pravj

#!/usr/bin/env bash

# bash script to generate tree structure of a directory
# Pravendra Singh (@hackpravj)

pwd=$(pwd)
find $pwd -print | sed -e "s;$pwd;\.;g;s;[^/]*\/;|__;g;s;__|; |;g"

pravj

# Python 3 program to find the stem 
# of given list of words 

# function to find the stem (longest 
# common substring) from the string array 
def findstem(arr): 

	# Determine size of the array 
	n = len(arr) 

pravj

[{"name": "sample 1", "image": "url 1"}, {"name": "sample 2", "image": "url 2"}]

pravj

var i = 99
while i > 0
    println(i + " bottles of beer on the wall, " + i + "bottles of beer.")
    var num = i - 1
    if i == 1 {
      var num = "no more"
    }
    println("Take one down and pass it around, " + num + "bottles of beer on the wall.")
println("No more bottles of beer on the wall, no more bottles of beer.")
println("Go to the store and buy some more, 99 bottles of beer on the wall.")

pravj

import cv2

# grayscale version of the single color image
image_gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)

# bilateral filter is effective when you want to
# keep the edges sharp while removing noise
image_gray = cv2.bilateralFilter(image_gray, 10, 50, 50)

# find contour in gray scale image after applying erosion and dilation

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# Reference: https://code.likeagirl.io/finding-dominant-colour-on-an-image-b4e075f98097
from sklearn.cluster import KMeans

# word block colors (color palette)
block_colors = []

# change dimension to (width x height, color-channels)
screen = screen.reshape((screen.shape[0] * screen.shape[1], 3))

# Collect 8 major colors in the image using KMeans clustering

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from gensim.models.keyedvectors import KeyedVectors

# load local word2vec model
model = KeyedVectors.load_word2vec_format(
    os.getenv('SEMANTRIS_SOLVER_WORD2VEC_PATH'),
    binary=True
)

# list of tuples containing the word and similarity score
associated_word_tuples = model.most_similar(word, topn=20)

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import pytesseract

image_text = pytesseract.image_to_string(cropped_image)

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import cv2
import numpy as np

# load and find the dimension attritbutes of the template image
template_img = cv2.imread('./gray-template.png', 0)
w, h = template_img.shape[::-1]

# OpenCV's template matching method
res_img = cv2.matchTemplate(
    screen2, template_img, cv2.TM_CCOEFF_NORMED

pravj

import cv2
import pyautogui

# using a screen size specific region
screen = pyautogui.screenshot(region=(200, 100, 1000, 700))
screen_img_gray = cv2.cvtColor(np.array(screen), cv2.COLOR_RGB2GRAY)