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.beta_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.beta_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.beta_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.beta_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.beta_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