From: https://www.kaggle.com/revanchist95/kernelaa90e7c809
Author: Quang Nguyen
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib as mp
import matplotlib.image as mpimg
import os
import random
file_names = os.listdir("../input/train/")
labels = pd.read_csv('../input/train.csv')
annotation = pd.read_csv("../input/labels.csv")
my_dpi = 96
def get_image(name):
path = os.path.join("../input/train", name)
image = mpimg.imread(path)
plt.figure(figsize=(800/my_dpi, 800/my_dpi), dpi=my_dpi)
imgplot = plt.imshow(image)
def get_label(name):
att = []
img_id = name.split(".")[0]
num_lab = labels.loc[labels["id"] == img_id]["attribute_ids"].values[0].split()
for lab in num_lab:
att.append(annotation.loc[annotation["attribute_id"] == int(lab)]["attribute_name"].values[0])
return(att)
name = random.choice(file_names)
get_image(name)
get_label(name)
import csv
with open("file_names.csv", 'w', newline='') as myfile:
wr = csv.writer(myfile, quoting=csv.QUOTE_ALL)
wr.writerow(file_names)