165 lines
7.6 KiB
Python
165 lines
7.6 KiB
Python
# layerstyle advance
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from .imagefunc import *
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from .segment_anything_func import *
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class ImageAutoCrop:
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def __init__(self):
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self.NODE_NAME = 'ImageAutoCrop'
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@classmethod
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def INPUT_TYPES(self):
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matting_method_list = ['RMBG 1.4', 'SegmentAnything']
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detect_mode = ['min_bounding_rect', 'max_inscribed_rect', 'mask_area']
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ratio_list = ['1:1', '3:2', '4:3', '16:9', '2:3', '3:4', '9:16', 'custom', 'detect_mask']
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return {
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"required": {
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"image": ("IMAGE", ), #
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"background_color": ("STRING", {"default": "#FFFFFF"}), # 背景颜色
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"aspect_ratio": (ratio_list,),
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"proportional_width": ("INT", {"default": 2, "min": 1, "max": 999, "step": 1}),
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"proportional_height": ("INT", {"default": 1, "min": 1, "max": 999, "step": 1}),
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"scale_to_longest_side": ("BOOLEAN", {"default": True}), # 是否按长边缩放
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"longest_side": ("INT", {"default": 1024, "min": 4, "max": 999999, "step": 1}),
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"detect": (detect_mode,),
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"border_reserve": ("INT", {"default": 100, "min": -9999, "max": 9999, "step": 1}),
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"ultra_detail_range": ("INT", {"default": 0, "min": 0, "max": 256, "step": 1}),
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"matting_method": (matting_method_list,),
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"sam_model": (list_sam_model(),),
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"grounding_dino_model": (list_groundingdino_model(),),
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"sam_threshold": ("FLOAT", {"default": 0.3, "min": 0, "max": 1.0, "step": 0.01}),
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"sam_prompt": ("STRING", {"default": "subject"}),
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},
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"optional": {
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}
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}
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RETURN_TYPES = ("IMAGE", "IMAGE", "MASK",)
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RETURN_NAMES = ("cropped_image", "box_preview", "cropped_mask",)
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FUNCTION = 'image_auto_crop'
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CATEGORY = '😺dzNodes/LayerUtility'
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def image_auto_crop(self, image, detect, border_reserve, aspect_ratio, proportional_width, proportional_height,
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background_color, ultra_detail_range, scale_to_longest_side, longest_side,
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matting_method, sam_model, grounding_dino_model, sam_threshold, sam_prompt
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):
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ret_images = []
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ret_box_previews = []
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ret_masks = []
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input_images = []
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input_masks = []
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crop_boxs = []
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for l in image:
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input_images.append(torch.unsqueeze(l, 0))
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m = tensor2pil(l)
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if m.mode == 'RGBA':
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input_masks.append(m.split()[-1])
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if len(input_masks) > 0 and len(input_masks) != len(input_images):
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input_masks = []
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log(f"Warning, {self.NODE_NAME} unable align alpha to image, drop it.", message_type='warning')
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if aspect_ratio == 'custom':
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ratio = proportional_width / proportional_height
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elif aspect_ratio == 'detect_mask':
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ratio = 0
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else:
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s = aspect_ratio.split(":")
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ratio = int(s[0]) / int(s[1])
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side_limit = longest_side if scale_to_longest_side else 0
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for i in range(len(input_images)):
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_image = tensor2pil(input_images[i]).convert('RGB')
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if len(input_masks) > 0:
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_mask = input_masks[i]
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else:
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if matting_method == 'SegmentAnything':
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sam_model = load_sam_model(sam_model)
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dino_model = load_groundingdino_model(grounding_dino_model)
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item = _image.convert('RGBA')
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boxes = groundingdino_predict(dino_model, item, sam_prompt, sam_threshold)
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(_, _mask) = sam_segment(sam_model, item, boxes)
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_mask = mask2image(_mask[0])
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else:
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_mask = RMBG(_image)
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if ultra_detail_range:
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_mask = tensor2pil(mask_edge_detail(input_images[i], pil2tensor(_mask), ultra_detail_range, 0.01, 0.99))
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bluredmask = gaussian_blur(_mask, 20).convert('L')
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x = 0
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y = 0
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width = 0
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height = 0
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x_offset = 0
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y_offset = 0
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if detect == "min_bounding_rect":
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(x, y, width, height) = min_bounding_rect(bluredmask)
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elif detect == "max_inscribed_rect":
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(x, y, width, height) = max_inscribed_rect(bluredmask)
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else:
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(x, y, width, height) = mask_area(bluredmask)
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canvas_width, canvas_height = _image.size
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x1 = x - border_reserve
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y1 = y - border_reserve
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x2 = x + width + border_reserve
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y2 = y + height + border_reserve
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if x1 < 0:
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canvas_width -= x1
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x_offset = -x1
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if y1 < 0:
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canvas_height -= y1
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y_offset = -y1
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if x2 > _image.width:
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canvas_width += x2 - _image.width
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if y2 > _image.height:
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canvas_height += y2 - _image.height
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crop_box = (x1 + x_offset, y1 + y_offset, width + border_reserve*2, height + border_reserve*2)
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crop_boxs.append(crop_box)
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if len(crop_boxs) > 0: # 批量图强制使用同一尺寸
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crop_box = crop_boxs[0]
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if aspect_ratio == 'detect_mask':
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ratio = crop_box[2] / crop_box[3]
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target_width, target_height = calculate_side_by_ratio(crop_box[2], crop_box[3], ratio,
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longest_side=side_limit)
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_canvas = Image.new('RGB', size=(canvas_width, canvas_height), color=background_color)
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_mask_canvas = Image.new('L', size=(canvas_width, canvas_height), color='black')
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if ultra_detail_range:
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_image = pixel_spread(_image, _mask)
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_canvas.paste(_image, box=(x_offset, y_offset), mask=_mask.convert('L'))
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_mask_canvas.paste(_mask, box=(x_offset, y_offset))
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preview_image = Image.new('RGB', size=(canvas_width, canvas_height), color='gray')
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preview_image.paste(_mask, box=(x_offset, y_offset))
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preview_image = draw_rect(preview_image,
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crop_box[0], crop_box[1], crop_box[2], crop_box[3],
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line_color="#F00000", line_width=(canvas_width + canvas_height)//200)
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ret_image = _canvas.crop((crop_box[0], crop_box[1], crop_box[0]+crop_box[2], crop_box[1]+crop_box[3]))
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ret_image = fit_resize_image(ret_image, target_width, target_height,
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fit='letterbox', resize_sampler=Image.LANCZOS,
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background_color=background_color)
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ret_mask = _mask_canvas.crop((crop_box[0], crop_box[1], crop_box[0]+crop_box[2], crop_box[1]+crop_box[3]))
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ret_mask = fit_resize_image(ret_mask, target_width, target_height,
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fit='letterbox', resize_sampler=Image.LANCZOS,
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background_color="#000000")
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ret_images.append(pil2tensor(ret_image))
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ret_box_previews.append(pil2tensor(preview_image))
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ret_masks.append(image2mask(ret_mask))
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log(f"{self.NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
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return (torch.cat(ret_images, dim=0),
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torch.cat(ret_box_previews, dim=0),
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torch.cat(ret_masks, dim=0),
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)
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NODE_CLASS_MAPPINGS = {
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"LayerUtility: ImageAutoCrop": ImageAutoCrop
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"LayerUtility: ImageAutoCrop": "LayerUtility: ImageAutoCrop(Advance)"
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} |