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@@ -0,0 +1 @@ +/detecto-1.2.2.tar.gz diff --git a/python-detecto.spec b/python-detecto.spec new file mode 100644 index 0000000..5487c6e --- /dev/null +++ b/python-detecto.spec @@ -0,0 +1,417 @@ +%global _empty_manifest_terminate_build 0 +Name: python-detecto +Version: 1.2.2 +Release: 1 +Summary: Build fully-functioning computer vision models with PyTorch +License: MIT License +URL: https://github.com/alankbi/detecto +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/6f/cf/3959004db3c861d89be02853cde49c5fd10204468dca248cd77a6d89ec43/detecto-1.2.2.tar.gz +BuildArch: noarch + +Requires: python3-matplotlib +Requires: python3-opencv-python +Requires: python3-pandas +Requires: python3-torch +Requires: python3-torchvision +Requires: python3-tqdm + +%description +[](https://detecto.readthedocs.io/en/latest/?badge=latest) +[](https://pepy.tech/project/detecto) +Detecto is a Python package that allows you to build fully-functioning computer vision and object detection models with just 5 lines of code. +Inference on still images and videos, transfer learning on custom datasets, and serialization of models to files are just a few of Detecto's features. +Detecto is also built on top of PyTorch, allowing an easy transfer of models between the two libraries. +The table below shows a few examples of Detecto's performance: +Still Image | Video +<img src="./assets/apple_orange.png" alt="Detecto still image" width="500px"> |  +# Installation +To install Detecto using pip, run the following command: +`pip3 install detecto` +Installing with pip should download all of Detecto's dependencies automatically. +However, if an issue arises, you can manually download the dependencies listed in the [requirements.txt](requirements.txt) file. +# Usage +The power of Detecto comes from its simplicity and ease of use. Creating and running a pre-trained +[Faster R-CNN ResNet-50 FPN](https://pytorch.org/docs/stable/torchvision/models.html#object-detection-instance-segmentation-and-person-keypoint-detection) +from PyTorch's model zoo takes 4 lines of code: +```python +from detecto.core import Model +from detecto.visualize import detect_video +model = Model() # Initialize a pre-trained model +detect_video(model, 'input_video.mp4', 'output.avi') # Run inference on a video +``` +Below are several more examples of things you can do with Detecto: +### Transfer Learning on Custom Datasets +Most of the times, you want a computer vision model that can detect custom objects. With Detecto, you can train a model on a custom dataset with 5 lines of code: +```python +from detecto.core import Model, Dataset +dataset = Dataset('custom_dataset/') # Load images and label data from the custom_dataset/ folder +model = Model(['dog', 'cat', 'rabbit']) # Train to predict dogs, cats, and rabbits +model.fit(dataset) +model.predict(...) # Start using your trained model! +``` +### Inference and Visualization +When using a model for inference, Detecto returns predictions in an easy-to-use format and provides several visualization tools: +```python +from detecto.core import Model +from detecto import utils, visualize +model = Model() +image = utils.read_image('image.jpg') # Helper function to read in images +labels, boxes, scores = model.predict(image) # Get all predictions on an image +predictions = model.predict_top(image) # Same as above, but returns only the top predictions +print(labels, boxes, scores) +print(predictions) +visualize.show_labeled_image(image, boxes, labels) # Plot predictions on a single image +images = [...] +visualize.plot_prediction_grid(model, images) # Plot predictions on a list of images +visualize.detect_video(model, 'input_video.mp4', 'output.avi') # Run inference on a video +visualize.detect_live(model) # Run inference on a live webcam +``` +### Advanced Usage +If you want more control over how you train your model, Detecto lets you do just that: +```python +from detecto import core, utils +from torchvision import transforms +import matplotlib.pyplot as plt +# Convert XML files to CSV format +utils.xml_to_csv('training_labels/', 'train_labels.csv') +utils.xml_to_csv('validation_labels/', 'val_labels.csv') +# Define custom transforms to apply to your dataset +custom_transforms = transforms.Compose([ + transforms.ToPILImage(), + transforms.Resize(800), + transforms.ColorJitter(saturation=0.3), + transforms.ToTensor(), + utils.normalize_transform(), +]) +# Pass