lelandbatey

Description

This simple script will take a picture of a whiteboard and use parts of the ImageMagick library with sane defaults to clean it up tremendously.

The script is here:

#!/bin/bash
convert "$1" -morphology Convolve DoG:15,100,0 -negate -normalize -blur 0x1 -channel RBG -level 60%,91%,0.1 "$2"

Results

hans

from collections import Counter, defaultdict
import itertools
import os
import random
import re

import numpy as np

EMBEDDING_FILE = "/u/nlp/data/depparser/nn/data/embeddings/en-cw.txt"
EMBEDDING_SERIALIZED = "embeddings.npz"

kvn219

shgidi

#https://gist.github.com/stared/dfb4dfaf6d9a8501cd1cc8b8cb806d2e
class PlotLosses(keras.callbacks.Callback):
    
    def __init__(self,imgs):
        super(PlotLosses, self).__init__()
        self.imgs=imgs
    
    def on_train_begin(self, logs={}):
        self.i = 0
        self.x = []

rajshah4

jdhao

"""
in this script, we calculate the image per channel mean and standard
deviation in the training set, do not calculate the statistics on the
whole dataset, as per here http://cs231n.github.io/neural-networks-2/#datapre
"""

import numpy as np
from os import listdir
from os.path import join, isdir
from glob import glob

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

jbburant

## GOAL:
## re-create a figure similar to Fig. 2 in Wilson et al. (2018), 
## Nature 554: 183-188. Available from:
## https://www.nature.com/articles/nature25479#s1
##
## combines a boxplot (or violin) with the raw data, by splitting each
## category location in two (box on left, raw data on right)


# initial set-up ----------------------------------------------------------

huatangzhi

# encoding:utf8

from scipy.io import loadmat
import pandas as pd
import numpy as np

mat_train = loadmat('devkit/cars_train_annos.mat')
mat_test = loadmat('devkit/cars_test_annos.mat')
meta = loadmat('devkit/cars_meta.mat')

myungsub

Papers from Super SloMo references

• Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation [Paper]
  • Huaizu Jiang, Deqing Sun, Varun Jampani, Ming-Hsuan Yang, Erik Learned-Miller, Jan Kautz
  • CVPR 2018 (splotlight)
• Video frame synthesis using deep voxel flow [Paper] [Code]
  • Z. Liu, R. Yeh, X. Tang, Y. Liu, and A. Agarwala.
  • ICCV 2017
• Video frame interpolation via adaptive separable convolution. [Paper] [Code]