syuji-higa

API_BASE_URL=http://localhost:8000

pinkerltm

#######################################################
## this code was used in a Jupyter notebook 
## therefore some jupyter specific lines are commented out


#%matplotlib inline
#%config InlineBackend.figure_format = 'retina'
import matplotlib.pyplot as plt
import numpy as np
import torch

sadimanna

'''
"Contrast Limited Adaptive Histogram Equalization"
by Karel Zuiderveld, karel@cv.ruu.nl
in "Graphics Gems IV", Academic Press, 1994

_Author_ -- Siladittya Manna

The below implementation does not assume that the 
X- and Y image resolutions are an integer multiple
of the X- and Y sizes of the contextual regions.

zongfan2

import cv2
from redis import ConnectionPool, Redis
import numpy as np
import json, time
from multithreading import Thread, Event

redis_config = {"server": "localhost",
                "passwd": '',
                "port": '6379',
                "db": 0}

hysts

#!/usr/bin/env python

import argparse
import json
import pathlib
from tensorboard.backend.event_processing import event_accumulator


def main():
    parser = argparse.ArgumentParser()

illume

""" Shows how to use flask and matplotlib together.

Shows SVG, and png.
The SVG is easier to style with CSS, and hook JS events to in browser.

python3 -m venv venv
. ./venv/bin/activate
pip install flask matplotlib
python flask_matplotlib.py
"""

stefanonardo

# MIT License
#
# Copyright (c) 2018 Stefano Nardo https://gist.github.com/stefanonardo
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:

mkocabas

mkdir coco
cd coco
mkdir images
cd images

wget http://images.cocodataset.org/zips/train2017.zip
wget http://images.cocodataset.org/zips/val2017.zip
wget http://images.cocodataset.org/zips/test2017.zip
wget http://images.cocodataset.org/zips/unlabeled2017.zip

jhjensen2

import numpy as np
import matplotlib.pyplot as plt
from IPython.display import clear_output
import time

n_particles = 100

for x in range(50):
  clear_output(wait=True)
  positions_x = [np.random.random() for i in range(n_particles)]

jotterbach

import seaborn as sns
import numpy as np
import numpy.random as rd
import matplotlib.pyplot as plt
from scipy.spatial import distance as dist
import scipy.linalg as la
import itertools as it

%matplotlib inline