in a CSV file instead of XML files for faster Dataset initialization speeds +dataset = core.Dataset('train_labels.csv', 'images/', transform=custom_transforms) +val_dataset = core.Dataset('val_labels.csv', 'val_images') # Validation dataset for training +# Create your own DataLoader with custom options +loader = core.DataLoader(dataset, batch_size=2, shuffle=True) +# Use MobileNet instead of the default ResNet +model = core.Model(['car', 'truck', 'boat', 'plane'], model_name='fasterrcnn_mobilenet_v3_large_fpn') +losses = model.fit(loader, val_dataset, epochs=15, learning_rate=0.001, verbose=True) +plt.plot(losses) # Visualize loss throughout training +plt.show() +model.save('model_weights.pth') # Save model to a file +# Directly access underlying torchvision model for even more control +torch_model = model.get_internal_model() +print(type(torch_model)) +``` +For more examples, visit the [docs](https://detecto.readthedocs.io/), which includes a [quickstart](https://detecto.readthedocs.io/en/latest/usage/quickstart.html) tutorial. +Alternatively, check out the [demo on Colab](https://colab.research.google.com/drive/1ISaTV5F-7b4i2QqtjTa7ToDPQ2k8qEe0). +# API Documentation +The full API documentation can be found at [detecto.readthedocs.io](https://detecto.readthedocs.io/en/latest/api/index.html). +The docs are split into three sections, each corresponding to one of Detecto's modules: +### Core +The [detecto.core](https://detecto.readthedocs.io/en/latest/api/core.html) module contains the central classes of the package: Dataset, DataLoader, and Model. +These are used to read in a labeled dataset and train a functioning object detection model. +### Utils +The [detecto.utils](https://detecto.readthedocs.io/en/latest/api/utils.html) module contains a variety of useful helper functions. +With it, you can read in images, convert XML files into CSV files, apply standard transforms to images, and more. +### Visualize +The [detecto.visualize](https://detecto.readthedocs.io/en/latest/api/visualize.html) module is used to display labeled images, plot predictions, and run object detection on videos. +# Contributing +All issues and pull requests are welcome! To run the code locally, first fork the repository and then run the following commands on your computer: +```bash +git clone https://github.com/<your-username>/detecto.git +cd detecto +# Recommended to create a virtual environment before the next step +pip3 install -r requirements.txt +``` +When adding code, be sure to write unit tests and docstrings where necessary. +Tests are located in `detecto/tests` and can be run using pytest: +`python3 -m pytest` +To generate the documentation locally, run the following commands: +```bash +cd docs +make html +``` +The documentation can then be viewed at `docs/_build/html/index.html`. +# Contact +Detecto was created by [Alan Bi](https://www.alanbi.com/). Feel free to reach out on [Twitter](https://twitter.com/alankbi) or through [email](mailto:alan.bi326@gmail.com)! + +%package -n python3-detecto +Summary: Build fully-functioning computer vision models with PyTorch +Provides: python-detecto +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-detecto +[](https://detecto.readthedocs.io/en/latest/?badge=latest) +[](https://pepy.tech/project/detecto) +Detecto is a Python package that allows you to build fully-functioning computer vision and object detection models with just 5 lines of code. +Inference on still images and videos, transfer learning on custom datasets, and serialization of models to files are just a few of Detecto's features. +Detecto is also built on top of PyTorch, allowing an easy transfer of models between the two libraries. +The table below shows a few examples of Detecto's performance: +Still Image | Video +<img src="./assets/apple_orange.png" alt="Detecto still image" width="500px"> |  +# Installation +To install Detecto using pip, run the following command: +`pip3 install detecto` +Installing with pip should download all of Detecto's dependencies automatically. +However, if an issue arises, you can manually download the dependencies listed in the [requirements.txt](requirements.txt) file. +# Usage +The power of Detecto comes from its simplicity and ease of use. Creating and running a pre-trained +[Faster R-CNN ResNet-50 FPN](https://pytorch.org/docs/stable/torchvision/models.html#object-detection-instance-segmentation-and-person-keypoint-detection) +from