app.py

import os
import copy
#import spaces
from main import run_main
from PIL import Image
import matplotlib 
import numpy as np
import gradio as gr
from utils import load_mask, load_mask_edit
from utils_mask import process_mask_to_follow_priority, mask_union, visualize_mask_list_clean
from pathlib import Path
from PIL import Image
from functools import partial
import time

LENGTH=512 #length of the square area displaying/editing images
TRANSPARENCY = 150 # transparency of the mask in display


def add_mask(mask_np_list_updated, mask_label_list):
    mask_new = np.zeros_like(mask_np_list_updated[0])
    mask_np_list_updated.append(mask_new)
    mask_label_list.append("new")
    return mask_np_list_updated, mask_label_list

def create_segmentation(mask_np_list):
    viridis = matplotlib.pyplot.get_cmap(name = 'viridis', lut = len(mask_np_list))
    segmentation = 0
    for i, m  in enumerate(mask_np_list):
        color = matplotlib.colors.to_rgb(viridis(i))
        color_mat = np.ones_like(m)                                                                            
        color_mat = np.stack([color_mat*color[0], color_mat*color[1],color_mat*color[2] ], axis = 2)
        color_mat = color_mat * m[:,:,np.newaxis]
        segmentation += color_mat
    segmentation = Image.fromarray(np.uint8(segmentation*255))
    return segmentation


#@spaces.GPU
def run_segmentation_wrapper(image):
    try:
        image, mask_np_list,mask_label_list = run_segmentation(image)
        #image = image.convert('RGB')
        segmentation = create_segmentation(mask_np_list)
        print("!!", len(mask_np_list))  
        max_val = len(mask_np_list)-1
        sliderup = gr.Slider(value = 0, minimum=0, maximum=max_val, step=1, visible=True)
        gr.Info('Segmentation finish. Select mask id and move to the next step.')
        return image, segmentation, mask_np_list, mask_label_list, image, sliderup, sliderup , 'Segmentation finish. Select mask id and move to the next step.'
    except:
        sliderup = gr.Slider(value = 0, minimum=0, maximum=1, step=1, visible=False)
        gr.Warning('Please upload an image before proceeding.')
        return None,None,None,None,None, sliderup, sliderup , 'Please upload an image before proceeding.'
        

def transparent_paste_with_mask(backimg, foreimg, mask_np,transparency = 128):
    backimg_solid_np =  np.array(backimg)
    bimg = backimg.copy()
    fimg = foreimg.copy()
    fimg.putalpha(transparency)
    bimg.paste(fimg, (0,0), fimg)

    bimg_np = np.array(bimg)
    mask_np = mask_np[:,:,np.newaxis]

    new_img_np = bimg_np*mask_np + (1-mask_np)* backimg_solid_np
    return Image.fromarray(np.uint8(new_img_np))

def show_segmentation(image, segmentation, flag):
    if flag is False:
        flag = True
        mask_np = np.ones([image.size[0],image.size[1]]).astype(np.uint8)
        image_edit = transparent_paste_with_mask(image, segmentation, mask_np ,transparency = TRANSPARENCY)
        return image_edit, flag
    else:
        flag = False
        return image,flag

def edit_mask_add(canvas,  image, idx, mask_np_list):
    mask_sel = mask_np_list[idx]
    mask_new = np.uint8(canvas["mask"][:, :, 0]/ 255.)
    mask_np_list_updated = []
    for midx, m  in enumerate(mask_np_list):
        if midx == idx:
            mask_np_list_updated.append(mask_union(mask_sel, mask_new))
        else:
            mask_np_list_updated.append(m)
    
    priority_list = [0 for _ in range(len(mask_np_list_updated))]
    priority_list[idx] = 1
    mask_np_list_updated = process_mask_to_follow_priority(mask_np_list_updated, priority_list)
    mask_ones = np.ones([mask_sel.shape[0], mask_sel.shape[1]]).astype(np.uint8)
    segmentation = create_segmentation(mask_np_list_updated)
    image_edit = transparent_paste_with_mask(image, segmentation, mask_ones ,transparency = TRANSPARENCY)
    return mask_np_list_updated, image_edit

def slider_release(index, image,  mask_np_list_updated, mask_label_list):
    if index > len(mask_np_list_updated)-1:
        return image, "out of range", ""
    else:
        mask_np = mask_np_list_updated[index]
        mask_label = mask_label_list[index]
        index = mask_label.rfind('-')
        mask_label = mask_label[:index]
        if mask_label == 'handbag':
            mask_prompt = "white handbag"
        elif mask_label == 'person':
            mask_prompt = "little boy"
        elif mask_label == 'wall-other-merged':
            mask_prompt = "white wall"
        elif mask_label == 'table-merged':
            mask_prompt = "table"
        else:
            mask_prompt = mask_label
        segmentation = create_segmentation(mask_np_list_updated)
        new_image = transparent_paste_with_mask(image, segmentation, mask_np, transparency = TRANSPARENCY)
    gr.Info('Edit '+ mask_label)
    return new_image, mask_label, mask_prompt
def image_change():
    return gr.Slider(value = 0, minimum=0, maximum=1, step=1, visible=False),gr.Button("Step 3. Run Editing (Check log for progress.)",interactive = False)

