LimaGuilherme

    import cv2
    import dlib
    import numpy as np
    
    initial_image = cv2.imread('images/image9.jpg')
    initial_image_in_rgb = cv2.cvtColor(initial_image, cv2.COLOR_BGR2RGB)
    reference_image = initial_image_in_rgb.copy()

    classifier_path = dlib.shape_predictor('classifier/shape_predictor_68_face_landmarks.dat')
    frontal_face_detector = dlib.get_frontal_face_detector()

LimaGuilherme

    import cv2
    
    group_of_people_image = cv2.imread('images/image7.jpg')
    frontal_face_classifier = cv2.CascadeClassifier('classifier/haarcascade_frontalface_default.xml')
    image_in_gray_scale = cv2.cvtColor(group_of_people_image, cv2.COLOR_BGR2GRAY)
    
    faces = frontal_face_classifier.detectMultiScale(image=image_in_gray_scale, scaleFactor=1.3, minNeighbors=6)

    for (x_axis, y_axis, weight, height) in faces:
        cv2.rectangle(group_of_people_image, (x_axis, y_axis), (x_axis + weight, y_axis + height), (255, 0, 0), 2)

LimaGuilherme

f = open('workfile' ,'w')
new_content = 'someContent'
f.write(new_content)
f.flush()

...

LimaGuilherme

f = open('workfile' ,'w')
new_content = 'someContent'
f.write(new_content)
f.seek(0)
line = f.readline()
print line
>> someContent

LimaGuilherme

f = open('workfile' ,'w')
new_content = 'someContent'
f.write(new_content)

LimaGuilherme

file_content = open('dados/some_sb.csv', encoding='utf-8', mode='w')

new_content = '1,someContent'

file_content.write(new_content)

LimaGuilherme

f = open('workfile' ,'r')

f.readline()
f.readlines()
f.read()

LimaGuilherme

file_content = open('dados/some_sb.csv', encoding='utf-8', mode='w')

new_content = '1,someContent'

file_content.write(new_content)

LimaGuilherme

f = open('workfile' ,'r', encoding='utf-8')

LimaGuilherme

import elasticsearch
import elasticsearch_dsl

from elasticsearch_dsl.connections import connections, get_connection

import config as config_module

config = config_module.get_config()