File size: 5,526 Bytes
e3f7bf2 7fd6b1d e3f7bf2 42de2d8 e3f7bf2 1e2d513 e3f7bf2 1e2d513 e3f7bf2 1e2d513 e3f7bf2 42de2d8 1e2d513 e3f7bf2 1e2d513 e3f7bf2 1e2d513 e3f7bf2 1e2d513 e3f7bf2 1e2d513 e3f7bf2 1e2d513 e3f7bf2 1e2d513 e3f7bf2 1e2d513 e3f7bf2 1e2d513 e3f7bf2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 |
#装lama-cleaner==1.2.4来得到其他环境依赖，但是使用的时候用lama_cleaner这个文件的
import base64
import requests
import os
from typing import List
import io
from PIL import Image
from pydantic import BaseModel
from lama_cleaner.server import process
from lama_cleaner.server import main#先初始化才能用process
#C:\Users\17331\.cache\torch\hub 缓存下载位置,记得删
class FakeArgs(BaseModel):
host: str = "0.0.0.0"
port: int = 7860
model: str = 'lama'
hf_access_token: str = ""
sd_enable_xformers: bool = False
sd_disable_nsfw: bool = False
sd_cpu_textencoder: bool = True
sd_controlnet: bool = False
sd_controlnet_method: str = "control_v11p_sd15_canny"
sd_local_model_path: str = ""
sd_run_local: bool = False
local_files_only: bool = False
cpu_offload: bool = False
device: str = "cpu"
gui: bool = False
gui_size: List[int] = [1000, 1000]
input: str = ''
disable_model_switch: bool = True
debug: bool = False
no_half: bool = False
disable_nsfw: bool = False
enable_xformers: bool = False
enable_interactive_seg: bool = True
interactive_seg_model: str = "vit_b"
interactive_seg_device: str = "cpu"
enable_remove_bg: bool = False
enable_anime_seg: bool = False
enable_realesrgan: bool = False
enable_gfpgan: bool = False
gfpgan_device: str = "cpu"
enable_restoreformer: bool = False
enable_gif: bool = False
quality: int = 95
model_dir: str = None
output_dir: str = None
def inpaint(img_path: str, mask_path: str) -> "img content (resp.content)":
# urllib3 1.26.4 兼容
image_bytes = open(img_path, 'rb')
mask_bytes = open(mask_path, 'rb')
# 将字节数据转换为Base64编码的字符串
files = {
"image": image_bytes,
"mask": mask_bytes
}
payload = {
"ldmSteps": 25,
"ldmSampler": "plms",
"zitsWireframe": True,
"hdStrategy": "Crop",
"hdStrategyCropMargin": 196,
"hdStrategyCropTrigerSize": 800,
"hdStrategyResizeLimit": 2048,
"prompt": "",
"negativePrompt": "",
"croperX": 307,
"croperY": 544,
"croperHeight": 512,
"croperWidth": 512,
"useCroper": False,
"sdMaskBlur": 5,
"sdStrength": 0.75,
"sdSteps": 50,
"sdGuidanceScale": 7.5,
"sdSampler": "uni_pc",
"sdSeed": -1,
"sdMatchHistograms": False,
"sdScale": 1,
"cv2Radius": 5,
"cv2Flag": "INPAINT_NS",
"paintByExampleSteps": 50,
"paintByExampleGuidanceScale": 7.5,
"paintByExampleSeed": -1,
"paintByExampleMaskBlur": 5,
"paintByExampleMatchHistograms": False,
"p2pSteps": 50,
"p2pImageGuidanceScale": 1.5,
"p2pGuidanceScale": 7.5,
"controlnet_conditioning_scale": 0.4,
"controlnet_method": "control_v11p_sd15_canny"
} # payload用data
resp = process(files=files, payload=payload)
return resp
def save_img(img_content: "要处理的图片数据", new_save_path: "新文件的保存路径(包含后缀)",
old_img_path: "旧文件路径") -> "void生成新的文件保存 ,传入旧文件路径是为了删除有问题的旧文件":
print(new_save_path)
try:
# 从 _io.BytesIO 对象中读取字节数据
resp_bytes = img_content.read()
# 使用 Image.open() 方法打开图片
img = Image.open(io.BytesIO(resp_bytes))
# 如果需要指定图像格式，可以在保存时指定
img.save(new_save_path, format="JPEG")
except Exception as e:
# 对于可能异常的图片->比如因为不合规导致resp.content没有正常返回
print(e, new_save_path, "图片返回有问题,跳过并删除图片.这里的路径是新保存路径")
os.remove(old_img_path)
if __name__ == '__main__':
main(FakeArgs())#初始化model
# 获取当前目录的子目录的路径
img_path = 'manga'
subdir_path = os.path.join(os.getcwd(), img_path)
# 图片素材获取（包含子目录下所有图片）
image_files = []
for root, dirs, files in os.walk(subdir_path):
for file in files:
if file.endswith(".jpg") or file.endswith(".png"):
image_files.append(os.path.relpath(os.path.join(root, file)))
# 创建处理后的子目录在与image_files同级目录下
processed_subdir_path = os.path.join(os.path.dirname(subdir_path), f"{img_path}1")
os.makedirs(processed_subdir_path, exist_ok=True)
# 对image_files进行某种处理，生成新图片，并保存在处理后的子目录中
for img_file in image_files:
# 处理图片的代码（这里仅作示例）
# 假设处理后的图片为new_img
img_dir = os.path.dirname(img_file)
new_img_dir = os.path.join(processed_subdir_path, img_dir)
os.makedirs(new_img_dir, exist_ok=True)
new_img_path = os.path.join(new_img_dir, os.path.basename(img_file))
if not os.path.exists(new_img_path):
# 如果已经处理过那么跳过
# 处理图片并保存
img_inpainted = inpaint(img_path=img_file, mask_path='mask/0.jpg') # 上传的遮罩保存都是0开始
save_img(img_content=img_inpainted, new_save_path=new_img_path, old_img_path=img_file)
else:
print(f"Skipping {new_img_path} as it already exists.")
|