nikgens

import cv2
import numpy as np
import imutils

def nothing(x):
    pass

# Load an image
img = cv2.imread('ellipse.jpg')

mariocj89

Introduction

When applications are running in production, they become black boxes that need to be traced and monitored. One of the simplest, yet main, ways to do so is logging. Logging allows us - at the time we develop our software - to instruct the program to emit information while the system is running that will be useful for us and our sysadmins.

zhanwenchen

import os
import tensorflow as tf

trained_checkpoint_prefix = 'checkpoints/dev'
export_dir = os.path.join('models', '0') # IMPORTANT: each model folder must be named '0', '1', ... Otherwise it will fail!

loaded_graph = tf.Graph()
with tf.Session(graph=loaded_graph) as sess:
    # Restore from checkpoint
    loader = tf.train.import_meta_graph(trained_checkpoint_prefix + '.meta')

lemon24

This gist tracks the creation of an almost fully functional sqlite3 server in Python 3, using only the multiprocessing.managers standard library module.

But why?

• To see if it can be done.
• To learn about multiprocessing managers.
• Aside from whole-database locking, SQLite (maybe, see below) supports table-level locking between multiple connections in the same process to the same database. A "SQLite server" makes it possible for users in different processes to use SQLite connections that live in the same process.

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TalhaUsuf

Introduction

When applications are running in production, they become black boxes that need to be traced and monitored. One of the simplest, yet main, ways to do so is logging. Logging allows us - at the time we develop our software - to instruct the program to emit information while the system is running that will be useful for us and our sysadmins.