Spaces:
Running
Running
File size: 4,938 Bytes
ea3b7ec |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 |
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 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:
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)
|