Ais otakbeku

This is a small demo of how to create a library in Rust and call it from Python (both CPython and PyPy) using the CFFI instead of ctypes.

CFFI is nice because:

	#!/usr/bin/python

	import os
	import errno
	import sqlite3
	import sys
	from time import time
	from cPickle import loads, dumps

	import logging

	import re
	import pyfpgrowth as fp
	import numpy as np


	class treeNode:
	def __init__(self, name_value, num_of_occur, parent_node):
	self.name = name_value
	self.count = num_of_occur
	self.next_node = None

	import cv2
	from datetime import timedelta
	import time

	# Manual timestamp extractor
	def get_frame_from_timestamp(filepath, timestamp):
	str_timestamp = time.strptime(timestamp,'%H:%M:%S')
	exact_time = timedelta(hours=str_timestamp.tm_hour,minutes=str_timestamp.tm_min,seconds=str_timestamp.tm_sec).total_seconds()
	cap = cv2.VideoCapture(filepath)
	camtime = cap.get(cv2.CAP_PROP_POS_MSEC)/1000

	# I _hope_ this is everything but there may be some other libs I forgot or you don't have,
	# so just use pip to install them if vqgan_clip.py complains about missing modules.
	# Make some folder, cd into it, and run this.
	git clone https://github.com/openai/CLIP
	git clone https://github.com/CompVis/taming-transformers
	pip install ftfy regex tqdm omegaconf pytorch-lightning
	pip install kornia
	pip install stegano
	apt install exempi
	pip install python-xmp-toolkit


	import torch
	import torchvision.models as models
	import torchvision.transforms as transforms
	import torch.nn.functional as F
	import os
	import requests
	import json
	import io
	from PIL import Image

	# XAI
	from captum.attr import Saliency
	from captum.attr import visualization as viz
	import torchvision

	def attribute_image_features(model, algorithm, input_data, target_label, **kwargs):
	model.zero_grad()
	tensor_attributions = algorithm.attribute(
	input_data,
	target=target_label,

	class ComplexImgAugTransform:
	# Based on imgaug documentation
	def __init__(self):
	sometimes = lambda aug: iaa.Sometimes(0.5, aug)
	self.aug = iaa.Sequential([# apply the following augmenters to most images
	iaa.Scale((299, 299)),
	iaa.Fliplr(0.5), # horizontally flip 50% of all images
	iaa.Flipud(0.2), # vertically flip 20% of all images
	# crop images by -5% to 10% of their height/width
	sometimes(iaa.CropAndPad(

	import cv2
	import matplotlib.pyplot as plt

	img = cv2.imread('/path/')
	img_blur = cv2.medianBlur(img[:,:,1], 5) # Using Green channel color
	plt.imshow(img_blur)

	_, test_img = cv2.threshold(img_blur, 160, 255, cv2.THRESH_BINARY)
	test_contour, _ = cv2.findContours(test_img.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
	largest_contour = max(test_contour, key=cv2.contourArea)

	import matplotlib.pyplot as plt
	import cv2

	img = cv2.imread('/path/')
	split_img = cv2.split(img) # Splitting channel

	histSize = 256
	histRange = (0, 256) # the upper boundary is exclusive
	accumulate = False