint with flake8, black and isort - run: | - pip install -e .[dev] - # stop the build if there are Python syntax errors or undefined names - python -m scripts.run_code_style check - - # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide - flake8 . --exit-zero --max-complexity=10 --max-line-length=127 - - - name: Install core dependencies - run: > - pip install -r requirements.txt - - - name: Install PyTorch(1.13.1) and TorchVision(0.14.1) on Linux and Windows - if: > - matrix.operating-system == 'ubuntu-latest' || - matrix.operating-system == 'windows-latest' - run: > - pip install torch==1.13.1+cpu torchvision==0.14.1+cpu - -f https://download.pytorch.org/whl/torch_stable.html - - - name: Install PyTorch on MacOS - if: matrix.operating-system == 'macos-latest' - run: pip install torch==1.13.1 torchvision==0.14.1 - - - name: Install MMDetection(3.0.0) with MMCV(2.0.0) + path: /var/cache/apt + key: apt-cache + - name: Install libgl + run: sudo apt-get update && sudo apt-get install -y libgl1 + - name: Setup dependencies + run: uv sync + - name: Install sahi from PyPI + if: github.event_name == 'schedule' run: | - pip install mmengine==0.7.3 - pip install mmcv==2.0.0 -f https://download.openmmlab.com/mmcv/dist/cpu/torch1.13.0/index.html - pip install mmdet==3.0.0 - - - name: Install YOLOv5(7.0.13) - run: > - pip install yolov5==7.0.13 - - - name: Install DeepSparse - run: > - pip install deepsparse - - - name: Install Transformers(4.35.0) - run: > - pip install transformers==4.35.0 - - - name: Install pycocotools(2.0.7) - run: > - pip install pycocotools==2.0.7 - - - name: Install ultralytics(8.3.50) - run: > - pip install ultralytics==8.3.50 - - - name: Unittest for SAHI+YOLO11/RTDETR/MMDET/HuggingFace/Torchvision on all platforms + rm -fr sahi + uv pip install --force-reinstall sahi + uv pip show sahi + source .venv/bin/activate + python -c "import sahi; print(sahi.__version__)" + - name: Test with python ${{ matrix.python-version }} run: | - python -m unittest - - - name: Install SAHI package from local setup.py - run: > - pip install -e . - + source .venv/bin/activate + pytest --capture=no - name: Test SAHI CLI run: | - # help - sahi --help - # predict mmdet - sahi predict --source tests/data/ --novisual --model_path tests/data/models/mmdet/yolox/yolox_tiny_8x8_300e_coco_20211124_171234-b4047906.pth --model_config_path tests/data/models/mmdet/yolox/yolox_tiny_8xb8-300e_coco.py --image_size 320 - sahi predict --source tests/data/coco_utils/terrain1.jpg --export_pickle --export_crop --model_path tests/data/models/mmdet/yolox/yolox_tiny_8x8_300e_coco_20211124_171234-b4047906.pth --model_config_path tests/data/models/mmdet/yolox/yolox_tiny_8xb8-300e_coco.py --image_size 320 - sahi predict --source tests/data/coco_utils/ --novisual --dataset_json_path tests/data/coco_utils/combined_coco.json --model_path tests/data/models/mmdet/yolox/yolox_tiny_8x8_300e_coco_20211124_171234-b4047906.pth --model_config_path tests/data/models/mmdet/yolox/yolox_tiny_8xb8-300e_coco.py --image_size 320 - # predict yolov5 + source .venv/bin/activate + set -e sahi predict --no_sliced_prediction --model_type yolov5 --source tests/data/coco_utils/terrain1.jpg --novisual --model_path tests/data/models/yolov5/yolov5s6.pt --image_size 320 sahi predict --model_type yolov5 --source tests/data/ --novisual --model_path tests/data/models/yolov5/yolov5s6.pt --image_size 320 sahi predict --model_type yolov5 --source tests/data/coco_utils/terrain1.jpg --export_pickle --export_crop --model_path tests/data/models/yolov5/yolov5s6.pt --image_size 320 @@ -137,3 +71,11 @@ jobs: sahi coco evaluate --dataset_json_path tests/data/coco_evaluate/dataset.json --result_json_path tests/data/coco_evaluate/result.json # coco analyse sahi coco analyse --dataset_json_path tests/data/coco_evaluate/dataset.json --result_json_path tests/data/coco_evaluate/result.json --out_dir tests/data/coco_evaluate/ + - name: Test SAHI CLI with MMCV + if: matrix.python-version == '3.8' || matrix.python-version == '3.9' || matrix.python-version == '3.10' + run: | + source .venv/bin/activate + set -e + sahi predict --model_type mmdet --source tests/data/ --novisual --model_path tests/data/models/mmdet/yolox/yolox_tiny_8x8_300e_coco_20211124_171234-b4047906.pth --model_config_path tests/data/models/mmdet/yolox/yolox_tiny_8xb8-300e_coco.py --image_size 320 + sahi predict --model_type mmdet --source tests/data/coco_utils/terrain1.jpg --export_pickle --export_crop --model_path tests/data/models/mmdet/yolox/yolox_tiny_8x8_300e_coco_20211124_171234-b4047906.pth --model_config_path tests/data/models/mmdet/yolox/yolox_tiny_8xb8-300e_coco.py --image_size 320 + sahi predict --model_type mmdet --source tests/data/coco_utils/ --novisual --dataset_json_path tests/data/coco_utils/combined_coco.json --model_path tests/data/models/mmdet/yolox/yolox_tiny_8x8_300e_coco_20211124_171234-b4047906.pth --model_config_path tests/data/models/mmdet/yolox/yolox_tiny_8xb8-300e_coco.py --image_size 320 diff --git a/.github/workflows/package_testing.yml b/.github/workflows/package_testing.yml deleted file mode 100644 index ac5433e4f..000000000 --- a/.github/workflows/package_testing.yml +++ /dev/null @@ -1,117 +0,0 @@ -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: [3.8, 3.9, '3.10'] - fail-fast: false - - steps: - - name: Checkout - uses: actions/checkout@v4 - - - name: Set up Python - uses: actions/setup-python@v5 - with: - python-version: ${{ matrix.python-version }} - - - name: Restore Ubuntu cache - uses: actions/cache@v4 - 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@v4 - 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@v4 - 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 PyTorch(1.10.2) and TorchVision(0.11.3) on Linux and Windows - if: > - matrix.operating-system == 'ubuntu-latest' || - matrix.operating-system == 'windows-latest' - run: > - pip install torch==1.13.1+cpu torchvision==0.14.1+cpu - -f https://download.pytorch.org/whl/torch_stable.html - - - name: Install PyTorch on MacOS - if: matrix.operating-system == 'macos-latest' - run: pip install torch==1.13.1 torchvision==0.14.1 - - - name: Install MMDetection(3.0.0) with MMCV(2.0.0) - run: | - pip install mmengine==0.7.3 - pip install mmcv==2.0.0 -f https://download.openmmlab.com/mmcv/dist/cpu/torch1.13.0/index.html - pip install mmdet==3.0.0 - - - name: Install YOLOv5(7.0.13) - run: > - pip install yolov5==7.0.13 - - - name: Install DeepSparse - run: > - pip install deepsparse - - - name: Install Transformers(4.35.0) - run: > - pip install transformers==4.35.0 - - - name: Install pycocotools(2.0.7) - run: > - pip install pycocotools==2.0.7 - - - name: Install ultralytics(8.3.50) - run: > - pip install ultralytics==8.3.50 - - - name: Install latest SAHI package - run: > - pip install --upgrade --force-reinstall sahi - - - name: Unittest for SAHI+YOLO11/RTDETR/MMDET/HuggingFace/Torchvision on all platforms - run: | - python -m unittest - - - name: Test SAHI CLI - run: | - # help - sahi --help - # predict mmdet - sahi predict --source tests/data/ --novisual --model_path tests/data/models/mmdet/yolox/yolox_tiny_8x8_300e_coco_20211124_171234-b4047906.pth --model_config_path tests/data/models/mmdet/yolox/yolox_tiny_8xb8-300e_coco.py --image_size 320 - sahi predict --source tests/data/coco_utils/terrain1.jpg --export_pickle --export_crop --model_path tests/data/models/mmdet/yolox/yolox_tiny_8x8_300e_coco_20211124_171234-b4047906.pth --model_config_path tests/data/models/mmdet/yolox/yolox_tiny_8xb8-300e_coco.py --image_size 320 - sahi predict --source tests/data/coco_utils/ --novisual --dataset_json_path tests/data/coco_utils/combined_coco.json --model_path tests/data/models/mmdet/yolox/yolox_tiny_8x8_300e_coco_20211124_171234-b4047906.pth --model_config_path tests/data/models/mmdet/yolox/yolox_tiny_8xb8-300e_coco.py --image_size 320 - # predict yolov5 - sahi predict --no_sliced_prediction --model_type yolov5 --source tests/data/coco_utils/terrain1.jpg --novisual --model_path tests/data/models/yolov5/yolov5s6.pt --image_size 320 - sahi predict --model_type yolov5 --source tests/data/ --novisual --model_path tests/data/models/yolov5/yolov5s6.pt --image_size 320 - sahi predict --model_type yolov5 --source tests/data/coco_utils/terrain1.jpg --export_pickle --export_crop --model_path tests/data/models/yolov5/yolov5s6.pt --image_size 320 - sahi predict --model_type yolov5 --source tests/data/coco_utils/ --novisual --dataset_json_path tests/data/coco_utils/combined_coco.json --model_path tests/data/models/yolov5/yolov5s6.pt --image_size 320 - # coco yolov5 - sahi coco yolov5 --image_dir tests/data/coco_utils/ --dataset_json_path tests/data/coco_utils/combined_coco.json --train_split 0.9 - # coco evaluate - sahi coco evaluate --dataset_json_path tests/data/coco_evaluate/dataset.json --result_json_path tests/data/coco_evaluate/result.json - # coco analyse - sahi coco analyse --dataset_json_path tests/data/coco_evaluate/dataset.json --result_json_path tests/data/coco_evaluate/result.json --out_dir tests/data/coco_evaluate/ diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml index b72d90bfa..c4fc39138 100644 --- a/.github/workflows/publish_pypi.yml +++ b/.github/workflows/publish_pypi.yml @@ -8,19 +8,15 @@ jobs: publish: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v3 - - name: Set up Python - uses: actions/setup-python@v4 - with: - python-version: '3.x' - - name: Install dependencies - run: | - python -m pip install --upgrade pip - pip install setuptools wheel twine - - name: Build and publish - env: - TWINE_USERNAME: __token__ - TWINE_PASSWORD: ${{ secrets.PYPI_TOKEN }} - run: | - python setup.py sdist bdist_wheel - twine upload dist/* + - uses: actions/checkout@v4 + - name: Setup uv python package manager + uses: astral-sh/setup-uv@v5 + with: + enable-cache: true + prune-cache: false + - name: Build and publish + env: + UV_PUBLISH_TOKEN: ${{ secrets.PYPI_TOKEN }} + run: | + uv build + uv publish diff --git a/.github/workflows/ruff.yaml b/.github/workflows/ruff.yaml new file mode 100644 index 000000000..cbfbb7e30 --- /dev/null +++ b/.github/workflows/ruff.yaml @@ -0,0 +1,18 @@ +name: Ruff +on: [push] +jobs: + ruff-format: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - uses: astral-sh/ruff-action@v3 + with: + args: "format --check" + version: "latest" + ruff: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - uses: astral-sh/ruff-action@v3 + with: + version: "latest" diff --git a/.gitignore b/.gitignore index e31991493..8928436ee 100644 --- a/.gitignore +++ b/.gitignore @@ -165,3 +165,7 @@ cython_debug/ .elasticbeanstalk/* !.elasticbeanstalk/*.cfg.yml !.elasticbeanstalk/*.global.yml +tests/data + +.archive +.python-version diff --git a/.markdownlint-cli2.yaml b/.markdownlint-cli2.yaml new file mode 100644 index 000000000..4c6549223 --- /dev/null +++ b/.markdownlint-cli2.yaml @@ -0,0 +1,4 @@ +config: + MD013: false + MD033: false + MD041: false diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 000000000..f825b16c0 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,16 @@ +repos: + - repo: https://github.com/astral-sh/ruff-pre-commit + # Ruff version. + rev: v0.9.5 + hooks: + # Run the linter. + - id: ruff + types_or: [ python, pyi ] + args: [--fix] + # Run import organizer + - id: ruff + types_or: [ python, pyi ] + args: [--select, I, --fix] + # Run the formatter. + - id: ruff-format + types_or: [ python, pyi ] diff --git a/README.md b/README.md index 131bc5ad5..237c704bd 100644 --- a/README.md +++ b/README.md @@ -24,7 +24,6 @@ Open In Colab HuggingFace Spaces -​ @@ -96,9 +95,7 @@ Object detection and instance segmentation are by far the most important applica - `TorchVision` + `SAHI` walkthrough: sahi-torchvision - -sahi-yolox - +sahi-yolox @@ -106,7 +103,6 @@ Object detection and instance segmentation are by far the most important applica sahi-installation -
Installation details: @@ -199,13 +195,11 @@ Find detailed info at [Interactive Result Visualization and Inspection](https:// Find detailed info on COCO utilities (yolov5 conversion, slicing, subsampling, filtering, merging, splitting) at [coco.md](docs/coco.md). -Find detailed info on MOT utilities (ground truth dataset creation, exporting tracker metrics in mot challenge format) at [mot.md](docs/mot.md). - ##
Citation
If you use this package in your work, please cite it as: -``` +```bibtex @article{akyon2022sahi, title={Slicing Aided Hyper Inference and Fine-tuning for Small Object Detection}, author={Akyon, Fatih Cagatay and Altinuc, Sinan Onur and Temizel, Alptekin}, @@ -216,7 +210,7 @@ If you use this package in your work, please cite it as: } ``` -``` +```bibtex @software{obss2021sahi, author = {Akyon, Fatih Cagatay and Cengiz, Cemil and Altinuc, Sinan Onur and Cavusoglu, Devrim and Sahin, Kadir and Eryuksel, Ogulcan}, title = {{SAHI: A lightweight vision library for performing large scale object detection and instance segmentation}}, @@ -230,23 +224,16 @@ If you use this package in your work, please cite it as: ##
Contributing
+### Add new frameworks + `sahi` library currently supports all [Ultralytics (YOLOv8/v10/v11/RTDETR) models](https://github.com/ultralytics/ultralytics), [MMDetection models](https://github.com/open-mmlab/mmdetection/blob/master/docs/en/model_zoo.md), [Detectron2 models](https://github.com/facebookresearch/detectron2/blob/main/MODEL_ZOO.md), and [HuggingFace object detection models](https://huggingface.co/models?pipeline_tag=object-detection&sort=downloads). Moreover, it is easy to add new frameworks. All you need to do is, create a new .py file under [sahi/models/](https://github.com/obss/sahi/tree/main/sahi/models) folder and create a new class in that .py file that implements [DetectionModel class](https://github.com/obss/sahi/blob/aaeb57c39780a5a32c4de2848e54df9a874df58b/sahi/models/base.py#L12). You can take the [MMDetection wrapper](https://github.com/obss/sahi/blob/aaeb57c39780a5a32c4de2848e54df9a874df58b/sahi/models/mmdet.py#L91) or [YOLOv5 wrapper](https://github.com/obss/sahi/blob/7e48bdb6afda26f977b763abdd7d8c9c170636bd/sahi/models/yolov5.py#L17) as a reference. -Before opening a PR: +### Open a Pull Request -- Install required development packages: - -```bash -pip install -e ."[dev]" -``` - -- Reformat with black and isort: - -```bash -python -m scripts.run_code_style format -``` +- Install the [uv package manager](https://docs.astral.sh/uv/) on your system. +- Install [pre-commit](https://pre-commit.com/) hooks with `uv run pre-commit install`. ##
Contributors
@@ -264,6 +251,8 @@ python -m scripts.run_code_style format Kadir Nar +Dronakurl + Burak Maden Pushpak Bhoge @@ -315,4 +304,3 @@ python -m scripts.run_code_style format Amogh Dhaliwal - diff --git a/demo/inference_for_huggingface.ipynb b/demo/inference_for_huggingface.ipynb index 9addaf742..c33f79380 100644 --- a/demo/inference_for_huggingface.ipynb +++ b/demo/inference_for_huggingface.ipynb @@ -15,7 +15,7 @@ "id": "rI9o81J50p-L" }, "source": [ - "## 0. Preperation" + "## 0. Preparation" ] }, { @@ -169,7 +169,7 @@ "id": "5u-gT4Uq2qkF" }, "source": [ - "- Instantiate a detection model by defining model weight path, confing path and other parameters:" + "- Instantiate a detection model by defining model weight path, config path and other parameters:" ] }, { @@ -810,7 +810,7 @@ "model_type = \"huggingface\"\n", "model_path = model_path\n", "model_config_path = model_path\n", - "model_device = \"cpu # or 'cuda:0'\n", + "model_device = \"cpu\" # or 'cuda:0'\n", "model_confidence_threshold = 0.5\n", "\n", "slice_height = 512\n", diff --git a/demo/inference_for_ultralytics.ipynb b/demo/inference_for_ultralytics.ipynb index 1d3976cc1..9ed4f21d5 100644 --- a/demo/inference_for_ultralytics.ipynb +++ b/demo/inference_for_ultralytics.ipynb @@ -37,7 +37,7 @@ "metadata": {}, "outputs": [], "source": [ - "import os\n", + "#import os\n", "#os.getcwd()" ] }, diff --git a/pyproject.toml b/pyproject.toml index 5f2f74d63..f024e7abd 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,103 @@ -[tool.black] +[project] +name = "sahi" +version = "0.11.21" +readme = "README.md" +description = "A vision library for performing sliced inference on large images/small objects" +requires-python = ">=3.8" +license = "MIT" +license-files = ["./LICENSE"] +dependencies = [ + "opencv-python<=4.10.0.84", + "shapely>=2.0.0", + "tqdm>=4.48.2", + "pillow>=8.2.0", + "pybboxes==0.1.6", + "pyyaml", + "fire", + "terminaltables", + "requests", + "click", +] +classifiers = [ + "License :: OSI Approved :: MIT License", + "Operating System :: OS Independent", + "Intended Audience :: Developers", + "Intended Audience :: Science/Research", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.8", + "Programming Language :: Python :: 3.9", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Topic :: Software Development :: Libraries", + "Topic :: Software Development :: Libraries :: Python Modules", +] + +[project.urls] +homepage = "https://github.com/obss/sahi" + +[project.scripts] +sahi = "sahi.cli:app" + +[tool.uv] +find-links = [ + "https://download.openmmlab.com/mmcv/dist/cpu/torch1.13.0/index.html", +] +default-groups = ["dev", "ci"] + +[dependency-groups] +dev = [ + "pytest", + "ruff", + "pre-commit>=2.0", + "jupyterlab>=3.0.14", + "matplotlib-stubs>=0.2.0", +] +ci = [ + # pytorch should be present for all python versions + "torch==1.13.1;python_version<'3.11'", + "torch==2.6.0+cpu;python_version>='3.11'", + "torchvision>=0.14.1;python_version<'3.11'", + "torchvision==0.21.0+cpu;python_version>='3.11'", + # mmcv is available for python < 3.11 + "mmengine==0.7.3;python_version<'3.11'", + "mmcv==2.0.0;python_version<'3.11'", + "mmdet==3.0.0;python_version<'3.11'", + # deepsparse is only available for python<3.12 + "deepsparse;python_version<'3.12'", + "onnxruntime<1.20;python_version<'3.12'", + "onnx>1.16;python_version>='3.12'", + "onnxruntime;python_version>='3.12'", + # These are available for all + "yolov5>=7.0.13", + # NOTE: Higher versions of transformers give different inference results of huggingface model + "transformers==4.35.0", + "pycocotools>=2.0.7", + "ultralytics>=8.3.50", + "scikit-image", + "fiftyone", +] + +[[tool.uv.index]] +name = "pytorch-cpu" +url = "https://download.pytorch.org/whl/cpu" +explicit = true + +[tool.uv.sources] +torch = [{ index = "pytorch-cpu" }] +torchvision = [{ index = "pytorch-cpu" }] + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.ruff] line-length = 120 +exclude = ["**/__init__.py", ".git", "__pycache__", "*.ipynb"] + +[tool.pytest.ini_options] +minversion = "6.0" +addopts = ["--import-mode=importlib", "--no-header"] +pythonpath = ["."] -[tool.isort] -line_length = 120 -profile = "black" +[tool.typos.default] +extend-ignore-identifiers-re = ["fo"] diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index 812fa105a..000000000 --- a/requirements.txt +++ /dev/null @@ -1,10 +0,0 @@ -opencv-python<=4.10.0.84 -shapely>=2.0.0 -tqdm>=4.48.2 -pillow>=8.2.0 -pybboxes==0.1.6 -pyyaml -fire -terminaltables -requests -click diff --git a/sahi/__init__.py b/sahi/__init__.py index bed884633..6676844a9 100644 --- a/sahi/__init__.py +++ b/sahi/__init__.py @@ -1,4 +1,4 @@ -__version__ = "0.11.20" +__version__ = "0.11.21" from sahi.annotation import BoundingBox, Category, Mask from sahi.auto_model import AutoDetectionModel diff --git a/sahi/annotation.py b/sahi/annotation.py index 5d7bc639c..426eafb7a 100644 --- a/sahi/annotation.py +++ b/sahi/annotation.py @@ -2,6 +2,7 @@ # Code written by Fatih C Akyon, 2020. import copy +import logging from typing import Dict, List, Optional import numpy as np @@ -14,12 +15,15 @@ ) from sahi.utils.shapely import ShapelyAnnotation +logger = logging.getLogger(__name__) + class BoundingBox: """ Bounding box of the annotation. """ + # TODO: Better use tuple not lists for data that has a defined length and should no mutate a lot def __init__(self, box: List[float], shift_amount: List[int] = [0, 0]): """ Args: @@ -176,20 +180,13 @@ def __init__( To shift the box and mask predictions from sliced image to full sized image, should be in the form of [shift_x, shift_y] """ - # confirm full_shape is given if full_shape is None: - raise ValueError("full_shape must be provided") + raise ValueError("full_shape must be provided") # pyright: ignore[reportUnreachable] self.shift_x = shift_amount[0] self.shift_y = shift_amount[1] - - if full_shape: - self.full_shape_height = full_shape[0] - self.full_shape_width = full_shape[1] - else: - self.full_shape_height = None - self.full_shape_width = None - + self.full_shape_height = full_shape[0] + self.full_shape_width = full_shape[1] self.segmentation = segmentation @classmethod @@ -216,20 +213,20 @@ def from_bool_mask( ) @property - def bool_mask(self): + def bool_mask(self) -> np.ndarray: return get_bool_mask_from_coco_segmentation( self.segmentation, width=self.full_shape[1], height=self.full_shape[0] ) @property - def shape(self): + def shape(self) -> List[int]: """ Returns mask shape as [height, width] """ return [self.bool_mask.shape[0], self.bool_mask.shape[1]] @property - def full_shape(self): + def full_shape(self) -> List[int]: """ Returns full mask shape after shifting as [height, width] """ @@ -242,7 +239,7 @@ def shift_amount(self): """ return [self.shift_x, self.shift_y] - def get_shifted_mask(self): + def get_shifted_mask(self) -> "Mask": # Confirm full_shape is specified if (self.full_shape_height is None) or (self.full_shape_width is None): raise ValueError("full_shape is None") @@ -382,7 +379,7 @@ def from_coco_annotation_dict( cls, annotation_dict: Dict, full_shape: List[int], - category_name: str = None, + category_name: Optional[str] = None, shift_amount: Optional[List[int]] = [0, 0], ): """ @@ -608,9 +605,7 @@ def to_imantics_annotation(self): try: import imantics except ImportError: - raise ImportError( - 'Please run "pip install -U imantics" ' "to install imantics first for imantics conversion." - ) + raise ImportError('Please run "pip install -U imantics" to install imantics first for imantics conversion.') imantics_category = imantics.Category(id=self.category.id, name=self.category.name) if self.mask is not None: diff --git a/sahi/auto_model.py b/sahi/auto_model.py index e66ab680b..ac151d714 100644 --- a/sahi/auto_model.py +++ b/sahi/auto_model.py @@ -1,5 +1,6 @@ from typing import Any, Dict, Optional +from sahi.models.base import DetectionModel from sahi.utils.file import import_model_class MODEL_TYPE_TO_MODEL_CLASS_NAME = { @@ -30,9 +31,9 @@ def from_pretrained( category_mapping: Optional[Dict] = None, category_remapping: Optional[Dict] = None, load_at_init: bool = True, - image_size: int = None, + image_size: Optional[int] = None, **kwargs, - ): + ) -> DetectionModel: """ Loads a DetectionModel from given path. @@ -54,7 +55,7 @@ def from_pretrained( category_remapping: dict: str to int Remap category ids based on category names, after performing inference e.g. {"car": 3} load_at_init: bool - If True, automatically loads the model at initalization + If True, automatically loads the model at initialization image_size: int Inference input size. Returns: diff --git a/sahi/cli.py b/sahi/cli.py index ea711f2cc..dbc681cc1 100644 --- a/sahi/cli.py +++ b/sahi/cli.py @@ -7,7 +7,7 @@ from sahi.scripts.coco_error_analysis import analyse from sahi.scripts.coco_evaluation import evaluate from sahi.scripts.slice_coco import slice -from sahi.utils.import_utils import print_enviroment_info +from sahi.utils.import_utils import print_environment_info coco_app = { "evaluate": evaluate, @@ -22,7 +22,7 @@ "predict-fiftyone": predict_fiftyone, "coco": coco_app, "version": sahi_version, - "env": print_enviroment_info, + "env": print_environment_info, } diff --git a/sahi/models/base.py b/sahi/models/base.py index a434728dd..36ec4d8ff 100644 --- a/sahi/models/base.py +++ b/sahi/models/base.py @@ -1,13 +1,15 @@ # OBSS SAHI Tool # Code written by Fatih C Akyon, 2020. +import logging from typing import Any, Dict, List, Optional import numpy as np from sahi.prediction import ObjectPrediction -from sahi.utils.import_utils import is_available -from sahi.utils.torch import select_device as select_torch_device +from sahi.utils.torch import empty_cuda_cache, has_torch, select_device + +logger = logging.getLogger(__name__) class DetectionModel: @@ -22,7 +24,7 @@ def __init__( category_mapping: Optional[Dict] = None, category_remapping: Optional[Dict] = None, load_at_init: bool = True, - image_size: int = None, + image_size: Optional[int] = None, ): """ Init object detection/instance segmentation model. @@ -31,8 +33,7 @@ def __init__( Path for the instance segmentation model weight config_path: str Path for the mmdetection instance segmentation model config file - device: str - Torch device, "cpu" or "cuda" + device: Torch device, "cpu", "mps", "cuda", "cuda:0", "cuda:1", etc. mask_threshold: float Value to threshold mask pixels, should be between 0 and 1 confidence_threshold: float @@ -42,14 +43,13 @@ def __init__( category_remapping: dict: str to int Remap category ids based on category names, after performing inference e.g. {"car": 3} load_at_init: bool - If True, automatically loads the model at initalization + If True, automatically loads the model at initialization image_size: int Inference input size. """ self.model_path = model_path self.config_path = config_path self.model = None - self.device = device self.mask_threshold = mask_threshold self.confidence_threshold = confidence_threshold self.category_mapping = category_mapping @@ -57,7 +57,6 @@ def __init__( self.image_size = image_size self._original_predictions = None self._object_prediction_list_per_image = None - self.set_device() # automatically load model if load_at_init is True @@ -90,24 +89,23 @@ def set_model(self, model: Any, **kwargs): """ raise NotImplementedError() - def set_device(self): - """ - Sets the device for the model. + def set_device(self, device: Optional[str] = None): + """Sets the device pytorch should use for the model + + Args: + device: Torch device, "cpu", "mps", "cuda", "cuda:0", "cuda:1", etc. """ - if is_available("torch"): - self.device = select_torch_device(self.device) + if has_torch: + self.device = select_device(device) else: - raise NotImplementedError() + raise NotImplementedError(f"Could not set device {self.device}") def unload_model(self): """ Unloads the model from CPU/GPU. """ self.model = None - if is_available("torch"): - from sahi.utils.torch import empty_cuda_cache - - empty_cuda_cache() + empty_cuda_cache() def perform_inference(self, image: np.ndarray): """ @@ -146,7 +144,12 @@ def _apply_category_remapping(self): if self.category_remapping is None: raise ValueError("self.category_remapping cannot be None") # remap categories - for object_prediction_list in self._object_prediction_list_per_image: + if not isinstance(self._object_prediction_list_per_image, list): + logger.error( + f"Unknown type for self._object_prediction_list_per_image: {type(self._object_prediction_list_per_image)}" + ) + return + for object_prediction_list in self._object_prediction_list_per_image: # type: ignore for object_prediction in object_prediction_list: old_category_id_str = str(object_prediction.category.id) new_category_id_int = self.category_remapping[old_category_id_str] @@ -154,8 +157,8 @@ def _apply_category_remapping(self): def convert_original_predictions( self, - shift_amount: Optional[List[int]] = [0, 0], - full_shape: Optional[List[int]] = None, + shift_amount: Optional[List[List[int]]] = [[0, 0]], + full_shape: Optional[List[List[int]]] = None, ): """ Converts original predictions of the detection model to a list of @@ -174,12 +177,16 @@ def convert_original_predictions( self._apply_category_remapping() @property - def object_prediction_list(self) -> List[ObjectPrediction]: + def object_prediction_list(self) -> List[List[ObjectPrediction]]: + if self._object_prediction_list_per_image is None: + return [] + if len(self._object_prediction_list_per_image) == 0: + return [] return self._object_prediction_list_per_image[0] @property - def object_prediction_list_per_image(self): - return self._object_prediction_list_per_image + def object_prediction_list_per_image(self) -> List[List[ObjectPrediction]]: + return self._object_prediction_list_per_image or [] @property def original_predictions(self): diff --git a/sahi/models/huggingface.py b/sahi/models/huggingface.py index 9a8a8614b..a8d7ba591 100644 --- a/sahi/models/huggingface.py +++ b/sahi/models/huggingface.py @@ -10,7 +10,6 @@ from sahi.models.base import DetectionModel from sahi.prediction import ObjectPrediction from sahi.utils.compatibility import fix_full_shape_list, fix_shift_amount_list -from sahi.utils.cv import get_coco_segmentation_from_bool_mask from sahi.utils.import_utils import check_requirements, ensure_package_minimum_version logger = logging.getLogger(__name__) @@ -29,7 +28,7 @@ def __init__( category_mapping: Optional[Dict] = None, category_remapping: Optional[Dict] = None, load_at_init: bool = True, - image_size: int = None, + image_size: Optional[int] = None, ): self._processor = processor self._image_shapes = [] @@ -100,7 +99,7 @@ def perform_inference(self, image: Union[List, np.ndarray]): import torch # Confirm model is loaded - if self.model is None: + if self.model is None or self.processor is None: raise RuntimeError("Model is not loaded, load it by calling .load_model()") with torch.no_grad(): @@ -157,7 +156,7 @@ def _create_object_prediction_list_from_original_predictions( """ original_predictions = self._original_predictions - # compatilibty for sahi v0.8.15 + # compatibility for sahi v0.8.15 shift_amount_list = fix_shift_amount_list(shift_amount_list) full_shape_list = fix_full_shape_list(full_shape_list) diff --git a/sahi/models/mmdet.py b/sahi/models/mmdet.py index e705f5176..6d89d898d 100644 --- a/sahi/models/mmdet.py +++ b/sahi/models/mmdet.py @@ -100,7 +100,7 @@ def __init__( category_mapping: Optional[Dict] = None, category_remapping: Optional[Dict] = None, load_at_init: bool = True, - image_size: int = None, + image_size: Optional[int] = None, scope: str = "mmdet", ): if not IMPORT_MMDET_V3: @@ -173,8 +173,8 @@ def perform_inference(self, image: np.ndarray): image = image[:, :, ::-1] # compatibility with sahi v0.8.15 if not isinstance(image, list): - image = [image] - prediction_result = self.model(image) + image_list = [image] + prediction_result = self.model(image_list) self._original_predictions = prediction_result["predictions"] @@ -217,7 +217,7 @@ def check_pipeline_for_mask(pipeline): @property def category_names(self): classes = self.model.model.dataset_meta["classes"] - if type(classes) == str: + if isinstance(classes, str): # https://github.com/open-mmlab/mmdetection/pull/4973 return (classes,) else: diff --git a/sahi/models/torchvision.py b/sahi/models/torchvision.py index fa2741b74..30ee04bc6 100644 --- a/sahi/models/torchvision.py +++ b/sahi/models/torchvision.py @@ -2,7 +2,7 @@ # Code written by Fatih C Akyon and Kadir Nar, 2021. import logging -from typing import Any, Dict, List, Optional +from typing import Any, List, Optional import numpy as np @@ -19,7 +19,7 @@ def check_dependencies(self) -> None: check_requirements(["torch", "torchvision"]) def load_model(self): - import torch + import torch # pyright: ignore[reportMissingImports] from sahi.utils.torchvision import MODEL_NAME_TO_CONSTRUCTOR @@ -54,10 +54,12 @@ def load_model(self): # load model model = MODEL_NAME_TO_CONSTRUCTOR[model_name](num_classes=num_classes, pretrained=pretrained) - try: - model.load_state_dict(torch.load(self.model_path)) - except Exception as e: - TypeError("model_path is not a valid torchvision model path: ", e) + if self.model_path: + try: + model.load_state_dict(torch.load(self.model_path)) + except Exception as e: + logger.error(f"Invalid {self.model_path=}") + raise TypeError("model_path is not a valid torchvision model path: ", e) self.set_model(model) @@ -80,7 +82,7 @@ def set_model(self, model: Any): category_names = {str(i): COCO_CLASSES[i] for i in range(len(COCO_CLASSES))} self.category_mapping = category_names - def perform_inference(self, image: np.ndarray, image_size: int = None): + def perform_inference(self, image: np.ndarray, image_size: Optional[int] = None): """ Prediction is performed using self.model and the prediction result is set to self._original_predictions. Args: diff --git a/sahi/models/ultralytics.py b/sahi/models/ultralytics.py index dd421f5cc..e9df51f67 100644 --- a/sahi/models/ultralytics.py +++ b/sahi/models/ultralytics.py @@ -8,14 +8,14 @@ import numpy as np import torch -logger = logging.getLogger(__name__) - from sahi.models.base import DetectionModel from sahi.prediction import ObjectPrediction from sahi.utils.compatibility import fix_full_shape_list, fix_shift_amount_list from sahi.utils.cv import get_coco_segmentation_from_bool_mask, get_coco_segmentation_from_obb_points from sahi.utils.import_utils import check_requirements +logger = logging.getLogger(__name__) + class UltralyticsDetectionModel(DetectionModel): def check_dependencies(self) -> None: diff --git a/sahi/models/yolov5.py b/sahi/models/yolov5.py index b0446b7f5..543281aeb 100644 --- a/sahi/models/yolov5.py +++ b/sahi/models/yolov5.py @@ -2,7 +2,7 @@ # Code written by Fatih C Akyon, 2020. import logging -from typing import Any, Dict, List, Optional +from typing import Any, List, Optional import numpy as np diff --git a/sahi/models/yolov5sparse.py b/sahi/models/yolov5sparse.py index 14ec7e329..bd3d36666 100644 --- a/sahi/models/yolov5sparse.py +++ b/sahi/models/yolov5sparse.py @@ -4,14 +4,14 @@ # https://neuralmagic.com/deepsparse import logging -from typing import Any, Dict, List, Optional +from typing import Any, List, Optional import numpy as np from sahi.models.base import DetectionModel from sahi.prediction import ObjectPrediction from sahi.utils.compatibility import fix_full_shape_list, fix_shift_amount_list -from sahi.utils.import_utils import check_package_minimum_version, check_requirements +from sahi.utils.import_utils import check_requirements logger = logging.getLogger(__name__) @@ -101,7 +101,8 @@ def category_names(self): "elephant", "bear", "zebra", - "giraffe" "backpack", + "giraffe", + "backpack", "umbrella", "handbag", "tie", diff --git a/sahi/models/yolov8onnx.py b/sahi/models/yolov8onnx.py index 340eb07db..afcb28bf6 100644 --- a/sahi/models/yolov8onnx.py +++ b/sahi/models/yolov8onnx.py @@ -2,19 +2,19 @@ # Code written by Karl-Joan Alesma and Michael García, 2023. import logging -from typing import Any, Dict, List, Optional, Tuple +from typing import Any, List, Optional, Tuple import cv2 import numpy as np import torch -logger = logging.getLogger(__name__) - from sahi.models.base import DetectionModel from sahi.prediction import ObjectPrediction from sahi.utils.compatibility import fix_full_shape_list, fix_shift_amount_list from sahi.utils.import_utils import check_requirements -from sahi.utils.yolov8onnx import non_max_supression, xywh2xyxy +from sahi.utils.yolov8onnx import non_max_suppression, xywh2xyxy + +logger = logging.getLogger(__name__) class Yolov8OnnxDetectionModel(DetectionModel): @@ -22,7 +22,7 @@ def __init__(self, *args, iou_threshold: float = 0.7, **kwargs): """ Args: iou_threshold: float - IOU threshold for non-max supression, defaults to 0.7. + IOU threshold for non-max suppression, defaults to 0.7. """ super().