src/utils/gradio_utils.py

import numpy as np
from tqdm import tqdm
import gradio as gr
import os 

import torch
import torch.nn.functional as F
import torch.utils.checkpoint

from accelerate import Accelerator
# from accelerate.logging import get_logger
from accelerate.utils import set_seed
from diffusers import AutoencoderKL, DDPMScheduler, DDIMScheduler, UNet2DConditionModel
# from huggingface_hub import HfFolder, Repository, whoami
from PIL import Image

from torchvision import transforms
from transformers import CLIPTextModel, CLIPTokenizer
from torch import autocast
from src.diffusers_ import StableDiffusionPipeline



def launch_source():
    image = Image.open('./tmp/train_images_source.png').convert("RGB")
    output = temp_save([image],num_rows=1)
    return output

def launch_opt800():
    image = Image.open('./tmp/train_images_step800.png').convert("RGB")
    output = temp_save([image],num_rows=1)
    return output

def launch_opt900():
    image = Image.open('./tmp/train_images_step900.png').convert("RGB")
    output = temp_save([image],num_rows=1)
    return output

def launch_opt1000():
    image = Image.open('./tmp/train_images_step1000.png').convert("RGB")
    output = temp_save([image],num_rows=1)
    return output

def launch_opt1100():
    image = Image.open('./tmp/train_images_step1100.png').convert("RGB")
    output = temp_save([image],num_rows=1)
    return output

def launch_optimize(img_in_real, prompt, n_hiper):

    os.makedirs("tmp", exist_ok=True)
    
    
    # Setting
    accelerator = Accelerator(
        gradient_accumulation_steps=1,
        mixed_precision="fp16",
    )

    seed = 2220000
    set_seed(seed)
    g_cuda = torch.Generator(device='cuda')
    g_cuda.manual_seed(seed)
    
    optimizer_class = torch.optim.Adam
    weight_dtype = torch.float16
     
    pretrained_model_name = 'CompVis/stable-diffusion-v1-4'   
        
    # Load pretrained models    
    tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_name, subfolder="tokenizer", use_auth_token=True)
    CLIP_text_encoder = CLIPTextModel.from_pretrained(pretrained_model_name, subfolder="text_encoder", use_auth_token=True)
    vae = AutoencoderKL.from_pretrained(pretrained_model_name, subfolder="vae", use_auth_token=True)
    unet = UNet2DConditionModel.from_pretrained(pretrained_model_name, subfolder="unet", use_auth_token=True)
    noise_scheduler = DDPMScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000)
    

    # Encode the input image.
    vae.to(accelerator.device, dtype=weight_dtype)
    input_image = img_in_real.convert("RGB")
    img_in_real.save(os.path.join("tmp", "train_images_source.png"))
    image_transforms = transforms.Compose(
        [
            transforms.Resize(512, interpolation=transforms.InterpolationMode.BILINEAR),
            transforms.CenterCrop(512),
            transforms.ToTensor(),
            transforms.Normalize([0.5], [0.5]),
        ]
    )
    init_image = image_transforms(input_image)
    init_image = init_image[None].to(device=accelerator.device, dtype=weight_dtype)
    with torch.inference_mode():
        init_latents = vae.encode(init_image).latent_dist.sample()
        init_latents = 0.18215 * init_latents

    # Encode the source and target text.
    CLIP_text_encoder.to(accelerator.device, dtype=weight_dtype)
    text_ids_src = tokenizer(prompt,padding="max_length",truncation=True,max_length=tokenizer.model_max_length,return_tensors="pt").input_ids
    text_ids_src = text_ids_src.to(device=accelerator.device)
    with torch.inference_mode():
        source_embeddings = CLIP_text_encoder(text_ids_src)[0].float()
    
    
    # del vae, CLIP_text_encoder
    del vae, CLIP_text_encoder
    if torch.cuda.is_available():
        torch.cuda.empty_cache()


    # For inference
    ddim_scheduler = DDIMScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", clip_sample=False, set_alpha_to_one=False)
    pipe = StableDiffusionPipeline.from_pretrained(pretrained_model_name, scheduler=ddim_scheduler, torch_dtype=torch.float16).to("cuda")
    num_samples = 1 
    guidance_scale = 7.5 
    num_inference_steps = 50
    height = 512
    width = 512
    
