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import matplotlib.pyplot as plt
import nmslib
import numpy as np
import os
import requests
import streamlit as st
from PIL import Image
from transformers import CLIPProcessor, FlaxCLIPModel
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"
CAPTIONS_FILE = os.path.join(IMAGES_DIR, "test-captions.json")
@st.cache(allow_output_mutation=True)
def load_example_images():
example_images = {}
image_names = os.listdir(IMAGES_DIR)
for image_name in image_names:
if image_name.find("_") < 0:
continue
image_class = image_name.split("_")[0]
if image_class in example_images.keys():
example_images[image_class].append(image_name)
else:
example_images[image_class] = [image_name]
example_image_list = sorted([v[np.random.randint(0, len(v))]
for k, v in example_images.items()][0:10])
return example_image_list
def get_image_thumbnail(image_filename):
image = Image.open(os.path.join(IMAGES_DIR, image_filename))
image = image.resize((100, 100))
return image
def download_and_prepare_image(image_url):
try:
image_raw = requests.get(image_url, stream=True,).raw
image = Image.open(image_raw).convert("RGB")
width, height = image.size
resize_mult = width / 224 if width < height else height / 224
image = image.resize((int(width // resize_mult),
int(height // resize_mult)))
width, height = image.size
left = int((width - 224) // 2)
top = int((height - 224) // 2)
right = int((width + 224) // 2)
bottom = int((height + 224) // 2)
image = image.crop((left, top, right, bottom))
return image
except Exception as e:
return None
def app():
filenames, index = utils.load_index(IMAGE_VECTOR_FILE)
model, processor = utils.load_model(MODEL_PATH, BASELINE_MODEL)
image2caption = utils.load_captions(CAPTIONS_FILE)
example_image_list = load_example_images()
st.title("Retrieve Images given Images")
st.markdown("""
This demo shows the image to image retrieval capabilities of this model, i.e.,
given an image file name as a query, we use our fine-tuned CLIP model
to project the query image to the image/caption embedding space and search
for nearby images (by cosine similarity) in this space.
Our fine-tuned CLIP model was previously used to generate image vectors for
our demo, and NMSLib was used for fast vector access.
Here are some randomly generated image files from our corpus, that you can
find similar images for by selecting the button below it. Alternatively you
can upload your own image from the Internet.
""")
suggest_idx = -1
col0, col1, col2, col3, col4 = st.columns(5)
col0.image(get_image_thumbnail(example_image_list[0]))
col1.image(get_image_thumbnail(example_image_list[1]))
col2.image(get_image_thumbnail(example_image_list[2]))
col3.image(get_image_thumbnail(example_image_list[3]))
col4.image(get_image_thumbnail(example_image_list[4]))
col0t, col1t, col2t, col3t, col4t = st.columns(5)
with col0t:
if st.button("Image-1"):
suggest_idx = 0
with col1t:
if st.button("Image-2"):
suggest_idx = 1
with col2t:
if st.button("Image-3"):
suggest_idx = 2
with col3t:
if st.button("Image-4"):
suggest_idx = 3
with col4t:
if st.button("Image-5"):
suggest_idx = 4
col5, col6, col7, col8, col9 = st.columns(5)
col5.image(get_image_thumbnail(example_image_list[5]))
col6.image(get_image_thumbnail(example_image_list[6]))
col7.image(get_image_thumbnail(example_image_list[7]))
col8.image(get_image_thumbnail(example_image_list[8]))
col9.image(get_image_thumbnail(example_image_list[9]))
col5t, col6t, col7t, col8t, col9t = st.columns(5)
with col5t:
if st.button("Image-6"):
suggest_idx = 5
with col6t:
if st.button("Image-7"):
suggest_idx = 6
with col7t:
if st.button("Image-8"):
suggest_idx = 7
with col8t:
if st.button("Image-9"):
suggest_idx = 8
with col9t:
if st.button("Image-10"):
suggest_idx = 9
image_url = st.text_input(
"OR provide an image URL",
value="https://static.eos.com/wp-content/uploads/2019/04/Main.jpg")
submit_button = st.button("Find Similar")
if submit_button or suggest_idx > -1:
image_name = None
if suggest_idx > -1:
image_name = example_image_list[suggest_idx]
image = Image.fromarray(plt.imread(os.path.join(IMAGES_DIR, image_name)))
else:
image = download_and_prepare_image(image_url)
st.image(image, caption="Input Image")
st.markdown("---")
if image is None:
st.error("Image could not be downloaded, please try another one!")
else:
inputs = processor(images=image, return_tensors="jax", padding=True)
query_vec = model.get_image_features(**inputs)
query_vec = np.asarray(query_vec)
ids, distances = index.knnQuery(query_vec, k=11)
result_filenames = [filenames[id] for id in ids]
rank = 0
for result_filename, score in zip(result_filenames, distances):
if image_name is not None and result_filename == image_name:
continue
caption = "{:s} (score: {:.3f})".format(result_filename, 1.0 - score)
col1, col2, col3 = st.columns([2, 10, 10])
col1.markdown("{:d}.".format(rank + 1))
col2.image(Image.open(os.path.join(IMAGES_DIR, result_filename)),
caption=caption)
caption_text = []
for caption in image2caption[result_filename]:
caption_text.append("* {:s}\n".format(caption))
col3.markdown("".join(caption_text))
rank += 1
st.markdown("---")
suggest_idx = -1
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