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import argparse
import os
from typing import Optional, Callable
import torch
from PIL import Image
from torch import Tensor, nn
from torchvision.transforms import transforms, functional
from tqdm.auto import tqdm
from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation
DEVICE = "cuda"
def parse_args():
parser = argparse.ArgumentParser(description="ClipSeg script.")
parser.add_argument(
"--sample_dir",
type=str,
required=True,
help="directory where samples are located",
)
parser.add_argument(
"--add_prompt",
type=str,
required=True,
action="append",
help="a prompt used to create a mask",
dest="prompts",
)
parser.add_argument(
"--mode",
type=str,
default='fill',
required=False,
help="Either replace, fill, add or subtract",
)
parser.add_argument(
"--threshold",
type=float,
default='0.3',
required=False,
help="threshold for including pixels in the mask",
)
parser.add_argument(
"--smooth_pixels",
type=int,
default=5,
required=False,
help="radius of a smoothing operation applied to the generated mask",
)
parser.add_argument(
"--expand_pixels",
type=int,
default=10,
required=False,
help="amount of expansion of the generated mask in all directions",
)
args = parser.parse_args()
return args
class MaskSample:
def __init__(self, filename: str):
self.image_filename = filename
self.mask_filename = os.path.splitext(filename)[0] + "-masklabel.png"
self.image = None
self.mask_tensor = None
self.height = 0
self.width = 0
self.image2Tensor = transforms.Compose([
transforms.ToTensor(),
])
self.tensor2Image = transforms.Compose([
transforms.ToPILImage(),
])
def get_image(self) -> Image:
if self.image is None:
self.image = Image.open(self.image_filename).convert('RGB')
self.height = self.image.height
self.width = self.image.width
return self.image
def get_mask_tensor(self) -> Tensor:
if self.mask_tensor is None and os.path.exists(self.mask_filename):
mask = Image.open(self.mask_filename).convert('L')
mask = self.image2Tensor(mask)
mask = mask.to(DEVICE)
self.mask_tensor = mask.unsqueeze(0)
return self.mask_tensor
def set_mask_tensor(self, mask_tensor: Tensor):
self.mask_tensor = mask_tensor
def add_mask_tensor(self, mask_tensor: Tensor):
mask = self.get_mask_tensor()
if mask is None:
mask = mask_tensor
else:
mask += mask_tensor
mask = torch.clamp(mask, 0, 1)
self.mask_tensor = mask
def subtract_mask_tensor(self, mask_tensor: Tensor):
mask = self.get_mask_tensor()
if mask is None:
mask = mask_tensor
else:
mask -= mask_tensor
mask = torch.clamp(mask, 0, 1)
self.mask_tensor = mask
def save_mask(self):
if self.mask_tensor is not None:
mask = self.mask_tensor.cpu().squeeze()
mask = self.tensor2Image(mask).convert('RGB')
mask.save(self.mask_filename)
class ClipSeg:
def __init__(self):
self.processor = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined")
self.model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined")
self.model.eval()
self.model.to(DEVICE)
self.smoothing_kernel_radius = None
self.smoothing_kernel = self.__create_average_kernel(self.smoothing_kernel_radius)
self.expand_kernel_radius = None
self.expand_kernel = self.__create_average_kernel(self.expand_kernel_radius)
@staticmethod
def __create_average_kernel(kernel_radius: Optional[int]):
if kernel_radius is None:
return None
kernel_size = kernel_radius * 2 + 1
kernel_weights = torch.ones(1, 1, kernel_size, kernel_size) / (kernel_size * kernel_size)
kernel = nn.Conv2d(in_channels=1, out_channels=1, kernel_size=kernel_size, bias=False, padding_mode='replicate', padding=kernel_radius)
kernel.weight.data = kernel_weights
kernel.requires_grad_(False)
kernel.to(DEVICE)
return kernel
@staticmethod
def __get_sample_filenames(sample_dir: str) -> [str]:
filenames = []
for filename in os.listdir(sample_dir):
ext = os.path.splitext(filename)[1].lower()
if ext in ['.jpg', '.jpeg', '.png', '.bmp', '.webp'] and '-masklabel.png' not in filename:
filenames.append(os.path.join(sample_dir, filename))
return filenames
def __process_mask(self, mask: Tensor, target_height: int, target_width: int, threshold: float) -> Tensor:
while len(mask.shape) < 4:
mask = mask.unsqueeze(0)
mask = torch.sigmoid(mask)
mask = mask.sum(1).unsqueeze(1)
if self.smoothing_kernel is not None:
mask = self.smoothing_kernel(mask)
mask = functional.resize(mask, [target_height, target_width])
mask = (mask > threshold).float()
if self.expand_kernel is not None:
mask = self.expand_kernel(mask)
mask = (mask > 0).float()
return mask
def mask_image(self, filename: str, prompts: [str], mode: str = 'fill', threshold: float = 0.3, smooth_pixels: int = 5, expand_pixels: int = 10):
"""
Masks a sample
Parameters:
filename (`str`): a sample filename
