def save_data_list(inpath, outpath, filenames, filename_bbox):
hr_images = []
lr_images = []
hr_segs = []
lr_segs = []
lr_size = int(LOAD_SIZE / LR_HR_RETIO)
cnt = 0
for key in filenames:
bbox = filename_bbox[key]
f_name = '%s/CUB_200_2011/images/%s.jpg' % (inpath, key)
img = get_image(f_name, LOAD_SIZE, is_crop=True, bbox=bbox)
img = img.astype('uint8')
seg_name = '%s/CUB_200_2011/segmentations/%s.png' % (inpath, key)
seg = get_image(seg_name, LOAD_SIZE, is_crop=True, bbox=bbox)
seg = seg.astype('uint8')
hr_images.append(img)
lr_img = scipy.misc.imresize(img, [lr_size, lr_size], 'bicubic')
lr_images.append(lr_img)
hr_segs.append(seg)
lr_seg = scipy.misc.imresize(seg, [lr_size, lr_size], 'bicubic')
lr_segs.append(lr_seg)
cnt += 1
if cnt % 100 == 0:
print('Load %d......' % cnt)
#
print('images', len(hr_images), hr_images[0].shape, lr_images[0].shape)
print('segmentations', len(hr_segs), hr_segs[0].shape, lr_segs[0].shape)
#
outfile = outpath + str(LOAD_SIZE) + 'images.pickle'
with open(outfile, 'wb') as f_out:
pickle.dump(hr_images, f_out)
print('save to: ', outfile)
#
outfile = outpath + str(lr_size) + 'images.pickle'
with open(outfile, 'wb') as f_out:
pickle.dump(lr_images, f_out)
print('save to: ', outfile)
outfile = outpath + str(LOAD_SIZE) + 'segs.pickle'
with open(outfile, 'wb') as f_out:
pickle.dump(hr_segs, f_out)
print('save to: ', outfile)
#
outfile = outpath + str(lr_size) + 'segs.pickle'
with open(outfile, 'wb') as f_out:
pickle.dump(lr_segs, f_out)
print('save to: ', outfile)
def save_data_list(inpath, outpath, filenames, filename_bbox):
hr_images = []
lr_images = []
lr_size = int(LOAD_SIZE / LR_HR_RETIO)
cnt = 0
for key in filenames:
bbox = filename_bbox[key]
f_name = '%s/CUB_200_2011/images/%s.jpg' % (inpath, key)
img = get_image(f_name, LOAD_SIZE, is_crop=True, bbox=bbox)
img = img.astype('uint8')
hr_images.append(img)
lr_img = scipy.misc.imresize(img, [lr_size, lr_size], 'bicubic')
lr_images.append(lr_img)
cnt += 1
if cnt % 100 == 0:
print('Load %d......' % cnt)
#
print('images', len(hr_images), hr_images[0].shape, lr_images[0].shape)
#
outfile = outpath + str(LOAD_SIZE) + 'images.pickle'
with open(outfile, 'wb') as f_out:
pickle.dump(hr_images, f_out)
print('save to: ', outfile)
#
outfile = outpath + str(lr_size) + 'images.pickle'
with open(outfile, 'wb') as f_out:
pickle.dump(lr_images, f_out)
print('save to: ', outfile)
def save_data_list(inpath, outpath, filenames, filename_bbox):
hr_images = []
lr_images = []
lr_size = int(LOAD_SIZE / LR_HR_RETIO)
cnt = 0
for key in filenames:
bbox = filename_bbox[key]
f_name = '%s/CUB_200_2011/images/%s.jpg' % (inpath, key)
img = get_image(f_name, LOAD_SIZE, is_crop=True, bbox=bbox)
img = img.astype('uint8')
hr_images.append(img)
lr_img = skimage.transform.resize(img, [lr_size, lr_size], order=3)
lr_images.append(lr_img)
cnt += 1
if cnt % 100 == 0:
print('Load %d......' % cnt)
#
print('images', len(hr_images), hr_images[0].shape, lr_images[0].shape)
#
outfile = outpath + str(LOAD_SIZE) + 'images.pickle'
with open(outfile, 'wb') as f_out:
pickle.dump(hr_images, f_out)
print('save to: ', outfile)
#
outfile = outpath + str(lr_size) + 'images.pickle'
with open(outfile, 'wb') as f_out:
pickle.dump(lr_images, f_out)
print('save to: ', outfile)
def save_data_list(inpath, outpath, filenames, filename_bbox):
lr_size = int(LOAD_SIZE / LR_HR_RETIO)
cnt = 0
ids = range(len(filenames))
shuffle(ids)
with open(outpath + 'File_Ids.pickle', 'wb') as f:
pickle.dump(ids, f)
numfiles = len(ids) / cfg.NUM_BATCH_IN_FILE
numfiles = int(numfiles) + 1
for file_id in range(numfiles):
hr_images = [] #np.array([],dtype=np.)
