Python get_image_files Examples

Example #1

File: convert.py Project: Seth-Park/fundus-diabetes-detection

def main(datadir, convert_dir, crop_size):
    try:
        os.mkdir(convert_dir)
    except OSError:
        pass

    filenames = data_util.get_image_files(datadir)

    print('Resizing images in {} to {}'.format(datadir, convert_dir))

    n = len(filenames)

    batch_size = 500
    batches = n // batch_size + 1
    p = Pool()

    args = []

    for f in filenames:
        args.append((convert_size, (datadir, convert_dir, f, crop_size)))

    for i in range(batches):
        print('batch {:>2} / {}'.format(i + 1, batches))
        p.map(convert, args[i * batch_size : (i + 1) * batch_size])

    p.close()
    p.join()
    print('Done')

Example #2

File: convert.py Project: sonyeric/fundus-diabetes-detection

def main(datadir, convert_dir, crop_size):
    try:
        os.mkdir(convert_dir)
    except OSError:
        pass

    filenames = data_util.get_image_files(datadir)

    print('Resizing images in {} to {}'.format(datadir, convert_dir))

    n = len(filenames)

    batch_size = 500
    batches = n // batch_size + 1
    p = Pool()

    args = []

    for f in filenames:
        args.append((convert_size, (datadir, convert_dir, f, crop_size)))

    for i in range(batches):
        print('batch {:>2} / {}'.format(i + 1, batches))
        p.map(convert, args[i * batch_size:(i + 1) * batch_size])

    p.close()
    p.join()
    print('Done')

Example #3

File: test_data_util.py Project: Seth-Park/fundus-diabetes-detection

import numpy as np
from time import time
import pdb
import skimage
import matplotlib.pyplot as plt
import data_util


DATA_DIR = "converted"

files = data_util.get_image_files(DATA_DIR)

images = data_util.load_images(files)

MEAN = data_util.compute_mean(files)
STD = data_util.compute_std(files)

images_normalized = []
for img in images:
    img = img - MEAN[:, np.newaxis, np.newaxis]
    img = img / STD[:, np.newaxis, np.newaxis]
    images_normalized.append(img)

images_normalized = np.array(images_normalized)
original_augmented = data_util.parallel_augment(images)
normalized_augmented = data_util.parallel_augment(images_normalized)

original = images[3]
normalized = images_normalized[3]
original = original.transpose(1, 2, 0)
normalized = normalized.transpose(1, 2, 0)

Example #4

import numpy as np
import cPickle
import data_util

DATA_DIR = '/nikel/dhpark/fundus/kaggle/original/training/train_medium'

files = data_util.get_image_files(DATA_DIR)
mean = data_util.compute_mean_across_channels(files)
std = data_util.compute_std_across_channels(files)

print("computing done")
print("dumping...")
mean.dump("mean.dat")
std.dump("std.dat")

Example #5

import numpy as np
from time import time
import data
import data_util
from matplotlib import pyplot as plt

aug_params = {
    'zoom_range': (1 / 1.15, 1.15),
    'rotation_range': (0, 360),
    'shear_range': (0, 0),
    'translation_range': (-40, 40),
    'do_flip': True,
    'allow_stretch': True,
}

files = data_util.get_image_files('testing')
X = data_util.load_images(files)
mean, std = data_util.compute_mean_and_std(files)
print(mean, std)

print("Number of images: {}".format(len(X)))

# start = time()
# result = data.batch_perturb_and_augment(X, 500, 500, aug_params=aug_params, sigma=0.5)
# end = time()
# print("Processing without parallelization took {} seconds".format(end - start))

#start = time()
#result = data.parallel_perturb_and_augment(X, 500, 500, aug_params=aug_params, sigma=0.5)
#result = data.parallel_perturb_and_augment(X, 500, 500)
#end = time()

Example #6

File: test_run.py Project: Seth-Park/fundus-diabetes-detection

import data_util
from matplotlib import pyplot as plt



aug_params = {
    'zoom_range': (1 / 1.15, 1.15),
    'rotation_range': (0, 360),
    'shear_range': (0, 0),
    'translation_range': (-40, 40),
    'do_flip': True,
    'allow_stretch': True,
}


files = data_util.get_image_files('testing')
X = data_util.load_images(files)
mean, std = data_util.compute_mean_and_std(files)
print(mean, std)

print("Number of images: {}".format(len(X)))

# start = time()
# result = data.batch_perturb_and_augment(X, 500, 500, aug_params=aug_params, sigma=0.5)
# end = time()
# print("Processing without parallelization took {} seconds".format(end - start))

#start = time()
#result = data.parallel_perturb_and_augment(X, 500, 500, aug_params=aug_params, sigma=0.5)
#result = data.parallel_perturb_and_augment(X, 500, 500)
#end = time()