juergenhoetzel

user=juergenhoetzel
while curl -s "https://api.github.com/users/$user/starred?per_page=100&page=${page:-1}" \
		    |jq -r -e '.[].full_name' && [[ ${PIPESTATUS[1]} != 4 ]]; do
    let page++
done

yoonjechoi

# ==================================================================
# module list
# ------------------------------------------------------------------
# darknet       latest (git)
# torch         latest (git)
# python        3.8    (apt)
# pytorch       latest (pip)
# onnx          latest (pip)
# theano        latest (git)
# tensorflow    latest (pip)

tvst

import streamlit as st
import os
import sys
import importlib.util

# Parse command-line arguments.
if len(sys.argv) > 1:
    folder = os.path.abspath(sys.argv[1])
else:
    folder = os.path.abspath(os.getcwd())

ines

"""
Example of a Streamlit app for an interactive Prodigy dataset viewer that also lets you
run simple training experiments for NER and text classification.

Requires the Prodigy annotation tool to be installed: https://prodi.gy
See here for details on Streamlit: https://streamlit.io.
"""
import streamlit as st
from prodigy.components.db import connect
from prodigy.models.ner import EntityRecognizer, merge_spans, guess_batch_size

Saafke

Google Summer of Code 2019 with OpenCV

Learning-based Super Resolution

Student: Xavier Weber
Mentors: Vladimir Tyan & Yida Wang
Student on the same project: Fanny Monori

Link to accomplished work:

• PR in the opencv_contrib repository: opencv_contrib/pull/2231

pculliton

import base64
import numpy as np
from pycocotools import _mask as coco_mask
import typing as t
import zlib
def encode_binary_mask(mask: np.ndarray) -> t.Text:
 """Converts a binary mask into OID challenge encoding ascii text."""

 # check input mask --
 if mask.dtype != np.bool:

schiffty

def frames_to_TC (frames):
    h = int(frames / 86400) 
    m = int(frames / 1440) % 60 
    s = int((frames % 1440)/24) 
    f = frames % 1440 % 24
    return ( "%02d:%02d:%02d:%02d" % ( h, m, s, f))


# Breakdown of the steps above:
# Hours: Divide frames by 86400 (# of frames in an hour at 24fps). Round down to nearest integer.

duhaime

import warnings
from skimage.measure import compare_ssim
from skimage.transform import resize
from scipy.stats import wasserstein_distance
from scipy.misc import imsave
from scipy.ndimage import imread
import numpy as np
import cv2

##

dayitv89

import os
# convert all image like `<Any_name>_2x.png` to `<Any_name>@2x.png`
[os.rename(f, f.replace('_2x', '@2x')) for f in os.listdir('.') if not f.startswith('.')]

willmasters

Updated 2025-01-17 thanks to Yemster's comment.

This should work on any architecture of Amazon Linux 2. 
(_Although not tested , should also work for Amazon Linux 2023_).

**Prereq** 
- visit https://johnvansickle.com/ffmpeg/ to grab the link to the relevant tarball for your specific server architecture.
- Use `uname -a` to find out your arch if unknown

### TL;DR