PyTorch's model zoo takes 4 lines of code: +```python +from detecto.core import Model +from detecto.visualize import detect_video +model = Model() # Initialize a pre-trained model +detect_video(model, 'input_video.mp4', 'output.avi') # Run inference on a video +``` +Below are several more examples of things you can do with Detecto: +### Transfer Learning on Custom Datasets +Most of the times, you want a computer vision model that can detect custom objects. With Detecto, you can train a model on a custom dataset with 5 lines of code: +```python +from detecto.core import Model, Dataset +dataset = Dataset('custom_dataset/') # Load images and label data from the custom_dataset/ folder +model = Model(['dog', 'cat', 'rabbit']) # Train to predict dogs, cats, and rabbits +model.fit(dataset) +model.predict(...) # Start using your trained model! +``` +### Inference and Visualization +When using a model for inference, Detecto returns predictions in an easy-to-use format and provides several visualization tools: +```python +from detecto.core import Model +from detecto import utils, visualize +model = Model() +image = utils.read_image('image.jpg') # Helper function to read in images +labels, boxes, scores = model.predict(image) # Get all predictions on an image +predictions = model.predict_top(image) # Same as above, but returns only the top predictions +print(labels, boxes, scores) +print(predictions) +visualize.show_labeled_image(image, boxes, labels) # Plot predictions on a single image +images = [...] +visualize.plot_prediction_grid(model, images) # Plot predictions on a list of images +visualize.detect_video(model, 'input_video.mp4', 'output.avi') # Run inference on a video +visualize.detect_live(model) # Run inference on a live webcam +``` +### Advanced Usage +If you want more control over how you train your model, Detecto lets you do just that: +```python +from detecto import core, utils +from torchvision import transforms +import matplotlib.pyplot as plt +# Convert XML files to CSV format +utils.xml_to_csv('training_labels/', 'train_labels.csv') +utils.xml_to_csv('validation_labels/', 'val_labels.csv') +# Define custom transforms to apply to your dataset +custom_transforms = transforms.Compose([ + transforms.ToPILImage(), + transforms.Resize(800), + transforms.ColorJitter(saturation=0.3), + transforms.ToTensor(), + utils.normalize_transform(), +]) +# Pass in a CSV file instead of XML files for faster Dataset initialization speeds +dataset = core.Dataset('train_labels.csv', 'images/', transform=custom_transforms) +val_dataset = core.Dataset('val_labels.csv', 'val_images') # Validation dataset for training +# Create your own DataLoader with custom options +loader = core.DataLoader(dataset, batch_size=2, shuffle=True) +# Use MobileNet instead of the default ResNet +model = core.Model(['car', 'truck', 'boat', 'plane'], model_name='fasterrcnn_mobilenet_v3_large_fpn') +losses = model.fit(loader, val_dataset, epochs=15, learning_rate=0.001, verbose=True) +plt.plot(losses) # Visualize loss throughout training +plt.show() +model.save('model_weights.pth') # Save model to a file +# Directly access underlying torchvision model for even more control +torch_model = model.get_internal_model() +print(type(torch_model)) +``` +For more examples, visit the [docs](https://detecto.readthedocs.io/), which includes a [quickstart](https://detecto.readthedocs.io/en/latest/usage/quickstart.html) tutorial. +Alternatively, check out the [demo on Colab](https://colab.research.google.com/drive/1ISaTV5F-7b4i2QqtjTa7ToDPQ2k8qEe0). +# API Documentation +The full API documentation can be found at [detecto.readthedocs.io](https://detecto.readthedocs.io/en/latest/api/index.html). +The docs are split into three sections, each corresponding to one of Detecto's modules: +### Core +The [detecto.core](https://detecto.readthedocs.io/en/latest/api/core.html) module contains the central classes of the package: Dataset, DataLoader, and Model. +These are used to read in a labeled dataset and train a functioning object detection model. +### Utils +The [detecto.utils](https://detecto.readthedocs.io/en/latest/api/utils.html) module contains a variety of useful helper functions. +With it, you can read in images, convert XML files into CSV files, apply standard transforms to images, and more. +### Visualize +The [detecto.visualize](https://detecto.readthedocs.io/en/latest/api/visualize.html) module