    
    # Optimize hiper embedding
    n_hiper = int(n_hiper)
    hiper_embeddings = source_embeddings[:,-n_hiper:].clone().detach()
    src_embeddings = source_embeddings[:,:-n_hiper].clone().detach()
    hiper_embeddings.requires_grad_(True)
    
    
    optimizer = optimizer_class(
        [hiper_embeddings],  
        lr=5e-3,
        betas=(0.9, 0.999),
        eps=1e-08,
    )

    unet, optimizer = accelerator.prepare(unet, optimizer)

    emb_train_steps = 1101
    # emb_train_steps = 201
    def train_loop(optimizer, hiper_embeddings):
        inf_images=[]    
        for step in tqdm(range(emb_train_steps)):
            with accelerator.accumulate(unet):
                
                noise = torch.randn_like(init_latents)
                bsz = init_latents.shape[0]
                timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=init_latents.device)
                timesteps = timesteps.long()

                noisy_latents = noise_scheduler.add_noise(init_latents, noise, timesteps)

                source_embeddings = torch.cat([src_embeddings, hiper_embeddings], 1)
                noise_pred = unet(noisy_latents, timesteps, source_embeddings).sample
                loss = F.mse_loss(noise_pred.float(), noise.float(), reduction="mean")

                accelerator.backward(loss)
                optimizer.step()
                optimizer.zero_grad(set_to_none=True)
            
            # Check inference
            if step in [800,900,1000,1100]:
                inf_emb = torch.cat([src_embeddings, hiper_embeddings.clone().detach()], 1)
                
                with autocast("cuda"), torch.inference_mode():     
                    images = pipe(text_embeddings=inf_emb, height=height, width=width, num_images_per_prompt=num_samples,
                        num_inference_steps=num_inference_steps, guidance_scale=guidance_scale, generator=g_cuda).images
                    inf_images.append(images[0])
                    images[0].save(os.path.join("tmp", "train_images_step{}.png".format(step)))
                del images

            if step in [800,900,1000,1100]:
                torch.save(hiper_embeddings.cpu(), os.path.join("tmp", "hiper_embeddings_step{}.pt".format(step)))
                

        accelerator.wait_for_everyone()
    
    out_image = train_loop(optimizer, hiper_embeddings)
    image = Image.open('./tmp/train_images_source.png').convert("RGB")
    output = temp_save([image],num_rows=1)

    return "tmp", output





def launch_main(dest, step, fpath_z_gen, seed):
    seed = int(seed)
    set_seed(seed)
    g_cuda = torch.Generator(device='cuda')
    g_cuda.manual_seed(seed)


    # Load pretrained models    
    pretrained_model_name = 'CompVis/stable-diffusion-v1-4'   
    scheduler = DDIMScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", clip_sample=False, set_alpha_to_one=False) 
    pipe = StableDiffusionPipeline.from_pretrained(pretrained_model_name, scheduler=scheduler, torch_dtype=torch.float16).to("cuda")
    tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_name, subfolder="tokenizer", use_auth_token=True)
    CLIP_text_encoder = CLIPTextModel.from_pretrained(pretrained_model_name, subfolder="text_encoder", use_auth_token=True)


    # Encode the target text.
    text_ids_tgt = tokenizer(dest, padding="max_length", truncation=True, max_length=tokenizer.model_max_length, return_tensors="pt").input_ids
    CLIP_text_encoder.to('cuda', dtype=torch.float32)
    with torch.inference_mode():
        target_embedding = CLIP_text_encoder(text_ids_tgt.to('cuda'))[0].to('cuda')
    del CLIP_text_encoder    


    # Concat target and hiper embeddings
    step = int(step.replace("Step ",""))
    hiper_embeddings = torch.load('./tmp/hiper_embeddings_step{}.pt'.format(step)).to("cuda")
    n_hiper = hiper_embeddings.shape[1]
    inference_embeddings =torch.cat([target_embedding[:, :-n_hiper], hiper_embeddings*0.8], 1)


