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dataset.py
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dataset.py
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import os
import json
import numpy as np
IMAGE_SIZE = 75
IMAGE_DIR = os.getcwd() + "/images/"
def read_dataset(file='train.json'):
with open(file) as f:
print('Reading', file)
data = json.load(f)
print('Dataset size', len(data))
return data
def write_dataset(file, data):
print('Writing to', file)
with open(file, 'w') as f:
json.dump(data, f, separators=(',', ':'))
def write_response(ids, results):
f = open('results.csv', 'w')
f.write('id,is_iceberg\n')
for i in range(len(ids)):
f.write(str(ids[i]) + ',' + str(results[i][0]) + '\n')
f.close()
def visualize(data):
from PIL import Image
for item in data:
# print(item['id'], item['is_iceberg'], item['inc_angle'])
band1 = np.asarray(item['band_1']).reshape(IMAGE_SIZE, IMAGE_SIZE)
band2 = np.asarray(item['band_2']).reshape(IMAGE_SIZE, IMAGE_SIZE)
# total_max = max(total_max, np.max(band2))
# total_min = min(total_min, np.min(band2))
scaled1 = ((band1 - 46) * (255.0 / 35.0))
scaled2 = ((band2 - 46) * (255.0 / 21.0))
Image.fromarray(scaled1.astype(np.uint8)).save(
IMAGE_DIR + ('ice_' if item['is_iceberg'] else 'ship_') + item['id'] + '_1.jpg'
)
Image.fromarray(scaled2.astype(np.uint8)).save(
IMAGE_DIR + ('ice_' if item['is_iceberg'] else 'ship_') + item['id'] + '_2.jpg'
)
def check_last_100(data):
for item in data[-100:-1]:
print(item['is_iceberg'])
# visualize(read_dataset())
if __name__ == '__main__':
visualize(read_dataset('train.json'))