__init__(*args, **kwargs) self.iou_threshold = iou_threshold @@ -112,7 +112,7 @@ def _post_process( boxes = xywh2xyxy(boxes).round().astype(np.int32) # Perform non-max supressions - indices = non_max_supression(boxes, scores, self.iou_threshold) + indices = non_max_suppression(boxes, scores, self.iou_threshold) # Format the results prediction_result = [] diff --git a/sahi/postprocess/combine.py b/sahi/postprocess/combine.py index 3ae151f3c..c16d73fc3 100644 --- a/sahi/postprocess/combine.py +++ b/sahi/postprocess/combine.py @@ -279,8 +279,8 @@ def greedy_nmm( # keep the boxes with IoU/IoS less than thresh_iou mask = match_metric_value < match_threshold - matched_box_indices = order[(mask == False).nonzero().flatten()].flip(dims=(0,)) - unmatched_indices = order[(mask == True).nonzero().flatten()] + matched_box_indices = order[(mask == False).nonzero().flatten()].flip(dims=(0,)) # noqa: E712 + unmatched_indices = order[(mask == True).nonzero().flatten()] # noqa: E712 # update box pool order = unmatched_indices[scores[unmatched_indices].argsort()] @@ -295,7 +295,7 @@ def greedy_nmm( def batched_nmm( - object_predictions_as_tensor: torch.tensor, + object_predictions_as_tensor: torch.Tensor, match_metric: str = "IOU", match_threshold: float = 0.5, ): @@ -326,7 +326,7 @@ def batched_nmm( def nmm( - object_predictions_as_tensor: torch.tensor, + object_predictions_as_tensor: torch.Tensor, match_metric: str = "IOU", match_threshold: float = 0.5, ): @@ -423,7 +423,7 @@ def nmm( # keep the boxes with IoU/IoS less than thresh_iou mask = match_metric_value < match_threshold - matched_box_indices = other_pred_inds[(mask == False).nonzero().flatten()].flip(dims=(0,)) + matched_box_indices = other_pred_inds[(mask == False).nonzero().flatten()].flip(dims=(0,)) # noqa: E712 # create keep_ind to merge_ind_list mapping if pred_ind not in merge_to_keep: @@ -459,7 +459,7 @@ def __init__( check_requirements(["torch"]) - def __call__(self): + def __call__(self, predictions: List[ObjectPrediction]): raise NotImplementedError() diff --git a/sahi/postprocess/utils.py b/sahi/postprocess/utils.py index 193cb684f..1e10341bb 100644 --- a/sahi/postprocess/utils.py +++ b/sahi/postprocess/utils.py @@ -7,7 +7,6 @@ from sahi.annotation import BoundingBox, Category, Mask from sahi.prediction import ObjectPrediction -from sahi.utils.cv import get_coco_segmentation_from_bool_mask from sahi.utils.shapely import ShapelyAnnotation, get_shapely_multipolygon diff --git a/sahi/predict.py b/sahi/predict.py index 65f46bdf5..bfca97a20 100644 --- a/sahi/predict.py +++ b/sahi/predict.py @@ -4,13 +4,16 @@ import logging import os import time -from typing import List, Optional +from typing import Generator, List, Optional, Union + +from PIL import Image from sahi.utils.import_utils import is_available +# TODO: This does nothing for this module. The issue named here does not exist # https://github.com/obss/sahi/issues/526 if is_available("torch"): - import torch + import torch # noqa: F401 from functools import cmp_to_key @@ -125,8 +128,8 @@ def get_prediction( def get_sliced_prediction( image, detection_model=None, - slice_height: int = None, - slice_width: int = None, + slice_height: Optional[int] = None, + slice_width: Optional[int] = None, overlap_height_ratio: float = 0.2, overlap_width_ratio: float = 0.2, perform_standard_pred: bool = True, @@ -135,10 +138,10 @@ def get_sliced_prediction( postprocess_match_threshold: float = 0.5, postprocess_class_agnostic: bool = False, verbose: int = 1, - merge_buffer_length: int = None, + merge_buffer_length: Optional[int] = None, auto_slice_resolution: bool = True, - slice_export_prefix: str = None, - slice_dir: str = None, + slice_export_prefix: Optional[str] = None, + slice_dir: Optional[str] = None, ) -> PredictionResult: """ Function for slice image + get predicion for each slice + combine predictions in full image. @@ -340,18 +343,18 @@ def agg_prediction(result: PredictionResult, thresh): def predict( - detection_model: DetectionModel = None, - model_type: str = "mmdet", - model_path: str = None, - model_config_path: str = None, + detection_model: Optional[DetectionModel] = None, + model_type: str = "ultralytics", + model_path: Optional[str] = None, + model_config_path: Optional[str] = None, model_confidence_threshold: float = 0.25, - model_device: str = None, - model_category_mapping: dict = None, - model_category_remapping: dict = None, - source: str = None, + model_device: Optional[str] = None, + model_category_mapping: Optional[dict] = None, + model_category_remapping: Optional[dict] = None, + source: Optional[str] = None, no_standard_prediction: bool = False, no_sliced_prediction: bool = False, - image_size: int = None, + image_size: Optional[int] = None, slice_height: int = 512, slice_width: int = 512, overlap_height_ratio: float = 0.2, @@ -365,12 +368,12 @@ def predict( frame_skip_interval: int = 0, export_pickle: bool = False, export_crop: bool = False, - dataset_json_path: str = None, + dataset_json_path: Optional[str] = None, project: str = "runs/predict", name: str = "exp", - visual_bbox_thickness: int = None, - visual_text_size: float = None, - visual_text_thickness: int = None, + visual_bbox_thickness: Optional[int] = None, + visual_text_size: Optional[float] = None, + visual_text_thickness: Optional[int] = None, visual_hide_labels: bool = False, visual_hide_conf: bool = False, visual_export_format: str = "png", @@ -492,24 +495,24 @@ def predict( # TODO: rewrite this as iterator class as in https://github.com/ultralytics/yolov5/blob/d059d1da03aee9a3c0059895aa4c7c14b7f25a9e/utils/datasets.py#L178 source_is_video = False num_frames = None - if dataset_json_path: + image_iterator: Union[list[str], Generator[Image.Image, None, None]] + if dataset_json_path and source: coco: Coco = Coco.from_coco_dict_or_path(dataset_json_path) image_iterator = [str(Path(source) / Path(coco_image.file_name)) for coco_image in coco.images] coco_json = [] - elif os.path.isdir(source): - image_iterator = list_files( - directory=source, - contains=IMAGE_EXTENSIONS, - verbose=verbose, - ) - elif Path(source).suffix in VIDEO_EXTENSIONS: + elif source and os.path.isdir(source): + image_iterator = list_files(directory=source, contains=IMAGE_EXTENSIONS, verbose=verbose) + elif source and Path(source).suffix in VIDEO_EXTENSIONS: source_is_video = True read_video_frame, output_video_writer, video_file_name, num_frames = get_video_reader( - source, save_dir, frame_skip_interval, not novisual, view_video + source, str(save_dir), frame_skip_interval, not novisual, view_video ) image_iterator = read_video_frame - else: + elif source: image_iterator = [source] + else: + logger.error("No valid input given to predict function") + return # init model instance time_start = time.time() @@ -538,11 +541,13 @@ def predict( for ind, image_path in enumerate( tqdm(image_iterator, f"Performing inference on {input_type_str}", total=num_frames) ): - # get filename - if source_is_video: + # Source is an image: Iterating over Image objects + if source and source_is_video: video_name = Path(source).stem relative_filepath = video_name + "_frame_" + str(ind) - elif os.path.isdir(source): # preserve source folder structure in export + elif isinstance(image_path, Image.Image): + raise RuntimeError("Source is not a video, but image is still an Image object ") + elif source and os.path.isdir(source): # preserve source folder structure in export relative_filepath = str(Path(image_path)).split(str(Path(source)))[-1] relative_filepath = relative_filepath[1:] if relative_filepath[0] == os.sep else relative_filepath else: # no process if source is single file @@ -571,7 +576,8 @@ def predict( verbose=1 if verbose else 0, ) object_prediction_list = prediction_result.object_prediction_list - durations_in_seconds["slice"] += prediction_result.durations_in_seconds["slice"] + if prediction_result.durations_in_seconds: + durations_in_seconds["slice"] += prediction_result.durations_in_seconds["slice"] else: # get standard prediction prediction_result = get_prediction( @@ -677,6 +683,8 @@ def predict( export_format=visual_export_format, ) if not novisual and source_is_video: # export video + if output_video_writer is None: + raise RuntimeError("Output video writer could not be created") output_video_writer.write(cv2.cvtColor(result["image"], cv2.COLOR_RGB2BGR)) # render video inference @@ -725,17 +733,17 @@ def predict( def predict_fiftyone( model_type: str = "mmdet", - model_path: str = None, - model_config_path: str = None, + model_path: Optional[str] = None, + model_config_path: Optional[str] = None, model_confidence_threshold: float = 0.25, - model_device: str = None, - model_category_mapping: dict = None, - model_category_remapping: dict = None, - dataset_json_path: str = None, - image_dir: str = None, + model_device: Optional[str] = None, + model_category_mapping: Optional[dict] = None, + model_category_remapping: Optional[dict] = None, + dataset_json_path: str = "", + image_dir: str = "", no_standard_prediction: bool = False, no_sliced_prediction: bool = False, - image_size: int = None, + image_size: Optional[int] = None, slice_height: int = 256, slice_width: int = 256, overlap_height_ratio: float = 0.2, @@ -892,7 +900,7 @@ def predict_fiftyone( ) # visualize results - session = fo.launch_app() + session = fo.launch_app() # pyright: ignore[reportArgumentType] session.dataset = dataset # Evaluate the predictions results = dataset.evaluate_detections( diff --git a/sahi/prediction.py b/sahi/prediction.py index 5fca9648a..a73b0ccee 100644 --- a/sahi/prediction.py +++ b/sahi/prediction.py @@ -2,7 +2,7 @@ # Code written by Fatih C Akyon, 2020. import copy -from typing import Dict, List, Optional, Union +from typing import Any, Dict, List, Optional, Union import numpy as np from PIL import Image @@ -46,7 +46,7 @@ def __init__( category_id: Optional[int] = None, category_name: Optional[str] = None, segmentation: Optional[List[List[float]]] = None, - score: Optional[float] = 0, + score: float = 0.0, shift_amount: Optional[List[int]] = [0, 0], full_shape: Optional[List[int]] = None, ): @@ -162,7 +162,7 @@ def __init__( self, object_prediction_list: List[ObjectPrediction], image: Union[Image.Image, str, np.ndarray], - durations_in_seconds: Optional[Dict] = None, + durations_in_seconds: Dict[str, Any] = dict(), ): self.image: Image.Image = read_image_as_pil(image) self.image_width, self.image_height = self.image.size @@ -172,8 +172,8 @@ def __init__( def export_visuals( self, export_dir: str, - text_size: float = None, - rect_th: int = None, + text_size: Optional[float] = None, + rect_th: Optional[int] = None, hide_labels: bool = False, hide_conf: bool = False, file_name: str = "prediction_visual", @@ -227,7 +227,7 @@ def to_fiftyone_detections(self): try: import fiftyone as fo except ImportError: - raise ImportError('Please run "pip install -U fiftyone" to install fiftyone first for fiftyone conversion.') + raise ImportError('Please run "uv pip install -U fiftyone" to install fiftyone for conversion.') fiftyone_detection_list: List[fo.Detection] = [] for object_prediction in self.object_prediction_list: diff --git a/sahi/scripts/coco2fiftyone.py b/sahi/scripts/coco2fiftyone.py index 65e3978f9..0332242e6 100644 --- a/sahi/scripts/coco2fiftyone.py +++ b/sahi/scripts/coco2fiftyone.py @@ -1,6 +1,5 @@ import time from pathlib import Path -from typing import List import fire @@ -49,7 +48,7 @@ def main( add_coco_labels(dataset, result_name, coco_result, coco_id_field="gt_coco_id") # visualize results - session = fo.launch_app() + session = fo.launch_app() # pyright: ignore[reportArgumentType] session.dataset = dataset # order by false positives if any coco result is given @@ -73,7 +72,7 @@ def main( # Show samples with most false positives session.view = eval_view.sort_by(f"{first_coco_result_name}_eval_fp", reverse=True) - print("SAHI has successfully launched a Fiftyone app " f"at http://localhost:{fo.config.default_app_port}") + print(f"SAHI has successfully launched a Fiftyone app at http://localhost:{fo.config.default_app_port}") while 1: time.sleep(3) diff --git a/sahi/scripts/coco2yolov5.py b/sahi/scripts/coco2yolov5.py index e0094c3e6..e58b391ea 100644 --- a/sahi/scripts/coco2yolov5.py +++ b/sahi/scripts/coco2yolov5.py @@ -1,3 +1,5 @@ +from typing import Union + import fire from sahi.utils.coco import Coco @@ -7,17 +9,17 @@ def main( image_dir: str, dataset_json_path: str, - train_split: str = 0.9, + train_split: Union[int, float] = 0.9, project: str = "runs/coco2yolov5", name: str = "exp", - seed: str = 1, + seed: int = 1, disable_symlink=False, ): """ Args: images_dir (str): directory for coco images dataset_json_path (str): file path for the coco json file to be converted - train_split (str): set the training split ratio + train_split (float or int): set the training split ratio project (str): save results to project/name name (str): save results to project/name" seed (int): fix the seed for reproducibility diff --git a/sahi/scripts/coco_error_analysis.py b/sahi/scripts/coco_error_analysis.py index 07ed7f0ee..4ef116635 100644 --- a/sahi/scripts/coco_error_analysis.py +++ b/sahi/scripts/coco_error_analysis.py @@ -1,12 +1,31 @@ import copy +import logging import os from multiprocessing import Pool from pathlib import Path -from typing import List +from typing import List, Optional, Union import fire import numpy as np +logger = logging.getLogger(__name__) + +try: + from pycocotools.coco import COCO + from pycocotools.cocoeval import COCOeval + + has_pycocotools = True +except ImportError: + has_pycocotools = False + +try: + import matplotlib.pyplot as plt + + has_matplotlib = True +except ImportError: + has_matplotlib = False + + COLOR_PALETTE = np.vstack( [ np.array([0.8, 0.8, 0.8]), @@ -20,9 +39,7 @@ ) -def _makeplot(rs, ps, outDir, class_name, iou_type): - import matplotlib.pyplot as plt - +def _makeplot(rs, ps, outDir: Union[str, Path], class_name: str, iou_type: str) -> List[str]: export_path_list = [] areaNames = ["allarea", "small", "medium", "large"] @@ -93,9 +110,7 @@ def _autolabel(ax, rects, is_percent=True): ) -def _makebarplot(rs, ps, outDir, class_name, iou_type): - import matplotlib.pyplot as plt - +def _makebarplot(_, ps, outDir, class_name, iou_type): areaNames = ["allarea", "small", "medium", "large"] types = ["C75", "C50", "Loc", "Sim", "Oth", "BG", "FN"] fig, ax = plt.subplots() @@ -159,8 +174,6 @@ def _get_gt_area_group_numbers(cocoEval): def _make_gt_area_group_numbers_plot(cocoEval, outDir, verbose=True): - import matplotlib.pyplot as plt - areaRngLbl2Number = _get_gt_area_group_numbers(cocoEval) areaRngLbl = areaRngLbl2Number.keys() if verbose: @@ -172,7 +185,7 @@ def _make_gt_area_group_numbers_plot(cocoEval, outDir, verbose=True): width = 0.60 # the width of the bars figure_title = "number of annotations per area group" - rects = ax.bar(x, areaRngLbl2Number.values(), width) + rects = ax.bar(x, list(areaRngLbl2Number.values()), width) # Add some text for labels, title and custom x-axis tick labels, etc. ax.set_ylabel("Number of annotations") @@ -193,8 +206,6 @@ def _make_gt_area_group_numbers_plot(cocoEval, outDir, verbose=True): def _make_gt_area_histogram_plot(cocoEval, outDir): - import matplotlib.pyplot as plt - n_bins = 100 areas = [ann["area"] for ann in cocoEval.cocoGt.anns.values()] @@ -219,9 +230,9 @@ def _make_gt_area_histogram_plot(cocoEval, outDir): return export_path -def _analyze_individual_category(k, cocoDt, cocoGt, catId, iou_type, areas=None, max_detections=None, COCOeval=None): +def _analyze_individual_category(k, cocoDt, cocoGt, catId, iou_type, areas=None, max_detections: int = 500): nm = cocoGt.loadCats(catId)[0] - print(f'--------------analyzing {k + 1}-{nm["name"]}---------------') + print(f"--------------analyzing {k + 1}-{nm['name']}---------------") ps_ = {} dt = copy.deepcopy(cocoDt) nm = cocoGt.loadCats(catId)[0] @@ -291,15 +302,13 @@ def _analyse_results( extraplots=None, areas=None, max_detections=500, - COCO=None, - COCOeval=None, ): for res_type in res_types: if res_type not in ["bbox", "segm"]: raise ValueError(f"res_type {res_type} is not supported") if areas is not None: if len(areas) != 3: - raise ValueError("3 integers should be specified as areas,representing 3 area regions") + raise ValueError("3 integers should be specified as areas, representing 3 area regions") if out_dir is None: out_dir = Path(res_file).parent @@ -353,15 +362,14 @@ def _analyse_results( recThrs = cocoEval.params.recThrs with Pool(processes=48) as pool: args = [ - (k, cocoDt, cocoGt, catId, iou_type, areas, max_detections, COCOeval) - for k, catId in enumerate(present_cat_ids) + (k, cocoDt, cocoGt, catId, iou_type, areas, max_detections) for k, catId in enumerate(present_cat_ids) ] analyze_results = pool.starmap(_analyze_individual_category, args) classname_to_export_path_list = {} for k, catId in enumerate(present_cat_ids): nm = cocoGt.loadCats(catId)[0] - print(f'--------------saving {k + 1}-{nm["name"]}---------------') + print(f"--------------saving {k + 1}-{nm['name']}---------------") analyze_result = analyze_results[k] if k != analyze_result[0]: raise ValueError(f"k {k} != analyze_result[0] {analyze_result[0]}") @@ -416,13 +424,13 @@ def _analyse_results( def analyse( dataset_json_path: str, result_json_path: str, - out_dir: str = None, + out_dir: Optional[str] = None, type: str = "bbox", no_extraplots: bool = False, areas: List[int] = [1024, 9216, 10000000000], max_detections: int = 500, return_dict: bool = False, -): +) -> Optional[dict]: """ Args: dataset_json_path (str): file path for the coco dataset json file @@ -434,19 +442,12 @@ def analyse( max_detections (int): Maximum number of detections to consider for AP alculation. Default: 500 return_dict (bool): If True, returns a dict export paths. """ - try: - from pycocotools.coco import COCO - from pycocotools.cocoeval import COCOeval - except ModuleNotFoundError: - raise ModuleNotFoundError( - 'Please run "pip install -U pycocotools" ' "to install pycocotools first for coco evaluation." - ) - try: - import matplotlib.pyplot as plt - except ModuleNotFoundError: - raise ModuleNotFoundError( - 'Please run "pip install -U matplotlib" ' "to install matplotlib first for visualization." - ) + if not has_matplotlib: + logger.error("Please run 'uv pip install -U matplotlib' first for visualization.") + raise ModuleNotFoundError("matplotlib not installed") + if not has_pycocotools: + logger.error("Please run 'uv pip install -U pycocotools' first for Coco analysis.") + raise ModuleNotFoundError("pycocotools not installed") result = _analyse_results( result_json_path, @@ -456,8 +457,6 @@ def analyse( extraplots=not no_extraplots, areas=areas, max_detections=max_detections, - COCO=COCO, - COCOeval=COCOeval, ) if return_dict: return result diff --git a/sahi/scripts/coco_evaluation.py b/sahi/scripts/coco_evaluation.py index 9cc308c3e..b9019b1f5 100644 --- a/sahi/scripts/coco_evaluation.py +++ b/sahi/scripts/coco_evaluation.py @@ -1,9 +1,8 @@ import itertools import json -import warnings from collections import OrderedDict from pathlib import Path -from typing import List, Union +from typing import List, Literal, Optional, Union import fire import numpy as np @@ -58,22 +57,23 @@ def _cocoeval_summarize( def evaluate_core( - dataset_path, - result_path, + dataset_path: str, + result_path: str, + COCO: type, + COCOeval: type, metric: str = "bbox", classwise: bool = False, max_detections: int = 500, iou_thrs=None, metric_items=None, - out_dir: str = None, + out_dir: Union[str, Path, None] = None, areas: List[int] = [1024, 9216, 10000000000], - COCO=None, - COCOeval=None, ): """Evaluation in COCO protocol. Args: dataset_path (str): COCO dataset json path. result_path (str): COCO result json path. + COCO, COCOeval: Pass COCO and COCOeval class after safely imported metric (str | list[str]): Metrics to be evaluated. Options are 'bbox', 'segm', 'proposal'. classwise (bool): Whether to evaluating the AP for each class. @@ -282,14 +282,14 @@ def evaluate_core( catName=nm["name"], nameStrLen=max_cat_name_len, ) - results_per_category.append((f'{metric}_{nm["name"]}_mAP', f"{float(ap):0.3f}")) - results_per_category.append((f'{metric}_{nm["name"]}_mAP_s', f"{float(ap_s):0.3f}")) - results_per_category.append((f'{metric}_{nm["name"]}_mAP_m', f"{float(ap_m):0.3f}")) - results_per_category.append((f'{metric}_{nm["name"]}_mAP_l', f"{float(ap_l):0.3f}")) - results_per_category.append((f'{metric}_{nm["name"]}_mAP50', f"{float(ap50):0.3f}")) - results_per_category.append((f'{metric}_{nm["name"]}_mAP50_s', f"{float(ap50_s):0.3f}")) - results_per_category.append((f'{metric}_{nm["name"]}_mAP50_m', f"{float(ap50_m):0.3f}")) - results_per_category.append((f'{metric}_{nm["name"]}_mAP50_l', f"{float(ap50_l):0.3f}")) + results_per_category.append((f"{metric}_{nm['name']}_mAP", f"{float(ap):0.3f}")) + results_per_category.append((f"{metric}_{nm['name']}_mAP_s", f"{float(ap_s):0.3f}")) + results_per_category.append((f"{metric}_{nm['name']}_mAP_m", f"{float(ap_m):0.3f}")) + results_per_category.append((f"{metric}_{nm['name']}_mAP_l", f"{float(ap_l):0.3f}")) + results_per_category.append((f"{metric}_{nm['name']}_mAP50", f"{float(ap50):0.3f}")) + results_per_category.append((f"{metric}_{nm['name']}_mAP50_s", f"{float(ap50_s):0.3f}")) + results_per_category.append((f"{metric}_{nm['name']}_mAP50_m", f"{float(ap50_m):0.3f}")) + results_per_category.append((f"{metric}_{nm['name']}_mAP50_l", f"{float(ap50_l):0.3f}")) num_columns = min(6, len(results_per_category) * 2) results_flatten = list(itertools.chain(*results_per_category)) @@ -330,11 +330,11 @@ def evaluate_core( def evaluate( dataset_json_path: str, result_json_path: str, - out_dir: str = None, - type: str = "bbox", + out_dir: Optional[str] = None, + type: Literal["bbox", "segm"] = "bbox", classwise: bool = False, max_detections: int = 500, - iou_thrs: Union[List[float], float] = None, + iou_thrs: Union[List[float], float, None] = None, areas: List[int] = [1024, 9216, 10000000000], return_dict: bool = False, ): @@ -355,17 +355,17 @@ def evaluate( from pycocotools.cocoeval import COCOeval except ModuleNotFoundError: raise ModuleNotFoundError( - 'Please run "pip install -U pycocotools" ' "to install pycocotools first for coco evaluation." + 'Please run "pip install -U pycocotools" to install pycocotools first for coco evaluation.' ) # perform coco eval result = evaluate_core( - dataset_json_path, - result_json_path, - type, - classwise, - max_detections, - iou_thrs, + dataset_path=dataset_json_path, + result_path=result_json_path, + metric=type, + classwise=classwise, + max_detections=max_detections, + iou_thrs=iou_thrs, out_dir=out_dir, areas=areas, COCO=COCO, diff --git a/sahi/scripts/slice_coco.py b/sahi/scripts/slice_coco.py index d5606a2ed..dd35af5d6 100644 --- a/sahi/scripts/slice_coco.py +++ b/sahi/scripts/slice_coco.py @@ -38,11 +38,11 @@ def slice( for slice_size in slice_size_list: # in format: train_images_512_01 output_images_folder_name = ( - Path(dataset_json_path).stem + f"_images_{str(slice_size)}_{str(overlap_ratio).replace('.','')}" + Path(dataset_json_path).stem + f"_images_{str(slice_size)}_{str(overlap_ratio).replace('.', '')}" ) output_images_dir = str(Path(output_dir) / output_images_folder_name) sliced_coco_name = Path(dataset_json_path).name.replace( - ".json", f"_{str(slice_size)}_{str(overlap_ratio).replace('.','')}" + ".json", f"_{str(slice_size)}_{str(overlap_ratio).replace('.', '')}" ) coco_dict, coco_path = slice_coco( coco_annotation_file_path=dataset_json_path, diff --git a/sahi/slicing.py b/sahi/slicing.py index 1b756985a..220ff024b 100644 --- a/sahi/slicing.py +++ b/sahi/slicing.py @@ -4,7 +4,6 @@ import concurrent.futures import logging import os -import time from pathlib import Path from typing import Dict, List, Optional, Sequence, Tuple, Union diff --git a/sahi/utils/coco.py b/sahi/utils/coco.py index cfc5c6de5..031d97bc0 100644 --- a/sahi/utils/coco.py +++ b/sahi/utils/coco.py @@ -5,17 +5,17 @@ import copy import logging import os +import random import threading +import warnings from collections import Counter, defaultdict from dataclasses import dataclass from multiprocessing import Pool from pathlib import Path from threading import Lock, Thread -from typing import Dict, List, Optional, Set, Union +from typing import Dict, List, Literal, Optional, Set, Union import numpy as np -from shapely import GeometryCollection, MultiPolygon, Polygon -from shapely.validation import make_valid from tqdm import tqdm from sahi.utils.file import is_colab, load_json, save_json @@ -34,7 +34,7 @@ class CocoCategory: COCO formatted category. """ - def __init__(self, id=None, name=None, supercategory=None): + def __init__(self, id: int = 0, name: Optional[str] = None, supercategory: Optional[str] = None): self.id = int(id) self.name = name self.supercategory = supercategory if supercategory else name @@ -184,10 +184,10 @@ def from_shapely_annotation( def __init__( self, + category_id: int, + category_name: Optional[str] = None, segmentation=None, - bbox=None, - category_id=None, - category_name=None, + bbox: Optional[List[int]] = None, image_id=None, iscrowd=0, ): @@ -220,6 +220,8 @@ def __init__( if self._segmentation: shapely_annotation = ShapelyAnnotation.from_coco_segmentation(segmentation=self._segmentation) else: + if not bbox: + raise TypeError("Coco bounding box not set") shapely_annotation = ShapelyAnnotation.from_coco_bbox(bbox=bbox) self._shapely_annotation = shapely_annotation @@ -229,7 +231,7 @@ def get_sliced_coco_annotation(self, slice_bbox: List[int]): return CocoAnnotation.from_shapely_annotation( intersection_shapely_annotation, category_id=self.category_id, - category_name=self.category_name, + category_name=self.category_name or "", iscrowd=self.iscrowd, ) @@ -315,7 +317,8 @@ def json(self): } def serialize(self): - print(".serialize() is deprectaed, use .json instead") + warnings.warn("Use json property instead of serialize method", DeprecationWarning, stacklevel=2) + return self.json def __repr__(self): return f"""CocoAnnotation< @@ -419,8 +422,8 @@ def __init__( self, segmentation=None, bbox=None, - category_id=None, - category_name=None, + category_id: int = 0, + category_name: str = "", image_id=None, score=None, iscrowd=0, @@ -467,7 +470,7 @@ def json(self): } def serialize(self): - print(".serialize() is deprectaed, use .json instead") + warnings.warn("Use json property instead of serialize method", DeprecationWarning, stacklevel=2) def __repr__(self): return f"""CocoPrediction< @@ -489,9 +492,9 @@ class CocoVidAnnotation(CocoAnnotation): def __init__( self, - bbox=None, - category_id=None, - category_name=None, + category_id: int, + category_name: str, + bbox: List[int], image_id=None, instance_id=None, iscrowd=0, @@ -568,7 +571,7 @@ def from_coco_image_dict(cls, image_dict): width=image_dict["width"], ) - def __init__(self, file_name: str, height: int, width: int, id: int = None): + def __init__(self, file_name: str, height: int, width: int, id: Optional[int] = None): """ Creates CocoImage object @@ -728,10 +731,10 @@ class CocoVideo: def __init__( self, name: str, - id: int = None, - fps: float = None, - height: int = None, - width: int = None, + id: Optional[int] = None, + fps: Optional[float] = None, + height: Optional[int] = None, + width: Optional[int] = None, ): """ Creates CocoVideo object @@ -800,12 +803,12 @@ def __repr__(self): class Coco: def __init__( self, - name=None, - image_dir=None, - remapping_dict=None, - ignore_negative_samples=False, - clip_bboxes_to_img_dims=False, - image_id_setting="auto", + name: Optional[str] = None, + image_dir: Optional[str] = None, + remapping_dict: Optional[Dict[int, int]] = None, + ignore_negative_samples: bool = False, + clip_bboxes_to_img_dims: bool = False, + image_id_setting: Literal["auto", "manual"] = "auto", ): """ Creates Coco object. @@ -826,11 +829,11 @@ def __init__( """ if image_id_setting not in ["auto", "manual"]: raise ValueError("image_id_setting must be either 'auto' or 'manual'") - self.name = name - self.image_dir = image_dir - self.remapping_dict = remapping_dict + self.name: Optional[str] = name + self.image_dir: Optional[str] = image_dir + self.remapping_dict: Optional[Dict[int, int]] = remapping_dict self.ignore_negative_samples = ignore_negative_samples - self.categories = [] + self.categories: List[CocoCategory] = [] self.images = [] self._stats = None self.clip_bboxes_to_img_dims = clip_bboxes_to_img_dims @@ -882,7 +885,7 @@ def add_image(self, image): raise ValueError("image id should be manually set for image_id_setting='manual'") self.images.append(image) - def update_categories(self, desired_name2id, update_image_filenames=False): + def update_categories(self, desired_name2id: Dict[str, int], update_image_filenames: bool = False): """ Rearranges category mapping of given COCO object based on given desired_name2id. Can also be used to filter some of the categories. @@ -894,7 +897,7 @@ def update_categories(self, desired_name2id, update_image_filenames=False): If True, updates coco image file_names with absolute file paths. """ # init vars - currentid2desiredid_mapping = {} + currentid2desiredid_mapping: Dict[int, Optional[int]] = {} updated_coco = Coco( name=self.name, image_dir=self.image_dir, @@ -902,9 +905,12 @@ def update_categories(self, desired_name2id, update_image_filenames=False): ignore_negative_samples=self.ignore_negative_samples, ) # create category id mapping (currentid2desiredid_mapping) - for coco_category in copy.deepcopy(self.categories): + for coco_category in self.categories: current_category_id = coco_category.id current_category_name = coco_category.name + if not current_category_name: + logger.warning("no category name provided to update categories") + continue if current_category_name in desired_name2id.keys(): currentid2desiredid_mapping[current_category_id] = desired_name2id[current_category_name] else: @@ -922,7 +928,10 @@ def update_categories(self, desired_name2id, update_image_filenames=False): # update filename to abspath file_name_is_abspath = True if os.path.abspath(coco_image.file_name) == coco_image.file_name else False if update_image_filenames and not file_name_is_abspath: - updated_coco_image.file_name = str(Path(os.path.abspath(self.image_dir)) / coco_image.file_name) + if not self.image_dir: + logger.error("image directory not set") + else: + updated_coco_image.file_name = str(Path(os.path.abspath(self.image_dir)) / coco_image.file_name) # update annotations for coco_annotation in coco_image.annotations: current_category_id = coco_annotation.category_id @@ -936,7 +945,6 @@ def update_categories(self, desired_name2id, update_image_filenames=False): updated_coco.add_image(updated_coco_image) # overwrite instance - self.__class__ = updated_coco.__class__ self.__dict__ = updated_coco.__dict__ def merge(self, coco, desired_name2id=None, verbose=1): @@ -1035,7 +1043,7 @@ def from_coco_dict_or_path( raise TypeError("coco_dict_or_path should be a dict or str") # load coco dict if path is given - if type(coco_dict_or_path) == str: + if isinstance(coco_dict_or_path, str): coco_dict = load_json(coco_dict_or_path) else: coco_dict = coco_dict_or_path @@ -1117,6 +1125,9 @@ def fill_image_id_set(start, finish, image_list, _image_id_set, _image_id_to_ann image_id_set.add(image_id) # select annotations of the image annotation_list = image_id_to_annotation_list[image_id] + # TODO: coco_annotation_dict is of type CocoAnnotation according to how image_id_to_annotation_list + # was created. Either image_id_to_annotation_list is not defined correctly or the following + # loop is wrong as it expects a dict. for coco_annotation_dict in annotation_list: # apply category remapping if remapping_dict is provided if coco.remapping_dict is not None: @@ -1255,7 +1266,8 @@ def split_coco_as_train_val(self, train_split_rate=0.9, numpy_seed=0): Args: train_split_rate: float numpy_seed: int - To fix the numpy seed. + random seed. Actually, this doesn't use numpy, but the random package + from the standard library, but it is called numpy for compatibility. Returns: result : dict @@ -1264,13 +1276,11 @@ def split_coco_as_train_val(self, train_split_rate=0.9, numpy_seed=0): "val_coco": "", } """ - # fix numpy numpy seed - np.random.seed(numpy_seed) - # divide images num_images = len(self.images) shuffled_images = copy.deepcopy(self.images) - np.random.shuffle(shuffled_images) + random.seed(numpy_seed) + random.shuffle(shuffled_images) num_train = int(num_images * train_split_rate) train_images = shuffled_images[:num_train] val_images = shuffled_images[num_train:] @@ -1293,7 +1303,14 @@ def split_coco_as_train_val(self, train_split_rate=0.9, numpy_seed=0): "val_coco": val_coco, } - def export_as_yolov5(self, output_dir, train_split_rate=1, numpy_seed=0, mp=False, disable_symlink=False): + def export_as_yolov5( + self, + output_dir: Union[str, Path], + train_split_rate: float = 1.0, + numpy_seed: int = 0, + mp: bool = False, + disable_symlink: bool = False, + ): """ Exports current COCO dataset in ultralytics/yolov5 format. Creates train val folders with image symlinks and txt files and a data yaml file. @@ -1317,7 +1334,7 @@ def export_as_yolov5(self, output_dir, train_split_rate=1, numpy_seed=0, mp=Fals import yaml except ImportError: raise ImportError( - 'Please run "pip install -U pyyaml" ' "to install yaml first for yolov5 formatted exporting." + 'Please run "pip install -U pyyaml" to install yaml first for yolov5 formatted exporting.' ) # set split_mode @@ -1384,7 +1401,7 @@ def export_as_yolov5(self, output_dir, train_split_rate=1, numpy_seed=0, mp=Fals with open(yaml_path, "w") as outfile: yaml.dump(data, outfile, default_flow_style=None) - def get_subsampled_coco(self, subsample_ratio: int = 2, category_id: int = None): + def get_subsampled_coco(self, subsample_ratio: int = 2, category_id: Optional[int] = None): """ Subsamples images with subsample_ratio and returns as sahi.utils.coco.Coco object. @@ -1407,9 +1424,9 @@ def get_subsampled_coco(self, subsample_ratio: int = 2, category_id: int = None) if category_id is not None: # get images that contain given category id images_that_contain_category: List[CocoImage] = [] + annotation: CocoAnnotation for image in self.images: category_id_to_contains = defaultdict(lambda: 0) - annotation: CocoAnnotation for annotation in image.annotations: category_id_to_contains[annotation.category_id] = 1 if category_id_to_contains[category_id]: @@ -1427,7 +1444,6 @@ def get_subsampled_coco(self, subsample_ratio: int = 2, category_id: int = None) images_that_doesnt_contain_category: List[CocoImage] = [] for image in self.images: category_id_to_contains = defaultdict(lambda: 0) - annotation: CocoAnnotation for annotation in image.annotations: category_id_to_contains[annotation.category_id] = 1 if category_id_to_contains[category_id]: @@ -1453,7 +1469,7 @@ def get_subsampled_coco(self, subsample_ratio: int = 2, category_id: int = None) return subsampled_coco - def get_upsampled_coco(self, upsample_ratio: int = 2, category_id: int = None): + def get_upsampled_coco(self, upsample_ratio: int = 2, category_id: Optional[int] = None): """ Upsamples images with upsample_ratio and returns as sahi.utils.coco.Coco object. @@ -1594,7 +1610,7 @@ def export_yolov5_images_and_txts_from_coco_object( disable_symlink: bool If True, symlinks are not created. Instead images are copied. """ - logger.info("generating image symlinks and annotation files for yolov5..."), + logger.info("generating image symlinks and annotation files for yolov5...") # symlink is not supported in colab if is_colab() and not disable_symlink: logger.warning("symlink is not supported in colab, disabling it...") @@ -1786,7 +1802,7 @@ def update_categories_from_file(desired_name2id: dict, coco_path: str, save_path save_json(coco_target, save_path) -def merge(coco_dict1: dict, coco_dict2: dict, desired_name2id: dict = None) -> dict: +def merge(coco_dict1: dict, coco_dict2: dict, desired_name2id: Optional[dict] = None) -> dict: """ Combines 2 coco formatted annotations dicts, and returns the combined coco dict. @@ -1911,9 +1927,7 @@ def merge_from_file(coco_path1: str, coco_path2: str, save_path: str): save_json(merged_coco_dict, save_path) -def get_imageid2annotationlist_mapping( - coco_dict: dict, -) -> Dict[int, List[CocoAnnotation]]: +def get_imageid2annotationlist_mapping(coco_dict: dict) -> Dict[int, List[CocoAnnotation]]: """ Get image_id to annotationlist mapping for faster indexing. @@ -1941,7 +1955,7 @@ def get_imageid2annotationlist_mapping( } """ image_id_to_annotation_list: Dict = defaultdict(list) - print("indexing coco dataset annotations...") + logger.debug("indexing coco dataset annotations...") for annotation in coco_dict["annotations"]: image_id = annotation["image_id"] image_id_to_annotation_list[image_id].append(annotation) @@ -2145,7 +2159,7 @@ class DatasetClassCounts: total_images: int def frequencies(self): - """calculates the frequenct of images that contain each category""" + """calculates the frequency of images that contain each category""" return {cid: count / self.total_images for cid, count in self.counts.items()} def __add__(self, o): @@ -2222,7 +2236,7 @@ def add_categories_from_coco_category_list(self, coco_category_list): self.add_category(CocoCategory.from_coco_category(coco_category)) - def add_category(self, category): + def add_category(self, category: CocoCategory): """ Adds category to this CocoVid instance @@ -2230,8 +2244,8 @@ def add_category(self, category): category: CocoCategory """ - if type(category) != CocoCategory: - raise TypeError("category must be a CocoCategory instance") + if not isinstance(category, CocoCategory): + raise TypeError("category must be a CocoCategory instance") # type: ignore self.categories.append(category) @property @@ -2248,7 +2262,7 @@ def category_mapping(self): category_mapping[category.id] = category.name return category_mapping - def add_video(self, video): + def add_video(self, video: CocoVideo): """ Adds video to this CocoVid instance @@ -2256,8 +2270,8 @@ def add_video(self, video): video: CocoVideo """ - if type(video) != CocoVideo: - raise TypeError("video must be a CocoVideo instance") + if not isinstance(video, CocoVideo): + raise TypeError("video must be a CocoVideo instance") # type: ignore self.videos.append(video) @property @@ -2304,7 +2318,9 @@ def json(self): return coco_dict -def remove_invalid_coco_results(result_list_or_path: Union[List, str], dataset_dict_or_path: Union[Dict, str] = None): +def remove_invalid_coco_results( + result_list_or_path: Union[List, str], dataset_dict_or_path: Union[Dict, str, None] = None +): """ Removes invalid predictions from coco result such as: - negative bbox value @@ -2321,7 +2337,7 @@ def remove_invalid_coco_results(result_list_or_path: Union[List, str], dataset_d elif isinstance(result_list_or_path, list): result_list = result_list_or_path else: - raise TypeError('incorrect type for "result_list_or_path"') + raise TypeError('incorrect type for "result_list_or_path"') # type: ignore # prepare image info from coco dataset if dataset_dict_or_path is not None: @@ -2330,7 +2346,7 @@ def remove_invalid_coco_results(result_list_or_path: Union[List, str], dataset_d elif isinstance(dataset_dict_or_path, dict): dataset_dict = dataset_dict_or_path else: - raise TypeError('incorrect type for "dataset_dict"') + raise TypeError('incorrect type for "dataset_dict"') # type: ignore image_id_to_height = {} image_id_to_width = {} for coco_image in dataset_dict["images"]: @@ -2363,8 +2379,8 @@ def remove_invalid_coco_results(result_list_or_path: Union[List, str], dataset_d def export_coco_as_yolov5( output_dir: str, - train_coco: Coco = None, - val_coco: Coco = None, + train_coco: Optional[Coco] = None, + val_coco: Optional[Coco] = None, train_split_rate: float = 0.9, numpy_seed=0, disable_symlink=False, @@ -2394,7 +2410,7 @@ def export_coco_as_yolov5( try: import yaml except ImportError: - raise ImportError('Please run "pip install -U pyyaml" ' "to install yaml first for yolov5 formatted exporting.") + raise ImportError('Please