class BatchLoader(object):
"""
This class abstracts away the loading of images.
Images can either be loaded singly, or in a batch. The latter is used for
the asyncronous data layer to preload batches while other processing is
performed.
"""
def __init__(self, params, result):
self.result = result
self.batch_size = params['batch_size']
self.pascal_root = params['pascal_root']
self.im_shape = params['im_shape']
self.im_mean = params['im_mean']
# get list of image indexes.
list_file = params['split'] + '.txt'
self.indexlist = [line.rstrip('\n') for line in open(
osp.join(self.pascal_root, 'ImageSets/Main', list_file))]
self._cur = 0 # current image
# this class does some simple data-manipulations
self.transformer = SimpleTransformer()
self.transformer.set_mean(self.im_mean)
print "BatchLoader initialized with {} images".format(
len(self.indexlist))
def load_next_image(self):
"""
Load the next image in a batch.
"""
# Did we finish an epoch?
if self._cur == len(self.indexlist):
self._cur = 0
shuffle(self.indexlist)
# Load an image
index = self.indexlist[self._cur] # Get the image index
image_file_name = index + '.jpg'
im = np.asarray(Image.open(
osp.join(self.pascal_root, 'JPEGImages', image_file_name)))
im = scipy.misc.imresize(im, self.im_shape) # resize
# do a simple horizontal flip as data augmentation
flip = np.random.choice(2)*2-1
im = im[:, ::flip, :]
# Load and prepare ground truth
multilabel = np.zeros(20).astype(np.float32)
anns = load_pascal_annotation(index, self.pascal_root)
for label in anns['gt_classes']:
# in the multilabel problem we don't care how MANY instances
# there are of each class. Only if they are present.
# The "-1" is b/c we are not interested in the background
# class.
multilabel[label - 1] = 1
self._cur += 1
return self.transformer.preprocess(im), multilabel
