%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
| 
# 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//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
| 
# 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//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
| 
# 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//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 - 1.2.2-1
- Package Spec generated