app.py

import csv
import sys
import pickle
from collections import Counter
import numpy as np
import gradio as gr
import gdown
import torchvision
from torchvision.datasets import ImageFolder

from SimSearch import FaissCosineNeighbors, SearchableTrainingSet
from ExtractEmbedding import QueryToEmbedding
from CHMCorr import chm_classify_and_visualize
from visualization import plot_from_reranker_output

csv.field_size_limit(sys.maxsize)

concat = lambda x: np.concatenate(x, axis=0)

# Embeddings
gdown.cached_download(
    url="https://drive.google.com/uc?id=116CiA_cXciGSl72tbAUDoN-f1B9Frp89",
    path="./embeddings.pkl",
    quiet=False,
    md5="002b2a7f5c80d910b9cc740c2265f058",
)

gdown.download(id="1SDtq6ap7LPPpYfLbAxaMGGmj0EAV_m_e")

# CUB training set
gdown.cached_download(
    url="https://drive.google.com/uc?id=1iR19j7532xqPefWYT-BdtcaKnsEokIqo",
    path="./CUB_train.zip",
    quiet=False,
    md5="1bd99e73b2fea8e4c2ebcb0e7722f1b1",
)

# EXTRACT training set
torchvision.datasets.utils.extract_archive(
    from_path="CUB_train.zip",
    to_path="data/",
    remove_finished=False,
)

# CHM Weights
gdown.cached_download(
    url="https://drive.google.com/u/0/uc?id=1zsJRlAsoOn5F0GTCprSFYwDDfV85xDy6&export=download",
    path="pas_psi.pt",
    quiet=False,
    md5="6b7b4d7bad7f89600fac340d6aa7708b",
)


# Caluclate Accuracy
with open(f"./embeddings.pickle", "rb") as f:
    Xtrain = pickle.load(f)
# FIXME: re-run the code to get the embeddings in the right format
with open(f"./labels.pickle", "rb") as f:
    ytrain = pickle.load(f)

searcher = SearchableTrainingSet(Xtrain, ytrain)
searcher.build_index()

# Extract label names
training_folder = ImageFolder(root="./data/train/")
id_to_bird_name = {
    x[1]: x[0].split("/")[-2].replace(".", " ") for x in training_folder.imgs
}


def search(query_image, searcher=searcher):
    query_embedding = QueryToEmbedding(query_image)
    scores, indices, labels = searcher.search(query_embedding, k=50)

    result_ctr = Counter(labels[0][:20]).most_common(5)

    top1_label = result_ctr[0][0]
    top_indices = []

    for a, b in zip(labels[0][:20], indices[0][:20]):
        if a == top1_label:
            top_indices.append(b)

    gallery_images = [training_folder.imgs[int(X)][0] for X in top_indices[:5]]
    predicted_labels = {id_to_bird_name[X[0]]: X[1] / 20.0 for X in result_ctr}

    print("gallery_images:", gallery_images)

    # CHM Prediction
    kNN_results = (top1_label, result_ctr[0][1], gallery_images)
    support_files = [training_folder.imgs[int(X)][0] for X in indices[0]]

    print(support_files)
    support_labels = [training_folder.imgs[int(X)][1] for X in indices[0]]
    print(support_labels)

    support = [support_files, support_labels]

    chm_output = chm_classify_and_visualize(
        query_image, kNN_results, support, training_folder
    )

    viz_plot = plot_from_reranker_output(chm_output, draw_arcs=False)

    return predicted_labels, gallery_images, viz_plot


demo = gr.Interface(
    search,
    gr.Image(type="filepath"),
    ["label", "gallery", "plot"],
    examples=[["./examples/bird.jpg"]],
    description="WIP - kNN on CUB dataset",
    title="Work in Progress - CHM-Corr",
)

if __name__ == "__main__":
    demo.launch()