app.py

import gradio as gr
import PIL
from PIL import Image
import numpy as np
import os
import uuid
import torch
from torch import autocast
import cv2

from io import BytesIO
import requests
import PIL
from PIL import Image
import numpy as np
import os
import uuid
import torch
from torch import autocast
import cv2
from matplotlib import pyplot as plt
from torchvision import transforms
from diffusers import DiffusionPipeline

import io
import logging
import multiprocessing
import random
import time
import imghdr
from pathlib import Path
from typing import Union
from loguru import logger

from lama_cleaner.model_manager import ModelManager
from lama_cleaner.schema import Config

try:
    torch._C._jit_override_can_fuse_on_cpu(False)
    torch._C._jit_override_can_fuse_on_gpu(False)
    torch._C._jit_set_texpr_fuser_enabled(False)
    torch._C._jit_set_nvfuser_enabled(False)
except:
    pass


from lama_cleaner.helper import (
    load_img,
    numpy_to_bytes,
    resize_max_size,
)

NUM_THREADS = str(multiprocessing.cpu_count())

# fix libomp problem on windows https://github.com/Sanster/lama-cleaner/issues/56
os.environ["KMP_DUPLICATE_LIB_OK"] = "True"

os.environ["OMP_NUM_THREADS"] = NUM_THREADS
os.environ["OPENBLAS_NUM_THREADS"] = NUM_THREADS
os.environ["MKL_NUM_THREADS"] = NUM_THREADS
os.environ["VECLIB_MAXIMUM_THREADS"] = NUM_THREADS
os.environ["NUMEXPR_NUM_THREADS"] = NUM_THREADS
if os.environ.get("CACHE_DIR"):
    os.environ["TORCH_HOME"] = os.environ["CACHE_DIR"]

os.environ["TORCH_HOME"] = './'

BUILD_DIR = os.environ.get("LAMA_CLEANER_BUILD_DIR", "app/build")

from share_btn import community_icon_html, loading_icon_html, share_js

HF_TOKEN_SD = os.environ.get('HF_TOKEN_SD')

device = "cuda" if torch.cuda.is_available() else "cpu"
print(f'device = {device}')

def get_image_ext(img_bytes):
    w = imghdr.what("", img_bytes)
    if w is None:
        w = "jpeg"
    return w
    
def diffuser_callback(i, t, latents):
    pass

def preprocess_image(image):
    w, h = image.size
    w, h = map(lambda x: x - x % 32, (w, h))  # resize to integer multiple of 32
    image = image.resize((w, h), resample=PIL.Image.LANCZOS)
    image = np.array(image).astype(np.float32) / 255.0
    image = image[None].transpose(0, 3, 1, 2)
    image = torch.from_numpy(image)
    return 2.0 * image - 1.0

def preprocess_mask(mask):
    mask = mask.convert("L")
    w, h = mask.size
    w, h = map(lambda x: x - x % 32, (w, h))  # resize to integer multiple of 32
    mask = mask.resize((w // 8, h // 8), resample=PIL.Image.NEAREST)
    mask = np.array(mask).astype(np.float32) / 255.0
    mask = np.tile(mask, (4, 1, 1))
    mask = mask[None].transpose(0, 1, 2, 3)  # what does this step do?
    mask = 1 - mask  # repaint white, keep black
    mask = torch.from_numpy(mask)
    return mask
    
def load_img_1_(nparr, gray: bool = False):
    # alpha_channel = None
    # nparr = np.frombuffer(img_bytes, np.uint8)
    if gray:
        np_img = cv2.imdecode(nparr, cv2.IMREAD_GRAYSCALE)
    else:
        np_img = cv2.imdecode(nparr, cv2.IMREAD_UNCHANGED)
        if len(np_img.shape) == 3 and np_img.shape[2] == 4:
            alpha_channel = np_img[:, :, -1]
            np_img = cv2.cvtColor(np_img, cv2.COLOR_BGRA2RGB)
        else:
            np_img = cv2.cvtColor(np_img, cv2.COLOR_BGR2RGB)