    # Generate target images 
    num_samples = 1
    guidance_scale = 7.5 
    num_inference_steps = 50 
    height = 512
    width = 512 

    with autocast("cuda"), torch.inference_mode():        
        image_set = []
        for idx, embd in enumerate([inference_embeddings]):
            for i in range(10):
                images = pipe(
                    text_embeddings=embd,
                    height=height,
                    width=width,
                    num_images_per_prompt=num_samples,
                    num_inference_steps=num_inference_steps,
                    guidance_scale=guidance_scale,
                    generator=g_cuda
                ).images
                image_set.append(images[0])
                
    out_image = temp_save(image_set,num_rows=5)
    return out_image
        



def set_visible_true():
    return gr.update(visible=True)

def set_visible_false():
    return gr.update(visible=False)


CSS_main = """
    body {
    font-family: "HelveticaNeue-Light", "Helvetica Neue Light", "Helvetica Neue", Helvetica, Arial, "Lucida Grande", sans-serif; 
    font-weight:300;
    font-size:18px;
    margin-left: auto;
    margin-right: auto;
    padding-left: 10px;
    padding-right: 10px;
    width: 800px;
    }

    h1 {
        font-size:32px;
        font-weight:300;
        text-align: center;
    }

    h2 {
        font-size:32px;
        font-weight:300;
        text-align: center;
    }

    #lbl_gallery_input{
        font-family: 'Helvetica', 'Arial', sans-serif;
        text-align: center;
        color: #fff;
        font-size: 28px;
        display: inline
    }


    #lbl_gallery_comparision{
        font-family: 'Helvetica', 'Arial', sans-serif;
        text-align: center;
        color: #fff;
        font-size: 28px;
    }

    .disclaimerbox {
        background-color: #eee;		
        border: 1px solid #eeeeee;
        border-radius: 10px ;
        -moz-border-radius: 10px ;
        -webkit-border-radius: 10px ;
        padding: 20px;
    }

    video.header-vid {
        height: 140px;
        border: 1px solid black;
        border-radius: 10px ;
        -moz-border-radius: 10px ;
        -webkit-border-radius: 10px ;
    }

    img.header-img {
        height: 140px;
        border: 1px solid black;
        border-radius: 10px ;
        -moz-border-radius: 10px ;
        -webkit-border-radius: 10px ;
    }

    img.rounded {
        border: 1px solid #eeeeee;
        border-radius: 10px ;
        -moz-border-radius: 10px ;
        -webkit-border-radius: 10px ;
    }

    a:link
    {
        color: #941120;
        text-decoration: none;
    }
    a:visited
    {
        color: #941120;
        text-decoration: none;
    }
    a:hover {
        color: #941120;
    }

    td.dl-link {
        height: 160px;
        text-align: center;
        font-size: 22px;
    }

    .layered-paper-big { /* modified from: http://css-tricks.com/snippets/css/layered-paper/ */
        box-shadow:
        0px 0px 1px 1px rgba(0,0,0,0.35), /* The top layer shadow */
        5px 5px 0 0px #fff, /* The second layer */
        5px 5px 1px 1px rgba(0,0,0,0.35), /* The second layer shadow */
        10px 10px 0 0px #fff, /* The third layer */
        10px 10px 1px 1px rgba(0,0,0,0.35), /* The third layer shadow */
        15px 15px 0 0px #fff, /* The fourth layer */
        15px 15px 1px 1px rgba(0,0,0,0.35), /* The fourth layer shadow */
        20px 20px 0 0px #fff, /* The fifth layer */
        20px 20px 1px 1px rgba(0,0,0,0.35), /* The fifth layer shadow */
        25px 25px 0 0px #fff, /* The fifth layer */
        25px 25px 1px 1px rgba(0,0,0,0.35); /* The fifth layer shadow */
        margin-left: 10px;
        margin-right: 45px;
    }

    .paper-big { /* modified from: http://css-tricks.com/snippets/css/layered-paper/ */
        box-shadow:
        0px 0px 1px 1px rgba(0,0,0,0.35); /* The top layer shadow */

        margin-left: 10px;
        margin-right: 45px;
    }


    .layered-paper { /* modified from: http://css-tricks.com/snippets/css/layered-paper/ */
        box-shadow:
        0px 0px 1px 1px rgba(0,0,0,0.35), /* The top layer shadow */
        5px 5px 0 0px #fff, /* The second layer */
        5px 5px 1px 1px rgba(0,0,0,0.35), /* The second layer shadow */
        10px 10px 0 0px #fff, /* The third layer */
        10px 10px 1px 1px rgba(0,0,0,0.35); /* The third layer shadow */
        margin-top: 5px;
        margin-left: 10px;
        margin-right: 30px;
        margin-bottom: 5px;
    }