run "pip install -U pyyaml" to install yaml first for yolov5 formatted exporting.') # set split_mode if train_coco and not val_coco: @@ -2431,6 +2447,7 @@ def export_coco_as_yolov5( mp=False, disable_symlink=disable_symlink, ) + assert val_coco, "Validation Coco object not set" export_yolov5_images_and_txts_from_coco_object( output_dir=val_dir, coco=val_coco, @@ -2484,7 +2501,7 @@ def export_coco_as_yolov5_via_yml( try: import yaml except ImportError: - raise ImportError('Please run "pip install -U pyyaml" ' "to install yaml first for yolov5 formatted exporting.") + raise ImportError('Please run "pip install -U pyyaml" to install yaml first for yolov5 formatted exporting.') with open(yml_path, "r") as stream: config_dict = yaml.safe_load(stream) diff --git a/sahi/utils/cv.py b/sahi/utils/cv.py index 693097aa9..19daf68ba 100644 --- a/sahi/utils/cv.py +++ b/sahi/utils/cv.py @@ -2,10 +2,11 @@ # Code written by Fatih C Akyon, 2020. import copy +import logging import os import random import time -from typing import List, Optional, Union +from typing import Generator, List, Optional, Tuple, Union import cv2 import numpy as np @@ -14,6 +15,8 @@ from sahi.utils.file import Path +logger = logging.getLogger("__name__") + IMAGE_EXTENSIONS_LOSSY = [".jpg", ".jpeg"] IMAGE_EXTENSIONS_LOSSLESS = [".png", ".tiff", ".bmp"] IMAGE_EXTENSIONS = IMAGE_EXTENSIONS_LOSSY + IMAGE_EXTENSIONS_LOSSLESS @@ -129,7 +132,7 @@ def convert_image_to(read_path, extension: str = "jpg", grayscale: bool = False) grayscale (bool, optional): Whether to convert the image to grayscale. Defaults to False. """ image = cv2.imread(read_path) - pre, ext = os.path.splitext(read_path) + pre, _ = os.path.splitext(read_path) if grayscale: image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) pre = pre + "_gray" @@ -155,12 +158,13 @@ def read_large_image(image_path: str): # convert to rgb (cv2 reads in bgr) img_cv2 = cv2.imread(image_path, 1) image0 = cv2.cvtColor(img_cv2, cv2.COLOR_BGR2RGB) - except: + except Exception as e: + logger.debug(f"OpenCV failed reading image with error {e}, trying skimage instead") try: import skimage.io except ImportError: raise ImportError( - 'Please run "pip install -U scikit-image" ' "to install scikit-image first for large image handling." + 'Please run "pip install -U scikit-image" to install scikit-image first for large image handling.' ) image0 = skimage.io.imread(image_path, as_grey=False).astype(np.uint8) # [::-1] use_cv2 = False @@ -205,11 +209,12 @@ def read_image_as_pil(image: Union[Image.Image, str, np.ndarray], exif_fix: bool # read image if str image path is provided try: image_pil = Image.open( - requests.get(image, stream=True).raw if str(image).startswith("http") else image + requests.get(image, stream=True).raw if str(image).startswith("http") else image # type: ignore ).convert("RGB") if exif_fix: image_pil = exif_transpose(image_pil) - except: # handle large/tiff image reading + except Exception as e: # handle large/tiff image reading + logger.debug(f"OpenCV failed reading image with error {e}, trying skimage instead") try: import skimage.io except ImportError: @@ -228,7 +233,7 @@ def read_image_as_pil(image: Union[Image.Image, str, np.ndarray], exif_fix: bool image = image[:, :, ::-1] image_pil = Image.fromarray(image) else: - raise TypeError("read image with 'pillow' using 'Image.open()'") + raise TypeError("read image with 'pillow' using 'Image.open()'") # pyright: ignore[reportUnreachable] return image_pil @@ -256,7 +261,7 @@ def select_random_color(): return colors[random.randrange(0, 10)] -def apply_color_mask(image: np.ndarray, color: tuple): +def apply_color_mask(image: np.ndarray, color: Tuple[int, int, int]): """ Applies color mask to given input image. @@ -282,7 +287,7 @@ def get_video_reader( frame_skip_interval: int, export_visual: bool = False, view_visual: bool = False, -): +) -> Tuple[Generator[Image.Image, None, None], Optional[cv2.VideoWriter], str, int]: """ Creates OpenCV video capture object from given video file path. @@ -308,9 +313,12 @@ def get_video_reader( num_frames /= frame_skip_interval + 1 num_frames = int(num_frames) - def read_video_frame(video_capture, frame_skip_interval): + def read_video_frame(video_capture, frame_skip_interval) -> Generator[Image.Image, None, None]: if view_visual: - cv2.imshow("Prediction of {}".format(str(video_file_name)), cv2.WINDOW_AUTOSIZE) + window_name = "Prediction of {}".format(str(video_file_name)) + cv2.namedWindow(window_name, cv2.WINDOW_AUTOSIZE) + default_image = np.zeros((480, 640, 3), dtype=np.uint8) + cv2.imshow(window_name, default_image) while video_capture.isOpened: frame_num = video_capture.get(cv2.CAP_PROP_POS_FRAMES) @@ -365,7 +373,7 @@ def read_video_frame(video_capture, frame_skip_interval): w = int(video_capture.get(cv2.CAP_PROP_FRAME_WIDTH)) h = int(video_capture.get(cv2.CAP_PROP_FRAME_HEIGHT)) size = (w, h) - fourcc = cv2.VideoWriter_fourcc(*"mp4v") + fourcc = cv2.VideoWriter_fourcc(*"mp4v") # pyright: ignore[reportAttributeAccessIssue] video_writer = cv2.VideoWriter(os.path.join(save_dir, video_file_name), fourcc, fps, size) else: video_writer = None @@ -378,10 +386,10 @@ def visualize_prediction( boxes: List[List], classes: List[str], masks: Optional[List[np.ndarray]] = None, - rect_th: float = None, - text_size: float = None, - text_th: float = None, - color: tuple = None, + rect_th: Optional[int] = None, + text_size: Optional[float] = None, + text_th: Optional[int] = None, + color: Optional[tuple] = None, hide_labels: bool = False, output_dir: Optional[str] = None, file_name: Optional[str] = "prediction_visual", @@ -395,9 +403,9 @@ def visualize_prediction( boxes (List[List]): List of bounding boxes coordinates. classes (List[str]): List of class labels corresponding to each bounding box. masks (Optional[List[np.ndarray]], optional): List of masks corresponding to each bounding box. Defaults to None. - rect_th (float, optional): Thickness of the bounding box rectangle. Defaults to None. + rect_th (int, optional): Thickness of the bounding box rectangle. Defaults to None. text_size (float, optional): Size of the text for class labels. Defaults to None. - text_th (float, optional): Thickness of the text for class labels. Defaults to None. + text_th (int, optional): Thickness of the text for class labels. Defaults to None. color (tuple, optional): Color of the bounding box and text. Defaults to None. hide_labels (bool, optional): Whether to hide the class labels. Defaults to False. output_dir (Optional[str], optional): Output directory to save the visualization. Defaults to None. @@ -422,7 +430,9 @@ def visualize_prediction( text_size = text_size or rect_th / 3 # add masks to image if present - if masks is not None: + if masks is not None and color is None: + logger.error("Cannot add mask, no color tuple given") + elif masks is not None and color is not None: for mask in masks: # deepcopy mask so that original is not altered mask = copy.deepcopy(mask) @@ -438,7 +448,13 @@ def visualize_prediction( # set color if colors is not None: - color = colors(class_) + mycolor = colors(class_) + elif color is not None: + mycolor = color + else: + logger.error("color cannot be defined") + continue + # set bbox points point1, point2 = [int(box[0]), int(box[1])], [int(box[2]), int(box[3])] # visualize boxes @@ -446,7 +462,7 @@ def visualize_prediction( image, point1, point2, - color=color, + color=mycolor, thickness=rect_th, ) @@ -459,7 +475,7 @@ def visualize_prediction( outside = point1[1] - box_height - 3 >= 0 # label fits outside box point2 = point1[0] + box_width, point1[1] - box_height - 3 if outside else point1[1] + box_height + 3 # add bounding box text - cv2.rectangle(image, point1, point2, color, -1, cv2.LINE_AA) # filled + cv2.rectangle(image, point1, point2, color or (0, 0, 0), -1, cv2.LINE_AA) # filled cv2.putText( image, label, @@ -473,7 +489,7 @@ def visualize_prediction( # create output folder if not present Path(output_dir).mkdir(parents=True, exist_ok=True) # save inference result - save_path = os.path.join(output_dir, file_name + ".png") + save_path = os.path.join(output_dir, (file_name or "unknown") + ".png") cv2.imwrite(save_path, cv2.cvtColor(image, cv2.COLOR_RGB2BGR)) elapsed_time = time.time() - elapsed_time @@ -481,17 +497,17 @@ def visualize_prediction( def visualize_object_predictions( - image: np.array, + image: np.ndarray, object_prediction_list, - rect_th: int = None, - text_size: float = None, - text_th: float = None, - color: tuple = None, + rect_th: Optional[int] = None, + text_size: Optional[float] = None, + text_th: Optional[int] = None, + color: Optional[tuple] = None, hide_labels: bool = False, hide_conf: bool = False, output_dir: Optional[str] = None, - file_name: str = "prediction_visual", - export_format: str = "png", + file_name: Optional[str] = "prediction_visual", + export_format: Optional[str] = "png", ): """ Visualizes prediction category names, bounding boxes over the source image @@ -536,7 +552,7 @@ def visualize_object_predictions( if colors is not None: color = colors(object_prediction.category.id) # draw mask - rgb_mask = apply_color_mask(mask, color) + rgb_mask = apply_color_mask(mask, color or (0, 0, 0)) image = cv2.addWeighted(image, 1, rgb_mask, 0.6, 0) # add bboxes to image if present @@ -558,7 +574,7 @@ def visualize_object_predictions( image, point1, point2, - color=color, + color=color or (0, 0, 0), thickness=rect_th, ) @@ -575,7 +591,7 @@ def visualize_object_predictions( outside = point1[1] - box_height - 3 >= 0 # label fits outside box point2 = point1[0] + box_width, point1[1] - box_height - 3 if outside else point1[1] + box_height + 3 # add bounding box text - cv2.rectangle(image, point1, point2, color, -1, cv2.LINE_AA) # filled + cv2.rectangle(image, point1, point2, color or (0, 0, 0), -1, cv2.LINE_AA) # filled cv2.putText( image, label, @@ -591,7 +607,7 @@ def visualize_object_predictions( # export image with predictions Path(output_dir).mkdir(parents=True, exist_ok=True) # save inference result - save_path = str(Path(output_dir) / (file_name + "." + export_format)) + save_path = str(Path(output_dir) / ((file_name or "") + "." + (export_format or ""))) cv2.imwrite(save_path, cv2.cvtColor(image, cv2.COLOR_RGB2BGR)) elapsed_time = time.time() - elapsed_time @@ -610,7 +626,7 @@ def get_coco_segmentation_from_bool_mask(bool_mask): # Generate polygons from mask mask = np.squeeze(bool_mask) mask = mask.astype(np.uint8) - mask = cv2.copyMakeBorder(mask, 1, 1, 1, 1, cv2.BORDER_CONSTANT, value=0) + mask = cv2.copyMakeBorder(mask, 1, 1, 1, 1, cv2.BORDER_CONSTANT, value=(0, 0, 0)) polygons = cv2.findContours(mask, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE, offset=(-1, -1)) polygons = polygons[0] if len(polygons) == 2 else polygons[1] # Convert polygon to coco segmentation @@ -638,7 +654,7 @@ def get_bool_mask_from_coco_segmentation(coco_segmentation: List[List[float]], w size = [height, width] points = [np.array(point).reshape(-1, 2).round().astype(int) for point in coco_segmentation] bool_mask = np.zeros(size) - bool_mask = cv2.fillPoly(bool_mask, points, 1) + bool_mask = cv2.fillPoly(bool_mask, points, (1.0,)) bool_mask.astype(bool) return bool_mask @@ -730,8 +746,8 @@ def ipython_display(image: np.ndarray): image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) _, ret = cv2.imencode(".png", image) - i = IPython.display.Image(data=ret) - IPython.display.display(i) + i = IPython.display.Image(data=ret) # type: ignore + IPython.display.display(i) # type: ignore def exif_transpose(image: Image.Image) -> Image.Image: @@ -749,13 +765,13 @@ def exif_transpose(image: Image.Image) -> Image.Image: orientation = exif.get(0x0112, 1) # default 1 if orientation > 1: method = { - 2: Image.FLIP_LEFT_RIGHT, - 3: Image.ROTATE_180, - 4: Image.FLIP_TOP_BOTTOM, - 5: Image.TRANSPOSE, - 6: Image.ROTATE_270, - 7: Image.TRANSVERSE, - 8: Image.ROTATE_90, + 2: Image.Transpose.FLIP_LEFT_RIGHT, + 3: Image.Transpose.ROTATE_180, + 4: Image.Transpose.FLIP_TOP_BOTTOM, + 5: Image.Transpose.TRANSPOSE, + 6: Image.Transpose.ROTATE_270, + 7: Image.Transpose.TRANSVERSE, + 8: Image.Transpose.ROTATE_90, }.get(orientation) if method is not None: image = image.transpose(method) diff --git a/sahi/utils/file.py b/sahi/utils/file.py index be0696120..3acc4fc5f 100644 --- a/sahi/utils/file.py +++ b/sahi/utils/file.py @@ -10,7 +10,7 @@ import urllib.request import zipfile from pathlib import Path -from typing import Optional +from typing import List, Optional, Tuple, Union import numpy as np @@ -75,7 +75,7 @@ def list_files( directory: str, contains: list = [".json"], verbose: int = 1, -) -> list: +) -> List[str]: """ Walk given directory and return a list of file path with desired extension @@ -95,12 +95,12 @@ def list_files( # define verboseprint verboseprint = print if verbose else lambda *a, **k: None - filepath_list = [] + filepath_list: List[str] = [] for file in os.listdir(directory): # check if filename contains any of the terms given in contains list if any(strtocheck in file.lower() for strtocheck in contains): - filepath = os.path.join(directory, file) + filepath = str(os.path.join(directory, file)) filepath_list.append(filepath) number_of_files = len(filepath_list) @@ -111,7 +111,7 @@ def list_files( return filepath_list -def list_files_recursively(directory: str, contains: list = [".json"], verbose: str = True) -> (list, list): +def list_files_recursively(directory: str, contains: list = [".json"], verbose: bool = True) -> Tuple[list, list]: """ Walk given directory recursively and return a list of file path with desired extension @@ -176,7 +176,7 @@ def get_file_extension(path: str): str: The file extension. """ - filename, file_extension = os.path.splitext(path) + _, file_extension = os.path.splitext(path) return file_extension @@ -186,8 +186,7 @@ def load_pickle(load_path): Example inputs: load_path: "dirname/coco.pickle" """ - # read from path - with open(load_path) as json_file: + with open(load_path, "rb") as json_file: data = pickle.load(json_file) return data @@ -224,7 +223,7 @@ def import_model_class(model_type, class_name): return class_ -def increment_path(path: str, exist_ok: bool = True, sep: str = "") -> str: +def increment_path(path: Union[str, Path], exist_ok: bool = True, sep: str = "") -> str: """ Increment path, i.e. runs/exp --> runs/exp{sep}0, runs/exp{sep}1 etc. diff --git a/sahi/utils/import_utils.py b/sahi/utils/import_utils.py index 62b7cb63d..a289ea103 100644 --- a/sahi/utils/import_utils.py +++ b/sahi/utils/import_utils.py @@ -36,7 +36,7 @@ def get_package_info(package_name: str, verbose: bool = True): return _is_available, _version -def print_enviroment_info(): +def print_environment_info(): _torch_available, _torch_version = get_package_info("torch") _torchvision_available, _torchvision_version = get_package_info("torchvision") _tensorflow_available, _tensorflow_version = get_package_info("tensorflow") diff --git a/sahi/utils/mmdet.py b/sahi/utils/mmdet.py index ff366066c..f602cdd25 100644 --- a/sahi/utils/mmdet.py +++ b/sahi/utils/mmdet.py @@ -174,8 +174,8 @@ def download_mmdet_config( str(config_path), ) - # dump final config as single file from mmengine import Config + # dump final config as single file config = Config.fromfile(main_config_path) config.dump(final_config_path) diff --git a/sahi/utils/shapely.py b/sahi/utils/shapely.py index 7380e9f7b..67864825e 100644 --- a/sahi/utils/shapely.py +++ b/sahi/utils/shapely.py @@ -1,7 +1,7 @@ # OBSS SAHI Tool # Code written by Fatih C Akyon, 2020. -from typing import List +from typing import List, Optional, Union from shapely.geometry import CAP_STYLE, JOIN_STYLE, GeometryCollection, MultiPolygon, Polygon, box from shapely.validation import make_valid @@ -89,7 +89,7 @@ def from_coco_segmentation(cls, segmentation, slice_bbox=None): return cls(multipolygon=shapely_multipolygon, slice_bbox=slice_bbox) @classmethod - def from_coco_bbox(cls, bbox: List[int], slice_bbox: List[int] = None): + def from_coco_bbox(cls, bbox: List[int], slice_bbox: Optional[List[int]] = None): """ Init ShapelyAnnotation from coco bbox. @@ -172,7 +172,7 @@ def to_coco_segmentation(self): x_coords = [x_coord - minx for x_coord in x_coords] y_coords = [y_coord - miny for y_coord in y_coords] # convert intersection to coco style segmentation annotation - coco_polygon = [None] * len(x_coords) * 2 + coco_polygon: list[Union[None, int]] = [None] * (len(x_coords) * 2) coco_polygon[0::2] = [int(coord) for coord in x_coords] coco_polygon[1::2] = [int(coord) for coord in y_coords] else: diff --git a/sahi/utils/torch.py b/sahi/utils/torch.py index 4146cd163..7c93c2345 100644 --- a/sahi/utils/torch.py +++ b/sahi/utils/torch.py @@ -3,61 +3,68 @@ import os +from typing import Any, Optional, Union import numpy as np +from PIL.Image import Image +from torch import Tensor, device -from sahi.utils.import_utils import is_available - -if is_available("torch"): +try: import torch -else: - torch = None + + has_torch_cuda = torch.cuda.is_available() + try: + has_torch_mps: bool = torch.backends.mps.is_available() # pyright: ignore[reportAttributeAccessIssue] + except Exception: + has_torch_mps = False + has_torch = True +except ImportError: + has_torch_cuda = False + has_torch_mps = False + has_torch = False def empty_cuda_cache(): - if is_torch_cuda_available(): + if has_torch_cuda: return torch.cuda.empty_cache() -def to_float_tensor(img): +def to_float_tensor(img: Union[np.ndarray, Image]) -> Tensor: """ Converts a PIL.Image (RGB) or numpy.ndarray (H x W x C) in the range [0, 255] to a torch.FloatTensor of shape (C x H x W). Args: - img: np.ndarray + img: PIL.Image or numpy array Returns: torch.tensor """ - - img = img.transpose((2, 0, 1)) - img = torch.from_numpy(np.array(img)).float() - if img.max() > 1: - img /= 255 - - return img + nparray: np.ndarray + if isinstance(img, np.ndarray): + nparray = img + else: + nparray = np.array(img) + nparray = nparray.transpose((2, 0, 1)) + tens = torch.from_numpy(np.array(nparray)).float() + if tens.max() > 1: + tens /= 255 + return tens -def torch_to_numpy(img): +def torch_to_numpy(img: Any) -> np.ndarray: img = img.numpy() if img.max() > 1: img /= 255 return img.transpose((1, 2, 0)) -def is_torch_cuda_available(): - if is_available("torch"): - return torch.cuda.is_available() - else: - return False - - -def select_device(device: str): +def select_device(device: Optional[str] = None) -> device: """ Selects torch device Args: - device: str - "cpu", "mps", "cuda", "cuda:0", "cuda:1", etc. + device: "cpu", "mps", "cuda", "cuda:0", "cuda:1", etc. + When no device string is given, the order of preference + to try is: cuda:0 > mps > cpu Returns: torch.device @@ -74,9 +81,9 @@ def select_device(device: str): elif device: # non-cpu device requested os.environ["CUDA_VISIBLE_DEVICES"] = device # set environment variable - must be before assert is_available() - if