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# import base64
# import requests
# import os
# from PIL import Image
# import io
# def inpaint(img_path:str,mask_path:str)->"img content (resp.content)":
# 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
# #不使用header
# #resp = requests.post("https://sanster-lama-cleaner-lama.hf.space/inpaint", data=payload, files=files)
# #使用自己的space
# resp = requests.post("https://rogerxavier-lama-cleaner-lama.hf.space/inpaint", data=payload, files=files)
# print("请求lama clenaer状态是",resp.status_code)
# return bytes(resp.content)
# 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:
# img = Image.open(io.BytesIO(img_content))
# # 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__':
# # 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.")
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('image.jpg', 'rb')
mask_bytes = open('mask.jpg', '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.")
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