from pycocotools.coco import COCO
import cPickle as pickle
if __name__ == '__main__':
# Specify datasets path.
refexp_filename='google_refexp_dataset_release/google_refexp_train_201511_coco_aligned.json'
coco_filename='external/coco/annotations/instances_train2014.json'
datasetDir = 'external/coco/'
datasetType = 'train2014'
# Create Refexp instance.
refexp = Refexp(refexp_filename, coco_filename)
# Get image ids of all images containing human beings
categoryIds = refexp.getCatIds(catNms=['person'])
imgIds = refexp.getImgIds(catIds=categoryIds)
nImgsAvailable = len(imgIds)
# Select 2 random images
nImgsDesired = 2
nImgs = min(nImgsDesired, nImgsAvailable)
np.random.seed(0)
randImgIndices = np.random.choice(np.arange(0, nImgs), size=(nImgs, 1), replace=False)
randImgIds = [imgIds[int(idx)] for idx in randImgIndices]
coco_imgs = refexp.loadImgs(randImgIds)
# The actual images as numpy arrays
images = [preprocess_image('%s/images/%s/%s' % (datasetDir, datasetType, img['file_name'])) for img in coco_imgs]
images = np.squeeze(np.asarray(images))
region = original.crop(
(int(bbox[0]), int(bbox[1]), int(bbox[0]) + int(bbox[2]),
int(bbox[1]) + int(bbox[3])))
region = region.resize((224, 224))
region = image.img_to_array(region)
region = np.expand_dims(region, axis=0)
region = preprocess_input(region)
reg_output = model.predict(region)
reg_output2 = model2.predict(region)
reg_output3 = model3.predict(region)
np.save(file=storage_dir + 'processed_b/' + number, arr=reg_output)
np.save(file=storage_dir + 'processed_flatten_b/' + number,
arr=reg_output2)
np.save(file=storage_dir + 'processed_predictions_b/' + number,
arr=reg_output3)
return output, output2, output3, reg_output, reg_output2, reg_output3
# print(file_id)
if __name__ == '__main__':
for file_id in refexp.getImgIds()[:20]:
print(file_id, "file_id")
print(type(file_id))
predict_image(file_id)
# predict_image('318556')
# features_loaded = np.load('block4_pool_features.npy')
# assert np.array_equal(block4_pool_features, features_loaded)
def preprocess_refexp_images(stream_num,
stream_size,
word_to_idx,
max_cap_len,
coco_dir,
category_names=[],
out_file='../keras_vgg_19/savedoc',
NO_PADDING=False):
coco_filename = coco_dir + '/annotations/instances_train2014.json'
refexp_filename = '../google_refexp_dataset_release/google_refexp_train_201511_coco_aligned.json'
refexp = Refexp(refexp_filename, coco_filename)
# choose categories/images
catIds = refexp.getCatIds(catNms=category_names)
imgIds = list(set(refexp.getImgIds(catIds=catIds)))
annIds = refexp.getAnnIds(imgIds=imgIds)
anns = refexp.loadAnns(ids=annIds)
refIds = []
bboxes = []
refImgIds = []
# get all refexp ids and bboxes and imageids in these annotations, except for captions with weird symbols
for ann in anns:
for ref_id in ann['refexp_ids']:
if ref_id not in [
146, 400, 923, 21409, 35384, 38589, 46630, 47673, 65639,
70715, 82742
]:
refIds.append(ref_id)
bboxes.append(ann['bbox'])
refImgIds.append(ann['image_id'])
# get caption sequences, with added start and stop tokens
captions = [
refexp.dataset['refexps'][ref_id]['raw'].encode('ascii')
for ref_id in refIds
]
caption_seqs = [[START_TOKEN] + text_to_word_sequence(c) + [STOP_TOKEN]
for c in captions]
caption_lengths = [len(seq) for seq in caption_seqs]
# filter out the long captions
refImgIds = [
img_id for i, img_id in enumerate(refImgIds)
if caption_lengths[i] <= max_cap_len
]
bboxes = [
bbox for i, bbox in enumerate(bboxes)
if caption_lengths[i] <= max_cap_len
]
caption_seqs = [
seq for i, seq in enumerate(caption_seqs)
if caption_lengths[i] <= max_cap_len
]
caption_lengths = [l for l in caption_lengths if l <= max_cap_len
] # do not move this before the other filter steps!
total_num_partial_captions = sum(caption_lengths)
# repeat image id and bounding box for each partial caption
repeated_ids = [[img_id] * (l - 1)
for img_id, l in zip(refImgIds, caption_lengths)]
image_ids = [img_id for rep_id in repeated_ids for img_id in rep_id]
repeated_bboxes = [[bbox] * (l - 1)
for bbox, l in zip(bboxes, caption_lengths)]
cap_bbox = [bbox for rep_bbox in repeated_bboxes for bbox in rep_bbox]
partial_caps, next_words = partial_captions_and_next_words(
caption_seqs, word_to_idx, max_cap_len
) #preprocess_captions(caption_seqs, word_to_idx, max_cap_len)
print(len(image_ids), len(partial_caps), len(cap_bbox))
assert (len(image_ids) == len(partial_caps))
assert (len(image_ids) == len(cap_bbox))
'''
# Determine how many (partial caption, image) examples to take to obtain
# `num_imgs_to_sample` total distinct images (including all partial captions)
if num_caps_to_sample < total_num_images:
number_of_items = 0
for i, l in enumerate(caption_lengths):
if i >= num_caps_to_sample:
break
number_of_items += l
else:
print total_num_images, ' were requested, but only ', num_caps_to_sample, \
' are available in this category. Processing all images in the category...'
number_of_items = len(partial_caps)
'''
X = [0, 0]
# TODO: handle the case where you request indices out of range
number_of_items = min(stream_size, total_num_partial_captions)
item_range = range((stream_num - 1) * stream_size,
stream_num * stream_size)
ids_and_bboxes = zip(image_ids, cap_bbox)
X[0] = ids_and_bboxes[((stream_num - 1) * stream_size):(stream_num *
stream_size)]
X[1] = np.asarray(partial_caps[item_range])
y = np.asarray(next_words)[item_range]
out = X, y
with open(out_file, 'w') as handle:
pickle.dump(out, handle)