dashboard_image2image.py

import matplotlib.pyplot as plt
import nmslib
import numpy as np
import os
import streamlit as st

from PIL import Image
from transformers import CLIPProcessor, FlaxCLIPModel

import utils

BASELINE_MODEL = "openai/clip-vit-base-patch32"
# MODEL_PATH = "/home/shared/models/clip-rsicd/bs128x8-lr5e-6-adam/ckpt-1"
MODEL_PATH = "flax-community/clip-rsicd-v2"

# IMAGE_VECTOR_FILE = "/home/shared/data/vectors/test-baseline.tsv"
# IMAGE_VECTOR_FILE = "/home/shared/data/vectors/test-bs128x8-lr5e-6-adam-ckpt-1.tsv"
IMAGE_VECTOR_FILE = "./vectors/test-bs128x8-lr5e-6-adam-ckpt-1.tsv"

# IMAGES_DIR = "/home/shared/data/rsicd_images"
IMAGES_DIR = "./images"


@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]
    return example_images


def app():
    filenames, index = utils.load_index(IMAGE_VECTOR_FILE)
    model, processor = utils.load_model(MODEL_PATH, BASELINE_MODEL)

    example_images = load_example_images()
    example_image_list = sorted([v[np.random.randint(0, len(v))] 
                                for k, v in example_images.items()][0:10])

    st.title("Image to Image Retrieval")
    st.markdown("""
        The CLIP model from OpenAI is trained in a self-supervised manner using 
        contrastive learning to project images and caption text onto a common 
        embedding space. We have fine-tuned the model (see [Model card](https://huggingface.co/flax-community/clip-rsicd-v2)) 
        using the RSICD dataset (10k images and ~50k captions from the remote 
        sensing domain). Click here for [more information about our project](https://github.com/arampacha/CLIP-rsicd).
        
        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. You can 
        copy paste one of these below or use one from the results of a text to 
        image search -- {:s}
    """.format(", ".join("`{:s}`".format(example) for example in example_image_list)))

    image_name = st.text_input("Provide an Image File Name")
    submit_button = st.button("Find Similar")

    if submit_button:
        image = Image.fromarray(plt.imread(os.path.join(IMAGES_DIR, image_name)))
        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]
        images, captions = [], []
        for result_filename, score in zip(result_filenames, distances):
            if result_filename == image_name:
                continue
            images.append(
                plt.imread(os.path.join(IMAGES_DIR, result_filename)))
            captions.append("{:s} (score: {:.3f})".format(result_filename, 1.0 - score))
        images = images[0:10]
        captions = captions[0:10]
        st.image(images[0:3], caption=captions[0:3])
        st.image(images[3:6], caption=captions[3:6])
        st.image(images[6:9], caption=captions[6:9])
        st.image(images[9:], caption=captions[9:])