kornia | Differentiable Computer Vision Library | Computer Vision library

 by   kornia Python Version: 0.7.2 License: Apache-2.0

kandi X-RAY | kornia Summary

kandi X-RAY | kornia Summary

kornia is a Python library typically used in Artificial Intelligence, Computer Vision, Deep Learning, Pytorch, Tensorflow applications. kornia has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. However kornia build file is not available. You can install using 'pip install kornia' or download it from GitHub, PyPI.

Inspired by existing packages, this library is composed by a subset of packages containing operators that can be inserted within neural networks to train models to perform image transformations, epipolar geometry, depth estimation, and low-level image processing such as filtering and edge detection that operate directly on tensors.
Support
    Quality
      Security
        License
          Reuse

            kandi-support Support

              kornia has a medium active ecosystem.
              It has 8273 star(s) with 842 fork(s). There are 126 watchers for this library.
              There were 2 major release(s) in the last 6 months.
              There are 212 open issues and 549 have been closed. On average issues are closed in 218 days. There are 37 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of kornia is 0.7.2

            kandi-Quality Quality

              kornia has 0 bugs and 0 code smells.

            kandi-Security Security

              kornia has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              kornia code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              kornia is licensed under the Apache-2.0 License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              kornia releases are available to install and integrate.
              Deployable package is available in PyPI.
              kornia has no build file. You will be need to create the build yourself to build the component from source.
              Installation instructions are not available. Examples and code snippets are available.
              kornia saves you 12152 person hours of effort in developing the same functionality from scratch.
              It has 46783 lines of code, 3572 functions and 373 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed kornia and discovered the below as its top functions. This is intended to give you an instant insight into kornia implemented functionality, and help decide if they suit your requirements.
            • Solve the pose of the pose of a pose problem
            • Transform points_1
            • Wrapper for linalg values
            • Convert a tensor to homogeneous space
            • Random crop generator
            • Generator for bbox
            • Adapted from uniform distribution
            • Clip the Boxes to the given tuple
            • Crop and resize a tensor
            • Generates random affine generator
            • Build the depth regression
            • Compute the forward computation
            • Return a copy of this tensor
            • Clip the tensor to the given bound
            • Create the sampler
            • Performs the forward computation
            • Center crop of a tensor
            • Forward computation
            • Convert a list of boxes to a 3D tensor
            • Generate random crop size generator
            • This is the main function
            • Generate random rectangle parameters
            • Center crop a tensor
            • Run the Homography Regression App
            • Calculate heatmap
            • Create 2D image histogram
            • Compute the motion from the essential solution
            Get all kandi verified functions for this library.

            kornia Key Features

            No Key Features are available at this moment for kornia.

            kornia Examples and Code Snippets

            How to run
            Pythondot img1Lines of Code : 106dot img1no licencesLicense : No License
            copy iconCopy
            pip install git+https://github.com/lukemelas/pytorch-pretrained-gans
            
            pip install hydra-core==1.1.0dev5 pytorch_lightning albumentations tqdm retry kornia
            
            config
            ├── data_gen
            │   ├── generated.yaml  # <- for generating data with 1 laten  
            Submitting to Image Matching Challenge 2021,Extracting the features
            Jupyter Notebookdot img2Lines of Code : 104dot img2no licencesLicense : No License
            copy iconCopy
            cd ..
            mkdir imc2021-sample-kornia-submission
            cd imc2021-sample-kornia-submission
            
            pip install torch torchvision kornia
            pip install kornia_moons --no-deps
            
            import matplotlib.pyplot as plt
            import numpy as np
            import cv2
            import torch
            import kornia as K
            i  
            Preliminary evaluation,Creating config json file,Reading results
            Jupyter Notebookdot img3Lines of Code : 65dot img3no licencesLicense : No License
            copy iconCopy
            cd ../imc2021-sample-kornia-submission
            
            hashname='dog-affnet-hardnet8-degensac'
            res_fname = os.path.join('../image-matching-benchmark/packed-val', f'{hashname}.json')
            
            with open(res_fname, 'r') as f:
                results = json.load(f)
            
            submission_name = resu  
            kornia - main video-face detection
            Pythondot img4Lines of Code : 87dot img4License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            import argparse
            
            import cv2
            import numpy as np
            import torch
            
            import kornia as K
            from kornia.contrib import FaceDetector, FaceDetectorResult, FaceKeypoint
            
