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# import library | |
import matplotlib.pyplot as plt | |
def get_complementary_color(source_color): | |
# creates dictionary of colors | |
colors = {1:("#A3218E","Red-violet"), | |
2:("#592F93","Violet"), | |
3:("#21409A","Blue-violet"), | |
4:("#0465B2","Blue"), |
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#!/usr/bin/env python3 | |
# -*- coding: utf-8 -*- | |
""" | |
Author: Wilame Lima | |
https://vallant.in/ | |
This program is free software; you can redistribute it and/or modify | |
it under the terms of the GNU General Public License as published by | |
the Free Software Foundation; either version 2 of the License, or | |
(at your option) any later version. |
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# turn camera on | |
video_capture = cv2.VideoCapture(0) | |
while True: | |
# get last recorded frame | |
_, color = video_capture.read() | |
# transform color -> grayscale | |
bw = cv2.cvtColor(color, cv2.COLOR_BGR2GRAY) | |
# detect the face and blur it | |
blur = find_and_blur(bw, color) |
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def find_and_blur(bw, color): | |
# detect al faces | |
faces = cascade.detectMultiScale(bw, 1.1, 4) | |
# get the locations of the faces | |
for (x, y, w, h) in faces: | |
# select the areas where the face was found | |
roi_color = color[y:y+h, x:x+w] | |
# blur the colored image | |
blur = cv2.GaussianBlur(roi_color, (101,101), 0) | |
# Insert ROI back into image |
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import cv2 | |
cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml') |
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text = 'Sociology is the scientific study of society, patterns of social relationships, social interaction, and culture of everyday life. It is a social science that uses various methods of empirical investigation and critical analysis to develop a body of knowledge about social order, acceptance, and change or social evolution. While some sociologists conduct research that may be applied directly to social policy and welfare, others focus primarily on refining the theoretical understanding of social processes. Subject matter ranges from the micro-sociology level of individual agency and interaction to the macro level of systems and the social structure.' | |
text = aruana.preprocess(text, remove_stopwords=True, stem=False) | |
print(text) |
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# import Aruana for text preprocessing | |
from aruana import Aruana | |
# start Aruana | |
aruana = Aruana('en') | |
# define text to be processed | |
text = "I told you that she was not happy" | |
text = aruana.preprocess(text, remove_stopwords=True, stem=False) | |
print(text) |
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doc_en = nlp_en(u'Brazil\'s new foreign minister believes climate change is a Marxist plot. He says that China is behind everything.') | |
doc_pt = nlp_pt(u'Novo ministro das Relações Exteriores do Brasil acredita que as mudanças climáticas são uma conspiração marxista. Para ele, a China está por trás de tudo isso.') | |
doc_fr = nlp_fr(u'Le nouveau Ministre des Affaires Etrangers brésilien croit que le changement climatique est une conspiration Marxiste. Selon lui, la Chine est derrière tout cela.') | |
sentences_en = list(doc_en.sents) | |
sentences_pt = list(doc_pt.sents) | |
sentences_fr = list(doc_fr.sents) | |
print(sentences_en) | |
print(sentences_pt) |
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lemmas_en = [token.lemma_ for token in doc_en] | |
lemmas_pt = [token.lemma_ for token in doc_pt] | |
lemmas_fr = [token.lemma_ for token in doc_fr] | |
print(lemmas_en) | |
print(lemmas_pt) | |
print(lemmas_fr) |
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