100 lines
4.5 KiB
Python
100 lines
4.5 KiB
Python
import torch
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from PIL import Image
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from .imagefunc import log, pil2tensor, tensor2pil, image2mask, RGB2RGBA
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from .imagefunc import draw_rounded_rectangle, gaussian_blur, mask_area, max_inscribed_rect, min_bounding_rect
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class LS_RoundedRectangle:
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def __init__(self):
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self.NODE_NAME = 'RoundedRectangle'
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@classmethod
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def INPUT_TYPES(self):
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detect_mode = ['mask_area', 'min_bounding_rect', 'max_inscribed_rect']
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return {
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"required": {
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"image": ("IMAGE",),
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"rounded_rect_radius": ("INT", {"default": 50, "min": 0, "max": 100, "step": 1}),
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"anti_aliasing": ("INT", {"default": 2, "min": 0, "max": 16, "step": 1}),
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"top": ("FLOAT", {"default": 8, "min": -100, "max": 100, "step": 0.1}),
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"bottom": ("FLOAT", {"default": 8, "min": -100, "max": 100, "step": 0.1}),
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"left": ("FLOAT", {"default": 8, "min": -100, "max": 100, "step": 0.1}),
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"right": ("FLOAT", {"default": 8, "min": -100, "max": 100, "step": 0.1}),
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"detect": (detect_mode,),
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"obj_ext_top": ("FLOAT", {"default": 8, "min": -100, "max": 100, "step": 0.1}),
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"obj_ext_bottom": ("FLOAT", {"default": 8, "min": -100, "max": 100, "step": 0.1}),
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"obj_ext_left": ("FLOAT", {"default": 8, "min": -100, "max": 100, "step": 0.1}),
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"obj_ext_right": ("FLOAT", {"default": 8, "min": -100, "max": 100, "step": 0.1}),
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},
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"optional": {
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"object_mask": ("MASK",),
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"crop_box": ("BOX",),
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}
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}
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RETURN_TYPES = ("IMAGE", "MASK",)
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RETURN_NAMES = ("image", "mask",)
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FUNCTION = 'rounded_rectangle'
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CATEGORY = '😺dzNodes/LayerUtility'
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def rounded_rectangle(self, image, rounded_rect_radius, anti_aliasing, top, bottom, left, right,
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detect, obj_ext_top, obj_ext_bottom, obj_ext_left, obj_ext_right,
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object_mask=None, crop_box=None):
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ret_images = []
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ret_masks = []
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for index, img in enumerate(image):
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orig_image = tensor2pil(torch.unsqueeze(img, 0)).convert('RGB')
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width, height = orig_image.size
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black_image = Image.new('L', (width, height), color="black")
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if crop_box is not None:
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w = crop_box[2] - crop_box[0]
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h = crop_box[3] - crop_box[1]
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x1 = crop_box[0] - int(obj_ext_left * w * 0.01)
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y1 = crop_box[1] - int(obj_ext_top * h * 0.01)
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x2 = crop_box[2] + int(obj_ext_right * w * 0.01)
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y2 = crop_box[3] + int(obj_ext_bottom * h * 0.01)
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bbox = [(x1, y1, x2, y2)]
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elif object_mask is not None:
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if object_mask.dim() == 2: object_mask = torch.unsqueeze(object_mask, 0)
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mask = object_mask[index] if index < len(object_mask) else object_mask[-1]
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mask = tensor2pil(mask)
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bluredmask = gaussian_blur(mask, 20).convert('L')
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x = -10
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y = -10
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w = 4
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h = 4
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if detect == "min_bounding_rect":
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(x, y, w, h) = min_bounding_rect(bluredmask)
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elif detect == "max_inscribed_rect":
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(x, y, w, h) = max_inscribed_rect(bluredmask)
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else:
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(x, y, w, h) = mask_area(mask)
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x1 = x - int(obj_ext_left * w * 0.01)
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y1 = y - int(obj_ext_top * h * 0.01)
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x2 = x + w + int(obj_ext_right * w * 0.01)
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y2 = y + h + int(obj_ext_bottom * h * 0.01)
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bbox = [(x1, y1, x2, y2)]
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else:
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bbox = [(int(left * width * 0.01),
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int(top * height * 0.01),
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width - int(right * width * 0.01),
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height - int(bottom * height * 0.01))
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]
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rect_mask = draw_rounded_rectangle(black_image, rounded_rect_radius, bbox, anti_aliasing, "white")
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ret_image = RGB2RGBA(orig_image, rect_mask)
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ret_images.append(pil2tensor(ret_image))
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ret_masks.append(image2mask(rect_mask))
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return (torch.cat(ret_images, dim=0), torch.cat(ret_masks, dim=0),)
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NODE_CLASS_MAPPINGS = {
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"LayerUtility: RoundedRectangle": LS_RoundedRectangle
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"LayerUtility: RoundedRectangle": "LayerUtility: RoundedRectangle"
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} |