Showing 9 open source projects for "opencv-mingw"

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  • 1
    Face Mask Detection

    Face Mask Detection

    Face Mask Detection system based on computer vision and deep learning

    Face Mask Detection system based on computer vision and deep learning using OpenCV and Tensorflow/Keras. Face Mask Detection System built with OpenCV, Keras/TensorFlow using Deep Learning and Computer Vision concepts in order to detect face masks in static images as well as in real-time video streams. Amid the ongoing COVID-19 pandemic, there are no efficient face mask detection applications which are now in high demand for transportation means, densely populated areas, residential districts, large-scale manufacturers and other enterprises to ensure safety. ...
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  • 2
    Semantic Segmentation in PyTorch

    Semantic Segmentation in PyTorch

    Semantic segmentation models, datasets & losses implemented in PyTorch

    Semantic segmentation models, datasets and losses implemented in PyTorch. PyTorch and Torchvision needs to be installed before running the scripts, together with PIL and opencv for data-preprocessing and tqdm for showing the training progress. PyTorch v1.1 is supported (using the new supported tensoboard); can work with earlier versions, but instead of using tensoboard, use tensoboardX. Poly learning rate, where the learning rate is scaled down linearly from the starting value down to zero during training. ...
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  • 3
    DCVGAN

    DCVGAN

    DCVGAN: Depth Conditional Video Generation, ICIP 2019.

    This paper proposes a new GAN architecture for video generation with depth videos and color videos. The proposed model explicitly uses the information of depth in a video sequence as additional information for a GAN-based video generation scheme to make the model understands scene dynamics more accurately. The model uses pairs of color video and depth video for training and generates a video using the two steps. Generate the depth video to model the scene dynamics based on the geometrical...
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  • 4
    Rainbow

    Rainbow

    Rainbow: Combining Improvements in Deep Reinforcement Learning

    Combining improvements in deep reinforcement learning. Results and pretrained models can be found in the releases. Data-efficient Rainbow can be run using several options (note that the "unbounded" memory is implemented here in practice by manually setting the memory capacity to be the same as the maximum number of timesteps).
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  • 5
    BossSensor

    BossSensor

    Hide screen when boss is approaching

    ...When the system detects that the trained face appears in the camera view, the program automatically triggers actions such as hiding the user’s screen or switching to a safe display. The software relies on libraries such as OpenCV, TensorFlow, and Python-based machine learning tools to perform face detection and classification. Training the system requires a dataset of labeled images representing the boss and other people so that the model can learn to differentiate between them.
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  • 6

    Face Recognition

    World's simplest facial recognition api for Python & the command line

    Face Recognition is the world's simplest face recognition library. It allows you to recognize and manipulate faces from Python or from the command line using dlib's (a C++ toolkit containing machine learning algorithms and tools) state-of-the-art face recognition built with deep learning. Face Recognition is highly accurate and is able to do a number of things. It can find faces in pictures, manipulate facial features in pictures, identify faces in pictures, and do face recognition on a...
    Downloads: 7 This Week
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  • 7
    Machine Learning for OpenCV

    Machine Learning for OpenCV

    M. Beyeler (2017). Machine Learning for OpenCV

    M. Beyeler (2017). Machine Learning for OpenCV: Intelligent image processing with Python. Packt Publishing Ltd., ISBN 978-178398028-4.
    Downloads: 0 This Week
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  • 8

    PyVocabularyTree

    A vocabulary tree for image classification using OpenCV

    A vocabulary tree for image classification have been designed to be integrated in mobile robotic applications. It is a learning schema based on decission trees, bags of features and inverted files. The design provides training and optimization parameters that have been characterized using several detectors and descriptors for several input datasets. Evaluation tests performed on public image databases allow to compare obtained results with previously published literature. All the tools...
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  • 9
    New Python bindings for the popular OpenCV image processing library.
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