kornia | Differentiable Computer Vision Library | Computer Vision library
kandi X-RAY | kornia Summary
kandi X-RAY | kornia Summary
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.
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Top functions reviewed by kandi - BETA
- 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
kornia Key Features
kornia Examples and Code Snippets
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
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
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
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
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
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
Trending Discussions on kornia
QUESTION
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 21:20If 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).
QUESTION
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 06:14So 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):
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install kornia
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.
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