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import os
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
from keras.applications import imagenet_utils
from keras.applications import resnet50
from keras.models import Model
IMAGE_DIR = '/Users/chunhuizhang/workspaces/00_datasets/images/INRIA Holidays dataset /jpg'
DATA_DIR = './data'
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 vectorize_images(image_dir, image_size, preprocessor,
model, vector_file, batch_size=32):
image_names = os.listdir(image_dir)
num_vecs = 0
fvec = open(vector_file, "w")
for image_batch in image_batch_generator(image_names, batch_size):
batched_images = []
for image_name in image_batch:
image = plt.imread(os.path.join(image_dir, image_name))
# image = imresize(image, (image_size, image_size))
image = np.asarray(Image.fromarray(image).resize((image_size, image_size)))
batched_images.append(image)
X = preprocessor(np.array(batched_images, dtype="float32"))
vectors = model.predict(X)
for i in range(vectors.shape[0]):
if num_vecs % 100 == 0:
print("{:d}/{:d} vectors generated".format(num_vecs, vectors.shape[0]))
image_vector = ",".join(["{:.5e}".format(v) for v in vectors[i].tolist()])
# print(image_batch[i], image_vector)
# print(type(image_batch[i]), type(image_vector))
fvec.write("{:s}\t{:s}\n".format(image_batch[i], image_vector))
num_vecs += 1
print("{:d} vectors generated".format(num_vecs))
fvec.close()
def generate_features(model, image_size, vector_file):
model = Model(input=model.input,
output=model.get_layer("avg_pool").output)
preprocessor = imagenet_utils.preprocess_input
vectorize_images(IMAGE_DIR, image_size, preprocessor, model, vector_file)
if __name__ == '__main__':
IMAGE_SIZE = 224
# vgg16_model = vgg16.VGG16(weights="imagenet", include_top=True)
# VECTOR_FILE = os.path.join(DATA_DIR, "vgg19-vectors.tsv")
# generate_features(vgg16_model, IMAGE_SIZE, VECTOR_FILE)
VECTOR_FILE = os.path.join(DATA_DIR, "resnet-vectors.tsv")
resnet_model = resnet50.ResNet50(weights="imagenet", include_top=True)
generate_features(resnet_model, IMAGE_SIZE, VECTOR_FILE)
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