    return np_img, alpha_channel
    
model = None
def model_process_pil(input):
    global model
    
    # input = request.files
    # RGB
    # origin_image_bytes = input["image"].read()
    image_pil = input['image']
    mask_pil = input['mask']
        
    image = np.array(image_pil)
    mask = np.array(mask_pil.convert("L"))
    # print(f'image_pil_ = {type(image_pil)}')
    # print(f'mask_pil_ = {type(mask_pil)}') 
    # mask_pil.save(f'./mask_pil.png')         
        
    #image, alpha_channel = load_img(image)
    # Origin image shape: (512, 512, 3)
    
    alpha_channel = (np.ones((image.shape[0],image.shape[1]))*255).astype(np.uint8)
    original_shape = image.shape
    interpolation = cv2.INTER_CUBIC    
    
    # form = request.form
    print(f'liuyz_3_here_', original_shape, alpha_channel, image.dtype, mask.dtype)

    size_limit = "Original" # image.shape[1] # : Union[int, str] = form.get("sizeLimit", "1080")
    if size_limit == "Original":
        size_limit = max(image.shape)
    else:
        size_limit = int(size_limit)

    config = Config(
        ldm_steps=25,
        ldm_sampler='plms',
        zits_wireframe=True,
        hd_strategy='Original',
        hd_strategy_crop_margin=196,
        hd_strategy_crop_trigger_size=1280,
        hd_strategy_resize_limit=2048,
        prompt='',
        use_croper=False,
        croper_x=0,
        croper_y=0,
        croper_height=512,
        croper_width=512,
        sd_mask_blur=5,
        sd_strength=0.75,
        sd_steps=50,
        sd_guidance_scale=7.5,
        sd_sampler='ddim',
        sd_seed=42,
        cv2_flag='INPAINT_NS',
        cv2_radius=5,
    )
   
    # print(f'config = {config}')
    
    print(f'config/alpha_channel/size_limit = {config} / {alpha_channel} / {size_limit}')
    if config.sd_seed == -1:
        config.sd_seed = random.randint(1, 999999999)
    
    # logger.info(f"Origin image shape: {original_shape}")
    print(f"Origin image shape: {original_shape} / {image[250][250]}")
    image = resize_max_size(image, size_limit=size_limit, interpolation=interpolation)
    # logger.info(f"Resized image shape: {image.shape}")
    print(f"Resized image shape: {image.shape} / {image[250][250]} / {image.dtype}")
    
    # mask, _ = load_img(mask, gray=True)
    #mask = np.array(mask_pil)
    mask = resize_max_size(mask, size_limit=size_limit, interpolation=interpolation)
    print(f"mask image shape: {mask.shape} / {type(mask)} / {mask[250][250]} / {mask.dtype}")

    if model is None:
        return None
        
    start = time.time()
    res_np_img = model(image, mask, config)
    logger.info(f"process time: {(time.time() - start) * 1000}ms, {res_np_img.shape}")
    print(f"process time_1_: {(time.time() - start) * 1000}ms, {alpha_channel.shape}, {res_np_img.shape} / {res_np_img[250][250]} / {res_np_img.dtype}")

    torch.cuda.empty_cache()

