| import cv2 |
| import numpy as np |
|
|
| from ..supported_preprocessor import Preprocessor, PreprocessorParameter |
| from ..utils import resize_image_with_pad, visualize_inpaint_mask |
|
|
|
|
| class PreprocessorLamaInpaint(Preprocessor): |
| def __init__(self): |
| super().__init__(name="inpaint_only+lama") |
| self.tags = ["Inpaint"] |
| self.returns_image = True |
| self.model = None |
| self.slider_resolution = PreprocessorParameter(visible=False) |
| self.accepts_mask = True |
| self.requires_mask = True |
|
|
| def __call__( |
| self, |
| input_image, |
| resolution, |
| slider_1=None, |
| slider_2=None, |
| slider_3=None, |
| **kwargs |
| ): |
| img = input_image |
| H, W, C = img.shape |
| assert C == 4, "No mask is provided!" |
| raw_color = img[:, :, 0:3].copy() |
| raw_mask = img[:, :, 3:4].copy() |
|
|
| res = 256 |
|
|
| img_res, remove_pad = resize_image_with_pad(img, res) |
|
|
| if self.model is None: |
| from annotator.lama import LamaInpainting |
|
|
| self.model = LamaInpainting() |
| |
| prd_color = self.model(img_res) |
| prd_color = remove_pad(prd_color) |
| prd_color = cv2.resize(prd_color, (W, H)) |
|
|
| alpha = raw_mask.astype(np.float32) / 255.0 |
| fin_color = prd_color.astype(np.float32) * alpha + raw_color.astype( |
| np.float32 |
| ) * (1 - alpha) |
| fin_color = fin_color.clip(0, 255).astype(np.uint8) |
|
|
| result = np.concatenate([fin_color, raw_mask], axis=2) |
| return Preprocessor.Result( |
| value=result, |
| display_images=[ |
| result[:, :, :3], |
| visualize_inpaint_mask(result), |
| ], |
| ) |
|
|
|
|
| Preprocessor.add_supported_preprocessor(PreprocessorLamaInpaint()) |
|
|