is used to display labeled images, plot predictions, and run object detection on videos. +# Contributing +All issues and pull requests are welcome! To run the code locally, first fork the repository and then run the following commands on your computer: +```bash +git clone https://github.com/<your-username>/detecto.git +cd detecto +# Recommended to create a virtual environment before the next step +pip3 install -r requirements.txt +``` +When adding code, be sure to write unit tests and docstrings where necessary. +Tests are located in `detecto/tests` and can be run using pytest: +`python3 -m pytest` +To generate the documentation locally, run the following commands: +```bash +cd docs +make html +``` +The documentation can then be viewed at `docs/_build/html/index.html`. +# Contact +Detecto was created by [Alan Bi](https://www.alanbi.com/). Feel free to reach out on [Twitter](https://twitter.com/alankbi) or through [email](mailto:alan.bi326@gmail.com)! + +%package help +Summary: Development documents and examples for detecto +Provides: python3-detecto-doc +%description help +[](https://detecto.readthedocs.io/en/latest/?badge=latest) +[](https://pepy.tech/project/detecto) +Detecto is a Python package that allows you to build fully-functioning computer vision and object detection models with just 5 lines of code. +Inference on still images and videos, transfer learning on custom datasets, and serialization of models to files are just a few of Detecto's features. +Detecto is also built on top of PyTorch, allowing an easy transfer of models between the two libraries. +The table below shows a few examples of Detecto's performance: +Still Image | Video +<img src="./assets/apple_orange.png" alt="Detecto still image" width="500px"> |  +# Installation +To install Detecto using pip, run the following command: +`pip3 install detecto` +Installing with pip should download all of Detecto's dependencies automatically. +However, if an issue arises, you can manually download the dependencies listed in the [requirements.txt](requirements.txt) file. +# Usage +The power of Detecto comes from its simplicity and ease of use. Creating and running a pre-trained +[Faster R-CNN ResNet-50 FPN](https://pytorch.org/docs/stable/torchvision/models.html#object-detection-instance-segmentation-and-person-keypoint-detection) +from PyTorch's model zoo takes 4 lines of code: +```python +from detecto.core import Model +from detecto.visualize import detect_video +model = Model() # Initialize a pre-trained model +detect_video(model, 'input_video.mp4', 'output.avi') # Run inference on a video +``` +Below are several more examples of things you can do with Detecto: +### Transfer Learning on Custom Datasets +Most of the times, you want a computer vision model that can detect custom objects. With Detecto, you can train a model on a custom dataset with 5 lines of code: +```python +from detecto.core import Model, Dataset +dataset = Dataset('custom_dataset/') # Load images and label data from the custom_dataset/ folder +model = Model(['dog', 'cat', 'rabbit']) # Train to predict dogs, cats, and rabbits +model.fit(dataset) +model.predict(...) # Start using your trained model! +``` +### Inference and Visualization +When using a model for inference, Detecto returns predictions in an easy-to-use format and provides several visualization tools: +```python +from detecto.core import Model +from detecto import utils, visualize +model = Model() +image = utils.read_image('image.jpg') # Helper function to read in images +labels, boxes, scores = model.predict(image) # Get all predictions on an image +predictions = model.predict_top(image) # Same as above, but returns only the top predictions +print(labels, boxes, scores) +print(predictions) +visualize.show_labeled_image(image, boxes, labels) # Plot predictions on a single image +images = [...] +visualize.plot_prediction_grid(model, images) # Plot predictions on a list of images +visualize.detect_video(model, 'input_video.mp4', 'output.avi') # Run inference on a video +visualize.detect_live(model) # Run inference on a live webcam +``` +### Advanced Usage +If you want more control over how you train your model, Detecto lets you do just that: +```python +from detecto import core, utils +from torchvision import transforms +import matplotlib.pyplot as plt +# Convert XML files to CSV format +utils.xml_to_csv('training_labels/', 