    if alpha_channel is not None:
        print(f"liuyz_here_10_: {alpha_channel.shape} / {alpha_channel.dtype} / {res_np_img.dtype}") 
        if alpha_channel.shape[:2] != res_np_img.shape[:2]:
            print(f"liuyz_here_20_: {alpha_channel.shape} / {res_np_img.shape}")
            alpha_channel = cv2.resize(
                alpha_channel, dsize=(res_np_img.shape[1], res_np_img.shape[0])
            )
        print(f"liuyz_here_30_: {alpha_channel.shape} / {res_np_img.shape} / {alpha_channel.dtype} / {res_np_img.dtype}")
        res_np_img = np.concatenate(
            (res_np_img, alpha_channel[:, :, np.newaxis]), axis=-1
        )
        print(f"liuyz_here_40_: {alpha_channel.shape} / {res_np_img.shape} / {alpha_channel.dtype} / {res_np_img.dtype}")
    print(f"process time_2_: {(time.time() - start) * 1000}ms, {alpha_channel.shape}, {res_np_img.shape} / {res_np_img[250][250]} / {res_np_img.dtype}")
    ext = 'png'
    image = Image.open(io.BytesIO(numpy_to_bytes(res_np_img, ext)))
    image.save(f'./result_image.png')
    return image # res_np_img.astype(np.uint8) # image
    
    '''
    ext = get_image_ext(origin_image_bytes)
    return ext
    '''
    
def model_process_filepath(input): #image, mask):
    global model
    # {'image': '/tmp/tmp8mn9xw93.png', 'mask': '/tmp/tmpn5ars4te.png'}
    # input = request.files
    # RGB
    origin_image_bytes = read_content(input["image"])
    print(f'origin_image_bytes = ', type(origin_image_bytes), len(origin_image_bytes))

    image, alpha_channel = load_img(origin_image_bytes)
    
    alpha_channel = (np.ones((image.shape[0],image.shape[1]))*255).astype(np.uint8)
    original_shape = image.shape
    interpolation = cv2.INTER_CUBIC

    image_pil = Image.fromarray(image)
    # mask_pil = Image.fromarray(mask).convert("L")
    
    # form = request.form
    # print(f'size_limit_1_ =  ', form["sizeLimit"], type(input["image"]))
    size_limit = "Original" #: Union[int, str] = form.get("sizeLimit", "1080")
    print(f'size_limit_2_ =  {size_limit}')
    if size_limit == "Original":
        size_limit = max(image.shape)
    else:
        size_limit = int(size_limit)
    print(f'size_limit_3_ =  {size_limit}')

    config = Config(
        ldm_steps=25,
        ldm_sampler='plms',
        zits_wireframe=True,
        hd_strategy='Original',
        hd_strategy_crop_margin=196,
        hd_strategy_crop_trigger_size=1280,
        hd_strategy_resize_limit=2048,
        prompt='',
        use_croper=False,
        croper_x=0,
        croper_y=0,
        croper_height=512,
        croper_width=512,
        sd_mask_blur=5,
        sd_strength=0.75,
        sd_steps=50,
        sd_guidance_scale=7.5,
        sd_sampler='ddim',
        sd_seed=42,
        cv2_flag='INPAINT_NS',
        cv2_radius=5,
    )
    
    print(f'config/alpha_channel/size_limit = {config} / {alpha_channel} / {size_limit}')
    if config.sd_seed == -1:
        config.sd_seed = random.randint(1, 999999999)

    logger.info(f"Origin image shape: {original_shape}")
    print(f"Origin image shape: {original_shape} / {image[250][250]}")
    image = resize_max_size(image, size_limit=size_limit, interpolation=interpolation)
    logger.info(f"Resized image shape: {image.shape} / {type(image)}")
    print(f"Resized image shape: {image.shape} / {image[250][250]}")

    mask, _ = load_img(read_content(input["mask"]), gray=True)
    mask = resize_max_size(mask, size_limit=size_limit, interpolation=interpolation)
    print(f"mask image shape: {mask.shape} / {type(mask)} / {mask[250][250]} / {alpha_channel}")

    if model is None:
        return None
        
    start = time.time()
    res_np_img = model(image, mask, config)
    logger.info(f"process time: {(time.time() - start) * 1000}ms, {res_np_img.shape}")
    print(f"process time_1_: {(time.time() - start) * 1000}ms, {alpha_channel.shape}, {res_np_img.shape} / {res_np_img[250][250]} / {res_np_img.dtype}")

    torch.cuda.empty_cache()

