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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() |
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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) |
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f = open('workfile' ,'w') | |
new_content = 'someContent' | |
f.write(new_content) | |
f.flush() | |
... |
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f = open('workfile' ,'w') | |
new_content = 'someContent' | |
f.write(new_content) | |
f.seek(0) | |
line = f.readline() | |
print line | |
>> someContent |
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f = open('workfile' ,'w') | |
new_content = 'someContent' | |
f.write(new_content) |
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file_content = open('dados/some_sb.csv', encoding='utf-8', mode='w') | |
new_content = '1,someContent' | |
file_content.write(new_content) |
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f = open('workfile' ,'r') | |
f.readline() | |
f.readlines() | |
f.read() |
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file_content = open('dados/some_sb.csv', encoding='utf-8', mode='w') | |
new_content = '1,someContent' | |
file_content.write(new_content) |
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f = open('workfile' ,'r', encoding='utf-8') |
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import elasticsearch | |
import elasticsearch_dsl | |
from elasticsearch_dsl.connections import connections, get_connection | |
import config as config_module | |
config = config_module.get_config() | |
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