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from PIL import Image, ImageDraw, ImageFont | |
import sys | |
ShowText = sys.argv[1] | |
font = ImageFont.truetype('arialbd.ttf', 15) #load the font | |
size = font.getsize(ShowText) #calc the size of text in pixels | |
image = Image.new('1', size, 1) #create a b/w image | |
draw = ImageDraw.Draw(image) | |
draw.text((0, 0), ShowText, font=font) #render the text to the bitmap |
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from pyspark.sql.functions import col | |
from pyspark.sql.functions import sum as spark_sum | |
def count_null(col_name): | |
return spark_sum(col(col_name).isNull().cast('integer')).alias(col_name) | |
# Build up a list of column expressions, one per column. |
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http://nadbordrozd.github.io/blog/2016/05/20/text-classification-with-word2vec/ | |
https://blog.keras.io/using-pre-trained-word-embeddings-in-a-keras-model.html | |
https://stackoverflow.com/questions/31321209/doc2vec-how-to-get-document-vectors | |
http://linanqiu.github.io/2015/10/07/word2vec-sentiment/ | |
https://rare-technologies.com/doc2vec-tutorial/ | |
https://radimrehurek.com/gensim/models/doc2vec.html#gensim.models.doc2vec.TaggedDocument | |
https://www.kaggle.com/tj2552/sentiment-classification-in-5-classes-doc2vec | |
https://github.com/ibrahimsharaf/Doc2vec |
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http://nadbordrozd.github.io/blog/2016/05/20/text-classification-with-word2vec/ | |
https://blog.keras.io/using-pre-trained-word-embeddings-in-a-keras-model.html | |
https://stackoverflow.com/questions/31321209/doc2vec-how-to-get-document-vectors | |
http://linanqiu.github.io/2015/10/07/word2vec-sentiment/ |
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"""Script to illustrate usage of tf.estimator.Estimator in TF v1.3""" | |
import tensorflow as tf | |
from tensorflow.examples.tutorials.mnist import input_data as mnist_data | |
from tensorflow.contrib import slim | |
from tensorflow.contrib.learn import ModeKeys | |
from tensorflow.contrib.learn import learn_runner | |
# Show debugging output |
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# USAGE | |
# python build_face_dataset.py --cascade haarcascade_frontalface_default.xml --output dataset/adrian | |
# import the necessary packages | |
from imutils.video import VideoStream | |
import argparse | |
import imutils | |
import time | |
import cv2 | |
import os |
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import os | |
cmd = "wget -c --progress=bar https://docs.microsoft.com/en-us/azure/cognitive-services/face/images/landmarks.1.jpg" | |
os.system(cmd) |