    if alpha_channel is not None:
        print(f"liuyz_here_10_: {alpha_channel.shape} / {alpha_channel.dtype} / {res_np_img.dtype}") 
        if alpha_channel.shape[:2] != res_np_img.shape[:2]:
            print(f"liuyz_here_20_: {alpha_channel.shape} / {res_np_img.shape}")
            alpha_channel = cv2.resize(
                alpha_channel, dsize=(res_np_img.shape[1], res_np_img.shape[0])
            )
        print(f"liuyz_here_30_: {alpha_channel.shape} / {res_np_img.shape} / {alpha_channel.dtype} / {res_np_img.dtype}")
        res_np_img = np.concatenate(
            (res_np_img, alpha_channel[:, :, np.newaxis]), axis=-1
        )
        print(f"liuyz_here_40_: {alpha_channel.shape} / {res_np_img.shape} / {alpha_channel.dtype} / {res_np_img.dtype}")
    ext = get_image_ext(origin_image_bytes)
    print(f"process time_2_: {(time.time() - start) * 1000}ms, {alpha_channel.shape}, {res_np_img.shape} / {res_np_img[250][250]} / {res_np_img.dtype} /{ext}")
  
    image = Image.open(io.BytesIO(numpy_to_bytes(res_np_img, ext)))
    image.save(f'./result_image.png')
    return  image # image

def model_process(image, mask, alpha_channel, ext):
    global model
    original_shape = image.shape
    interpolation = cv2.INTER_CUBIC

    # image_pil = Image.fromarray(image)
    # mask_pil = Image.fromarray(mask).convert("L")
    
    size_limit = "Original"
    print(f'size_limit_2_ =  {size_limit}')
    if size_limit == "Original":
        size_limit = max(image.shape)
    else:
        size_limit = int(size_limit)
    print(f'size_limit_3_ =  {size_limit}')

    config = Config(
        ldm_steps=25,
        ldm_sampler='plms',
        zits_wireframe=True,
        hd_strategy='Original',
        hd_strategy_crop_margin=196,
        hd_strategy_crop_trigger_size=1280,
        hd_strategy_resize_limit=2048,
        prompt='',
        use_croper=False,
        croper_x=0,
        croper_y=0,
        croper_height=512,
        croper_width=512,
        sd_mask_blur=5,
        sd_strength=0.75,
        sd_steps=50,
        sd_guidance_scale=7.5,
        sd_sampler='ddim',
        sd_seed=42,
        cv2_flag='INPAINT_NS',
        cv2_radius=5,
    )
    
    print(f'config/alpha_channel/size_limit = {config} / {alpha_channel} / {size_limit}')
    if config.sd_seed == -1:
        config.sd_seed = random.randint(1, 999999999)

    logger.info(f"Origin image shape: {original_shape}")
    print(f"Origin image shape: {original_shape} / {image[250][250]}")
    image = resize_max_size(image, size_limit=size_limit, interpolation=interpolation)
    logger.info(f"Resized image shape: {image.shape} / {type(image)}")
    print(f"Resized image shape: {image.shape} / {image[250][250]}")

    mask = resize_max_size(mask, size_limit=size_limit, interpolation=interpolation)
    print(f"mask image shape: {mask.shape} / {type(mask)} / {mask[250][250]} / {alpha_channel}")

    if model is None:
        return None
        
    start = time.time()
    res_np_img = model(image, mask, config)
    logger.info(f"process time: {(time.time() - start) * 1000}ms, {res_np_img.shape}")
    print(f"process time_1_: {(time.time() - start) * 1000}ms, {alpha_channel.shape}, {res_np_img.shape} / {res_np_img[250][250]} / {res_np_img.dtype}")

    torch.cuda.empty_cache()

