def random(dir_name):
print("Preparing random generator")
vecs = load_sparse_matrix(config.vectors_path(dir_name)).toarray()
s = vecs.shape[0]
def rf():
return vecs[np.random.randint(0, s)]
print("Random generator is ready")
return rf
def load_predictor(dir_name):
print("load predictor")
filenames = open(config.images_order(dir_name), 'r').readline().split(',')
vecs = load_sparse_matrix(config.vectors_path(dir_name))
knn = NearestNeighbors(metric='cosine', algorithm='brute')
knn.fit(vecs)
def similarity(vec, n_neighbors=6):
return _similar(vec, knn, filenames, n_neighbors)
print("Predictor loaded")
return similarity
def main(dir_name):
files = glob(config.images_glob_path(dir_name))
save(files, config.images_order(dir_name))
base_model = VGG19(weights='imagenet')
# Read about fc1 here http://cs231n.github.io/convolutional-networks/
model = Model(inputs=base_model.input,
outputs=base_model.get_layer('fc1').output)
vecs = vectorize_all(files, model, n_dims=4096)
save_sparse_matrix(config.vectors_path(dir_name), vecs)
print('Finished')
def load_predictor(owner_id):
filenames = open(config.images_order(owner_id), 'r').readline().split(',')
vecs = load_sparse_matrix(config.vectors_path(owner_id))
model = VGG16(include_top=False, weights='imagenet', pooling='max')
knn = NearestNeighbors(metric='cosine', algorithm='brute')
knn.fit(vecs)
def similarity(file_path):
vec = _vectorize(file_path, model)
return _similar(vec, knn, filenames)
return similarity
def load_predictor(dir_name):
filenames = open(config.images_order(dir_name), 'r').readline().split(',')
vecs = load_sparse_matrix(config.vectors_path(dir_name))
base_model = VGG19(weights='imagenet')
model = Model(inputs=base_model.input,
outputs=base_model.get_layer('fc1').output)
knn = NearestNeighbors(metric='cosine', algorithm='brute')
knn.fit(vecs)
def similarity(file_path, n_neighbors=6):
vec = _vectorize(file_path, model)
return _similar(vec, knn, filenames, n_neighbors)
return similarity
def main(owner_id):
model = VGG16(include_top=False, weights='imagenet', pooling='max')
files = glob(config.images_glob_path(owner_id))
save(files, config.images_order(owner_id))
vecs = vectorize_all(files, model)
save_sparse_matrix(config.vectors_path(owner_id), vecs)
