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""" | |
# Copyright (c) 2022, salesforce.com, inc. | |
# All rights reserved. | |
# SPDX-License-Identifier: BSD-3-Clause | |
# For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause | |
""" | |
import numpy as np | |
import streamlit as st | |
import torch | |
from lavis.models import BlipBase, load_model | |
from matplotlib import pyplot as plt | |
from PIL import Image | |
from scipy.ndimage import filters | |
from skimage import transform as skimage_transform | |
def resize_img(raw_img): | |
w, h = raw_img.size | |
scaling_factor = 240 / w | |
resized_image = raw_img.resize((int(w * scaling_factor), int(h * scaling_factor))) | |
return resized_image | |
def read_img(filepath): | |
raw_image = Image.open(filepath).convert("RGB") | |
return raw_image | |
def load_model_cache(name, model_type, is_eval, device): | |
return load_model(name, model_type, is_eval, device) | |
def init_bert_tokenizer(): | |
tokenizer = BlipBase.init_tokenizer() | |
return tokenizer | |
def getAttMap(img, attMap, blur=True, overlap=True): | |
attMap -= attMap.min() | |
if attMap.max() > 0: | |
attMap /= attMap.max() | |
attMap = skimage_transform.resize(attMap, (img.shape[:2]), order=3, mode="constant") | |
if blur: | |
attMap = filters.gaussian_filter(attMap, 0.02 * max(img.shape[:2])) | |
attMap -= attMap.min() | |
attMap /= attMap.max() | |
cmap = plt.get_cmap("jet") | |
attMapV = cmap(attMap) | |
attMapV = np.delete(attMapV, 3, 2) | |
if overlap: | |
attMap = ( | |
1 * (1 - attMap**0.7).reshape(attMap.shape + (1,)) * img | |
+ (attMap**0.7).reshape(attMap.shape + (1,)) * attMapV | |
) | |
return attMap | |
def load_blip_itm_model(device, model_type="base"): | |
model = load_model( | |
"blip_image_text_matching", model_type, is_eval=True, device=device | |
) | |
return model | |