def save_as_orig_mask(mask_np_list_updated, mask_label_list, input_folder="example_tmp"):
    print(mask_np_list_updated)
    try: 
        assert np.all(sum(mask_np_list_updated)==1)
    except:
        print("please check mask")
        # plt.imsave( "out_mask.png", mask_list_edit[0]) 
        import pdb; pdb.set_trace()
        
    for midx, (mask, mask_label) in enumerate(zip(mask_np_list_updated, mask_label_list)):
        # np.save(os.path.join(input_folder, "maskEDIT{}_{}.npy".format(midx, mask_label)),mask )
        np.save(os.path.join(input_folder, "mask{}_{}.npy".format(midx, mask_label)),mask )
    savepath = os.path.join(input_folder, "seg_current.png")
    visualize_mask_list_clean(mask_np_list_updated, savepath)
    
def save_as_edit_mask(mask_np_list_updated, mask_label_list, input_folder="example_tmp"):
    print(mask_np_list_updated)
    try: 
        assert np.all(sum(mask_np_list_updated)==1)
    except:
        print("please check mask")
        # plt.imsave( "out_mask.png", mask_list_edit[0]) 
        import pdb; pdb.set_trace()
    for midx, (mask, mask_label) in enumerate(zip(mask_np_list_updated, mask_label_list)):
        np.save(os.path.join(input_folder, "maskEdited{}_{}.npy".format(midx, mask_label)), mask)
    savepath = os.path.join(input_folder, "seg_edited.png")
    visualize_mask_list_clean(mask_np_list_updated, savepath)
  


def button_clickable(is_clickable):
    return gr.Button(interactive=is_clickable)



def load_pil_img():
    from PIL import Image
    return Image.open("example_tmp/text/out_text_0.png")

def change_image(img):
    return None
                

import shutil
if os.path.isdir("./example_tmp"):
    shutil.rmtree("./example_tmp")




from segment import run_segmentation

with gr.Blocks() as demo:
    image = gr.State() # store mask
    image_loaded = gr.State()
    segmentation    = gr.State()

    mask_np_list    = gr.State([])
    mask_label_list = gr.State([])
    mask_np_list_updated = gr.State([])
    true    = gr.State(True)
    false    = gr.State(False)
    block_flag = gr.State(0)
    num_tokens_global = gr.State(5)
    with gr.Row():
        gr.Markdown("""# D-Edit""")

    with gr.Tab(label="d-edit"): 
        with gr.Row():
            with gr.Column():
                canvas = gr.Image(value = "./img.png", type="numpy",  label="Draw Mask", show_label=True, height=LENGTH, width=LENGTH, interactive=True)
                gr.Markdown("""<p style="text-align: left; font-size: 16px">Each image requires a single segmentation and optimization operation.<br>Afterwards, you can modify the mask ID and prompt for image editing. <br>The link of D-edit paper: https://arxiv.org/abs/2403.04880v2</p>""")

            with gr.Column():
                result_info0 = gr.Text(label="Response")
                segment_button  = gr.Button("Step 1. Run segmentation")
                flag = gr.State(False)

                # mask_np_list_updated.value = copy.deepcopy(mask_np_list.value) #!!
                mask_np_list_updated = mask_np_list
                gr.Markdown("""<p style="text-align: center; font-size: 20px">Edit Mask (Do not change it during the optimization)</p>""")
                slider =  gr.Slider(0, 20, step=1, label = 'mask id',  visible=False)
                label = gr.Text(label='label')

            
                
    #with gr.Tab(label="2 Optimization"):
    #    with gr.Row():
    #        with gr.Column():
                result_info = gr.Text(label="Response")
                
                opt_flag = gr.State(0)
                gr.Markdown("""<p style="text-align: center; font-size: 20px">Optimization settings</p>""")
                with gr.Accordion(label="Advanced settings", open=False):
                    num_tokens = gr.Number(value="5", label="num tokens to represent each object", interactive= True)
                    num_tokens_global = num_tokens
                    embedding_learning_rate = gr.Textbox(value="0.00025", label="Embedding optimization: Learning rate", interactive= True )
                    max_emb_train_steps =  gr.Number(value="6", label="embedding optimization: Training steps", interactive= True )
                    
                    diffusion_model_learning_rate = gr.Textbox(value="0.0002", label="UNet Optimization: Learning rate", interactive= True )
                    max_diffusion_train_steps = gr.Number(value="28", label="UNet Optimization: Learning rate: Training steps", interactive= True )
                    
                    train_batch_size = gr.Number(value="20", label="Batch size", interactive= True )
                    gradient_accumulation_steps=gr.Number(value="2", label="Gradient accumulation", interactive= True )
                
