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Syntopically reading "The Power of Habit"

Pravendra Singh pravj

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Syntopically reading "The Power of Habit"
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View semantris-blocks-contour-detection.py
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
View semantris-blocks-k-means.py
# 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
View semantris-associated-word.py
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)
View semantris-arcade-ocr.py
import pytesseract
image_text = pytesseract.image_to_string(cropped_image)
View semantris-arcade-template-matching.py
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
View semantris-arcade-gray-scale-conversion.py
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)
@pravj
pravj / docker_ce_setup.sh
Created Jul 8, 2018
Shell script to setup Docker Community Edition on an Ubuntu:16.04
View docker_ce_setup.sh
#
# Setup the Docker repository
#
echo "update apt package index"
sudo apt-get update
echo "allow apt to use HTTPS"
sudo apt-get install \
apt-transport-https \
View radio-head.js
document.addEventListener('DOMContentLoaded', function() {
document.querySelectorAll('label')[0].classList.add('button-press');
document.addEventListener('click', function(e) {
var e = e || window.event,
target = e.target || e.srcElement,
text = target.textContent || target.innerText;
if (target.tagName.toLowerCase() == 'label') {
var labels = document.querySelectorAll("label");
View radio-head.css
/*
Hide radio button (the round disc)
we will use just the label to create pushbutton effect
*/
input[type=radio] {
display:none;
margin:10px;
}
/*
@pravj
pravj / NBA-CHA-TOR-event.json
Created Feb 16, 2017
Data for an event in the NBA game between Toronto Raptors and Charlotte Hornets
View NBA-CHA-TOR-event.json
[{"eventId": "6", "visitor": {"abbreviation": "CHA", "players": [{"playerid": 101107, "lastname": "Williams", "jersey": "2", "firstname": "Marvin", "position": "F"}, {"playerid": 203087, "lastname": "Lamb", "jersey": "3", "firstname": "Jeremy", "position": "G"}, {"playerid": 201587, "lastname": "Batum", "jersey": "5", "firstname": "Nicolas", "position": "G-F"}, {"playerid": 202391, "lastname": "Lin", "jersey": "7", "firstname": "Jeremy", "position": "G"}, {"playerid": 1626151, "lastname": "Harrison", "jersey": "9", "firstname": "Aaron", "position": "G"}, {"playerid": 202689, "lastname": "Walker", "jersey": "15", "firstname": "Kemba", "position": "G"}, {"playerid": 203798, "lastname": "Hairston", "jersey": "19", "firstname": "PJ", "position": "F-G"}, {"playerid": 203148, "lastname": "Roberts", "jersey": "22", "firstname": "Brian", "position": "G"}, {"playerid": 203584, "lastname": "Daniels", "jersey": "30", "firstname": "Troy", "position": "G"}, {"playerid": 203469, "lastname": "Zeller", "jersey": "40", "first
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