    .vert-cent {
        position: relative;
        top: 50%;
        transform: translateY(-50%);
    }

    hr
    {
        border: 0;
        height: 1px;
        background-image: linear-gradient(to right, rgba(0, 0, 0, 0), rgba(0, 0, 0, 0.75), rgba(0, 0, 0, 0));
    }

    .card {
        /* width: 130px;
        height: 195px;
        width: 1px;
        height: 1px; */
        position: relative;
        display: inline-block;
        /* margin: 50px; */
    }
    .card .img-top {
        display: none;
        position: absolute;
        top: 0;
        left: 0;
        z-index: 99;
    }
    .card:hover .img-top {
        display: inline;
    }
    details {
    user-select: none;
    }

    details>summary span.icon {
    width: 24px;
    height: 24px;
    transition: all 0.3s;
    margin-left: auto;
    }

    details[open] summary span.icon {
    transform: rotate(180deg);
    }

    summary {
    display: flex;
    cursor: pointer;
    }

    summary::-webkit-details-marker {
    display: none;
    }

    ul {
    display: table;
    margin: 0 auto;
    text-align: left;
    }

    .dark {
        padding: 1em 2em;
        background-color: #333;
        box-shadow: 3px 3px 3px #333;
        border: 1px #333;
    }
    .column {
        float: left;
        width: 20%;
        padding: 0.5%;
    }

    .galleryImg {
    transition: opacity 0.3s;
    -webkit-transition: opacity 0.3s;
    filter: grayscale(100%);
    /* filter: blur(2px); */
    -webkit-transition : -webkit-filter 250ms linear;
    /* opacity: 0.5; */
    cursor: pointer; 
    }



    .selected {	
    /* outline: 100px solid var(--hover-background) !important; */
    /* outline-offset: -100px; */
    filter: grayscale(0%);
    -webkit-transition : -webkit-filter 250ms linear;
    /*opacity: 1.0 !important; */
    }
    
    .galleryImg:hover {
    filter: grayscale(0%);
    -webkit-transition : -webkit-filter 250ms linear;

    }

    .row {
    margin-bottom: 1em;
    padding: 0px 1em;
    }
    /* Clear floats after the columns */
    .row:after {
    content: "";
    display: table;
    clear: both;
    }
    
    /* The expanding image container */
    #gallery {
    position: relative;
    /*display: none;*/
    }

    #section_comparison{
        position: relative;
        width: 100%;
        height: max-content;
    }

    /* SLIDER
    -------------------------------------------------- */

    .slider-container {
        position: relative;
        height: 384px;
        width: 512px;
        cursor: grab;
        overflow: hidden;
        margin: auto;
    }
    .slider-after {
        display: block;
        position: absolute;
        top: 0;
        right: 0;
        bottom: 0;
        left: 0;
        width: 100%;
        height: 100%;
        overflow: hidden;
    }
    .slider-before {
        display: block;
        position: absolute;
        top: 0;
        /* right: 0; */
        bottom: 0;
        left: 0;
        width: 100%;
        height: 100%;
        z-index: 15;
        overflow: hidden;
    }
    .slider-before-inset {
        position: absolute;
        top: 0;
        bottom: 0;
        left: 0;
    }
    .slider-after img,
    .slider-before img {
        object-fit: cover;
        position: absolute;
        width: 100%;
        height: 100%;
        object-position: 50% 50%;
        top: 0;
        bottom: 0;
        left: 0;
        -webkit-user-select: none;
        -khtml-user-select: none;
        -moz-user-select: none;
        -o-user-select: none;
        user-select: none;
    }

    #lbl_inset_left{
        text-align: center;
        position: absolute;
        top: 384px;
        width: 150px;
        left: calc(50% - 256px);
        z-index: 11;
        font-size: 16px;
        color: #fff;
        margin: 10px;
    }
    .inset-before {
        position: absolute;
        width: 150px;
        height: 150px;
        box-shadow: 3px 3px 3px #333;
        border: 1px #333;
        border-style: solid;
        z-index: 16;
        top: 410px;
        left: calc(50% - 256px);
        margin: 10px;
        font-size: 1em;
        background-repeat: no-repeat;
        pointer-events: none;
    }

