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import jax
import flax
import matplotlib.pyplot as plt
import nmslib
import numpy as np
import os
import requests
import streamlit as st
from tempfile import NamedTemporaryFile
from torchvision.transforms import Compose, Resize, ToPILImage
from transformers import CLIPProcessor, FlaxCLIPModel
from PIL import Image
import utils
BASELINE_MODEL = "openai/clip-vit-base-patch32"
MODEL_PATH = "flax-community/clip-rsicd-v2"
IMAGE_VECTOR_FILE = "./vectors/test-bs128x8-lr5e-6-adam-ckpt-1.tsv"
IMAGES_DIR = "./images"
DEMO_IMAGES_DIR = "./demo-images"
def split_image(X):
num_rows = X.shape[0] // 224
num_cols = X.shape[1] // 224
Xc = X[0 : num_rows * 224, 0 : num_cols * 224, :]
patches = []
for j in range(num_rows):
for i in range(num_cols):
patches.append(Xc[j * 224 : (j + 1) * 224,
i * 224 : (i + 1) * 224,
:])
return num_rows, num_cols, patches
def get_patch_probabilities(patches, searched_feature,
image_preprocesor,
model, processor):
images = [image_preprocesor(patch) for patch in patches]
text = "An aerial image of {:s}".format(searched_feature)
inputs = processor(images=images,
text=text,
return_tensors="jax",
padding=True)
outputs = model(**inputs)
probs = jax.nn.softmax(outputs.logits_per_text, axis=-1)
probs_np = np.asarray(probs)[0]
return probs_np
def get_image_ranks(probs):
temp = np.argsort(-probs)
ranks = np.empty_like(temp)
ranks[temp] = np.arange(len(probs))
return ranks
def download_and_prepare_image(image_url):
"""
Take input image and resize it to 672x896
"""
try:
image_raw = requests.get(image_url, stream=True,).raw
image = Image.open(image_raw).convert("RGB")
width, height = image.size
# print("WID,HGT:", width, height)
if width < 224 or height < 224:
return None
# take the short edge and reduce to 672
if width < height:
resize_factor = 672 / width
image = image.resize((672, int(height * resize_factor)))
image = image.crop((0, 0, 672, 896))
else:
resize_factor = 672 / height
image = image.resize((int(width * resize_factor), 896))
image = image.crop((0, 0, 896, 672))
return np.asarray(image)
except Exception as e:
# print(e)
return None
def app():
model, processor = utils.load_model(MODEL_PATH, BASELINE_MODEL)
st.title("Find Features in Images")
st.markdown("""
This demo shows the ability of the model to find specific features
(specified as text queries) in the image. As an example, say you wish to
find the parts of the following image that contain a `beach`, `houses`,
or `ships`. We partition the image into tiles of (224, 224) and report
how likely each of them are to contain each text features.
""")
st.image("demo-images/st_tropez_1.png")
st.image("demo-images/st_tropez_2.png")
st.markdown("""
For this image and the queries listed above, our model reports that the
two left tiles are most likely to contain a `beach`, the two top right
tiles are most likely to contain `houses`, and the two bottom right tiles
are likely to contain `boats`.
We have provided a few representative images from [Unsplash](https://unsplash.com/s/photos/aerial-view)
that you can experiment with. Use the image name to put in an initial feature
to look for, this will show the original image, and you will get more ideas
for features that you can ask the model to identify.
""")
image_file = st.selectbox(
"Sample Image File",
options=[
"-- select one --",
"St-Tropez-Port.jpg",
"Acopulco-Bay.jpg",
"Highway-through-Forest.jpg",
"Forest-with-River.jpg",
"Eagle-Bay-Coastline.jpg",
"Multistoreyed-Buildings.jpg",
"Street-View-Malayasia.jpg",
])
image_url = st.text_input(
"OR provide an image URL",
value="https://static.eos.com/wp-content/uploads/2019/04/Main.jpg")
searched_feature = st.text_input("Feature to find", value="beach")
if st.button("Find"):
if image_file.startswith("--"):
image = download_and_prepare_image(image_url)
else:
image = plt.imread(os.path.join("demo-images", image_file))
if image is None:
st.error("Image could not be downloaded, please try another one")
else:
st.image(image, caption="Input Image")
st.markdown("---")
num_rows, num_cols, patches = split_image(image)
image_preprocessor = Compose([
ToPILImage(),
Resize(224)
])
num_rows, num_cols, patches = split_image(image)
patch_probs = get_patch_probabilities(
patches,
searched_feature,
image_preprocessor,
model,
processor)
patch_ranks = get_image_ranks(patch_probs)
pid = 0
for i in range(num_rows):
cols = st.columns(num_cols)
for col in cols:
caption = "#{:d} p({:s})={:.3f}".format(
patch_ranks[pid] + 1, searched_feature, patch_probs[pid])
col.image(patches[pid], caption=caption)
pid += 1
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