app.py

# import os
# os.system("pip uninstall -y gradio")
# os.system("pip install gradio==3.41.0")

import os
import copy
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
from main import run_main
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

def load_mask_ui(input_folder="example_tmp",load_edit = False):
    if not load_edit:
        mask_list, mask_label_list = load_mask(input_folder)
    else:
        mask_list, mask_label_list = load_mask_edit(input_folder) 
        
    mask_np_list = []
    for  m  in mask_list:
        mask_np_list. append( m.cpu().numpy())

    return mask_np_list, mask_label_list

def load_image_ui(load_edit, input_folder="example_tmp"):
    #try:
    if 1:
        image, mask_np_list, mask_label_list = load_mask_ui(input_folder, load_edit = load_edit)
        #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, interactive=True)
        return image, segmentation, mask_np_list, mask_label_list, image, sliderup, sliderup 
    #except:
    #    print("Image folder invalid: The folder should contain image.png")
    #    return None, None, None, None, None, None


def run_segmentation_wrapper(image):
    mask_np_list,mask_label_list = run_segmentation(image)
    for img_path in Path("example_tmp").iterdir():
        if img_path.name in ["img_512.png"]:
            image = Image.open(img_path)
    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, interactive=True)
    return image, segmentation, mask_np_list, mask_label_list, image, sliderup, sliderup , 'Segmentatin finish.'
        

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]

    try:
        new_img_np = bimg_np*mask_np + (1-mask_np)* backimg_solid_np
        return Image.fromarray(new_img_np) 
    except:
        import pdb; pdb.set_trace()

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):
        return image, "out of range"
    else:
        mask_np = mask_np_list_updated[index]
        mask_label = mask_label_list[index]
        segmentation = create_segmentation(mask_np_list_updated)
        new_image = transparent_paste_with_mask(image, segmentation, mask_np, transparency = TRANSPARENCY)
    return new_image, mask_label

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 image_change():
    directory_path = "./example_tmp/"
    for filename in os.listdir(directory_path):
        file_path = os.path.join(directory_path, filename)
        if os.path.isfile(file_path) or os.path.islink(file_path):
            os.unlink(file_path)
        elif os.path.isdir(file_path):
            shutil.rmtree(file_path)
    return gr.Button("1.2 Load original masks",visible = False)

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")

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="1 Edit mask"):
        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)
                
                segment_button  = gr.Button("1.1 Run segmentation")
                
                text_button  = gr.Button("Waiting 1.1 to complete",visible = False)


                flag = gr.State(False)

            # mask_np_list_updated.value = copy.deepcopy(mask_np_list.value) #!!
            mask_np_list_updated = mask_np_list
            with gr.Column():
                result_info0 = gr.Text(label="Response")
                gr.Markdown("""<p style="text-align: center; font-size: 20px">Edit Mask (Optional)</p>""")
                slider =  gr.Slider(0, 20, step=1, label = 'mask id',  interactive=False)
                label = gr.Text(label='label')
                slider.release(slider_release, 
                        inputs = [slider, image_loaded,   mask_np_list_updated, mask_label_list], 
                        outputs= [canvas, label]
                    )

        


        canvas.upload(image_change, inputs=[], outputs=[text_button])
                
                
    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 (SD)</p>""")
                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="0.0001", label="Embedding optimization: Learning rate", interactive= True )
                max_emb_train_steps =  gr.Number(value="200", label="embedding optimization: Training steps", interactive= True )
                
                diffusion_model_learning_rate = gr.Textbox(value="0.00005", label="UNet Optimization: Learning rate", interactive= True )
                max_diffusion_train_steps = gr.Number(value="200", label="UNet Optimization: Learning rate: Training steps", interactive= True )
                
                train_batch_size = gr.Number(value="5", label="Batch size", interactive= True )
                gradient_accumulation_steps=gr.Number(value="5", label="Gradient accumulation", interactive= True )
                
                add_button  = gr.Button("Run optimization")
                def run_optimization_wrapper (
                        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,                 
                            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()
                        print('finish')
                        return "Optimization finished!"
                    except:
                        return "CUDA out of memory, use a smaller batch size or try another picture."

                
                def immediate_update():
                    return gr.Button("Run Optimization (Check Log for Completion).", interactive=True)
                
                add_button.click(run_optimization_wrapper, 
                        inputs = [
                            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], api_name=False, concurrency_limit=45)
                add_button.click(fn=immediate_update, inputs=[], outputs=[add_button])
                #add_button.update()
                def change_text():
                    return gr.Textbox("Optimization Finished!", interactive = False)


    with gr.Tab(label="3 Editing"):
        with gr.Tab(label="3.1 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 (SD)</p>""")
                    
                    tgt_prompt =  gr.Textbox(value="White bag", label="Editing: Text prompt", interactive= True )
                    slider2 = gr.Slider(0, 20, step=1, label = 'mask id',  interactive=False)
                    #tgt_index = gr.Number(value="0", label="Editing: Object index", interactive= True )
                    guidance_scale = gr.Textbox(value="6", label="Editing: CFG guidance scale", interactive= True )
                    num_sampling_steps = gr.Number(value="50", 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_button  = gr.Button("Run Editing (Check Log for Completion)")
                    def run_edit_text_wrapper(
                            num_tokens,
                            guidance_scale,
                            num_sampling_steps ,
                            strength ,
                            edge_thickness,
                            tgt_prompt ,
                            tgt_index
                    ):
                            
                        run_edit_text = partial(
                            run_main,
                            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()
                        return load_pil_img()
                        
                    add_button.click(run_edit_text_wrapper, 
                        inputs = [num_tokens_global,
                                    guidance_scale,
                                    num_sampling_steps,
                                    strength ,
                                    edge_thickness,
                                    tgt_prompt ,
                                    slider2
                                ],        
                        outputs = [canvas_text_edit],queue=True,
                    )
                    
        slider.change(
            lambda x: x,
            inputs=[slider],
            outputs=[slider2]
        )

        text_button.click(load_image_ui, [false] , 
                        [image_loaded, segmentation,  mask_np_list, mask_label_list, canvas, slider, 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)