peteflorence

Here's a simple implementation of bilinear interpolation on tensors using PyTorch.

I wrote this up since I ended up learning a lot about options for interpolation in both the numpy and PyTorch ecosystems. More generally than just interpolation, too, it's also a nice case study in how PyTorch magically can put very numpy-like code on the GPU (and by the way, do autodiff for you too).

For interpolation in PyTorch, this open issue calls for more interpolation features. There is now a nn.functional.grid_sample() feature but at least at first this didn't look like what I needed (but we'll come back to this later).

In particular I wanted to take an image, W x H x C, and sample it many times at different random locations. Note also that this is different than upsampling which exhaustively samples and also doesn't give us fle

balzer82

#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Tensorflow DNNRegressor in Python
# CC-BY-2.0 Paul Balzer
# see: http://www.cbcity.de/deep-learning-tensorflow-dnnregressor-einfach-erklaert
#
TRAINING = True
WITHPLOT = False

decrispell

/**
 * Convert standard camera intrinsic and extrinsic parameters to a vtkCamera instance for rendering
 * Assume square pixels and 0 skew (for now).
 *
 * focal_len : camera focal length (units pixels)
 * nx,ny : image dimensions in pixels
 * principal_pt: camera principal point,
 *    i.e. the intersection of the principal ray with the image plane (units pixels)
 * camera_rot, camera_trans : rotation, translation matrix mapping world points to camera coordinates
 * depth_min, depth_max : needed to set the clipping range

ernestum

import numpy as np
from scipy.ndimage.interpolation import map_coordinates
from scipy.ndimage.filters import gaussian_filter

def elastic_transform(image, alpha, sigma, random_state=None):
    """Elastic deformation of images as described in [Simard2003]_.
    .. [Simard2003] Simard, Steinkraus and Platt, "Best Practices for
       Convolutional Neural Networks applied to Visual Document Analysis", in
       Proc. of the International Conference on Document Analysis and
       Recognition, 2003.

berak

#include <opencv2/opencv.hpp>
#include <opencv2/core/core_c.h> // needed for IplImage ;(

#include <dlib/image_processing.h>
#include <dlib/opencv/cv_image.h>

// IMPORTANT:
// do **not** use namespace cv or dlib,
// but prefix everything correctly !!!

tzutalin

#include <iostream>
#include <string>
#include <ctime>
#include <cstdlib>

namespace da{
struct nullstream: std::ostream{
    nullstream(): std::ostream(0){}
};

tnarihi

amroamroamro

Epipolar Geometry example

MATLAB code to show epipolar geometry with OpenCV (using mexopencv), based on code from OpenCV-Python Tutorials.

bearpaw

Caffe + Ubuntu 12.04 64bit + CUDA 6.5 配置说明

本步骤能实现用Intel核芯显卡来进行显示， 用NVIDIA GPU进行计算。

1. 安装开发所需的依赖包

安装开发所需要的一些基本包

sudo apt-get install build-essential
sudo apt-get install vim cmake git
sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev libhdf5-serial-dev

tony612

• create tasks T{NNNN} asign them
• create a branch with name like "T{NNNN}-boo-hoo"
• git checkout -b T1234-boo-foo
• commit changes on that branch until it gets ready to be reviewed
• git commit -am 'first'
• git commit -am 'now it works'
• check if it's lint free (NOTE: it runs lint against only modified files)
• arc lint
• push a review request to the server. This will create a diff with id D{NNNN}
• arc diff