    #lbl_inset_right{
        text-align: center;
        position: absolute;
        top: 384px;
        width: 150px;
        right: calc(50% - 256px);
        z-index: 11;
        font-size: 16px;
        color: #fff;
        margin: 10px;
    }
    .inset-after {
        position: absolute;
        width: 150px;
        height: 150px;
        box-shadow: 3px 3px 3px #333;
        border: 1px #333;
        border-style: solid;
        z-index: 16;
        top: 410px;
        right: calc(50% - 256px);
        margin: 10px;
        font-size: 1em;
        background-repeat: no-repeat;
        pointer-events: none;
    }

    #lbl_inset_input{
        text-align: center;
        position: absolute;
        top: 384px;
        width: 150px;
        left: calc(50% - 256px + 150px + 20px);
        z-index: 11;
        font-size: 16px;
        color: #fff;
        margin: 10px;
    }
    .inset-target {
        position: absolute;
        width: 150px;
        height: 150px;
        box-shadow: 3px 3px 3px #333;
        border: 1px #333;
        border-style: solid;
        z-index: 16;
        top: 410px;
        right: calc(50% - 256px + 150px + 20px);
        margin: 10px;
        font-size: 1em;
        background-repeat: no-repeat;
        pointer-events: none;
    }

    .slider-beforePosition {
        background: #121212;
        color: #fff;
        left: 0;
        pointer-events: none;
        border-radius: 0.2rem;
        padding: 2px 10px;
    }
    .slider-afterPosition {
        background: #121212;
        color: #fff;
        right: 0;
        pointer-events: none;
        border-radius: 0.2rem;
        padding: 2px 10px;
    }
    .beforeLabel {
        position: absolute;
        top: 0;
        margin: 1rem;
        font-size: 1em;
        -webkit-user-select: none;
        -khtml-user-select: none;
        -moz-user-select: none;
        -o-user-select: none;
        user-select: none;
    }
    .afterLabel {
        position: absolute;
        top: 0;
        margin: 1rem;
        font-size: 1em;
        -webkit-user-select: none;
        -khtml-user-select: none;
        -moz-user-select: none;
        -o-user-select: none;
        user-select: none;
    }

    .slider-handle {
        height: 101px;
        width: 41px;
        position: absolute;
        left: 50%;
        top: 50%;
        margin-left: -20px;
        margin-top: -21px;
        border: 2px solid #fff;
        border-radius: 1000px;
        z-index: 20;
        pointer-events: none;
        box-shadow: 0 0 10px rgb(12, 12, 12);
    }
    .handle-left-arrow,
    .handle-right-arrow {
        width: 0;
        height: 0;
        border: 6px inset transparent;
        position: absolute;
        top: 50%;
        margin-top: -6px;
    }
    .handle-left-arrow {
        border-right: 6px solid #fff;
        left: 50%;
        margin-left: -17px;
    }
    .handle-right-arrow {
        border-left: 6px solid #fff;
        right: 50%;
        margin-right: -17px;
    }
    .slider-handle::before {
        bottom: 50%;
        margin-bottom: 20px;
        box-shadow: 0 0 10px rgb(12, 12, 12);
    }
    .slider-handle::after {
        top: 50%;
        margin-top: 20.5px;
        box-shadow: 0 0 5px rgb(12, 12, 12);
    }
    .slider-handle::before,
    .slider-handle::after {
        content: " ";
        display: block;
        width: 2px;
        background: #fff;
        height: 9999px;
        position: absolute;
        left: 50%;
        margin-left: -1.5px;
    }  


    /* 
    -------------------------------------------------
    The editing results shown below inversion results
    -------------------------------------------------
    */
    .edit_labels{
        font-weight:500;
        font-size: 24px;
        color: #fff;
        height: 20px;
        margin-left: 20px;
        position: relative; 
        top: 20px;
    }

    .open > a:hover {
        color: #555;
        background-color: red;
    }

    #directions { padding-top:30; padding-bottom:0; margin-bottom: 0px; height: 20px; }
    #custom_task { padding-top:0; padding-bottom:0; margin-bottom: 0px; height: 20px; }
    #slider_ddim {accent-color: #941120;}
    #slider_ddim::-webkit-slider-thumb {background-color: #941120;}
    #slider_xa {accent-color: #941120;}
    #slider_xa::-webkit-slider-thumb {background-color: #941120;}
    #slider_edit_mul {accent-color: #941120;}
    #slider_edit_mul::-webkit-slider-thumb {background-color: #941120;}

