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| import streamlit as st | |
| st.set_page_config(page_title='ITR', page_icon="🧊", layout='centered') | |
| st.title("LCM-Independent for Pascal Dataset") | |
| import faiss | |
| import numpy as np | |
| from PIL import Image | |
| import json | |
| import zipfile | |
| import pandas as pd | |
| import pickle | |
| import pickletools | |
| from transformers import AutoTokenizer, CLIPTextModelWithProjection | |
| # loading the train dataset | |
| # with open('clip_train.pkl', 'rb') as f: | |
| # temp_d = pickletools.dis(f) | |
| # train_xv = temp_d['image'].astype(np.float64) # Array of image features : np ndarray | |
| # train_xt = temp_d['text'].astype(np.float64) # Array of text features : np ndarray | |
| # train_yv = temp_d['label'] # Array of labels | |
| # train_yt = temp_d['label'] # Array of labels | |
| # ids = list(temp_d['ids']) # image names == len(images) | |
| train_yt = np.load("train_yt.npy") | |
| # loading the test dataset | |
| # with open('clip_test.pkl', 'rb') as f: | |
| # temp_d = pickletools.dis(f) | |
| # test_xv = temp_d['image'].astype(np.float64) | |
| # test_xt = temp_d['text'].astype(np.float64) | |
| # test_yv = temp_d['label'] | |
| # test_yt = temp_d['label'] | |
| test_xt = np.load("test_xt.npy") | |
| # Map the image ids to the corresponding image URLs | |
| image_map_name = 'pascal_dataset.csv' | |
| df = pd.read_csv(image_map_name) | |
| image_list = list(df['image']) | |
| class_list = list(df['class']) | |
| zip_path = "pascal_raw.zip" | |
| zip_file = zipfile.ZipFile(zip_path) | |
| text_model = CLIPTextModelWithProjection.from_pretrained("openai/clip-vit-base-patch32") | |
| text_tokenizer = AutoTokenizer.from_pretrained("openai/clip-vit-base-patch32") | |
| d = 1024 | |
| text_index = faiss.index_factory(d, "Flat", faiss.METRIC_INNER_PRODUCT) | |
| faiss.read_index("text_index.index") | |
| def T2Isearch(query, k=50): | |
| # Encode the text query | |
| inputs = text_tokenizer([query], padding=True, return_tensors="pt") | |
| outputs = text_model(**inputs) | |
| query_embedding = outputs.text_embeds | |
| query_vector = query_embedding.detach().numpy() | |
| # query_vector = test_xt[0] | |
| # query_vector = np.array([query_embedding]) | |
| faiss.normalize_L2(query_vector) | |
| # text_index.nprobe = index.ntotal | |
| text_index.nprobe = 100 | |
| # Search for the nearest neighbors in the FAISS text index | |
| D, I = text_index.search(query_vector, k) | |
| # get rank of all classes wrt to query | |
| classes_all = [] | |
| Y = train_yt | |
| neighbor_ys = Y[I] | |
| class_freq = np.zeros(Y.shape[1]) | |
| for neighbor_y in neighbor_ys: | |
| classes = np.where(neighbor_y > 0.5)[0] | |
| for _class in classes: | |
| class_freq[_class] += 1 | |
| count = 0 | |
| for i in range(len(class_freq)): | |
| if class_freq[i]>0: | |
| count +=1 | |
| ranked_classes = np.argsort(-class_freq) # chosen order of pivots -- predicted sequence of all labels for the query | |
| ranked_classes_after_knn = ranked_classes[:count] # predicted sequence of top labels after knn search | |
| lis = ['aeroplane', 'bicycle','bird','boat','bottle','bus','car','cat','chair','cow','diningtable','dog','horse','motorbike','person','pottedplant','sheep','sofa','train','tvmonitor'] | |
| class_ = lis[ranked_classes_after_knn[0]-1] | |
| # Map the image ids to the corresponding image URLs | |
| for i in range(len(image_list)): | |
| if class_list[i] == class_ : | |
| image_name = image_list[i] | |
| image_data = zip_file.open("pascal_raw/images/dataset/"+ image_name) | |
| image = Image.open(image_data) | |
| st.image(image, width=600) | |
| query = st.text_input("Enter your search query here:") | |
| if st.button("Search"): | |
| if query: | |
| T2Isearch(query) |