LaiTool/resources/scripts/lama/lama_inpaint.py

import io
import os
import sys
from typing import Union
import cv2
import torch
import numpy as np
from PIL import Image


sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding="utf-8")

# 判断sys.argv 的长度，如果小于2，说明没有传入参数，设置初始参数
# if len(sys.argv) < 2:
#     sys.argv = [
#         "C:/Users/27698/Desktop/LAITool/resources/scripts/lama/lama_inpaint.exe",
#         "-l",
#         "C:\\Users\\27698\\Desktop\\测试\\mjTest\\data\\mask\\temp\\1717508661218.png",
#         "C:\\Users\\27698\\Desktop\\测试\\mjTest\\data\\mask\\mask_temp_1717508662659.png",
#         "C:\\Users\\27698\\Desktop\\测试\\mjTest\\data\\mask\\temp\\1717508564042.png",
#     ]
print(sys.argv)

if getattr(sys, "frozen", False):
    cript_directory = os.path.dirname(sys.executable)
elif __file__:
    cript_directory = os.path.dirname(__file__)

link_name = os.path.join(os.path.expanduser("~"), "big_lama.pt")
cu_name = os.path.join(cript_directory, "model\\big-lama.pt")
mode_pa = link_name

if len(sys.argv) < 2:
    # # 判断model_path是否存在，如果不存在，设置默认值
    if not os.path.exists(link_name):
        os.system(f'mklink "{link_name}" "{cu_name}"')
    print("Params: <runtime-config.json>")
    sys.exit(0)


def get_image(image):
    if isinstance(image, Image.Image):
        img = np.array(image)
    elif isinstance(image, np.ndarray):
        img = image.copy()
    else:
        raise Exception("Input image should be either PIL Image or numpy array!")

    if img.ndim == 3:
        img = np.transpose(img, (2, 0, 1))  # chw
    elif img.ndim == 2:
        img = img[np.newaxis, ...]

    assert img.ndim == 3

    img = img.astype(np.float32) / 255
    return img


def ceil_modulo(x, mod):
    if x % mod == 0:
        return x
    return (x // mod + 1) * mod


def scale_image(img, factor, interpolation=cv2.INTER_AREA):
    if img.shape[0] == 1:
        img = img[0]
    else:
        img = np.transpose(img, (1, 2, 0))

    img = cv2.resize(img, dsize=None, fx=factor, fy=factor, interpolation=interpolation)

    if img.ndim == 2:
        img = img[None, ...]
    else:
        img = np.transpose(img, (2, 0, 1))
    return img


def pad_img_to_modulo(img, mod):
    channels, height, width = img.shape
    out_height = ceil_modulo(height, mod)
    out_width = ceil_modulo(width, mod)
    return np.pad(
        img,
        ((0, 0), (0, out_height - height), (0, out_width - width)),
        mode="symmetric",
    )


def prepare_img_and_mask(image, mask, device, pad_out_to_modulo=8, scale_factor=None):
    out_image = get_image(image)
    out_mask = get_image(mask)

    if scale_factor is not None:
        out_image = scale_image(out_image, 1)
        out_mask = scale_image(out_mask, scale_factor, interpolation=cv2.INTER_NEAREST)

    if pad_out_to_modulo is not None and pad_out_to_modulo > 1:
        out_image = pad_img_to_modulo(out_image, pad_out_to_modulo)
        out_mask = pad_img_to_modulo(out_mask, pad_out_to_modulo)

    out_image = torch.from_numpy(out_image).unsqueeze(0).to(device)
    out_mask = torch.from_numpy(out_mask).unsqueeze(0).to(device)

    out_mask = (out_mask > 0) * 1

    return out_image, out_mask


class LamaInpaint:
    def __init__(
        self,
        device,
        model_path=None,
    ) -> None:
        if model_path is None:
            model_path = os.path.join(cript_directory, "model\\big-lama.pt")

        self.model = torch.jit.load(model_path, map_location=device)
        self.model.eval()
        self.model.to(device)
        self.device = device

    def run(
        self,
        image: Union[Image.Image, np.ndarray],
        mask: Union[Image.Image, np.ndarray],
    ):
        if isinstance(image, np.ndarray):
            orig_height, orig_width = image.shape[:2]
        else:
            orig_height, orig_width = np.array(image).shape[:2]

        # image_width = image.shape[1]
        # mask_width = mask.shape[1]
        scale = image.width / mask.width
        image, mask = prepare_img_and_mask(image, mask, self.device, 8, scale)
        with torch.inference_mode():
            inpainted = self.model(image, mask)
            cur_res = inpainted[0].permute(1, 2, 0).detach().cpu().numpy()
            cur_res = np.clip(cur_res * 255, 0, 255).astype("uint8")
            cur_res = cur_res[:orig_height, :orig_width]
            return cur_res


try:
    de = "cpu"
    if torch.cuda.is_available():
        de = "cuda"

    lama = LamaInpaint(de, mode_pa)

    image_path = sys.argv[2]
    mask_path = sys.argv[3]
    output_path = sys.argv[4]

    # 若是没有传递mask_path，需要自己计算mask区域
    # 使用Image.open打开图片
    image = Image.open(image_path).convert("RGB")
    mask = Image.open(mask_path).convert("L")

    res = lama.run(image, mask)
    # 将修复后的图片保存到本地
    img = Image.fromarray(res)
    # 使用 save 方法将图像保存到文件
    img.save(output_path)
    sys.exit(0)

except Exception as e:
    print(e)
    sys.exit(str(e))