'train_labels.csv') +utils.xml_to_csv('validation_labels/', 'val_labels.csv') +# Define custom transforms to apply to your dataset +custom_transforms = transforms.Compose([ + transforms.ToPILImage(), + transforms.Resize(800), + transforms.ColorJitter(saturation=0.3), + transforms.ToTensor(), + utils.normalize_transform(), +]) +# Pass in a CSV file instead of XML files for faster Dataset initialization speeds +dataset = core.Dataset('train_labels.csv', 'images/', transform=custom_transforms) +val_dataset = core.Dataset('val_labels.csv', 'val_images') # Validation dataset for training +# Create your own DataLoader with custom options +loader = core.DataLoader(dataset, batch_size=2, shuffle=True) +# Use MobileNet instead of the default ResNet +model = core.Model(['car', 'truck', 'boat', 'plane'], model_name='fasterrcnn_mobilenet_v3_large_fpn') +losses = model.fit(loader, val_dataset, epochs=15, learning_rate=0.001, verbose=True) +plt.plot(losses) # Visualize loss throughout training +plt.show() +model.save('model_weights.pth') # Save model to a file +# Directly access underlying torchvision model for even more control +torch_model = model.get_internal_model() +print(type(torch_model)) +``` +For more examples, visit the [docs](https://detecto.readthedocs.io/), which includes a [quickstart](https://detecto.readthedocs.io/en/latest/usage/quickstart.html) tutorial. +Alternatively, check out the [demo on Colab](https://colab.research.google.com/drive/1ISaTV5F-7b4i2QqtjTa7ToDPQ2k8qEe0). +# API Documentation +The full API documentation can be found at [detecto.readthedocs.io](https://detecto.readthedocs.io/en/latest/api/index.html). +The docs are split into three sections, each corresponding to one of Detecto's modules: +### Core +The [detecto.core](https://detecto.readthedocs.io/en/latest/api/core.html) module contains the central classes of the package: Dataset, DataLoader, and Model. +These are used to read in a labeled dataset and train a functioning object detection model. +### Utils +The [detecto.utils](https://detecto.readthedocs.io/en/latest/api/utils.html) module contains a variety of useful helper functions. +With it, you can read in images, convert XML files into CSV files, apply standard transforms to images, and more. +### Visualize +The [detecto.visualize](https://detecto.readthedocs.io/en/latest/api/visualize.html) module is used to display labeled images, plot predictions, and run object detection on videos. +# Contributing +All issues and pull requests are welcome! To run the code locally, first fork the repository and then run the following commands on your computer: +```bash +git clone https://github.com/<your-username>/detecto.git +cd detecto +# Recommended to create a virtual environment before the next step +pip3 install -r requirements.txt +``` +When adding code, be sure to write unit tests and docstrings where necessary. +Tests are located in `detecto/tests` and can be run using pytest: +`python3 -m pytest` +To generate the documentation locally, run the following commands: +```bash +cd docs +make html +``` +The documentation can then be viewed at `docs/_build/html/index.html`. +# Contact +Detecto was created by [Alan Bi](https://www.alanbi.com/). Feel free to reach out on [Twitter](https://twitter.com/alankbi) or through [email](mailto:alan.bi326@gmail.com)! + +%prep +%autosetup -n detecto-1.2.2 + +%build +%py3_build + +%install +%py3_install +install -d -m755 %{buildroot}/%{_pkgdocdir} +if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi +if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi +if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi +if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi +pushd %{buildroot} +if [ -d usr/lib ]; then + find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/lib64 ]; then + find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/bin ]; then + find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/sbin ]; then + find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst +fi +touch doclist.lst +if [ -d usr/share/man ]; then + find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst +fi +popd +mv %{buildroot}/filelist.lst . +mv %{buildroot}/doclist.lst . + +%files -n python3-detecto -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Wed May 10 2023 Python_Bot <Python_Bot@openeuler.org> - 1.2.2-1 +- Package Spec generated @@ -0,0 +1 @@ +7be1289cd62815a5354811d3bb712f74 detecto-1.2.2.tar.gz |