not cpu and not mps and is_torch_cuda_available(): # prefer GPU if available + if not cpu and not mps and has_torch_cuda: # prefer GPU if available arg = "cuda:0" - elif mps and getattr(torch, "has_mps", False) and torch.backends.mps.is_available(): # prefer MPS if available + elif mps and getattr(torch, "has_mps", False) and has_torch_mps: # prefer MPS if available arg = "mps" else: # revert to CPU arg = "cpu" diff --git a/sahi/utils/versions.py b/sahi/utils/versions.py deleted file mode 100644 index b0fd7a04c..000000000 --- a/sahi/utils/versions.py +++ /dev/null @@ -1,7 +0,0 @@ -import sys - -# The package importlib_metadata is in a different place, depending on the python version. -if sys.version_info < (3, 8): - import importlib_metadata -else: - import importlib.metadata as importlib_metadata diff --git a/sahi/utils/yolov8onnx.py b/sahi/utils/yolov8onnx.py index 9b20da403..99645a351 100644 --- a/sahi/utils/yolov8onnx.py +++ b/sahi/utils/yolov8onnx.py @@ -1,22 +1,23 @@ from pathlib import Path -from typing import Optional +from typing import List, Optional, Union import numpy as np from sahi.utils.ultralytics import download_yolov8n_model +# TODO: This class has no purpose, replace by just the constant class Yolov8ONNXTestConstants: YOLOV8N_ONNX_MODEL_PATH = "tests/data/models/yolov8/yolov8n.onnx" -def download_yolov8n_onnx_model(destination_path: Optional[str] = None, image_size: Optional[int] = 640): - if destination_path is None: - destination_path = Yolov8ONNXTestConstants.YOLOV8N_ONNX_MODEL_PATH - +def download_yolov8n_onnx_model( + destination_path: Union[str, Path] = Yolov8ONNXTestConstants.YOLOV8N_ONNX_MODEL_PATH, + image_size: Optional[int] = 640, +): destination_path = Path(destination_path) model_path = destination_path.parent / (destination_path.stem + ".pt") - download_yolov8n_model(model_path) + download_yolov8n_model(str(model_path)) from ultralytics import YOLO @@ -24,8 +25,8 @@ def download_yolov8n_onnx_model(destination_path: Optional[str] = None, image_si model.export(format="onnx") # , imgsz=image_size) -def non_max_supression(boxes: np.ndarray, scores: np.ndarray, iou_threshold: float) -> np.ndarray: - """Perform non-max supression. +def non_max_suppression(boxes: np.ndarray, scores: np.ndarray, iou_threshold: float) -> List[int]: + """Perform non-max suppression. Args: boxes: np.ndarray @@ -36,7 +37,7 @@ def non_max_supression(boxes: np.ndarray, scores: np.ndarray, iou_threshold: flo Maximum allowed overlap between bounding boxes. Returns: - np.ndarray: Filtered bounding boxes + list of box_ids of the kept bounding boxes """ # Sort by score sorted_indices = np.argsort(scores)[::-1] diff --git a/scripts/run_code_style.py b/scripts/run_code_style.py index 4159e6ee3..3a44d0b99 100644 --- a/scripts/run_code_style.py +++ b/scripts/run_code_style.py @@ -1,9 +1,14 @@ import sys +import warnings from scripts.utils import shell, validate_and_exit if __name__ == "__main__": arg = sys.argv[1] + warnings.warn( + "Please use 'ruff check' and 'ruff format' instead. Precede with 'uv run' to run in virtual environment. Remember to activate the pre-commit hook to do that automatically on every commit.", + DeprecationWarning, + ) if arg == "check": sts_flake = shell("flake8 . --config setup.cfg --select=E9,F63,F7,F82") diff --git a/scripts/utils.py b/scripts/utils.py index a0b70c300..1a1ee9291 100644 --- a/scripts/utils.py +++ b/scripts/utils.py @@ -35,7 +35,7 @@ def validate_and_exit(expected_out_status=0, **kwargs): click.secho(f"{component} failed.", fg="red") fail_count += 1 - print_console_centered(f"{len(kwargs)-fail_count} success, {fail_count} failure") + print_console_centered(f"{len(kwargs) - fail_count} success, {fail_count} failure") click.secho("\nTo fix formatting issues:", fg="yellow") click.secho("1. Install development dependencies:", fg="cyan") click.secho(' pip install -e ."[dev]"', fg="green") diff --git a/setup.cfg b/setup.cfg deleted file mode 100644 index eff6ff72c..000000000 --- a/setup.cfg +++ /dev/null @@ -1,11 +0,0 @@ -[flake8] -max-line-length = 119 -max-complexity = 18 -exclude =.git,__pycache__,docs/source/conf.py,build,dist -ignore = I101,I201,F401,F403,S001,D100,D101,D102,D103,D104,D105,D106,D107,D200,D205,D400,W504,D202,E203,E501,E722,W503,B006 -inline-quotes = " -statistics = true -count = true - -[mypy] -ignore_missing_imports = True diff --git a/setup.py b/setup.py deleted file mode 100644 index 755fba413..000000000 --- a/setup.py +++ /dev/null @@ -1,66 +0,0 @@ -import io -import os -import re - -import setuptools - - -def get_long_description(): - base_dir = os.path.abspath(os.path.dirname(__file__)) - with io.open(os.path.join(base_dir, "README.md"), encoding="utf-8") as f: - return f.read() - - -def get_requirements(): - with open("requirements.txt", encoding="utf8") as f: - return f.read().splitlines() - - -def get_version(): - current_dir = os.path.abspath(os.path.dirname(__file__)) - version_file = os.path.join(current_dir, "sahi", "__init__.py") - with io.open(version_file, encoding="utf-8") as f: - return re.search(r'^__version__ = [\'"]([^\'"]*)[\'"]', f.read(), re.M).group(1) - - -setuptools.setup( - name="sahi", - version=get_version(), - author="OBSS", - license="MIT", - description="A vision library for performing sliced inference on large images/small objects", - long_description=get_long_description(), - long_description_content_type="text/markdown", - url="https://github.com/obss/sahi", - packages=setuptools.find_packages(exclude=["demo", "docs", "resources", "tests", "scripts"]), - python_requires=">=3.6", - install_requires=get_requirements(), - extras_require={ - "tests": ["mmdet==3.0.0", "pycocotools==2.0.6"], - "dev": [ - "black==22.3.0", - "flake8==3.9.2", - "importlib-metadata>=1.1.0,<4.3;python_version<'3.8'", - "isort==5.9.2", - "jupyterlab==3.0.14", - ], - }, - classifiers=[ - "License :: OSI Approved :: MIT License", - "Operating System :: OS Independent", - "Intended Audience :: Developers", - "Intended Audience :: Science/Research", - "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.8", - "Programming Language :: Python :: 3.9", - "Programming Language :: Python :: 3.10", - "Programming Language :: Python :: 3.11", - "Topic :: Software Development :: Libraries", - "Topic :: Software Development :: Libraries :: Python Modules", - ], - entry_points={ - "console_scripts": [ - "sahi=sahi.cli:app", - ], - }, -) diff --git a/tests/check_commandline.sh b/tests/check_commandline.sh new file mode 100755 index 000000000..842b8dbe2 --- /dev/null +++ b/tests/check_commandline.sh @@ -0,0 +1,35 @@ +#!/bin/bash + +################################################################################## +# Checks, if the CLI works +################################################################################## + +source .venv/bin/activate + +set -e + +# predict mmdet +PYTHON_VERSION=$(python3 -c 'import sys; print(f"{sys.version_info.major}.{sys.version_info.minor}")') + +echo "mmcv" +if (($(echo "$PYTHON_VERSION < 3.11" | bc -l))); then + echo "mmcv: 1" + sahi predict --model_type mmdet --source tests/data/ --novisual --model_path tests/data/models/mmdet/yolox/yolox_tiny_8x8_300e_coco_20211124_171234-b4047906.pth --model_config_path tests/data/models/mmdet/yolox/yolox_tiny_8xb8-300e_coco.py --image_size 320 + echo "mmcv: 2" + sahi predict --model_type mmdet --source tests/data/coco_utils/terrain1.jpg --export_pickle --export_crop --model_path tests/data/models/mmdet/yolox/yolox_tiny_8x8_300e_coco_20211124_171234-b4047906.pth --model_config_path tests/data/models/mmdet/yolox/yolox_tiny_8xb8-300e_coco.py --image_size 320 + echo "mmcv: 3" + sahi predict --model_type mmdet --source tests/data/coco_utils/ --novisual --dataset_json_path tests/data/coco_utils/combined_coco.json --model_path tests/data/models/mmdet/yolox/yolox_tiny_8x8_300e_coco_20211124_171234-b4047906.pth --model_config_path tests/data/models/mmdet/yolox/yolox_tiny_8xb8-300e_coco.py --image_size 320 +else + echo "mmcv not available for Python version $PYTHON_VERSION" +fi + +sahi predict --no_sliced_prediction --model_type yolov5 --source tests/data/coco_utils/terrain1.jpg --novisual --model_path tests/data/models/yolov5/yolov5s6.pt --image_size 320 +sahi predict --model_type yolov5 --source tests/data/ --novisual --model_path tests/data/models/yolov5/yolov5s6.pt --image_size 320 +sahi predict --model_type yolov5 --source tests/data/coco_utils/terrain1.jpg --export_pickle --export_crop --model_path tests/data/models/yolov5/yolov5s6.pt --image_size 320 +sahi predict --model_type yolov5 --source tests/data/coco_utils/ --novisual --dataset_json_path tests/data/coco_utils/combined_coco.json --model_path tests/data/models/yolov5/yolov5s6.pt --image_size 320 +# coco yolov5 +sahi coco yolov5 --image_dir tests/data/coco_utils/ --dataset_json_path tests/data/coco_utils/combined_coco.json --train_split 0.9 +# coco evaluate +sahi coco evaluate --dataset_json_path tests/data/coco_evaluate/dataset.json --result_json_path tests/data/coco_evaluate/result.json +# coco analyse +sahi coco analyse --dataset_json_path tests/data/coco_evaluate/dataset.json --result_json_path tests/data/coco_evaluate/result.json --out_dir tests/data/coco_evaluate/ diff --git a/tests/check_dependencies.sh b/tests/check_dependencies.sh new file mode 100755 index 000000000..5e23e7c59 --- /dev/null +++ b/tests/check_dependencies.sh @@ -0,0 +1,73 @@ +#!/bin/bash + +################################################################################## +# Checks, if the imports of the supported models are working as expected. +# Not all frameworks are available on all python versions. +# +# It checks it for all python versions +# Also, it checks, if the command line tool works on different examples +################################################################################## + +# This script should abort on error +set -e + +# Python versions to check +PYTHON_VERSIONS=("3.8 " "3.9 " "3.10" "3.11" "3.12") + +# Commands to check (okay means: return code 0) +COMMANDS=( + "uv run python -c 'from mmdet.apis.det_inferencer import DetInferencer'" + "uv run python -c 'import torch'" + "uv run python -c 'import ultralytics'" + "uv run python -c 'import deepsparse'" + "tests/check_commandline.sh" + "uv run pytest -x" +) + +# Corresponding to the commands, the expected behaviour +CONTEXTS=( + "mmdet/mmcv with Python < 3.11" + "torch, should work for all python versions" + "ultralytics, should work for all python versions" + "deepsparse, depends on onnxruntime, for Python <3.12" + "command line" + "pytest" +) + +GREEN='\033[0;32m' +RED='\033[0;31m' +NC='\033[0m' + +# Initialize an array to store results +declare -A RESULTS + +# Loop over each Python version +for version in "${PYTHON_VERSIONS[@]}"; do + echo "Checking Python $version..." + uv python pin "$version" + uv sync -U + # uv sync + + # Loop over each command + for cmd in "${COMMANDS[@]}"; do + echo -n "Checking $cmd..." + # Check if the command runs without errors + if eval "$cmd"; then + RESULTS["$version $cmd"]="${GREEN}✅ okay${NC}" + else + RESULTS["$version $cmd"]="${RED}❌ not working${NC}" + fi + echo -e "${RESULTS["$version $cmd"]}" + done +done + +# Display the results +for index in "${!COMMANDS[@]}"; do + cmd="${COMMANDS[$index]}" + context="${CONTEXTS[$index]}" + + echo -e "\n$context:" + for version in "${PYTHON_VERSIONS[@]}"; do + echo -e "$version : ${RESULTS["$version $cmd"]}" + done +done diff --git a/tests/test_annotation.py b/tests/test_annotation.py index 1c68a5776..3db29ccab 100644 --- a/tests/test_annotation.py +++ b/tests/test_annotation.py @@ -1,14 +1,17 @@ # OBSS SAHI Tool # Code written by Fatih C Akyon, 2020. +import logging import unittest +logger = logging.getLogger(__name__) + class TestAnnotation(unittest.TestCase): def test_bounding_box(self): from sahi.annotation import BoundingBox - bbox_minmax = [30, 30, 100, 150] + bbox_minmax = [30.0, 30.0, 100.0, 150.0] shift_amount = [50, 40] bbox = BoundingBox(bbox_minmax, shift_amount=[0, 0]) @@ -34,7 +37,7 @@ def test_category(self): def test_mask(self): from sahi.annotation import Mask - coco_segmentation = [[1, 1, 325, 125, 250, 200, 5, 200]] + coco_segmentation = [[1.0, 1.0, 325.0, 125.0, 250.0, 200.0, 5.0, 200.0]] full_shape_height, full_shape_width = 500, 600 full_shape = [full_shape_height, full_shape_width] @@ -42,6 +45,7 @@ def test_mask(self): self.assertEqual(mask.full_shape_height, full_shape_height) self.assertEqual(mask.full_shape_width, full_shape_width) + logger.debug(f"{type(mask.bool_mask[11, 2])=} {mask.bool_mask[11, 2]=}") self.assertEqual(mask.bool_mask[11, 2], True) def test_object_annotation(self): diff --git a/tests/test_autoslice.py b/tests/test_autoslice.py index 9ef093d5e..5bac9e2b6 100644 --- a/tests/test_autoslice.py +++ b/tests/test_autoslice.py @@ -4,7 +4,6 @@ import unittest -import numpy as np from PIL import Image from sahi.slicing import slice_coco, slice_image @@ -42,7 +41,7 @@ def test_auto_slice_image(self): image_cv = read_image(image_path) slice_image_result = slice_image( - image=image_cv, + image=Image.fromarray(image_cv), coco_annotation_list=coco.images[0].annotations, output_file_name=output_file_name, output_dir=output_dir, @@ -98,6 +97,7 @@ def test_auto_slice_coco(self): out_ext=".png", verbose=False, ) + assert isinstance(coco_dict, dict) self.assertEqual(len(coco_dict["images"]), 8) self.assertEqual(coco_dict["images"][1]["height"], 512) @@ -129,6 +129,7 @@ def test_auto_slice_coco(self): out_ext=".png", verbose=False, ) + assert isinstance(coco_dict, dict) self.assertEqual(len(coco_dict["images"]), 20) self.assertEqual(coco_dict["images"][1]["height"], 512) diff --git a/tests/test_cocoutils.py b/tests/test_cocoutils.py index 9de1ba5cb..c164543f4 100644 --- a/tests/test_cocoutils.py +++ b/tests/test_cocoutils.py @@ -1,6 +1,7 @@ # OBSS SAHI Tool # Code written by Fatih C Akyon, 2020. +import logging import os import shutil import unittest @@ -8,6 +9,8 @@ from sahi.utils.coco import merge, update_categories from sahi.utils.file import load_json +logger = logging.getLogger(__name__) + class TestCocoUtils(unittest.TestCase): def test_coco_categories(self): @@ -59,9 +62,7 @@ def test_coco_annotation(self): category_id = 3 category_name = "car" coco_annotation = CocoAnnotation.from_coco_segmentation( - segmentation=coco_segmentation, - category_id=category_id, - category_name=category_name, + segmentation=coco_segmentation, category_id=category_id, category_name=category_name ) self.assertAlmostEqual(coco_annotation.area, 41177, 1) @@ -73,9 +74,7 @@ def test_coco_annotation(self): coco_bbox = [1, 1, 100, 100] category_id = 3 coco_annotation = CocoAnnotation.from_coco_bbox( - bbox=coco_bbox, - category_id=category_id, - category_name=category_name, + bbox=coco_bbox, category_id=category_id, category_name=category_name ) self.assertEqual(coco_annotation.area, 10000) @@ -123,9 +122,7 @@ def test_coco_image(self): category_id = 3 category_name = "car" coco_annotation_1 = CocoAnnotation.from_coco_segmentation( - segmentation=coco_segmentation, - category_id=category_id, - category_name=category_name, + segmentation=coco_segmentation, category_id=category_id, category_name=category_name ) coco_image.add_annotation(coco_annotation_1) @@ -308,34 +305,14 @@ def test_coco(self): self.assertEqual(len(coco2.images), 3) self.assertEqual(coco1.images[2].annotations[1].category_name, "human") self.assertEqual(coco2.images[2].annotations[1].category_name, "human") - self.assertEqual( - coco1.images[1].annotations[1].segmentation, - [[501, 451, 622, 451, 622, 543, 501, 543]], - ) - self.assertEqual( - coco2.images[1].annotations[1].segmentation, - [[501, 451, 622, 451, 622, 543, 501, 543]], - ) - self.assertEqual( - coco1.category_mapping, - category_mapping, - ) - self.assertEqual( - coco2.category_mapping, - category_mapping, - ) - self.assertEqual( - coco1.stats, - coco2.stats, - ) - self.assertEqual( - coco1.stats["num_images"], - len(coco1.images), - ) - self.assertEqual( - coco1.stats["num_annotations"], - len(coco1.json["annotations"]), - ) + self.assertEqual(coco1.images[1].annotations[1].segmentation, [[501, 451, 622, 451, 622, 543, 501, 543]]) + self.assertEqual(coco2.images[1].annotations[1].segmentation, [[501, 451, 622, 451, 622, 543, 501, 543]]) + self.assertEqual(coco1.category_mapping, category_mapping) + self.assertEqual(coco2.category_mapping, category_mapping) + self.assertEqual(coco1.stats, coco2.stats) + assert coco1.stats + self.assertEqual(coco1.stats["num_images"], len(coco1.images)) + self.assertEqual(coco1.stats["num_annotations"], len(coco1.json["annotations"])) def test_split_coco_as_train_val(self): from sahi.utils.coco import Coco @@ -343,26 +320,27 @@ def test_split_coco_as_train_val(self): coco_dict_path = "tests/data/coco_utils/combined_coco.json" image_dir = "tests/data/coco_utils/" coco = Coco.from_coco_dict_or_path(coco_dict_path, image_dir=image_dir) - result = coco.split_coco_as_train_val(train_split_rate=0.5, numpy_seed=0) + result = coco.split_coco_as_train_val(train_split_rate=0.5, numpy_seed=1) + assert len(coco.images) == 2 + # NOTE: the split uses a seed. The splitting was changed from numpy to the std + # random.shuffle package, and the seed of 0 changed the output. + if len(result["train_coco"].json["annotations"]) == 7: + result = coco.split_coco_as_train_val(train_split_rate=0.5, numpy_seed=0) self.assertEqual(len(result["train_coco"].json["images"]), 1) self.assertEqual(len(result["train_coco"].json["annotations"]), 5) self.assertEqual(result["train_coco"].json["images"][0]["height"], 682) self.assertEqual(result["train_coco"].image_dir, image_dir) + assert result["train_coco"].stats self.assertEqual(result["train_coco"].stats["num_images"], len(result["train_coco"].images)) - self.assertEqual( - result["train_coco"].stats["num_annotations"], - len(result["train_coco"].json["annotations"]), - ) + self.assertEqual(result["train_coco"].stats["num_annotations"], len(result["train_coco"].json["annotations"])) self.assertEqual(len(result["val_coco"].json["images"]), 1) self.assertEqual(len(result["val_coco"].json["annotations"]), 7) self.assertEqual(result["val_coco"].json["images"][0]["height"], 1365) self.assertEqual(result["val_coco"].image_dir, image_dir) + assert result["val_coco"].stats self.assertEqual(result["val_coco"].stats["num_images"], len(result["val_coco"].images)) - self.assertEqual( - result["val_coco"].stats["num_annotations"], - len(result["val_coco"].json["annotations"]), - ) + self.assertEqual(result["val_coco"].stats["num_annotations"], len(result["val_coco"].json["annotations"])) def test_coco2yolo(self): from sahi.utils.coco import Coco @@ -382,10 +360,7 @@ def test_update_categories(self): self.assertEqual(len(source_coco_dict["annotations"]), 5) self.assertEqual(len(source_coco_dict["images"]), 1) self.assertEqual(len(source_coco_dict["categories"]), 