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| import pickle | |
| from pathlib import Path | |
| import gradio as gr | |
| import torch | |
| # from loguru import logger | |
| from PIL import Image | |
| # from sentence_transformers import SentenceTransformer, util | |
| import os | |
| import argparse | |
| import re | |
| import time | |
| import numpy as np | |
| from numpy.__config__ import show | |
| import torch | |
| from misc.model import img_embedding, joint_embedding | |
| from torch.utils.data import DataLoader, dataset | |
| from misc.dataset import TextDataset | |
| from misc.utils import collate_fn_cap_padded | |
| from torch.utils.data import DataLoader | |
| from misc.utils import load_obj | |
| from misc.evaluation import recallTopK | |
| from misc.utils import show_imgs | |
| import sys | |
| from misc.dataset import TextEncoder | |
| import requests | |
| from io import BytesIO | |
| from translate import Translator | |
| device = torch.device("cpu") | |
| batch_size = 1 | |
| topK = 5 | |
| T2I = "Text 2 Image" | |
| I2I = "Image 2 Image" | |
| model_path = "data/best_model.pth.tar" | |
| # model = SentenceTransformer("clip-ViT-B-32") | |
| def download_url_img(url): | |
| try: | |
| response = requests.get(url, timeout=3) | |
| except Exception as e: | |
| print(str(e)) | |
| return False, [] | |
| if response is not None and response.status_code == 200: | |
| input_image_data = response.content | |
| image=Image.open(BytesIO(input_image_data)) | |
| return True, image | |
| return False, [] | |
| def search(mode, text): | |
| # translator = Translator(from_lang="chinese",to_lang="english") | |
| # text = translator.translate(text) | |
| dataset = torch.Tensor(encoder.encode(text)).unsqueeze(dim=0) | |
| dataset_loader = DataLoader(dataset, batch_size=batch_size, num_workers=1, pin_memory=True, collate_fn=collate_fn_cap_padded) | |
| caps_enc = list() | |
| for i, (caps, length) in enumerate(dataset_loader, 0): | |
| input_caps = caps.to(device) | |
| with torch.no_grad(): | |
| _, output_emb = join_emb(None, input_caps, length) | |
| caps_enc.append(output_emb.cpu().data.numpy()) | |
| caps_stack = np.vstack(caps_enc) | |
| imgs_url = [os.path.join("http://images.cocodataset.org/train2017", img_path.strip().split('_')[-1]) for img_path in imgs_path] | |
| recall_imgs = recallTopK(caps_stack, imgs_emb, imgs_url, ks=100) | |
| # Cat image downloaded from https://www.flickr.com/photos/blacktigersdream/23119711630 | |
| # cat_image = "./cat_example.jpg" | |
| # Dog example downloaded from https://upload.wikimedia.org/wikipedia/commons/1/18/Dog_Breeds.jpg | |
| # dog_image = "./dog_example.jpg" | |
| res = [] | |
| idx = 0 | |
| for img_url in recall_imgs: | |
| if idx == topK: | |
| break | |
| b, img = download_url_img(img_url) | |
| if b: | |
| res.append(img) | |
| idx += 1 | |
| return res | |
| if __name__ == "__main__": | |
| # print("Loading model from:", model_path) | |
| checkpoint = torch.load(model_path, map_location=lambda storage, loc: storage) | |
| join_emb = joint_embedding(checkpoint['args_dict']) | |
| join_emb.load_state_dict(checkpoint["state_dict"]) | |
| for param in join_emb.parameters(): | |
| param.requires_grad = False | |
| join_emb.to(device) | |
| join_emb.eval() | |
| encoder = TextEncoder() | |
| imgs_emb_file_path = "./coco_img_emb" | |
| imgs_emb, imgs_path = load_obj(imgs_emb_file_path) | |
| print("prepare done!") | |
| iface = gr.Interface( | |
| fn=search, | |
| inputs=[ | |
| gr.inputs.Radio([T2I]), | |
| gr.inputs.Textbox( | |
| lines=1, label="Text query", placeholder="Introduce the search text...", | |
| ), | |
| ], | |
| theme="grass", | |
| outputs=[ | |
| gr.outputs.Image(type="auto", label="1st Best match"), | |
| gr.outputs.Image(type="auto", label="2nd Best match"), | |
| gr.outputs.Image(type="auto", label="3rd Best match"), | |
| gr.outputs.Image(type="auto", label="4rd Best match"), | |
| gr.outputs.Image(type="auto", label="5rd Best match") | |
| ], | |
| title="HUST毕业设计-图文检索系统", | |
| description="请输入图片或文本，将为您展示相关的图片：", | |
| ) | |
| iface.launch(share=False) | |