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import matplotlib.pyplot as plt
import numpy as np
import os
from PIL import Image
import itertools
from random import shuffle
from keras.utils import np_utils
from keras.applications import imagenet_utils
import itertools
import os
from random import shuffle
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
from keras.applications import imagenet_utils
from keras.utils import np_utils
IMAGE_DIR = '/Users/chunhuizhang/workspaces/00_datasets/images/INRIA Holidays dataset /jpg'
# IMAGE_DIR = os.path.join(DATA_DIR, "holiday-photos")
image_cache = {}
def pair_generator(triples, image_cache, datagens, batch_size=32):
while True:
# shuffle once per batch
indices = np.random.permutation(np.arange(len(triples)))
num_batches = len(triples) // batch_size
for bid in range(num_batches):
batch_indices = indices[bid * batch_size: (bid + 1) * batch_size]
batch = [triples[i] for i in batch_indices]
X1 = np.zeros((batch_size, 224, 224, 3))
X2 = np.zeros((batch_size, 224, 224, 3))
Y = np.zeros((batch_size, 2))
for i, (image_filename_l, image_filename_r, label) in enumerate(batch):
if datagens is None or len(datagens) == 0:
X1[i] = image_cache[image_filename_l]
X2[i] = image_cache[image_filename_r]
else:
X1[i] = datagens[0].random_transform(image_cache[image_filename_l])
X2[i] = datagens[1].random_transform(image_cache[image_filename_r])
Y[i] = [1, 0] if label == 0 else [0, 1]
yield [X1, X2], Y
def image_batch_generator(image_names, batch_size):
num_batches = len(image_names) // batch_size
for i in range(num_batches):
batch = image_names[i * batch_size: (i + 1) * batch_size]
yield batch
batch = image_names[(i + 1) * batch_size:]
yield batch
def show_img(sid, img_file, img_title):
plt.subplot(sid)
plt.title(img_title)
plt.xticks([])
plt.yticks([])
img = np.asarray(Image.fromarray(plt.imread(img_file)).resize((512, 512)))
plt.imshow(img)
def get_random_image(img_groups, group_names, gid):
gname = group_names[gid]
photos = img_groups[gname]
pid = np.random.choice(np.arange(len(photos)), size=1)[0]
pname = photos[pid]
return gname + pname + ".jpg"
def create_triples(image_dir):
img_groups = {}
for img_file in os.listdir(image_dir):
prefix, suffix = img_file.split(".")
gid, pid = prefix[0:4], prefix[4:]
if gid in img_groups:
img_groups[gid].append(pid)
else:
img_groups[gid] = [pid]
pos_triples, neg_triples = [], []
# positive pairs are any combination of images in same group
for key in img_groups.keys():
triples = [(key + x[0] + ".jpg", key + x[1] + ".jpg", 1)
for x in itertools.combinations(img_groups[key], 2)]
pos_triples.extend(triples)
# need equal number of negative examples
group_names = list(img_groups.keys())
# pos:neg == 1:1
for i in range(len(pos_triples)):
g1, g2 = np.random.choice(np.arange(len(group_names)), size=2, replace=False)
left = get_random_image(img_groups, group_names, g1)
right = get_random_image(img_groups, group_names, g2)
neg_triples.append((left, right, 0))
pos_triples.extend(neg_triples)
shuffle(pos_triples)
return pos_triples
def load_image(image_name, imagenet=False):
if image_name not in image_cache:
image = plt.imread(os.path.join(IMAGE_DIR, image_name)).astype(np.float32)
image = image.astype(np.uint8)
image = np.asarray(Image.fromarray(image).resize((224, 224)))
if imagenet:
image = imagenet_utils.preprocess_input(image)
else:
image = np.divide(image, 256)
image_cache[image_name] = image
return image_cache[image_name]
def generate_image_triples_batch(image_triples, batch_size, shuffle=False):
while True:
# loop once per epoch
if shuffle:
indices = np.random.permutation(np.arange(len(image_triples)))
else:
indices = np.arange(len(image_triples))
shuffled_triples = [image_triples[ix] for ix in indices]
num_batches = len(shuffled_triples) // batch_size
for bid in range(num_batches):
# loop once per batch
images_left, images_right, labels = [], [], []
batch = shuffled_triples[bid * batch_size: (bid + 1) * batch_size]
for i in range(batch_size):
lhs, rhs, label = batch[i]
images_left.append(load_image(lhs, imagenet=True))
images_right.append(load_image(rhs))
labels.append(label)
Xlhs = np.array(images_left)
Xrhs = np.array(images_right)
Y = np_utils.to_categorical(np.array(labels), num_classes=2)
yield ([Xlhs, Xrhs], Y)
def train_test_split(triples, splits):
assert sum(splits) == 1.0
split_pts = np.cumsum(np.array([0.] + splits))
indices = np.random.permutation(np.arange(len(triples)))
shuffled_triples = [triples[i] for i in indices]
data_splits = []
for sid in range(len(splits)):
start = int(split_pts[sid] * len(triples))
end = int(split_pts[sid + 1] * len(triples))
data_splits.append(shuffled_triples[start:end])
return data_splits
def batch_to_vectors(batch, vec_size, vec_dict):
X1 = np.zeros((len(batch), vec_size))
X2 = np.zeros((len(batch), vec_size))
Y = np.zeros((len(batch), 2))
for tid in range(len(batch)):
X1[tid] = vec_dict[batch[tid][0]]
X2[tid] = vec_dict[batch[tid][1]]
Y[tid] = [1, 0] if batch[tid][2] == 0 else [0, 1]
return ([X1, X2], Y)
def data_generator(triples, vec_size, vec_dict, batch_size=32):
while True:
# shuffle once per batch
indices = np.random.permutation(np.arange(len(triples)))
num_batches = len(triples) // batch_size
for bid in range(num_batches):
batch_indices = indices[bid * batch_size: (bid + 1) * batch_size]
batch = [triples[i] for i in batch_indices]
yield batch_to_vectors(batch, vec_size, vec_dict)
if __name__ == '__main__':
show_img(131, os.path.join(IMAGE_DIR, "115200.jpg"), "original")
show_img(132, os.path.join(IMAGE_DIR, "115201.jpg"), "similar")
show_img(133, os.path.join(IMAGE_DIR, "123700.jpg"), "different")
plt.tight_layout()
plt.show()
triples_data = create_triples(IMAGE_DIR)
print("# image triples:", len(triples_data))
print([x for x in triples_data[0:5]])
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