                add_button  = gr.Button("Step 2. Run optimization")
                def run_optimization_wrapper (
                        mask_np_list,
                        mask_label_list,
                        image,
                        opt_flag,                     
                        num_tokens,
                        embedding_learning_rate , 
                        max_emb_train_steps , 
                        diffusion_model_learning_rate , 
                        max_diffusion_train_steps,
                        train_batch_size,
                        gradient_accumulation_steps,
                ):
                    try:
                        run_optimization = partial(
                            run_main,  
                            mask_np_list=mask_np_list, 
                            mask_label_list=mask_label_list,
                            image_gt=np.array(image),
                            num_tokens=int(num_tokens),
                            embedding_learning_rate = float(embedding_learning_rate), 
                            max_emb_train_steps = int(max_emb_train_steps), 
                            diffusion_model_learning_rate= float(diffusion_model_learning_rate), 
                            max_diffusion_train_steps = int(max_diffusion_train_steps),
                            train_batch_size=int(train_batch_size),
                            gradient_accumulation_steps=int(gradient_accumulation_steps)
                        )
                        run_optimization()
                        gr.Info("Optimization Finished! Move to the next step.")
                        return "Optimization finished! Move to the next step.",gr.Button("Step 3. Run Editing (Check log for progress.)",interactive = True)
                    except Exception as e:
                        print(e)
                        gr.Error("e")
                        return "Error: use a smaller batch size or try latter.",gr.Button("Step 3. Run Editing (Check log for progress.)",interactive = False)



    #with gr.Tab(label="3 Editing"):
        with gr.Tab(label="Text-based editing"):
        
            with gr.Row():
                with gr.Column():
                    canvas_text_edit = gr.Image(value = None, type = "pil", label="Editing results", show_label=True,visible = True)
                    # canvas_text_edit = gr.Gallery(label = "Edited results")
                
                with gr.Column():
                    gr.Markdown("""<p style="text-align: center; font-size: 20px">Editing setting</p>""")
                    tgt_prompt =  gr.Textbox(value="text prompt", label="Editing: Text prompt", interactive= True )
                    with gr.Accordion(label="Advanced settings", open=False):
                        slider2 = gr.Slider(0, 20, step=1, label = 'mask id',  visible=False)
                        guidance_scale = gr.Textbox(value="5", label="Editing: CFG guidance scale", interactive= True )
                        num_sampling_steps = gr.Number(value="20", label="Editing: Sampling steps", interactive= True )
                        edge_thickness = gr.Number(value="10", label="Editing: Edge thickness", interactive= True )
                        strength = gr.Textbox(value="0.5", label="Editing: Mask strength", interactive= True )
                    
                    add_button2  = gr.Button("Step 3. Run Editing (Check log for progress.)",interactive = False)
                    def run_edit_text_wrapper(
                            mask_np_list,
                            mask_label_list,
                            image,
                            num_tokens,
                            guidance_scale,
                            num_sampling_steps ,
                            strength ,
                            edge_thickness,
                            tgt_prompt ,
                            tgt_index
                    ):
                            
                        run_edit_text = partial(
                            run_main,
                            mask_np_list=mask_np_list, 
                            mask_label_list=mask_label_list,
                            image_gt=np.array(image),
                            load_trained=True,
                            text=True,
                            num_tokens = int(num_tokens_global.value),
                            guidance_scale = float(guidance_scale),
                            num_sampling_steps = int(num_sampling_steps),
                            strength = float(strength),
                            edge_thickness = int(edge_thickness),
                            num_imgs = 1,
                            tgt_prompt = tgt_prompt,
                            tgt_index = int(tgt_index)
                        )
                        run_edit_text()
                        gr.Info('Image editing completed.')
                        return load_pil_img()

                    example_inps = [['./img.png'],['./img2.png'],['./img3.png'],['./img4.png']]
                    gr.Examples(examples=example_inps, inputs=[canvas],
                            label='examples', cache_examples='lazy', outputs=[],
                            fn=change_image)


        canvas.upload(image_change, inputs=[], outputs=[slider,add_button2])            
        add_button.click(run_optimization_wrapper, 
                        inputs = [
                            mask_np_list,
                            mask_label_list,
                            image_loaded,
                            opt_flag,
                            num_tokens,
                            embedding_learning_rate , 
                            max_emb_train_steps , 
                            diffusion_model_learning_rate , 
                            max_diffusion_train_steps,
                            train_batch_size,
                            gradient_accumulation_steps
                        ], 
                        outputs = [result_info,add_button2], api_name=False, concurrency_limit=45)
                
        add_button2.click(run_edit_text_wrapper, 
                        inputs = [  mask_np_list,
                                    mask_label_list,
                                    image_loaded,num_tokens_global,
                                    guidance_scale,
                                    num_sampling_steps,
                                    strength ,
                                    edge_thickness,
                                    tgt_prompt ,
                                    slider2
                                ],        
                        outputs = [canvas_text_edit],queue=True)

        slider.release(slider_release, 
                        inputs = [slider, image_loaded,   mask_np_list_updated, mask_label_list], 
                        outputs= [canvas, label,tgt_prompt])

        slider.change(
            lambda x: x,
            inputs=[slider],
            outputs=[slider2]
        )


        segment_button.click(run_segmentation_wrapper, 
                [canvas] ,
                [image_loaded, segmentation,  mask_np_list, mask_label_list, canvas, slider, slider2, result_info0] )



demo.queue().launch(debug=True)