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import streamlit as st
from text2image import get_model, get_tokenizer, get_image_transform
from utils import text_encoder
from torchvision import transforms
from PIL import Image
from jax import numpy as jnp
import pandas as pd
import numpy as np
import requests
import psutil
import time
import jax
import gc
preprocess = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),
])
def pad_to_square(image, size=224):
ratio = float(size) / max(image.size)
new_size = tuple([int(x * ratio) for x in image.size])
image = image.resize(new_size, Image.ANTIALIAS)
new_image = Image.new("RGB", size=(size, size), color=(128, 128, 128))
new_image.paste(image, ((size - new_size[0]) // 2, (size - new_size[1]) // 2))
return new_image
def image_encoder(image, model):
image = np.transpose(image, (0, 2, 3, 1))
features = model.get_image_features(image)
features /= jnp.linalg.norm(features, keepdims=True)
return features
def gen_image_batch(image_url, image_size=224, pixel_size=10):
n_pixels = image_size // pixel_size + 1
image_batch = []
masks = []
image_raw = requests.get(image_url, stream=True).raw
image = Image.open(image_raw).convert("RGB")
image = pad_to_square(image, size=image_size)
gray = np.ones_like(image) * 128
mask = np.ones_like(image)
image_batch.append(image)
masks.append(mask)
for i in range(0, n_pixels):
for j in range(i+1, n_pixels):
m = mask.copy()
m[:min(i*pixel_size, image_size) + 1, :] = 0
m[min(j*pixel_size, image_size) + 1:, :] = 0
neg_m = 1 - m
image_batch.append(image * m + gray * neg_m)
masks.append(m)
for i in range(0, n_pixels+1):
for j in range(i+1, n_pixels+1):
m = mask.copy()
m[:, :min(i*pixel_size + 1, image_size)] = 0
m[:, min(j*pixel_size + 1, image_size):] = 0
neg_m = 1 - m
image_batch.append(image * m + gray * neg_m)
masks.append(m)
return image_batch, masks
def get_heatmap(image_url, text, pixel_size=10, iterations=3):
tokenizer = get_tokenizer()
model = get_model()
image_size = model.config.vision_config.image_size
text_embedding = text_encoder(text, model, tokenizer)
images, masks = gen_image_batch(image_url, image_size=image_size, pixel_size=pixel_size)
input_image = images[0].copy()
images = np.stack([preprocess(image) for image in images], axis=0)
image_embeddings = jnp.asarray(image_encoder(images, model))
sims = []
scores = []
mask_val = jnp.zeros_like(masks[0])
for e, m in zip(image_embeddings, masks):
sim = jnp.matmul(e, text_embedding.T)
sims.append(sim)
if len(sims) > 1:
scores.append(sim * m)
mask_val += 1 - m
score = jnp.mean(jnp.clip(jnp.array(scores) - sims[0], 0, jnp.inf), axis=0)
for i in range(iterations):
score = jnp.clip(score - jnp.mean(score), 0, jnp.inf)
score = (score - jnp.min(score)) / (jnp.max(score) - jnp.min(score))
return np.asarray(score), input_image
def app():
st.title("Zero-Shot Localization")
st.markdown(
"""
### 👋 Ciao!
Here you can find an exaple for zero shot localization that will show you where in an image the model sees an object.
🤌 Italian mode on! 🤌
For example, try typing "gatto" (cat) or "cane" (dog) in the space for label and click "locate"!
"""
)
image_url = st.text_input(
"You can input the URL of an image here...",
value="https://www.tuttosuigatti.it/files/styles/full_width/public/images/featured/205/cani-e-gatti.jpg?itok=WAAiTGS6",
)
MAX_ITER = 1
col1, col2 = st.beta_columns([3, 1])
with col2:
pixel_size = st.selectbox(
"Pixel Size", options=range(10, 21, 5), index=0
)
iterations = st.selectbox(
"Refinement Steps", options=range(3, 30, 3), index=0
)
compute = st.button("LOCATE")
with col1:
caption = st.text_input(f"Insert label...")
if compute:
with st.spinner('Waiting for resources...'):
sleep_time = 5
print('CPU_load', psutil.cpu_percent())
while psutil.cpu_percent() > 90:
time.sleep(sleep_time)
if not caption or not image_url:
st.error("Please choose one image and at least one label")
else:
with st.spinner("Computing..."):
heatmap, image = get_heatmap(image_url, caption, pixel_size, iterations)
with col1:
st.image(image, use_column_width=True)
st.image(heatmap, use_column_width=True)
st.image(np.asarray(image) / 255.0 * heatmap, use_column_width=True)
gc.collect()
elif image_url:
image_raw = requests.get(image_url, stream=True, ).raw
image = Image.open(image_raw).convert("RGB")
with col1:
st.image(image)
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