Vadbeg

import coremltools as ct
import numpy as np
import torch
from coremltools.models.neural_network import quantization_utils

from PIL import Image
from colorizers import siggraph17, load_img
from colorizers.siggraph17 import ModelHead

Vadbeg

import Foundation
#if canImport(FoundationNetworking)
import FoundationNetworking
#endif

var semaphore = DispatchSemaphore (value: 0)

let parameters = [
  [
    "key": "image",

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[tool.black]
line-length = 88
target-version = ["py38"]

[tool.isort]
profile = "black"
multi_line_output = 3

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exclude: _pb2\.py$
repos:
- repo: https://github.com/psf/black
  rev: 22.3.0
  hooks:
    - id: black
      args: [ --skip-string-normalization ]
-   repo: https://github.com/pre-commit/pre-commit-hooks
    rev: v4.0.1
    hooks:

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variety.viewport.typeset = function(strAddress) {
	var driver = variety.viewport.driver;
	delete driver.typesetString;
	delete driver.typesetScale;
	var s = this.recoverStrFromMemory(strAddress);
	if(s == "0") s = "10";
    driver.typesetString = s.split('\n');
};

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1. News Scrapper
  GitHub: https://github.com/Vadbeg/NewsScrapper
  Description:
    Project for reading news from rss files. Includes command line utility and web application. 
    The main thing about this project is fasts, flexible and easy acces to news in format you want.
    You can export every article into fb2, pdf and html.
  Languages:
    1. Python
  Technologies:
    1. Django

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def add_backgound(image):
    """
    Adds background to given image using PIL
    """
    
    image_shape = image.shape
    image_height = image_shape[0]
    image_width = image_shape[1]
    
    backgound = create_blank_image(image_height, 

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def create_blank_image(height, width, rgb_color=(0, 0, 255)):
    """
    Creates np.array, each channel of which is filled with value from rgb_color
    
    Was stolen from:
    :source: https://stackoverflow.com/questions/4337902/how-to-fill-opencv-image-with-one-solid-color
    """
    
    image = np.zeros((height, width, 3), np.uint8)
    color = tuple(reversed(rgb_color))

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def remove_background(img, threshold):
    """
    This method removes background from your image
    
    :param img: cv2 image
    :type img: np.array
    :param threshold: threshold value for cv2.threshold
    :type threshold: float
    :return: RGBA image
    :rtype: np.ndarray