Inpaint-Anything / fill_anything.py
RysonFeng
Add source code
cdb26a4
import cv2
import sys
import argparse
import numpy as np
import torch
from pathlib import Path
from matplotlib import pyplot as plt
from typing import Any, Dict, List
from sam_segment import predict_masks_with_sam
from stable_diffusion_inpaint import fill_img_with_sd
from utils import load_img_to_array, save_array_to_img, dilate_mask, \
show_mask, show_points
def setup_args(parser):
parser.add_argument(
"--input_img", type=str, required=True,
help="Path to a single input img",
)
parser.add_argument(
"--point_coords", type=float, nargs='+', required=True,
help="The coordinate of the point prompt, [coord_W coord_H].",
)
parser.add_argument(
"--point_labels", type=int, nargs='+', required=True,
help="The labels of the point prompt, 1 or 0.",
)
parser.add_argument(
"--text_prompt", type=str, required=True,
help="Text prompt",
)
parser.add_argument(
"--dilate_kernel_size", type=int, default=None,
help="Dilate kernel size. Default: None",
)
parser.add_argument(
"--output_dir", type=str, required=True,
help="Output path to the directory with results.",
)
parser.add_argument(
"--sam_model_type", type=str,
default="vit_h", choices=['vit_h', 'vit_l', 'vit_b'],
help="The type of sam model to load. Default: 'vit_h"
)
parser.add_argument(
"--sam_ckpt", type=str, required=True,
help="The path to the SAM checkpoint to use for mask generation.",
)
parser.add_argument(
"--seed", type=int,
help="Specify seed for reproducibility.",
)
parser.add_argument(
"--deterministic", action="store_true",
help="Use deterministic algorithms for reproducibility.",
)
if __name__ == "__main__":
"""Example usage:
python fill_anything.py \
--input_img FA_demo/FA1_dog.png \
--point_coords 750 500 \
--point_labels 1 \
--text_prompt "a teddy bear on a bench" \
--dilate_kernel_size 15 \
--output_dir ./results \
--sam_model_type "vit_h" \
--sam_ckpt sam_vit_h_4b8939.pth
"""
parser = argparse.ArgumentParser()
setup_args(parser)
args = parser.parse_args(sys.argv[1:])
device = "cuda" if torch.cuda.is_available() else "cpu"
img = load_img_to_array(args.input_img)
masks, _, _ = predict_masks_with_sam(
img,
[args.point_coords],
args.point_labels,
model_type=args.sam_model_type,
ckpt_p=args.sam_ckpt,
device=device,
)
masks = masks.astype(np.uint8) * 255
# dilate mask to avoid unmasked edge effect
if args.dilate_kernel_size is not None:
masks = [dilate_mask(mask, args.dilate_kernel_size) for mask in masks]
# visualize the segmentation results
img_stem = Path(args.input_img).stem
out_dir = Path(args.output_dir) / img_stem
out_dir.mkdir(parents=True, exist_ok=True)
for idx, mask in enumerate(masks):
# path to the results
mask_p = out_dir / f"mask_{idx}.png"
img_points_p = out_dir / f"with_points.png"
img_mask_p = out_dir / f"with_{Path(mask_p).name}"
# save the mask
save_array_to_img(mask, mask_p)
# save the pointed and masked image
dpi = plt.rcParams['figure.dpi']
height, width = img.shape[:2]
plt.figure(figsize=(width/dpi/0.77, height/dpi/0.77))
plt.imshow(img)
plt.axis('off')
show_points(plt.gca(), [args.point_coords], args.point_labels,
size=(width*0.04)**2)
plt.savefig(img_points_p, bbox_inches='tight', pad_inches=0)
show_mask(plt.gca(), mask, random_color=False)
plt.savefig(img_mask_p, bbox_inches='tight', pad_inches=0)
plt.close()
# fill the masked image
for idx, mask in enumerate(masks):
if args.seed is not None:
torch.manual_seed(args.seed)
mask_p = out_dir / f"mask_{idx}.png"
img_filled_p = out_dir / f"filled_with_{Path(mask_p).name}"
img_filled = fill_img_with_sd(
img, mask, args.text_prompt, device=device)
save_array_to_img(img_filled, img_filled_p)