            
            def draw_keypoint(img: np.ndarray, det: FaceDetectorResult, kpt_type: FaceKeypoint) -> np.n  
            kornia - main-train-object detection
            Pythondot img5Lines of Code : 85dot img5License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            import hydra
            import torch
            import torchvision
            import torchvision.transforms as T
            from hydra.core.config_store import ConfigStore
            from hydra.utils import to_absolute_path
            
            import kornia as K
            from kornia.x import Configuration, ModelCheckpoint, ObjectDe  
            kornia - main-train-semantic segmentation
            Pythondot img6Lines of Code : 67dot img6License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            import hydra
            import numpy as np
            import torch
            import torch.nn as nn
            import torchvision
            from hydra.core.config_store import ConfigStore
            from hydra.utils import to_absolute_path
            
            import kornia as K
            from kornia.x import Configuration, Lambda, SemanticSeg  

            Community Discussions

            QUESTION

            Cannot create the calibration cache for the QAT model in tensorRT
            Asked 2022-Mar-14 at 21:20

            I've trained a quantized model (with help of quantized-aware-training method in pytorch). I want to create the calibration cache to do inference in INT8 mode by TensorRT. When create calib cache, I get the following warning and the cache is not created:

            ...

            ANSWER

            Answered 2022-Mar-14 at 21:20

            If the ONNX model has Q/DQ nodes in it, you may not need calibration cache because quantization parameters such as scale and zero point are included in the Q/DQ nodes. You can run the Q/DQ ONNX model directly in TensorRT execution provider in OnnxRuntime (>= v1.9.0).

            Source https://stackoverflow.com/questions/71368760

            QUESTION

            How do I rotate a PyTorch image tensor around it's center in a way that supports autograd?
            Asked 2020-Oct-05 at 06:14

            I'd like to randomly rotate an image tensor (B, C, H, W) around it's center (2d rotation I think?). I would like to avoid using NumPy and Kornia, so that I basically only need to import from the torch module. I'm also not using torchvision.transforms, because I need it to be autograd compatible. Essentially I'm trying to create an autograd compatible version of torchvision.transforms.RandomRotation() for visualization techniques like DeepDream (so I need to avoid artifacts as much as possible).

            ...

            ANSWER

            Answered 2020-Oct-05 at 06:14

            So the grid generator and the sampler are sub-modules of the Spatial Transformer (JADERBERG, Max, et al.). These sub-modules are not trainable, they let you apply a learnable, as well as non-learnable, spatial transformation. Here I take these two submodules and use them to rotate an image by theta using PyTorch's functions F.affine_grid and F.affine_sample (these functions are implementations of the generator and the sampler, respectively):

            Source https://stackoverflow.com/questions/64197754

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install kornia

            You can install using 'pip install kornia' or download it from GitHub, PyPI.
            You can use kornia like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

            Support

            We appreciate all contributions. If you are planning to contribute back bug-fixes, please do so without any further discussion. If you plan to contribute new features, utility functions or extensions, please first open an issue and discuss the feature with us. Please, consider reading the CONTRIBUTING notes. The participation in this open source project is subject to Code of Conduct.
            Find more information at:

            Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items

            Find more libraries
            Install
          • PyPI

            pip install kornia

          • CLONE
          • HTTPS

            https://github.com/kornia/kornia.git

          • CLI

            gh repo clone kornia/kornia

          • sshUrl

            git@github.com:kornia/kornia.git

          • Stay Updated

            Subscribe to our newsletter for trending solutions and developer bootcamps

            Agree to Sign up and Terms & Conditions

            Share this Page

            share link