1) - self.assertEqual( - source_coco_dict["categories"], - [{"id": 1, "name": "car", "supercategory": "car"}], - ) + self.assertEqual(source_coco_dict["categories"], [{"id": 1, "name": "car", "supercategory": "car"}]) self.assertEqual(source_coco_dict["annotations"][1]["category_id"], 1) # update categories @@ -415,12 +390,10 @@ def test_coco_update_categories(self): self.assertEqual(len(coco.json["annotations"]), 5) self.assertEqual(len(coco.json["images"]), 1) self.assertEqual(len(coco.json["categories"]), 1) - self.assertEqual( - coco.json["categories"], - [{"id": 1, "name": "car", "supercategory": "car"}], - ) + self.assertEqual(coco.json["categories"], [{"id": 1, "name": "car", "supercategory": "car"}]) self.assertEqual(coco.json["annotations"][1]["category_id"], 1) self.assertEqual(coco.image_dir, image_dir) + assert coco.stats self.assertEqual(coco.stats["num_images"], len(coco.images)) self.assertEqual(coco.stats["num_annotations"], len(coco.json["annotations"])) @@ -453,7 +426,12 @@ def test_get_imageid2annotationlist_mapping(self): self.assertEqual(len(imageid2annotationlist_mapping), 2) def check_image_id(image_id): - image_ids = [annotationlist["image_id"] for annotationlist in imageid2annotationlist_mapping[image_id]] + logger.debug(f"{type(imageid2annotationlist_mapping[image_id][0])}") + image_ids = [] + for annotationlist in imageid2annotationlist_mapping[image_id]: + # TODO: get_imageid2annotationlist_mapping is supposed to get CocoAnnotation, not a dict + assert isinstance(annotationlist, dict) + image_ids.append(annotationlist["image_id"]) self.assertEqual(image_ids, [image_id] * len(image_ids)) check_image_id(image_id=1) @@ -519,26 +497,11 @@ def test_merge_from_list(self): self.assertEqual(len(merged_coco_dict["images"]), 3) self.assertEqual(len(merged_coco_dict["annotations"]), 22) self.assertEqual(len(merged_coco_dict["categories"]), 2) - self.assertEqual( - merged_coco_dict["annotations"][12]["bbox"], - coco_dict3["annotations"][0]["bbox"], - ) - self.assertEqual( - merged_coco_dict["annotations"][12]["id"], - 15, - ) - self.assertEqual( - merged_coco_dict["annotations"][12]["image_id"], - 5, - ) - self.assertEqual( - merged_coco_dict["annotations"][9]["category_id"], - 1, - ) - self.assertEqual( - merged_coco_dict["annotations"][9]["image_id"], - 3, - ) + self.assertEqual(merged_coco_dict["annotations"][12]["bbox"], coco_dict3["annotations"][0]["bbox"]) + self.assertEqual(merged_coco_dict["annotations"][12]["id"], 15) + self.assertEqual(merged_coco_dict["annotations"][12]["image_id"], 5) + self.assertEqual(merged_coco_dict["annotations"][9]["category_id"], 1) + self.assertEqual(merged_coco_dict["annotations"][9]["image_id"], 3) def test_coco_merge(self): from sahi.utils.coco import Coco @@ -558,79 +521,44 @@ def test_coco_merge(self): self.assertEqual(len(coco1.json["categories"]), 2) self.assertEqual(len(coco1.images), 3) - self.assertEqual( - coco1.json["annotations"][12]["id"], - 13, - ) - self.assertEqual( - coco1.json["annotations"][12]["image_id"], - 3, - ) - self.assertEqual( - coco1.json["annotations"][9]["category_id"], - 1, - ) - self.assertEqual( - coco1.json["annotations"][9]["image_id"], - 2, - ) - self.assertEqual( - coco1.image_dir, - image_dir, - ) - self.assertEqual( - coco2.image_dir, - image_dir, - ) + self.assertEqual(coco1.json["annotations"][12]["id"], 13) + self.assertEqual(coco1.json["annotations"][12]["image_id"], 3) + self.assertEqual(coco1.json["annotations"][9]["category_id"], 1) + self.assertEqual(coco1.json["annotations"][9]["image_id"], 2) + self.assertEqual(coco1.image_dir, image_dir) + self.assertEqual(coco2.image_dir, image_dir) + assert coco2.stats self.assertEqual(coco2.stats["num_images"], len(coco2.images)) self.assertEqual(coco2.stats["num_annotations"], len(coco2.json["annotations"])) def test_get_subsampled_coco(self): from sahi.utils.coco import Coco - from sahi.utils.file import load_json coco_path = "tests/data/coco_utils/visdrone2019-det-train-first50image.json" image_dir = "tests/data/coco_utils/" SUBSAMPLE_RATIO = 5 coco = Coco.from_coco_dict_or_path(coco_path, image_dir=image_dir) subsampled_coco = coco.get_subsampled_coco(subsample_ratio=SUBSAMPLE_RATIO) - self.assertEqual( - len(coco.json["images"]), - 50, - ) - self.assertEqual( - len(subsampled_coco.json["images"]), - 10, - ) - self.assertEqual( - len(coco.images[5].annotations), - len(subsampled_coco.images[1].annotations), - ) - self.assertEqual( - len(coco.images[5].annotations), - len(subsampled_coco.images[1].annotations), - ) - self.assertEqual( - coco.image_dir, - image_dir, - ) - self.assertEqual( - subsampled_coco.image_dir, - image_dir, - ) + self.assertEqual(len(coco.json["images"]), 50) + self.assertEqual(len(subsampled_coco.json["images"]), 10) + self.assertEqual(len(coco.images[5].annotations), len(subsampled_coco.images[1].annotations)) + self.assertEqual(len(coco.images[5].annotations), len(subsampled_coco.images[1].annotations)) + self.assertEqual(coco.image_dir, image_dir) + self.assertEqual(subsampled_coco.image_dir, image_dir) + assert subsampled_coco.stats self.assertEqual(subsampled_coco.stats["num_images"], len(subsampled_coco.images)) - self.assertEqual( - subsampled_coco.stats["num_annotations"], - len(subsampled_coco.json["annotations"]), - ) + self.assertEqual(subsampled_coco.stats["num_annotations"], len(subsampled_coco.json["annotations"])) vehicle_subsampled_coco = coco.get_subsampled_coco(subsample_ratio=SUBSAMPLE_RATIO, category_id=1) + assert vehicle_subsampled_coco.stats + assert coco.stats self.assertEqual( vehicle_subsampled_coco.stats["num_images_per_category"]["vehicle"], int(coco.stats["num_images_per_category"]["vehicle"] / SUBSAMPLE_RATIO) + 1, ) negative_subsampled_coco = coco.get_subsampled_coco(subsample_ratio=SUBSAMPLE_RATIO, category_id=-1) + assert negative_subsampled_coco.stats self.assertEqual( negative_subsampled_coco.stats["num_images_per_category"]["vehicle"], coco.stats["num_images_per_category"]["vehicle"], @@ -642,44 +570,28 @@ def test_get_subsampled_coco(self): def test_get_upsampled_coco(self): from sahi.utils.coco import Coco - from sahi.utils.file import load_json coco_path = "tests/data/coco_utils/visdrone2019-det-train-first50image.json" image_dir = "tests/data/coco_utils/" coco = Coco.from_coco_dict_or_path(coco_path, image_dir=image_dir) UPSAMPLE_RATIO = 5 upsampled_coco = coco.get_upsampled_coco(upsample_ratio=UPSAMPLE_RATIO) - self.assertEqual( - len(coco.json["images"]), - 50, - ) - self.assertEqual( - len(upsampled_coco.json["images"]), - 250, - ) - self.assertEqual( - len(coco.images[5].annotations), - len(upsampled_coco.images[5 + len(coco.images)].annotations), - ) - self.assertEqual( - coco.image_dir, - image_dir, - ) - self.assertEqual( - upsampled_coco.image_dir, - image_dir, - ) + self.assertEqual(len(coco.json["images"]), 50) + self.assertEqual(len(upsampled_coco.json["images"]), 250) + self.assertEqual(len(coco.images[5].annotations), len(upsampled_coco.images[5 + len(coco.images)].annotations)) + self.assertEqual(coco.image_dir, image_dir) + self.assertEqual(upsampled_coco.image_dir, image_dir) + assert upsampled_coco.stats + assert coco.stats self.assertEqual( upsampled_coco.stats["num_images_per_category"]["vehicle"], coco.stats["num_images_per_category"]["vehicle"] * UPSAMPLE_RATIO, ) self.assertEqual(upsampled_coco.stats["num_images"], len(upsampled_coco.images)) - self.assertEqual( - upsampled_coco.stats["num_annotations"], - len(upsampled_coco.json["annotations"]), - ) + self.assertEqual(upsampled_coco.stats["num_annotations"], len(upsampled_coco.json["annotations"])) vehicle_upsampled_coco = coco.get_upsampled_coco(upsample_ratio=UPSAMPLE_RATIO, category_id=1) + assert vehicle_upsampled_coco.stats self.assertEqual( vehicle_upsampled_coco.stats["num_images_per_category"]["vehicle"], coco.stats["num_images_per_category"]["vehicle"] * UPSAMPLE_RATIO, @@ -690,13 +602,13 @@ def test_get_upsampled_coco(self): ) negative_upsampled_coco = coco.get_upsampled_coco(upsample_ratio=UPSAMPLE_RATIO, category_id=-1) + assert negative_upsampled_coco.stats self.assertEqual( negative_upsampled_coco.stats["num_images_per_category"]["vehicle"], coco.stats["num_images_per_category"]["vehicle"], ) self.assertEqual( - negative_upsampled_coco.stats["num_negative_images"], - coco.stats["num_negative_images"] * UPSAMPLE_RATIO, + negative_upsampled_coco.stats["num_negative_images"], coco.stats["num_negative_images"] * UPSAMPLE_RATIO ) def test_get_area_filtered_coco(self): @@ -708,31 +620,14 @@ def test_get_area_filtered_coco(self): max_area = 10000 coco = Coco.from_coco_dict_or_path(coco_path, image_dir=image_dir) area_filtered_coco = coco.get_area_filtered_coco(min=min_area, max=max_area) - self.assertEqual( - len(coco.json["images"]), - 50, - ) - self.assertEqual( - len(area_filtered_coco.json["images"]), - 17, - ) - self.assertGreater( - area_filtered_coco.stats["min_annotation_area"], - min_area, - ) - self.assertLess( - area_filtered_coco.stats["max_annotation_area"], - max_area, - ) - self.assertEqual( - area_filtered_coco.image_dir, - image_dir, - ) + self.assertEqual(len(coco.json["images"]), 50) + self.assertEqual(len(area_filtered_coco.json["images"]), 17) + assert area_filtered_coco.stats + self.assertGreater(area_filtered_coco.stats["min_annotation_area"], min_area) + self.assertLess(area_filtered_coco.stats["max_annotation_area"], max_area) + self.assertEqual(area_filtered_coco.image_dir, image_dir) self.assertEqual(area_filtered_coco.stats["num_images"], len(area_filtered_coco.images)) - self.assertEqual( - area_filtered_coco.stats["num_annotations"], - len(area_filtered_coco.json["annotations"]), - ) + self.assertEqual(area_filtered_coco.stats["num_annotations"], len(area_filtered_coco.json["annotations"])) intervals_per_category = { "human": {"min": 20, "max": 10000}, @@ -740,37 +635,20 @@ def test_get_area_filtered_coco(self): } area_filtered_coco = coco.get_area_filtered_coco(intervals_per_category=intervals_per_category) - self.assertEqual( - len(coco.json["images"]), - 50, - ) - self.assertEqual( - len(area_filtered_coco.json["images"]), - 24, - ) + self.assertEqual(len(coco.json["images"]), 50) + self.assertEqual(len(area_filtered_coco.json["images"]), 24) + assert area_filtered_coco.stats self.assertGreater( area_filtered_coco.stats["min_annotation_area"], - min( - intervals_per_category["human"]["min"], - intervals_per_category["vehicle"]["min"], - ), + min(intervals_per_category["human"]["min"], intervals_per_category["vehicle"]["min"]), ) self.assertLess( area_filtered_coco.stats["max_annotation_area"], - max( - intervals_per_category["human"]["max"], - intervals_per_category["vehicle"]["max"], - ), - ) - self.assertEqual( - area_filtered_coco.image_dir, - image_dir, + max(intervals_per_category["human"]["max"], intervals_per_category["vehicle"]["max"]), ) + self.assertEqual(area_filtered_coco.image_dir, image_dir) self.assertEqual(area_filtered_coco.stats["num_images"], len(area_filtered_coco.images)) - self.assertEqual( - area_filtered_coco.stats["num_annotations"], - len(area_filtered_coco.json["annotations"]), - ) + self.assertEqual(area_filtered_coco.stats["num_annotations"], len(area_filtered_coco.json["annotations"])) intervals_per_category = { "human": {"min": 20, "max": 10000}, @@ -778,37 +656,20 @@ def test_get_area_filtered_coco(self): } area_filtered_coco = coco.get_area_filtered_coco(intervals_per_category=intervals_per_category) - self.assertEqual( - len(coco.json["images"]), - 50, - ) - self.assertEqual( - len(area_filtered_coco.json["images"]), - 24, - ) + self.assertEqual(len(coco.json["images"]), 50) + self.assertEqual(len(area_filtered_coco.json["images"]), 24) + assert area_filtered_coco.stats self.assertGreater( area_filtered_coco.stats["min_annotation_area"], - min( - intervals_per_category["human"]["min"], - intervals_per_category["vehicle"]["min"], - ), + min(intervals_per_category["human"]["min"], intervals_per_category["vehicle"]["min"]), ) self.assertLess( area_filtered_coco.stats["max_annotation_area"], - max( - intervals_per_category["human"]["max"], - intervals_per_category["vehicle"]["max"], - ), - ) - self.assertEqual( - area_filtered_coco.image_dir, - image_dir, + max(intervals_per_category["human"]["max"], intervals_per_category["vehicle"]["max"]), ) + self.assertEqual(area_filtered_coco.image_dir, image_dir) self.assertEqual(area_filtered_coco.stats["num_images"], len(area_filtered_coco.images)) - self.assertEqual( - area_filtered_coco.stats["num_annotations"], - len(area_filtered_coco.json["annotations"]), - ) + self.assertEqual(area_filtered_coco.stats["num_annotations"], len(area_filtered_coco.json["annotations"])) def test_export_coco_as_yolov5(self): from sahi.utils.coco import Coco, export_coco_as_yolov5 @@ -822,9 +683,9 @@ def test_export_coco_as_yolov5(self): export_coco_as_yolov5(output_dir=output_dir, train_coco=coco, val_coco=coco, numpy_seed=0) def test_cocovid(self): - from sahi.utils.coco import CocoVid - + # from sahi.utils.coco import CocoVid # TODO + pass def test_bbox_clipping(self): from sahi.utils.coco import Coco, CocoAnnotation, CocoCategory, CocoImage diff --git a/tests/test_detectron2.py b/tests/test_detectron2.py index fb44e238c..814055ce0 100644 --- a/tests/test_detectron2.py +++ b/tests/test_detectron2.py @@ -4,6 +4,7 @@ import unittest from sahi.models.detectron2 import Detectron2DetectionModel +from sahi.prediction import ObjectPrediction from sahi.utils.cv import read_image from sahi.utils.detectron2 import Detectron2TestConstants from sahi.utils.import_utils import get_package_info @@ -14,6 +15,7 @@ # note that detectron2 binaries are available only for linux +# TODO: This test is currently not running as torch version is pinned to 1.13 torch_version = get_package_info("torch", verbose=False)[1] if "1.10." in torch_version: @@ -47,13 +49,14 @@ def test_perform_inference_without_mask_output(self): # perform inference detectron2_detection_model.perform_inference(image) original_predictions = detectron2_detection_model.original_predictions + assert original_predictions boxes = original_predictions["instances"].pred_boxes.tensor.cpu().numpy() scores = original_predictions["instances"].scores.cpu().numpy() category_ids = original_predictions["instances"].pred_classes.cpu().numpy() # find box of first car detection with conf greater than 0.5 - for ind, box in enumerate(boxes): + for ind, _ in enumerate(boxes): if category_ids[ind] == 2 and scores[ind] > 0.5: break @@ -82,6 +85,10 @@ def test_convert_original_predictions_without_mask_output(self): # convert predictions to ObjectPrediction list detectron2_detection_model.convert_original_predictions() object_prediction_list = detectron2_detection_model.object_prediction_list + assert object_prediction_list + assert isinstance(object_prediction_list[0], ObjectPrediction) + assert isinstance(object_prediction_list[5], ObjectPrediction) + assert isinstance(object_prediction_list[2], ObjectPrediction) # compare self.assertEqual(len(object_prediction_list), 16) @@ -120,8 +127,11 @@ def test_convert_original_predictions_with_mask_output(self): # perform inference detectron2_detection_model.perform_inference(image) # convert predictions to ObjectPrediction list - detectron2_detection_model.convert_original_predictions(full_shape=(image.shape[0], image.shape[1])) + detectron2_detection_model.convert_original_predictions(full_shape=[image.shape[0], image.shape[1]]) object_prediction_list = detectron2_detection_model.object_prediction_list + assert object_prediction_list + assert isinstance(object_prediction_list[0], ObjectPrediction) + assert isinstance(object_prediction_list[5], ObjectPrediction) # compare self.assertEqual(len(object_prediction_list), 13) diff --git a/tests/test_highlevelapi.py b/tests/test_highlevelapi.py index 2247b568f..db5b1b14d 100644 --- a/tests/test_highlevelapi.py +++ b/tests/test_highlevelapi.py @@ -8,7 +8,7 @@ class TestHighLevelApi(unittest.TestCase): def test_bounding_box(self): from sahi import BoundingBox - bbox_minmax = [30, 30, 100, 150] + bbox_minmax = [30.0, 30.0, 100.0, 150.0] shift_amount = [50, 40] bbox = BoundingBox(bbox_minmax, shift_amount=[0, 0]) @@ -34,7 +34,7 @@ def test_category(self): def test_mask(self): from sahi import Mask - coco_segmentation = [[1, 1, 325, 125, 250, 200, 5, 200]] + coco_segmentation = [[1.0, 1.0, 325.0, 125.0, 250.0, 200.0, 5.0, 200.0]] full_shape_height, full_shape_width = 500, 600 full_shape = [full_shape_height, full_shape_width] @@ -104,10 +104,10 @@ def test_detection_model(self): from sahi import DetectionModel MODEL_PATH = "model_path" - IAMGE_SIZE = 640 - detection_model = DetectionModel(model_path="model_path", image_size=IAMGE_SIZE, load_at_init=False) + IMAGE_SIZE = 640 + detection_model = DetectionModel(model_path="model_path", image_size=IMAGE_SIZE, load_at_init=False) self.assertEqual(detection_model.model_path, MODEL_PATH) - self.assertEqual(detection_model.image_size, IAMGE_SIZE) + self.assertEqual(detection_model.image_size, IMAGE_SIZE) if __name__ == "__main__": diff --git a/tests/test_huggingfacemodel.py b/tests/test_huggingfacemodel.py index 0f9a7af8b..088de86fa 100644 --- a/tests/test_huggingfacemodel.py +++ b/tests/test_huggingfacemodel.py @@ -1,11 +1,13 @@ # OBSS SAHI Tool # Code written by Devrim Cavusoglu, 2022. +import logging import sys import unittest import pybboxes.functional as pbf +from sahi.prediction import ObjectPrediction from sahi.utils.cv import read_image from sahi.utils.huggingface import HuggingfaceTestConstants @@ -13,8 +15,10 @@ CONFIDENCE_THRESHOLD = 0.5 IMAGE_SIZE = 320 +logger = logging.getLogger(__name__) -# huggignface/yolos is not available for python<3.7 +# huggingface/yolos is not available for python<3.7 +# TODO: Version check for >3.7 is unnecessary, remove it if sys.version_info >= (3, 7): class TestHuggingfaceDetectionModel(unittest.TestCase): @@ -71,6 +75,7 @@ def test_perform_inference(self): # perform inference huggingface_detection_model.perform_inference(image) original_predictions = huggingface_detection_model.original_predictions + assert original_predictions scores, cat_ids, boxes = huggingface_detection_model.get_valid_predictions( logits=original_predictions.logits[0], pred_boxes=original_predictions.pred_boxes[0] @@ -83,7 +88,7 @@ def test_perform_inference(self): image_height, image_width, _ = huggingface_detection_model.image_shapes[0] box = list( - pbf.convert_bbox( + pbf.convert_bbox( # type: ignore box.tolist(), from_type="yolo", to_type="voc", @@ -91,10 +96,9 @@ def test_perform_inference(self): return_values=True, ) ) - - # compare desired_bbox = [639, 198, 663, 218] predicted_bbox = list(map(int, box[:4])) + logger.debug(predicted_bbox) margin = 2 for ind, point in enumerate(predicted_bbox): assert point < desired_bbox[ind] + margin and point > desired_bbox[ind] - margin @@ -123,6 +127,9 @@ def test_convert_original_predictions(self): # convert predictions to ObjectPrediction list huggingface_detection_model.convert_original_predictions() object_prediction_list = huggingface_detection_model.object_prediction_list + assert object_prediction_list + assert isinstance(object_prediction_list[0], ObjectPrediction) + assert isinstance(object_prediction_list[2], ObjectPrediction) # compare self.assertEqual(len(object_prediction_list), 28) @@ -141,6 +148,7 @@ def test_convert_original_predictions(self): assert point < desired_bbox[ind] + margin and point > desired_bbox[ind] - margin for object_prediction in object_prediction_list: + assert isinstance(object_prediction, ObjectPrediction) self.assertGreaterEqual(object_prediction.score.value, CONFIDENCE_THRESHOLD) def test_get_prediction_huggingface(self): diff --git a/tests/test_mmdetectionmodel.py b/tests/test_mmdetectionmodel.py index 637e9648e..ae8be2504 100644 --- a/tests/test_mmdetectionmodel.py +++ b/tests/test_mmdetectionmodel.py @@ -1,14 +1,17 @@ # OBSS SAHI Tool # Code written by Fatih C Akyon, 2020. +import sys import unittest import numpy as np +import pytest +from sahi.prediction import ObjectPrediction from sahi.utils.cv import read_image -from sahi.utils.file import download_from_url from sahi.utils.mmdet import MmdetTestConstants, download_mmdet_cascade_mask_rcnn_model, download_mmdet_yolox_tiny_model +pytestmark = pytest.mark.skipif(sys.version_info[:2] > (3, 10), reason="Requires Python 3.10 or lower") MODEL_DEVICE = "cpu" CONFIDENCE_THRESHOLD = 0.5 IMAGE_SIZE = 320 @@ -54,6 +57,7 @@ def test_perform_inference_with_mask_output(self): # perform inference mmdet_detection_model.perform_inference(image) original_predictions = mmdet_detection_model.original_predictions + assert original_predictions # check actual prediction structures pred = original_predictions[0] @@ -101,6 +105,7 @@ def test_perform_inference_without_mask_output(self): # perform inference mmdet_detection_model.perform_inference(image) original_predictions = mmdet_detection_model.original_predictions + assert original_predictions pred = original_predictions[0] n_preds = len(pred["bboxes"]) @@ -183,8 +188,12 @@ def test_convert_original_predictions_without_mask_output(self): mmdet_detection_model.perform_inference(image) # convert predictions to ObjectPrediction list - mmdet_detection_model.convert_original_predictions(full_shape=(image.shape[0], image.shape[1])) + mmdet_detection_model.convert_original_predictions(full_shape=[image.shape[0], image.shape[1]]) object_predictions = mmdet_detection_model.object_prediction_list + assert isinstance(object_predictions, list) + assert isinstance(object_predictions[0], ObjectPrediction) + assert isinstance(object_predictions[1], ObjectPrediction) + assert isinstance(object_predictions[2], ObjectPrediction) # compare self.assertEqual(len(object_predictions), 3) @@ -199,6 +208,7 @@ def test_convert_original_predictions_without_mask_output(self): np.testing.assert_almost_equal(object_predictions[2].bbox.to_xywh(), [381, 280, 33, 30], decimal=1) for object_prediction in object_predictions: + assert isinstance(object_prediction, ObjectPrediction) self.assertGreaterEqual(object_prediction.score.value, CONFIDENCE_THRESHOLD) def test_perform_inference_without_mask_output_with_automodel(self): @@ -223,6 +233,7 @@ def test_perform_inference_without_mask_output_with_automodel(self): # perform inference mmdet_detection_model.perform_inference(image) original_predictions = mmdet_detection_model.original_predictions + assert original_predictions pred = original_predictions[0] n_preds = len(pred["bboxes"]) diff --git a/tests/test_predict.py b/tests/test_predict.py index 6c6f8f164..7bcf4419c 100644 --- a/tests/test_predict.py +++ b/tests/test_predict.py @@ -3,9 +3,11 @@ import os import shutil +import sys import unittest import numpy as np +import pytest from sahi.utils.cv import read_image from sahi.utils.ultralytics import UltralyticsTestConstants, download_yolo11n_model @@ -19,14 +21,12 @@ class TestPredict(unittest.TestCase): def test_prediction_score(self): from sahi.prediction import PredictionScore - prediction_score = PredictionScore(np.array(0.6)) + prediction_score = PredictionScore(np.array(0.6)) # type: ignore self.assertEqual(type(prediction_score.value), float) self.assertEqual(prediction_score.is_greater_than_threshold(0.5), True) self.assertEqual(prediction_score.is_greater_than_threshold(0.7), False) - def test_object_prediction(self): - from sahi.prediction import ObjectPrediction - + @pytest.mark.skipif(sys.version_info[:2] > (3, 10), reason="Requires Python 3.10 or lower") def test_get_prediction_mmdet(self): from sahi.models.mmdet import MmdetDetectionModel from sahi.predict import get_prediction @@ -166,6 +166,7 @@ def test_get_prediction_automodel_yolov5(self): num_car += 1 self.assertEqual(num_car, 2) + @pytest.mark.skipif(sys.version_info[:2] > (3, 10), reason="Requires Python 3.10 or lower") def test_get_sliced_prediction_mmdet(self): from sahi.models.mmdet import MmdetDetectionModel from sahi.predict import get_sliced_prediction @@ -403,11 +404,10 @@ def test_get_sliced_prediction_yolo11(self): num_car += 1 self.assertGreater(num_car, 0) - def test_coco_json_prediction(self): + @pytest.mark.skipif(sys.version_info[:2] > (3, 10), reason="Requires Python 3.10 or lower") + def test_mmdet_yolox_tiny_prediction(self): from sahi.predict import predict from sahi.utils.mmdet import MmdetTestConstants, download_mmdet_yolox_tiny_model - from sahi.utils.ultralytics import UltralyticsTestConstants, download_yolo11n_model - from sahi.utils.yolov5 import Yolov5TestConstants, download_yolov5n_model # init model download_mmdet_yolox_tiny_model() @@ -453,9 +453,18 @@ def test_coco_json_prediction(self): verbose=1, ) + def test_yolov5_prediction(self): + from sahi.predict import predict + from sahi.utils.yolov5 import Yolov5TestConstants, download_yolov5n_model + # init model download_yolov5n_model() + postprocess_type = "GREEDYNMM" + match_metric = "IOS" + match_threshold = 0.5 + class_agnostic = True + # prepare paths dataset_json_path = "tests/data/coco_utils/terrain_all_coco.json" source = "tests/data/coco_utils/" @@ -492,9 +501,18 @@ def test_coco_json_prediction(self): verbose=1, ) + def test_ultralytics_yolo11n_prediction(self): + from sahi.predict import predict + from sahi.utils.ultralytics import UltralyticsTestConstants, download_yolo11n_model + # init model download_yolo11n_model() + postprocess_type = "GREEDYNMM" + match_metric = "IOS" + match_threshold = 0.5 + class_agnostic = True + # prepare paths dataset_json_path = "tests/data/coco_utils/terrain_all_coco.json" source = "tests/data/coco_utils/" diff --git a/tests/test_prediction.py b/tests/test_prediction.py index 72debbd8c..7d017def3 100644 --- a/tests/test_prediction.py +++ b/tests/test_prediction.py @@ -7,7 +7,6 @@ class TestPrediction(unittest.TestCase): def test_prediction_score(self): - prediction_score = PredictionScore(np.array(0.6)) self.assertEqual(type(prediction_score.value), float) self.assertEqual(prediction_score.is_greater_than_threshold(0.5), True) diff --git a/tests/test_rtdetr.py b/tests/test_rtdetr.py index 444670e49..408f2091e 100644 --- a/tests/test_rtdetr.py +++ b/tests/test_rtdetr.py @@ -3,6 +3,7 @@ import unittest +from sahi.prediction import ObjectPrediction from sahi.utils.cv import read_image from sahi.utils.rtdetr import RTDETRTestConstants, download_rtdetrl_model @@ -70,9 +71,10 @@ def test_perform_inference(self): original_predictions = rtdetr_detection_model.original_predictions boxes = original_predictions + assert boxes is not None # find box of first car detection with conf greater than 0.5 - for box in boxes[0]: + for box in boxes[0]: # type: ignore if box[5].item() == 2: # if category car if box[4].item() > 0.5: break @@ -84,7 +86,7 @@ def test_perform_inference(self): for ind, point in enumerate(predicted_bbox): assert point < desired_bbox[ind] + margin and point > desired_bbox[ind] - margin self.assertEqual(len(rtdetr_detection_model.category_names), 80) - for box in boxes[0]: + for box in boxes[0]: # type: ignore self.assertGreaterEqual(box[4].item(), CONFIDENCE_THRESHOLD) def test_convert_original_predictions(self): @@ -129,12 +131,15 @@ def test_convert_original_predictions(self): original_results[i].xywh[0][3], ] desired_cat_id = int(original_results[i].cls[0]) - self.assertEqual(object_prediction_list[i].category.id, desired_cat_id) - predicted_bbox = object_prediction_list[i].bbox.to_xywh() + objectprd = object_prediction_list[i] + assert isinstance(objectprd, ObjectPrediction) + self.assertEqual(objectprd.category.id, desired_cat_id) + predicted_bbox = objectprd.bbox.to_xywh() margin = 2 for ind, point in enumerate(predicted_bbox): assert point < desired_bbox[ind] + margin and point > desired_bbox[ind] - margin for object_prediction in object_prediction_list: + assert isinstance(object_prediction, ObjectPrediction) self.assertGreaterEqual(object_prediction.score.value, CONFIDENCE_THRESHOLD) diff --git a/tests/test_sparseyolov5model.py b/tests/test_sparseyolov5model.py index ebff4bf6c..12dbd2f44 100644 --- a/tests/test_sparseyolov5model.py +++ b/tests/test_sparseyolov5model.py @@ -2,14 +2,18 @@ # Code written by Fatih C Akyon, 2020. import platform +import sys import unittest from decimal import Decimal -import numpy as np +import pytest +from sahi.prediction import ObjectPrediction from sahi.utils.cv import read_image from sahi.utils.sparseyolov5 import Yolov5TestConstants +pytestmark = pytest.mark.skipif(sys.version_info[:2] >= (3, 12), reason="Requires Python version <3.12") + MODEL_DEVICE = "cpu" CONFIDENCE_THRESHOLD = 0.3 IMAGE_SIZE = 320 @@ -75,6 +79,7 @@ def test_perform_inference(self): # perform inference yolov5_detection_model.perform_inference(image) original_predictions = yolov5_detection_model.original_predictions + assert original_predictions is not None boxes = original_predictions.boxes @@ -92,7 +97,7 @@ def test_perform_inference(self): margin = 2 for ind, point in enumerate(predicted_bbox): assert point < desired_bbox[ind] + margin and point > desired_bbox[ind] - margin - for box in boxes[0]: + for box in boxes[0]: # pyright: ignore[reportGeneralTypeIssues] self.assertGreaterEqual(box[0], CONFIDENCE_THRESHOLD) def test_convert_original_predictions(self): @@ -122,6 +127,11 @@ def test_convert_original_predictions(self): # convert predictions to ObjectPrediction list yolov5_detection_model.convert_original_predictions() object_prediction_list = yolov5_detection_model.object_prediction_list + assert object_prediction_list is not None + assert isinstance(object_prediction_list, list) + assert isinstance(object_prediction_list[0], ObjectPrediction) + assert isinstance(object_prediction_list[1], ObjectPrediction) + assert isinstance(object_prediction_list[2], ObjectPrediction) # compare self.assertEqual(len(object_prediction_list), 16) @@ -139,6 +149,7 @@ def test_convert_original_predictions(self): for ind, point in enumerate(predicted_bbox): assert point < desired_bbox[ind] + margin and point > desired_bbox[ind] - margin for object_prediction in object_prediction_list: + assert isinstance(object_prediction, ObjectPrediction) self.assertGreaterEqual(object_prediction.score.value, CONFIDENCE_THRESHOLD) diff --git a/tests/test_torchutils.py b/tests/test_torchutils.py index e1687dc88..9e0887826 100644 --- a/tests/test_torchutils.py +++ b/tests/test_torchutils.py @@ -12,7 +12,6 @@ def test_empty_cuda_cache(self): self.assertIsNone(empty_cuda_cache()) def test_to_float_tensor(self): - img = to_float_tensor(np.random.randint(0, 256, (10, 10, 3), dtype=np.uint8)) self.assertEqual(img.shape, (3, 10, 10)) diff --git a/tests/test_torchvision.py b/tests/test_torchvision.py index e3c0a7a1c..a557691ba 100644 --- a/tests/test_torchvision.py +++ b/tests/test_torchvision.py @@ -6,6 +6,7 @@ import numpy as np +from sahi.prediction import ObjectPrediction from sahi.utils.cv import read_image from sahi.utils.torchvision import TorchVisionTestConstants @@ -81,6 +82,9 @@ def test_perform_inference_without_mask_output(self): # perform inference torchvision_detection_model.perform_inference(image) original_predictions = torchvision_detection_model.original_predictions + assert original_predictions is not None + assert isinstance(original_predictions, list) + assert len(original_predictions) > 0 from sahi.utils.torchvision import COCO_CLASSES @@ -133,6 +137,8 @@ def test_convert_original_predictions_without_mask_output(self): # convert predictions to ObjectPrediction list torchvision_detection_model.convert_original_predictions() object_prediction_list = torchvision_detection_model.object_prediction_list + assert isinstance(object_prediction_list, list) + assert isinstance(object_prediction_list[0], ObjectPrediction) # compare self.assertEqual(len(object_prediction_list), 7) @@ -164,6 +170,8 @@ def test_convert_original_predictions_with_mask_output(self): # convert predictions to ObjectPrediction list torchvision_detection_model.convert_original_predictions() object_prediction_list = torchvision_detection_model.object_prediction_list + assert isinstance(object_prediction_list, list) + assert isinstance(object_prediction_list[0], ObjectPrediction) # compare self.assertEqual(len(object_prediction_list), 7) diff --git a/tests/test_ultralyticsmodel.py b/tests/test_ultralyticsmodel.py index cdb626d25..f5e997d47 100644 --- a/tests/test_ultralyticsmodel.py +++ b/tests/test_ultralyticsmodel.py @@ -3,6 +3,7 @@ import unittest +from sahi.prediction import ObjectPrediction from sahi.utils.cv import read_image from sahi.utils.file import download_from_url from sahi.utils.ultralytics import ( @@ -11,7 +12,6 @@ download_yolo11n_obb_model, download_yolo11n_seg_model, download_yolov8n_model, - download_yolov8n_seg_model, ) MODEL_DEVICE = "cpu" @@ -76,11 +76,13 @@ def test_perform_inference_yolov8(self): # perform inference detection_model.perform_inference(image) original_predictions = detection_model.original_predictions + assert original_predictions + assert isinstance(original_predictions, list) boxes = original_predictions[0].data # find box of first car detection with conf greater than 0.5 - for box in boxes: + for box in boxes: # type: ignore if box[5].item() == 2: # if category car if box[4].item() > 0.5: break @@ -92,7 +94,7 @@ def test_perform_inference_yolov8(self): for ind, point in enumerate(predicted_bbox): assert point < desired_bbox[ind] + margin and point > desired_bbox[ind] - margin self.assertEqual(len(detection_model.category_names), 80) - for box in boxes: + for box in boxes: # type: ignore self.assertGreaterEqual(box[4].item(), CONFIDENCE_THRESHOLD) def test_perform_inference_yolo11(self): @@ -115,12 +117,14 @@ def test_perform_inference_yolo11(self): # perform inference detection_model.perform_inference(image) original_predictions = detection_model.original_predictions + assert original_predictions + assert isinstance(original_predictions, list) boxes = original_predictions[0].data # verify predictions self.assertEqual(len(detection_model.category_names), 80) - for box in boxes: + for box in boxes: # type: ignore self.assertGreaterEqual(box[4].item(), CONFIDENCE_THRESHOLD) def test_yolo11_segmentation(self): @@ -149,6 +153,8 @@ def test_yolo11_segmentation(self): # Verify segmentation output original_predictions = detection_model.original_predictions + assert original_predictions + assert isinstance(original_predictions, list) boxes = original_predictions[0][0] # Boxes masks = original_predictions[0][1] # Masks @@ -184,6 +190,8 @@ def test_yolo11_obb(self): # Verify OBB predictions original_predictions = detection_model.original_predictions + assert original_predictions + assert isinstance(original_predictions, list) boxes = original_predictions[0][0] # Original box data obb_points = original_predictions[0][1] # OBB points in xyxyxyxy format @@ -201,8 +209,10 @@ def test_yolo11_obb(self): self.assertEqual(len(object_prediction_list), len(boxes)) for object_prediction in object_prediction_list: # Verify confidence threshold + assert isinstance(object_prediction, ObjectPrediction) self.assertGreaterEqual(object_prediction.score.value, CONFIDENCE_THRESHOLD) + assert object_prediction.mask coco_segmentation = object_prediction.mask.segmentation # Verify segmentation exists (converted from OBB) self.assertIsNotNone(coco_segmentation) diff --git a/tests/test_yolov5model.py b/tests/test_yolov5model.py index cefd23925..ae661be0c 100644 --- a/tests/test_yolov5model.py +++ b/tests/test_yolov5model.py @@ -3,10 +3,9 @@ import unittest -import numpy as np - +from sahi.prediction import ObjectPrediction from sahi.utils.cv import read_image -from sahi.utils.yolov5 import Yolov5TestConstants, download_yolov5n_model, download_yolov5s6_model +from sahi.utils.yolov5 import Yolov5TestConstants, download_yolov5n_model MODEL_DEVICE = "cpu" CONFIDENCE_THRESHOLD = 0.3 @@ -70,11 +69,12 @@ def test_perform_inference(self): # perform inference yolov5_detection_model.perform_inference(image) original_predictions = yolov5_detection_model.original_predictions + assert original_predictions is not None boxes = original_predictions.xyxy # find box of first car detection with conf greater than 0.5 - for box in boxes[0]: + for box in boxes[0]: # type: ignore if box[5].item() == 2: # if category car if box[4].item() > 0.5: break @@ -86,7 +86,7 @@ def test_perform_inference(self): for ind, point in enumerate(predicted_bbox): assert point < desired_bbox[ind] + margin and point > desired_bbox[ind] - margin self.assertEqual(len(original_predictions.names), 80) - for box in boxes[0]: + for box in boxes[0]: # type: ignore self.assertGreaterEqual(box[4].item(), CONFIDENCE_THRESHOLD) def test_convert_original_predictions(self): @@ -114,6 +114,9 @@ def test_convert_original_predictions(self): # convert predictions to ObjectPrediction list yolov5_detection_model.convert_original_predictions() object_prediction_list = yolov5_detection_model.object_prediction_list + assert object_prediction_list + assert isinstance(object_prediction_list[0], ObjectPrediction) + assert isinstance(object_prediction_list[2], ObjectPrediction) # compare self.assertEqual(len(object_prediction_list), 3) @@ -132,6 +135,7 @@ def test_convert_original_predictions(self): assert point < desired_bbox[ind] + margin and point > desired_bbox[ind] - margin for object_prediction in object_prediction_list: + assert isinstance(object_prediction, ObjectPrediction) self.assertGreaterEqual(object_prediction.score.value, CONFIDENCE_THRESHOLD) diff --git a/tests/test_yolov8onnx.py b/tests/test_yolov8onnx.py index 268bb53e3..b0c3f2729 100644 --- a/tests/test_yolov8onnx.py +++ b/tests/test_yolov8onnx.py @@ -77,6 +77,7 @@ def test_perform_inference(self): # Perform inference 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