lr_images = [] #np.array([],dtype='uint8')
for i in range(
file_id * cfg.NUM_BATCH_IN_FILE * cfg.TRAIN.BATCH_SIZE,
min((file_id + 1) * cfg.NUM_BATCH_IN_FILE *
cfg.TRAIN.BATCH_SIZE, len(ids))):
id = ids[i]
key = filenames[id]
key = key[:-4]
bbox = filename_bbox[key]
f_name = '%s/../%s.jpg' % (inpath, key)
img = get_image(f_name, LOAD_SIZE, is_crop=True, bbox=bbox)
img = img.astype('uint8')
hr_images.append(img)
lr_img = scipy.misc.imresize(img, [lr_size, lr_size], 'bicubic')
lr_images.append(lr_img)
cnt += 1
if cnt % 100 == 0:
print('Load %d......' % cnt)
#
print('images', len(hr_images), hr_images[0].shape, lr_images[0].shape)
#
outfile = outpath + str(LOAD_SIZE) + 'images' + str(
file_id) + '.pickle'
# with open(outfile, 'wb') as f_out:
# pickle.dump(hr_images, f_out)
# print('save to: ', outfile)
#
outfile = outpath + str(lr_size) + 'images' + str(file_id) + '.pickle'
with open(outfile, 'wb') as f_out:
pickle.dump(lr_images, f_out)
print('save to: ', outfile)
def save_data_list(outpath, filenames):
hr_images = []
lr_images = []
lr_size = int(LOAD_SIZE / LR_HR_RATIO)
cnt = 0
for f_name in filenames:
print('processing %s' % f_name)
img = get_image(f_name, LOAD_SIZE, is_crop=False)
img = img.astype('uint8')
hr_images.append(img)
lr_img = scipy.misc.imresize(img, [lr_size, lr_size], 'bicubic')
lr_images.append(lr_img)
cnt += 1
if cnt % 50 == 0:
print('Load %d........' % cnt)
print('images', len(hr_images), len(lr_images), hr_images[0].shape,
lr_images[0].shape)
outfile = outpath + str(LOAD_SIZE) + 'images.pickle'
with open(outfile, 'wb') as f_out:
pickle.dump(hr_images, f_out)
print('save to: ', outfile)
outfile = outpath + str(lr_size) + 'images.pickle'
with open(outfile, 'wb') as f_out:
pickle.dump(lr_images, f_out)
print('save to: ', outfile)
outfile = outpath + 'filenames.pickle'
with open(outfile, 'wb') as f_out:
pickle.dump([os.path.basename(name) for name in filenames], f_out)
print('save to: ', outfile)
# TODO Use fake classes for now, switch to real data when it's available
outfile = outpath + 'class_info.pickle'
with open(outfile, 'wb') as f_out:
pickle.dump([0 for i in range(len(filenames))], f_out)
print('save to: ', outfile)
# get the corresponding captions
with open(DATA_DIR + '/text_c10/' + filename + '.txt', "r") as f:
captions = f.read().split('\n')
captions = [cap for cap in captions if len(cap) > 0]
# randomly select 1 caption and the corresponding embedding
j, caption = random.choice(list(enumerate(captions)))
embedding = embeddings[index][j]
# load the example true image (process as usual down to 76 x 76
lr_size = int(LOAD_SIZE / LR_HR_RATIO)
filename_bbox = load_bbox('Data/birds/')
bbox = filename_bbox[filename]
f_name = 'Data/birds/CUB_200_2011/images/%s.jpg' % filename
img = get_image(f_name, LOAD_SIZE, is_crop=True, bbox=bbox)
img = img.astype(np.float32)
true_img = scipy.misc.imresize(img, [lr_size, lr_size],
'bicubic').astype(np.float32)
print("Caption: ", caption)
# convert the embedding to a tensor and repeat BATCH_SIZE times to shape (BATCH_SIZE, 1024)
embedding = tf.constant(embedding)
batch_embeddings = tf.tile(tf.expand_dims(embedding, axis=0),
[BATCH_SIZE, 1])
batch_z = tf.random_normal([BATCH_SIZE, Z_DIM])
# get conditioning vector (from embedding) and KL divergence for use as a
# regularization term in the generator loss
