import os
import random
import numpy as np
import scipy.misc
class GetData():
def __init__(self, data_dir):
images_list =[]
labels_list = []
self.source_list = []
examples = 0
print("loading images")
label_dir = os.path.join(data_dir, "Labels")
image_dir = os.path.join(data_dir, "Images")
for label_root, dir, files in os.walk(label_dir):
for file in files:
if not file.endswith((".png", ".jpg", ".gif")):
continue
try:
folder = os.path.relpath(label_root, label_dir)
image_root = os.path.join(image_dir, folder)
image = scipy.misc.imread(os.path.join(image_root, file))
resize_image = scipy.misc.imresize(image, [256, 256], interp='nearest')
label = scipy.misc.imread(os.path.join(label_root, file))
resize_label = scipy.misc.imresize(label, [256, 256], interp='nearest')
images_list.append(resize_image[..., np.newaxis]/255)
labels_list.append((resize_label>1).astype(np.int64))
examples = examples + 1
except Exception as e:
print(e)
print("finished loading images")
self.examples = examples
print("Number of examples found: ", examples)
self.images = np.array(images_list)
self.labels = np.array(labels_list)
def next_batch(self, batch_size):
if len(self.source_list) < batch_size:
new_source = list(range(self.examples))
random.shuffle(new_source)
self.source_list.extend(new_source)
examples_idx = self.source_list[:batch_size]
del self.source_list[:batch_size]
return self.images[examples_idx,...], self.labels[examples_idx,...]
def next_batch_test(self, start, batch_size):
return self.images[start:start+batch_size, ...], self.labels[start:start+batch_size, ...]