    if alpha_channel is not None:
        print(f"liuyz_here_10_: {alpha_channel.shape} / {alpha_channel.dtype} / {res_np_img.dtype}") 
        if alpha_channel.shape[:2] != res_np_img.shape[:2]:
            print(f"liuyz_here_20_: {alpha_channel.shape} / {res_np_img.shape}")
            alpha_channel = cv2.resize(
                alpha_channel, dsize=(res_np_img.shape[1], res_np_img.shape[0])
            )
        print(f"liuyz_here_30_: {alpha_channel.shape} / {res_np_img.shape} / {alpha_channel.dtype} / {res_np_img.dtype}")
        res_np_img = np.concatenate(
            (res_np_img, alpha_channel[:, :, np.newaxis]), axis=-1
        )
        print(f"liuyz_here_40_: {alpha_channel.shape} / {res_np_img.shape} / {alpha_channel.dtype} / {res_np_img.dtype}")
    
    print(f"process time_2_: {(time.time() - start) * 1000}ms, {alpha_channel.shape}, {res_np_img.shape} / {res_np_img[250][250]} / {res_np_img.dtype} /{ext}")
  
    image = Image.open(io.BytesIO(numpy_to_bytes(res_np_img, ext)))
    return  image # image
    
model = ModelManager(
        name='lama',
        device=device,
        # hf_access_token=HF_TOKEN_SD,
        # sd_disable_nsfw=False,
        # sd_cpu_textencoder=True,
        # sd_run_local=True,
        # callback=diffuser_callback,
    )

'''
pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-inpainting", dtype=torch.float16, revision="fp16", use_auth_token=auth_token).to(device)

transform = transforms.Compose([
      transforms.ToTensor(),
      transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
      transforms.Resize((512, 512)),
])
'''

def read_content(file_path):
    """read the content of target file
    """
    with open(file_path, 'rb') as f:
        content = f.read()

    return content

image_type = 'pil'  #'filepath' #'pil'
def predict(input):
    print(f'liuyz_0_', input)  
    '''
    image_np = np.array(input["image"])
    print(f'image_np = {image_np.shape}')
    mask_np = np.array(input["mask"])
    print(f'mask_np = {mask_np.shape}')   
    ''' 
    '''
    image = dict["image"] # .convert("RGB") #.resize((512, 512))
    # target_size = (init_image.shape[0], init_image.shape[1])
    print(f'liuyz_1_', image.shape)
    print(f'liuyz_2_', image.convert("RGB").shape)
    print(f'liuyz_3_', image.convert("RGB").resize((512, 512)).shape)
    # mask = dict["mask"] # .convert("RGB") #.resize((512, 512))
    '''   
    if image_type == 'filepath':
        # input: {'image': '/tmp/tmp8mn9xw93.png', 'mask': '/tmp/tmpn5ars4te.png'}
        origin_image_bytes = read_content(input["image"])
        print(f'origin_image_bytes = ', type(origin_image_bytes), len(origin_image_bytes))    
        image, _ = load_img(origin_image_bytes) 
        mask, _ = load_img(read_content(input["mask"]), gray=True)       
        alpha_channel = (np.ones((image.shape[0],image.shape[1]))*255).astype(np.uint8)  
        ext = get_image_ext(origin_image_bytes)
        
        output = model_process(image, mask, alpha_channel, ext)
    elif image_type == 'pil':
        # input: {'image': pil, 'mask': pil}
        image_pil = input['image']
        mask_pil = input['mask']
            
        image = np.array(image_pil)
        mask = np.array(mask_pil.convert("L"))
        alpha_channel = (np.ones((image.shape[0],image.shape[1]))*255).astype(np.uint8)  
        ext = 'png'
        
        output = model_process(image, mask, alpha_channel, ext)
    
