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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
from matplotlib import pyplot as plt
from torchvision import transforms
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"]
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 read_content(file_path):
"""read the content of target file
"""
with open(file_path, 'rb') as f:
content = f.read()
return content
def get_image_enhancer(scale = 2, device='cuda:0'):
from basicsr.archs.rrdbnet_arch import RRDBNet
from realesrgan import RealESRGANer
from realesrgan.archs.srvgg_arch import SRVGGNetCompact
from gfpgan import GFPGANer
realesrgan_model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64,
num_block=23, num_grow_ch=32, scale=4
)
netscale = scale
model_realesrgan = 'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth'
upsampler = RealESRGANer(
scale=netscale,
model_path=model_realesrgan,
model=realesrgan_model,
tile=0,
tile_pad=10,
pre_pad=0,
half=False if device=='cpu' else True,
device=device
)
model_GFPGAN = 'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth'
img_enhancer = GFPGANer(
model_path=model_GFPGAN,
upscale=scale,
arch='clean',
channel_multiplier=2,
bg_upsampler=upsampler,
device=device
)
return img_enhancer
image_enhancer = get_image_enhancer(scale = 1, device=device)
model = None
def model_process(image, mask, img_enhancer):
global model,image_enhancer
if mask.shape[0] == image.shape[1] and mask.shape[1] == image.shape[0] and mask.shape[0] != mask.shape[1]:
# rotate image
image = np.transpose(image[::-1, ...][:, ::-1], axes=(1, 0, 2))[::-1, ...]
original_shape = image.shape
interpolation = cv2.INTER_CUBIC
size_limit = 1080 #1080 # "Original"
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,
)
if config.sd_seed == -1:
config.sd_seed = random.randint(1, 999999999)
logger.info(f"Origin image shape_0_: {original_shape} / {size_limit}")
image = resize_max_size(image, size_limit=size_limit, interpolation=interpolation)
logger.info(f"Resized image shape_1_: {image.shape}")
logger.info(f"mask image shape_0_: {mask.shape} / {type(mask)}")
mask = resize_max_size(mask, size_limit=size_limit, interpolation=interpolation)
logger.info(f"mask image shape_1_: {mask.shape} / {type(mask)}")
if model is None:
return None
res_np_img = model(image, mask, config)
torch.cuda.empty_cache()
image = Image.open(io.BytesIO(numpy_to_bytes(res_np_img, 'png')))
if image_enhancer is not None and img_enhancer:
start = time.time()
input_img_rgb = np.array(image)
input_img_bgr = input_img_rgb[...,[2,1,0]]
_, _, enhance_img = image_enhancer.enhance(input_img_bgr, has_aligned=False,
only_center_face=False, paste_back=True)
input_img_rgb = enhance_img[...,[2,1,0]]
img_enhance = Image.fromarray(np.uint8(input_img_rgb))
image = img_enhance
log_info = f"image_enhancer_: {(time.time() - start) * 1000}ms, {res_np_img.shape} "
logger.info(log_info)
return image # image
model = ModelManager(
name='lama',
device=device,
)
image_type = 'pil' # filepath'
def predict(input, img_enhancer):
if input is None:
return None
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)
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"))
output = model_process(image, mask, img_enhancer)
return output
css = '''
.container {max-width: 100%;margin: auto;padding-top: 1.5rem}
.output-image, .input-image, .image-preview {height: 600px !important;object-fit: contain}
#work-container {min-width: min(160px, 100%) !important;flex-grow: 0 !important}
#image_upload{min-height:610px}
#image_upload [data-testid="image"], #image_upload [data-testid="image"] > div{min-height: 620px}
#image_output{margin: 0 auto; text-align: center;width:640px}
#erase-container{margin: 0 auto; text-align: center;width:150px;border-width:5px;border-color:#2c9748}
#enhancer-checkbox{width:520px}
#enhancer-tip{width:450px}
#enhancer-tip-div{text-align: left}
#prompt-container{margin: 0 auto; text-align: center;width:fit-content;min-width: min(150px, 100%);flex-grow: 0; flex-wrap: nowrap;}
.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}
#image_upload .touch-none{display: flex}
@keyframes spin {
from {
transform: rotate(0deg);
}
to {
transform: rotate(360deg);
}
}
'''
image_blocks = gr.Blocks(css=css)
with image_blocks as demo:
with gr.Group():
with gr.Box(elem_id="work-container"):
with gr.Row(elem_id="input-container"):
with gr.Column():
image = gr.Image(source='upload', elem_id="image_upload",tool='sketch', type=f'{image_type}',
label="Upload(载入图片)", show_label=True).style(mobile_collapse=False)
with gr.Row(elem_id="prompt-container").style(mobile_collapse=False, equal_height=True):
with gr.Column(elem_id="erase-container"):
btn_erase = gr.Button(value = "Erase(擦除↓)",elem_id="erase_btn").style(
margin=True,
rounded=(True, True, True, True),
full_width=True,
).style(width=100)
with gr.Column(elem_id="enhancer-checkbox"):
enhancer_label = 'Enhanced image(processing is very slow, please check only for blurred images)【增强图像(处理很慢,请仅针对模糊图像做勾选)】'
img_enhancer = gr.Checkbox(label=enhancer_label).style(width=150)
with gr.Row(elem_id="output-container"):
with gr.Column():
image_out = gr.Image(label="Result", elem_id="image_output", visible=True).style(width=640)
btn_erase.click(fn=predict, inputs=[image, img_enhancer], outputs=[image_out])
image_blocks.launch() |