| /* | |
| MIT License | |
| Copyright (c) 2024 Maxwell Zeryck | |
| 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: | |
| The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. | |
| THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTW |
| #!python3 | |
| ''' | |
| This is a demo of how you can use the CoreML framework (via objc_util) to classify images in Pythonista. It downloads the trained 'MobileNet' CoreML model from the Internet, and uses it to classify images that are either taken with the camera, or picked from the photo library. | |
| ''' | |
| import requests | |
| import os | |
| import io | |
| import photos | |
| import dialogs |
| #!python3 | |
| from ctypes import c_int, c_void_p, POINTER, byref, c_ulong | |
| from objc_util import ( | |
| load_framework, c, ns, ObjCInstance, nsdata_to_bytes, NSString, NSData, NSNumber, | |
| ObjCClass, NSArray, NSDictionary | |
| ) | |
| from enum import Enum, IntFlag | |
| from typing import Union | |
| import datetime |
| # coding: utf-8 | |
| import photos | |
| import console | |
| from objc_util import * | |
| CIFilter, CIImage, CIContext, CIDetector, CIVector = map(ObjCClass, ['CIFilter', 'CIImage', 'CIContext', 'CIDetector', 'CIVector']) | |
| def take_photo(filename='.temp.jpg'): | |
| img = photos.capture_image() |
| #!/usr/bin/env python | |
| # | |
| # Convert a set of similarly-structured .xlsx files into a SQLite DB. | |
| # | |
| # For example, say you have hundreds of Excel files in a directory | |
| # called "big-analysis-project" and that each of these Excel files | |
| # has a worksheet containing the same set of columns. Rather than | |
| # having hundreds of separate Excel files, it would be handy to have | |
| # all their data inside one relational database management system. | |
| # |
| # coding: utf-8 | |
| from objc_util import * | |
| import threading | |
| NSBundle = ObjCClass('NSBundle') | |
| LocalAuthentication = NSBundle.bundleWithPath_('/System/Library/Frameworks/LocalAuthentication.framework') | |
| LocalAuthentication.load() | |
| LAContext = ObjCClass('LAContext') | |
| # authenticate() will raise one of these exceptions when authentication |
ffmpeg -i "Apache Sqoop Tutorial Part 1.mp4" -c copy -bsf:v h264_mp4toannexb -f mpegts intermediate1.ts
ffmpeg -i "Apache Sqoop Tutorial Part 2.mp4" -c copy -bsf:v h264_mp4toannexb -f mpegts intermediate2.ts
ffmpeg -i "Apache Sqoop Tutorial Part 3.mp4" -c copy -bsf:v h264_mp4toannexb -f mpegts intermediate3.ts
ffmpeg -i "Apache Sqoop Tutorial Part 4.mp4" -c copy -bsf:v h264_mp4toannexb -f mpegts intermediate4.ts
ffmpeg -i "concat:intermediate1.ts|intermediate2.ts|intermediate3.ts|intermediate4.ts" -c copy -bsf:a aac_adtstoasc "Apache Sqoop Tutorial.mp4"
| #!/bin/sh | |
| # Just copy and paste the lines below (all at once, it won't work line by line!) | |
| # MAKE SURE YOU ARE HAPPY WITH WHAT IT DOES FIRST! THERE IS NO WARRANTY! | |
| function abort { | |
| echo "$1" | |
| exit 1 | |
| } | |
| set -e |