app.py

import pickle
from operator import itemgetter

import cv2
import gradio as gr
import kornia.filters
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import matplotlib.pyplot as plt
import zipfile
# from skimage.transform import resize
from torchvision import transforms, models
from get_models import Resnet_with_skip

def create_retrieval_figure(res):
    fig = plt.figure(figsize=[10 * 3, 10 * 3])
    cols = 5
    rows = 2
    ax_query = fig.add_subplot(rows, 1, 1)
    plt.rcParams['figure.facecolor'] = 'white'
    plt.axis('off')
    ax_query.set_title('Top 10 most similar scarabs', fontsize=40)
    names = ""
    for i, image in zip(range(len(res)), res):
        current_image_path = image.split("/")[3]+"/"+image.split("/")[4]
        if i==0: continue
        if i < 11:
            archive = zipfile.ZipFile('dataset.zip', 'r')
            imgfile = archive.read(current_image_path)
            image = cv2.imdecode(np.frombuffer(imgfile, np.uint8), 1)
            # image_resized = cv2.resize(image, (224, 224), interpolation=cv2.INTER_LINEAR)
            ax = fig.add_subplot(rows, cols, i)
            plt.axis('off')
            plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
            item_uuid = current_image_path.split("/")[1].split("_photoUUID")[0].split("itemUUID_")[1]
            ax.set_title('Top {}'.format(i), fontsize=40)
            names = names + "Top " + str(i) + " item UUID is " + item_uuid + "\n"
    return fig, names

def knn_calc(image_name, query_feature, features):
    current_image_feature = features[image_name]
    criterion = torch.nn.CosineSimilarity(dim=1)
    dist = criterion(query_feature, current_image_feature).mean()
    dist = -dist.item()
    return dist

checkpoint_path = "multi_label.pth.tar"

resnet = models.resnet101(pretrained=True)
num_ftrs = resnet.fc.in_features
resnet.fc = nn.Linear(num_ftrs, 13)
model = Resnet_with_skip(resnet)
checkpoint = torch.load(checkpoint_path, map_location="cpu")
model.load_state_dict(checkpoint)
embedding_model_test = torch.nn.Sequential(*(list(model.children())[:-1]))

periods_model = models.resnet101(pretrained=True)
periods_model.fc = nn.Linear(num_ftrs, 5)
periods_checkpoint = torch.load("periods.pth.tar", map_location="cpu")
periods_model.load_state_dict(periods_checkpoint)

with open('query_images_paths.pkl', 'rb') as fp:
    query_images_paths = pickle.load(fp)

with open('features.pkl', 'rb') as fp:
    features = pickle.load(fp)



model.eval()
transform = transforms.Compose([
            transforms.Resize((224, 224)),
            transforms.Grayscale(num_output_channels=3),
            transforms.ToTensor(),
            transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
        ])
invTrans = transforms.Compose([transforms.Normalize(mean=[0., 0., 0.],
                                                    std=[1 / 0.5, 1 / 0.5, 1 / 0.5]),
                               transforms.Normalize(mean=[-0.5, -0.5, -0.5],
                                                    std=[1., 1., 1.]),
                               ])

labels = ['ankh', 'anthropomorphic', 'bands', 'beetle', 'bird', 'circles', 'cross', 'duck', 'head', 'ibex', 'lion', 'sa', 'snake']

periods_labels = ["MB1", "MB2", "LB", "Iron1", 'Iron2']
periods_model.eval()

def predict(inp):
    image_tensor = transform(inp)
    with torch.no_grad():
        classification, reconstruction = model(image_tensor.unsqueeze(0))
        periods_classification = periods_model(image_tensor.unsqueeze(0))
        recon_tensor = reconstruction[0].repeat(3, 1, 1)
        recon_tensor = invTrans(kornia.enhance.invert(recon_tensor))
        plot_recon = recon_tensor.permute(1, 2, 0).detach().numpy()
        w, h = inp.size
        # plot_recon = resize(plot_recon, (h, w))
        m = nn.Sigmoid()
        y = m(classification)
        preds = []
        for sample in y:
            for i in sample:
                if i >=0.8:
                    preds.append(1)
                else:
                    preds.append(0)
        confidences = {}
        true_labels = ""
        for i in range(len(labels)):
            if preds[i]==1:
                if true_labels=="":
                    true_labels = true_labels + labels[i]
                else:
                    true_labels = true_labels + "&" + labels[i]
        confidences[true_labels] = torch.tensor(1.0)

        periods_prediction = torch.nn.functional.softmax(periods_classification[0], dim=0)
        periods_confidences = {periods_labels[i]: periods_prediction[i] for i in range(len(periods_labels))}
        feature = embedding_model_test(image_tensor.unsqueeze(0))
        dists = dict()
        with torch.no_grad():
            for i, image_name in enumerate(query_images_paths):
                dist = knn_calc(image_name, feature, features)
                dists[image_name] = dist
        res = dict(sorted(dists.items(), key=itemgetter(1)))
    fig, names = create_retrieval_figure(res)
    return confidences, periods_confidences, plot_recon, fig, names


gr.Interface(fn=predict,
             inputs=gr.Image(type="pil"),
             title="ArcAid: Analysis of Archaeological Artifacts using Drawings",
             description="Easily classify artifacs, retrieve similar ones and generate drawings. "
                         "https://arxiv.org/abs/2211.09480.",
             # examples=['anth.jpg', 'beetle_snakes.jpg', 'bird.jpg', 'cross.jpg', 'ibex.jpg',
             #           'lion.jpg', 'lion2.jpg', 'sa.jpg'],
             outputs=[gr.Label(num_top_classes=1), gr.Label(num_top_classes=1), "image", 'plot', 'text'], ).launch(share=True, enable_queue=True)