Jan Minar
21a48b7262
We differ from the LayerStyle implementation; explain why we're right and they're wrong
127 lines
4.7 KiB
Python
127 lines
4.7 KiB
Python
import torch
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from PIL import Image
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import numpy as np
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try:
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from app import process_image
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except ImportError:
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# We're running as a module
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from .app import process_image
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from .utils.sam import model_to_config_map as sam_model_to_config_map
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# Format conversion helpers adapted from LayerStyle -- but LayerStyle has them
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# wrong: this is not the place to squeeze/unsqueeze.
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#
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# - [tensor2pil](https://github.com/chflame163/ComfyUI_LayerStyle/blob/28c1a4f3082d0af5067a7bc4b72951a8dd47b9b8/py/imagefunc.py#L131)
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# - [pil2tensor](https://github.com/chflame163/ComfyUI_LayerStyle/blob/28c1a4f3082d0af5067a7bc4b72951a8dd47b9b8/py/imagefunc.py#L111)
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#
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# LayerStyle wrongly, misguidedly, and confusingly, un/squeezes the batch
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# dimension in the helpers, and then in the main code, they have to reverse
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# that. The batch dimension is there for a reason, people, it's not something
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# to be abstracted away! So our version leaves that out.
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def tensor2pil(t_image: torch.Tensor) -> Image.Image:
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return Image.fromarray(np.clip(255.0 * t_image.cpu().numpy(), 0, 255).astype(np.uint8))
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def pil2tensor(image: Image.Image) -> torch.Tensor:
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return torch.from_numpy(np.array(image).astype(np.float32) / 255.0)
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class F2S2GenerateMask:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(cls):
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model_list = list(sam_model_to_config_map.keys())
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model_list.sort()
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return {
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"required": {
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"sam2_model": (model_list, {"default": "sam2_hiera_small.pt"}),
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"image": ("IMAGE",),
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"prompt": ("STRING", {"default": "subject"}),
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}
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}
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RETURN_TYPES = ("IMAGE", "MASK", "IMAGE",)
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RETURN_NAMES = ("annotated_image", "mask", "masked_image",)
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FUNCTION = "_process_image"
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CATEGORY = "💃rDancer"
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def _process_image(self, sam2_model: str, image: torch.Tensor, prompt: str = None):
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prompt = prompt.strip() if prompt else ""
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annotated_images, masks, masked_images = [], [], []
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# Convert image from tensor to PIL
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# the image has an extra batch dimension, despite the variable name
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for img in image:
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img = tensor2pil(img).convert("RGB")
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annotated_image, mask, masked_image = process_image(sam2_model, img, prompt)
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annotated_images.append(pil2tensor(annotated_image))
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masks.append(pil2tensor(mask))
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masked_images.append(pil2tensor(masked_image))
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annotated_images = torch.stack(annotated_images)
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masks = torch.stack(masks)
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masked_images = torch.stack(masked_images)
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return (annotated_images, masks, masked_images, )
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NODE_CLASS_MAPPINGS = {
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"RdancerFlorence2SAM2GenerateMask": F2S2GenerateMask
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}
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__all__ = ["NODE_CLASS_MAPPINGS"]
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if __name__ == "__main__":
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# detect which parameters are filenames -- those are images
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# the rest are prompts
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# call process_image with the images and prompts
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# save the output images
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# return the output images' filenames
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import sys
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import os
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import argparse
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from app import process_image
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# import rdancer_debug # will listen for debugger to attach
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def my_process_image(image_path, prompt):
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from utils.sam import SAM_CHECKPOINT
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image = Image.open(image_path).convert("RGB")
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annotated_image, mask, masked_image = process_image(SAM_CHECKPOINT, image, prompt)
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output_image_path, output_mask_path, output_masked_image_path = f"output_image_{os.path.basename(image_path)}", f"output_mask_{os.path.basename(image_path)}", f"output_masked_image_{os.path.basename(image_path)}"
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annotated_image.save(output_image_path)
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mask.save(output_mask_path)
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masked_image.save(output_masked_image_path)
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return output_image_path, output_mask_path, output_masked_image_path
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if len(sys.argv) < 2:
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print(f"Usage: python {os.path.basename(sys.argv[0])} <image_path>[ ...] [<prompt>]")
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sys.exit(1)
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# test which exist as filenames
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images = []
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prompts = []
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for arg in sys.argv[1:]:
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if not os.path.exists(arg):
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prompts.append(arg)
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else:
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images.append(arg)
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if len(prompts) > 1:
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raise ValueError("At most one prompt is required")
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if len(images) < 1:
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raise ValueError("At least one image is required")
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prompt = prompts[0].strip() if prompts else None
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print(f"Processing {len(images)} image{'' if len(images) == 1 else 's'} with prompt: {prompt}")
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from app import process_image
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for image in images:
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output_image_path, output_mask_path, output_masked_image_path = my_process_image(image, prompt)
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print(f"Saved output image to {output_image_path} and mask to {output_mask_path} and masked image to {output_masked_image_path}")
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