frankie567

import asyncio

import streamlit as st
from httpx_oauth.clients.google import GoogleOAuth2

st.title("Google OAuth2 flow")

"## Configuration"

client_id = st.text_input("Client ID")

ctmakro

# if input image is in range 0..1, please first multiply img by 255
# assume image is ndarray of shape [height, width, channels] where channels can be 1, 3 or 4
def imshow(img):
    import cv2
    import IPython
    _,ret = cv2.imencode('.jpg', img) 
    i = IPython.display.Image(data=ret)
    IPython.display.display(i)

iamtodor

# This is an example of using 
# https://github.com/tensorflow/models/blob/master/research/object_detection/dataset_tools/create_pascal_tf_record.py
# The structure should be like PASCAL VOC format dataset
# +Dataset
#   +Annotations
#   +JPEGImages
# python create_tfrecords_from_xml.py --image_dir=dataset/JPEGImages 
#                                      --annotations_dir=dataset/Annotations 
#                                      --label_map_path=object-detection.pbtxt 
#                                      --output_path=data.record

pydevops

• 0.1. References
• 0.2. Other cheatsheets
• 0.3. Manage multiple gcloud config configurations
  • 0.3.1. Switch gcloud context with gcloud config
• 0.4. Credentials
• 0.5. info
• 0.6. projects
• 0.7. zones & regions
• 0.8. Organization
• 0.9. billing

santisbon

Updating a feature branch

First we'll update your local master branch. Go to your local project and check out the branch you want to merge into (your local master branch)

$ git checkout master

Fetch the remote, bringing the branches and their commits from the remote repository. You can use the -p, --prune option to delete any remote-tracking references that no longer exist in the remote. Commits to master will be stored in a local branch, remotes/origin/master.

jshaw

Byobu Commands
==============

        byobu                              Screen manager


Level 0 Commands (Quick Start)
------------------------------

        <F2>                               Create a new window

larsmans

"""
Three ways of computing the Hellinger distance between two discrete
probability distributions using NumPy and SciPy.
"""

import numpy as np
from scipy.linalg import norm
from scipy.spatial.distance import euclidean