let5see/IOPaint
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

FcF use unique resize strategy

Browse Source
This commit is contained in:
Qing
2022-09-04 16:00:42 +08:00
parent c5d7baec79
commit 2119a5f905
6 changed files with 105 additions and 18 deletions
Download Patch File Download Diff File Expand all files Collapse all files

23
lama_cleaner/model/base.py
View File

@@ -4,7 +4,6 @@ from typing import Optional
import cv2
import torch
from loguru import logger
import numpy as np
from lama_cleaner.helper import boxes_from_mask, resize_max_size, pad_img_to_modulo
from lama_cleaner.schema import Config, HDStrategy
@@ -92,7 +91,6 @@ class InpaintModel:
inpaint_result = cv2.resize(inpaint_result,
(origin_size[1], origin_size[0]),
interpolation=cv2.INTER_CUBIC)
original_pixel_indices = mask < 127
inpaint_result[original_pixel_indices] = image[:, :, ::-1][original_pixel_indices]
@@ -101,7 +99,7 @@ class InpaintModel:
return inpaint_result
def _run_box(self, image, mask, box, config: Config):
def _crop_box(self, image, mask, box, config: Config):
"""
Args:
@@ -110,7 +108,7 @@ class InpaintModel:
box: [left,top,right,bottom]
Returns:
BGR IMAGE
BGR IMAGE, (l, r, r, b)
"""
box_h = box[3] - box[1]
box_w = box[2] - box[0]
@@ -131,7 +129,7 @@ class InpaintModel:
t = max(_t, 0)
b = min(_b, img_h)
# try to get more context when crop around image edge
# try to get more context when crop around image edge
if _l < 0:
r += abs(_l)
if _r > img_w:
@@ -151,4 +149,19 @@ class InpaintModel:
logger.info(f"box size: ({box_h},{box_w}) crop size: {crop_img.shape}")
return crop_img, crop_mask, [l, t, r, b]
def _run_box(self, image, mask, box, config: Config):
"""
Args:
image: [H, W, C] RGB
mask: [H, W, 1]
box: [left,top,right,bottom]
Returns:
BGR IMAGE
"""
crop_img, crop_mask, [l, t, r, b] = self._crop_box(image, mask, box, config)
return self._pad_forward(crop_img, crop_mask, config), [l, t, r, b]

34
lama_cleaner/model/fcf.py
View File

@@ -8,7 +8,7 @@ import torch.fft as fft
from lama_cleaner.schema import Config
from lama_cleaner.helper import load_model, get_cache_path_by_url, norm_img
from lama_cleaner.helper import load_model, get_cache_path_by_url, norm_img, boxes_from_mask, resize_max_size
from lama_cleaner.model.base import InpaintModel
from torch import conv2d, nn
import torch.nn.functional as F
@@ -1154,6 +1154,38 @@ class FcF(InpaintModel):
def is_downloaded() -> bool:
return os.path.exists(get_cache_path_by_url(FCF_MODEL_URL))
@torch.no_grad()
def __call__(self, image, mask, config: Config):
"""
images: [H, W, C] RGB, not normalized
masks: [H, W]
return: BGR IMAGE
"""
boxes = boxes_from_mask(mask)
crop_result = []
config.hd_strategy_crop_margin = 128
for box in boxes:
crop_image, crop_mask, crop_box = self._crop_box(image, mask, box, config)
origin_size = crop_image.shape[:2]
resize_image = resize_max_size(crop_image, size_limit=512)
resize_mask = resize_max_size(crop_mask, size_limit=512)
inpaint_result = self._pad_forward(resize_image, resize_mask, config)
# only paste masked area result
inpaint_result = cv2.resize(inpaint_result, (origin_size[1], origin_size[0]), interpolation=cv2.INTER_CUBIC)
original_pixel_indices = crop_mask < 127
inpaint_result[original_pixel_indices] = crop_image[:, :, ::-1][original_pixel_indices]
crop_result.append((inpaint_result, crop_box))
inpaint_result = image[:, :, ::-1]
for crop_image, crop_box in crop_result:
x1, y1, x2, y2 = crop_box
inpaint_result[y1:y2, x1:x2, :] = crop_image
return inpaint_result
def forward(self, image, mask, config: Config):
"""Input images and output images have same size
images: [H, W, C] RGB