prompts (`[str]`): a list of prompts used to create a mask
mode (`str`): can be one of
- replace: creates new masks for all samples, even if a mask already exists
- fill: creates new masks for all samples without a mask
- add: adds the new region to existing masks
- subtract: subtracts the new region from existing masks
threshold (`float`): threshold for including pixels in the mask
smooth_pixels (`int`): radius of a smoothing operation applied to the generated mask
expand_pixels (`int`): amount of expansion of the generated mask in all directions
"""
mask_sample = MaskSample(filename)
if mode == 'fill' and mask_sample.get_mask_tensor() is not None:
return
if self.smoothing_kernel_radius != smooth_pixels:
self.smoothing_kernel = self.__create_average_kernel(smooth_pixels)
self.smoothing_kernel_radius = smooth_pixels
if self.expand_kernel_radius != expand_pixels:
self.expand_kernel = self.__create_average_kernel(expand_pixels)
self.expand_kernel_radius = expand_pixels
inputs = self.processor(text=prompts, images=[mask_sample.get_image()] * len(prompts), padding="max_length", return_tensors="pt")
inputs.to(DEVICE)
with torch.no_grad():
outputs = self.model(**inputs)
predicted_mask = self.__process_mask(outputs.logits, mask_sample.height, mask_sample.width, threshold)
if mode == 'replace' or mode == 'fill':
mask_sample.set_mask_tensor(predicted_mask)
elif mode == 'add':
mask_sample.add_mask_tensor(predicted_mask)
elif mode == 'subtract':
mask_sample.subtract_mask_tensor(predicted_mask)
mask_sample.save_mask()
def mask_folder(
self,
sample_dir: str,
prompts: [str],
mode: str = 'fill',
threshold: float = 0.3,
smooth_pixels: int = 5,
expand_pixels: int = 10,
progress_callback: Callable[[int, int], None] = None,
error_callback: Callable[[str], None] = None,
):
"""
Masks all samples in a folder
Parameters:
sample_dir (`str`): directory where samples are located
prompts (`[str]`): a list of prompts used to create a mask
mode (`str`): can be one of
- replace: creates new masks for all samples, even if a mask already exists
- fill: creates new masks for all samples without a mask
- add: adds the new region to existing masks
- subtract: subtracts the new region from existing masks
threshold (`float`): threshold for including pixels in the mask
smooth_pixels (`int`): radius of a smoothing operation applied to the generated mask
expand_pixels (`int`): amount of expansion of the generated mask in all directions
progress_callback (`Callable[[int, int], None]`): called after every processed image
error_callback (`Callable[[str], None]`): called for every exception
"""
filenames = self.__get_sample_filenames(sample_dir)
self.mask_images(
filenames=filenames,
prompts=prompts,
mode=mode,
threshold=threshold,
smooth_pixels=smooth_pixels,
expand_pixels=expand_pixels,
progress_callback=progress_callback,
error_callback=error_callback,
)
def mask_images(
self,
filenames: [str],
prompts: [str],
mode: str = 'fill',
threshold: float = 0.3,
smooth_pixels: int = 5,
expand_pixels: int = 10,
progress_callback: Callable[[int, int], None] = None,
error_callback: Callable[[str], None] = None,
):
"""
Masks all samples in a list
Parameters:
filenames (`[str]`): a list of sample filenames
prompts (`[str]`): a list of prompts used to create a mask
mode (`str`): can be one of
- replace: creates new masks for all samples, even if a mask already exists
- fill: creates new masks for all samples without a mask
- add: adds the new region to existing masks
- subtract: subtracts the new region from existing masks
threshold (`float`): threshold for including pixels in the mask
smooth_pixels (`int`): radius of a smoothing operation applied to the generated mask
expand_pixels (`int`): amount of expansion of the generated mask in all directions
progress_callback (`Callable[[int, int], None]`): called after every processed image
error_callback (`Callable[[str], None]`): called for every exception
"""
if progress_callback is not None:
progress_callback(0, len(filenames))
for i, filename in enumerate(tqdm(filenames)):
try:
self.mask_image(filename, prompts, mode, threshold, smooth_pixels, expand_pixels)
except Exception as e:
if error_callback is not None:
error_callback(filename)
if progress_callback is not None:
progress_callback(i + 1, len(filenames))
def main():
args = parse_args()
clip_seg = ClipSeg()
clip_seg.mask_folder(
sample_dir=args.sample_dir,
prompts=args.prompts,
mode=args.mode,
threshold=args.threshold,
smooth_pixels=args.smooth_pixels,
expand_pixels=args.expand_pixels,
error_callback=lambda filename: print("Error while processing image " + filename)
)
if __name__ == "__main__":
main()
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