    # output = mask #output.images[0]
    # output = pipe(prompt = prompt, image=init_image, mask_image=mask,guidance_scale=7.5)
    # output = input["mask"]
    # output = None
    return output #, gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)

print(f'liuyz_500_here_')

css = '''
.container {max-width: 1150px;margin: auto;padding-top: 1.5rem}
#image_upload{min-height:512px}
#image_upload [data-testid="image"], #image_upload [data-testid="image"] > div{min-height: 512px}
#mask_radio .gr-form{background:transparent; border: none}
#word_mask{margin-top: .75em !important}
#word_mask textarea:disabled{opacity: 0.3}
.footer {margin-bottom: 45px;margin-top: 35px;text-align: center;border-bottom: 1px solid #e5e5e5}
.footer>p {font-size: .8rem; display: inline-block; padding: 0 10px;transform: translateY(10px);background: white}
.dark .footer {border-color: #303030}
.dark .footer>p {background: #0b0f19}
.acknowledgments h4{margin: 1.25em 0 .25em 0;font-weight: bold;font-size: 115%}
#image_upload .touch-none{display: flex}
@keyframes spin {
    from {
        transform: rotate(0deg);
    }
    to {
        transform: rotate(360deg);
    }
}
#share-btn-container {
    display: flex; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem;
}
#share-btn {
    all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important;
}
#share-btn * {
    all: unset;
}
#share-btn-container div:nth-child(-n+2){
    width: auto !important;
    min-height: 0px !important;
}
#share-btn-container .wrap {
    display: none !important;
}
'''

'''
sketchpad = Sketchpad()
imageupload = ImageUplaod()
interface = gr.Interface(fn=predict, inputs="image", outputs="image", sketchpad, imageupload)

interface.launch(share=True)
'''

'''
# gr.Interface(fn=predict, inputs="image", outputs="image").launch(share=True)

image = gr.Image(source='upload', tool='sketch', type="pil", label="Upload")# .style(height=400)
image_blocks = gr.Interface(
             fn=predict, 
             inputs=image,
             outputs=image,
             # examples=[["cheetah.jpg"]],
             )
             
image_blocks.launch(inline=True)

import gradio as gr

def greet(dict, name, is_morning, temperature):
    image = dict['image']
    target_size = (image.shape[0], image.shape[1])
    print(f'liuyz_1_', target_size)
    salutation = "Good morning" if is_morning else "Good evening"
    greeting = f"{salutation} {name}. It is {temperature} degrees today"
    celsius = (temperature - 32) * 5 / 9
    return image, greeting, round(celsius, 2)

image = gr.Image(source='upload', tool='sketch', label="上传")# .style(height=400)

demo = gr.Interface(
    fn=greet,
    inputs=[image, "text", "checkbox", gr.Slider(0, 100)],
    outputs=['image', "text", "number"],
)
demo.launch()
'''

image_blocks = gr.Blocks(css=css)
with image_blocks as demo:
    # gr.HTML(read_content("header.html"))
    with gr.Group():
        with gr.Box():
            with gr.Row():
                with gr.Column():
                    image = gr.Image(source='upload', tool='sketch',type=f'{image_type}', label="Upload").style(height=512)
                    with gr.Row(elem_id="prompt-container").style(mobile_collapse=False, equal_height=True):
                        # prompt = gr.Textbox(placeholder = 'Your prompt (what you want in place of what is erased)', show_label=False, elem_id="input-text")
                        btn = gr.Button("Done!").style(
                            margin=True,
                            rounded=(True, True, True, True),
                            full_width=True,
                        )                
                
                with gr.Column():
                    image_out = gr.Image(label="Output").style(height=512)
                    '''
                    with gr.Group(elem_id="share-btn-container"):
                        community_icon = gr.HTML(community_icon_html, visible=False)
                        loading_icon = gr.HTML(loading_icon_html, visible=False)
                        share_button = gr.Button("Share to community", elem_id="share-btn", visible=False)
                    '''
               
                

            # btn.click(fn=predict, inputs=[image, prompt], outputs=[image_out, community_icon, loading_icon, share_button])
            btn.click(fn=predict, inputs=[image], outputs=[image_out]) #, community_icon, loading_icon, share_button])
            #share_button.click(None, [], [], _js=share_js)
            
            
image_blocks.launch()