    #input_image [data-testid="image"]{
        height: unset;
    }
    #input_image_synth [data-testid="image"]{
        height: unset;
    }
"""


HTML_header = f"""
    <body>
    <center>
    <span style="font-size:32px">Highly Personalized Text Embedding for Image Manipulation by Stable Diffusion</span>
    <table align=center>
        <tr>
            <td align=center>
                <center>
                    <span style="font-size:24px; margin-left: 0px;"><a href='https://hiper0.github.io/'>[Project page]</a></span>
                    <span style="font-size:24px; margin-left: 20px;"><a href='https://github.com/HiPer0/HiPer'>[Github]</a></span>
                </center>
            </td>
        </tr>
    </table>
    </center>

    <center>
    <div align=center>
        <p align=left>
         We present a simple yet highly effective approach to personalization using highly personalized (HiPer) text embedding by decomposing the CLIP embedding space for personalization and content manipulation. Our method does not require model fine-tuning or identifiers, yet still enables manipulation of background, texture, and motion with just a single image and target text. 
        <br>
        </p>
    </div>
    </center>


    <hr>
    </body>
"""


HTML_input_header = f"""
    <p style="font-size:150%; padding: 0px">
    <span font-weight: 800; style=" color: #941120;"> Step 1: </span> select a real input image.
    </p>
"""


HTML_middle_header = f"""
    <p style="font-size:150%;">
    <span font-weight: 800; style=" color: #941120;"> Step 2: </span> select the editing options.
    </p>
"""


HTML_output_header = f"""
    <p style="font-size:150%;">
    <span font-weight: 800; style=" color: #941120;"> Step 3: </span> translated image!
    </p>
"""


import numpy as np
import torch
from PIL import Image, ImageDraw, ImageFont
import cv2
from typing import Optional, Union, Tuple, List, Callable, Dict
# from tqdm.notebook import tqdm

#codes for 'show_image' and 'text_under_image' are from 
# https://github.com/google/prompt-to-prompt/blob/main/prompt-to-prompt_stable.ipynb

def show_images(images, num_rows=2, offset_ratio=0.02):
    if type(images) is list:
        num_empty = len(images) % num_rows
    elif images.ndim == 4:
        num_empty = images.shape[0] % num_rows
    else:
        images = [images]
        num_empty = 0

    empty_images = np.ones(images[0].shape, dtype=np.uint8) * 255
    images = [image.astype(np.uint8) for image in images] + [empty_images] * num_empty
    num_items = len(images)

    h, w, c = images[0].shape
    offset = int(h * offset_ratio)
    num_cols = num_items // num_rows
    image_ = np.ones((h * num_rows + offset * (num_rows - 1),
                      w * num_cols + offset * (num_cols - 1), 3), dtype=np.uint8) * 255
    for i in range(num_rows):
        for j in range(num_cols):
            image_[i * (h + offset): i * (h + offset) + h:, j * (w + offset): j * (w + offset) + w] = images[
                i * num_cols + j]

    pil_img = Image.fromarray(image_)
    # pil_img.save(name)
    return pil_img
    
    
def text_under_image(image: np.ndarray, text: str, text_color: Tuple[int, int, int] = (0, 0, 0)):
    h, w, c = image.shape
    offset = int(h * .2)
    img = np.ones((h + offset, w, c), dtype=np.uint8) * 255
    font = cv2.FONT_HERSHEY_SIMPLEX
    img[:h] = image
    textsize = cv2.getTextSize(text, font, 1, 2)[0]
    text_x, text_y = (w - textsize[0]) // 2, h + offset - textsize[1] // 2
    cv2.putText(img, text, (text_x, text_y ), font, 1, text_color, 2)
    return img


def inf_save(inf_img, name):
    images = []
    for i in range(len(inf_img)):
        image = np.array(inf_img[i].resize((256,256)))
        image = text_under_image(image, name[i])
        images.append(image)
    inf_image = show_images(np.stack(images, axis=0),num_rows=1)
    return inf_image

def temp_save(inf_img,num_rows):
    images = []
    for i in range(len(inf_img)):
        image = np.array(inf_img[i].resize((256,256)))
        images.append(image)
    inf_image = show_images(np.stack(images, axis=0),num_rows=num_rows)
    return inf_image