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 = input['image'] mask = input['mask'] print(f'liuyz_2_here_', type(image), image.shape) image_pil = Image.fromarray(image) mask_pil = Image.fromarray(mask).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}") 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 ''' ext = get_image_ext(origin_image_bytes) response = make_response( send_file( io.BytesIO(numpy_to_bytes(res_np_img, ext)), mimetype=f"image/{ext}", ) ) response.headers["X-Seed"] = str(config.sd_seed) return response ''' 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 = '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': output = model_process_filepath(input) # dict["image"], dict["mask"]) elif image_type == 'pil': output = model_process_pil(input) # 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()