automatic import of python-detecto

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+/detecto-1.2.2.tar.gz
diff --git a/python-detecto.spec b/python-detecto.spec
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+%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
+[![Documentation Status](https://readthedocs.org/projects/detecto/badge/?version=latest)](https://detecto.readthedocs.io/en/latest/?badge=latest)
+[![Downloads](https://pepy.tech/badge/detecto)](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"> | ![Video demo of Detecto](assets/demo.gif)
+# 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
+[![Documentation Status](https://readthedocs.org/projects/detecto/badge/?version=latest)](https://detecto.readthedocs.io/en/latest/?badge=latest)
+[![Downloads](https://pepy.tech/badge/detecto)](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"> | ![Video demo of Detecto](assets/demo.gif)
+# 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
+[![Documentation Status](https://readthedocs.org/projects/detecto/badge/?version=latest)](https://detecto.readthedocs.io/en/latest/?badge=latest)
+[![Downloads](https://pepy.tech/badge/detecto)](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"> | ![Video demo of Detecto](assets/demo.gif)
+# 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
diff --git a/sources b/sources
new file mode 100644
index 0000000..a0147cf
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+7be1289cd62815a5354811d3bb712f74  detecto-1.2.2.tar.gz