Add application file

imageSearching.py

@@ -0,0 +1,105 @@
+import gradio as gr
+import torch
+import numpy as np
+import h5py
+import faiss
+import json
+import hydra
+import time
+import random
+from PIL import Image
+import io 
+import pickle
+
+def get_image(file, dataset_image_mask, processid_to_index, idx):
+    # idx = processid_to_index[query_id]
+    image_enc_padded = file["image"][idx].astype(np.uint8)
+    enc_length = dataset_image_mask[idx]
+    image_enc = image_enc_padded[:enc_length]
+    image = Image.open(io.BytesIO(image_enc))
+    return image
+
+def searchEmbeddings(id):
+    # get embeddings from file
+    embeddings_file = h5py.File('5m/extracted_features_of_all_keys.hdf5', 'r')
+
+    # variable and index initialization
+    dim = 768
+    count = 0
+    num_neighbors = 10
+
+    image_index = faiss.IndexFlatIP(dim)
+
+    # load dictionaries
+    with open("id_emb_dict.pickle", "rb") as f:
+        id_to_emb_dict = pickle.load(f)
+    with open("indx_to_id.pickle", "rb") as f:
+        indx_to_id_dict = pickle.load(f)
+
+    # get index
+    image_index = faiss.read_index("image_index.index")
+
+    # search for query
+    query = id_to_emb_dict[id]
+    query = query.astype(np.float32)
+    D, I = image_index.search(query, num_neighbors)
+
+    id_list = []
+    # need to convert I to id
+    i = 1
+    for indx in I[0]:
+        id = indx_to_id_dict[indx]
+        id_list.append(id)
+        # id_list.append(str(i) + ": " + id)
+        # i += 1
+
+    # get image data
+    dataset_hdf5_all_key = h5py.File('full5m/BIOSCAN_5M.hdf5', "r", libver="latest")['all_keys']
+    dataset_processid_list = [item.decode("utf-8") for item in dataset_hdf5_all_key["processid"][:]]
+    dataset_image_mask = dataset_hdf5_all_key["image_mask"][:]
+    processid_to_index = {pid: idx for idx, pid in enumerate(dataset_processid_list)}
+
+    image1 = get_image(dataset_hdf5_all_key, dataset_image_mask, processid_to_index, I[0][0])
+    image2 = get_image(dataset_hdf5_all_key, dataset_image_mask, processid_to_index, I[0][1])
+    image3 = get_image(dataset_hdf5_all_key, dataset_image_mask, processid_to_index, I[0][2])
+    image4 = get_image(dataset_hdf5_all_key, dataset_image_mask, processid_to_index, I[0][3])
+    image5 = get_image(dataset_hdf5_all_key, dataset_image_mask, processid_to_index, I[0][4])
+    image6 = get_image(dataset_hdf5_all_key, dataset_image_mask, processid_to_index, I[0][5])
+    image7 = get_image(dataset_hdf5_all_key, dataset_image_mask, processid_to_index, I[0][6])
+    image8 = get_image(dataset_hdf5_all_key, dataset_image_mask, processid_to_index, I[0][7])
+    image9 = get_image(dataset_hdf5_all_key, dataset_image_mask, processid_to_index, I[0][8])
+    image10 = get_image(dataset_hdf5_all_key, dataset_image_mask, processid_to_index, I[0][9])
+
+    # return id_list, id_list[0], id_list[1], id_list[2], id_list[3], id_list[4], id_list[5], id_list[6], id_list[7], id_list[8], id_list[9], image1, image2, image3, image4, image5, image6, image7, image8, image9, image10
+    # return id_list, indx_to_id_dict[I[0][0]], indx_to_id_dict[I[0][1]], indx_to_id_dict[I[0][2]], indx_to_id_dict[I[0][3]], indx_to_id_dict[I[0][4]], indx_to_id_dict[I[0][5]], indx_to_id_dict[I[0][6]], indx_to_id_dict[I[0][7]], indx_to_id_dict[I[0][8]], indx_to_id_dict[I[0][9]]
+    return id_list, image1, image2, image3, image4, image5, image6, image7, image8, image9, image10
+
+with gr.Blocks() as demo:
+    with gr.Column():
+        process_id = gr.Textbox(label="ID:", info="Enter a sample ID to search for")
+        process_id_list = gr.Textbox(label="Closest 10 matches:" )
+        search_btn = gr.Button("Search") 
+
+    with gr.Row():
+        image1 = gr.Image(label=1)
+        image2 = gr.Image(label=2)
+        image3 = gr.Image(label=3)
+        image4 = gr.Image(label=4)
+        image5 = gr.Image(label=5)
+    with gr.Row():
+        image6 = gr.Image(label=6)
+        image7 = gr.Image(label=7)
+        image8 = gr.Image(label=8)
+        image9 = gr.Image(label=9)
+        image10 = gr.Image(label=10)
+    
+    search_btn.click(fn=searchEmbeddings, inputs=process_id, 
+                     outputs=[process_id_list, image1, image2, image3, image4, image5, image6, image7, image8, image9, image10])
+
+
+# cant make functions with Image's as inputs
+# MUST be a way to format after
+
+
+# ARONZ671-20
+demo.launch(share=True)