# Documentation - Class name: ImageAutoCrop - Category: 😺dzNodes/LayerUtility - Output node: False - Repo Ref: https://github.com/chflame163/ComfyUI_LayerStyle 自动抠图并按照遮罩裁切图片。可指定生成图片的背景颜色、长宽比和大小。这个节点是为生成训练模型的图片素材而设计的。 *请参照 [SegmentAnythingUltra](https://github.com/chflame163/ComfyUI_LayerStyle/blob/main/README_CN.MD#SegmentAnythingUltra) 和 [RemBgUltra](https://github.com/chflame163/ComfyUI_LayerStyle/blob/main/README_CN.MD#RemBgUltra) 节点的模型安装方法安装模型。 # Input types ## Required - image - 输入图像。 - Comfy dtype: IMAGE - Python dtype: torch.Tensor - background_color - 背景颜色。 - Comfy dtype: STRING - Python dtype: str - aspect_ratio - 输出的宽高比。这里提供了常见的画幅比例， "custom"为自定义比例。 - Comfy dtype: STRING - Python dtype: str - proportional_width - 比例宽。如果aspect_ratio选项不是"custom"，此处设置将被忽略。 - Comfy dtype: INT - Python dtype: int - proportional_height - 比例高。如果aspect_ratio选项不是"custom"，此处设置将被忽略。 - Comfy dtype: INT - Python dtype: int - scale_to_longest_side - 允许按长边尺寸缩放。 - Comfy dtype: BOOLEAN - Python dtype: bool - longest_side - scale_to_longest_side被设置为True时，此项将作为是图像长边的长度。 - Comfy dtype: INT - Python dtype: int - detect - 探测方法，min_bounding_rect是最小外接矩形, max_inscribed_rect是最大内接矩形。 - Comfy dtype: STRING - Python dtype: str - border_reserve - 保留边框。在探测到的遮罩主体区域之外扩展裁切范围。 - Comfy dtype: INT - Python dtype: int - ultra_detail_range - 遮罩边缘超精细处理范围，0为不处理，可以节省生成时间。 - Comfy dtype: INT - Python dtype: int - matting_method - 生成遮罩的方法。有Segment Anything和 RMBG 1.4两种方法。RMBG 1.4运行速度更快。 - Comfy dtype: STRING - Python dtype: str - sam_model - 此处选择Segment Anything所使用的sam模型。 - Comfy dtype: STRING - Python dtype: str - grounding_dino_model - 此处选择Segment Anything所使用的grounding_dino模型。 - Comfy dtype: STRING - Python dtype: str - sam_threshold - Segment Anything的阈值。 - Comfy dtype: FLOAT - Python dtype: float - sam_prompt - Segment Anything的提示词。 - Comfy dtype: STRING - Python dtype: str # Output types - cropped_image - 裁切并更换背景后的图像。 - Comfy dtype: IMAGE - Python dtype: torch.Tensor - box_preview - 裁切位置预览。 - Comfy dtype: IMAGE - Python dtype: torch.Tensor - cropped_mask - 裁切后的遮罩。 - Comfy dtype: MASK - Python dtype: torch.Tensor # Usage tips - Infra type: CPU # Source code ``` class ImageAutoCrop: def __init__(self): pass @classmethod def INPUT_TYPES(self): matting_method_list = ['RMBG 1.4', 'SegmentAnything'] detect_mode = ['min_bounding_rect', 'max_inscribed_rect', 'mask_area'] ratio_list = ['1:1', '3:2', '4:3', '16:9', '2:3', '3:4', '9:16', 'custom', 'detect_mask'] return { "required": { "image": ("IMAGE", ), # "background_color": ("STRING", {"default": "#FFFFFF"}), # 背景颜色 "aspect_ratio": (ratio_list,), "proportional_width": ("INT", {"default": 2, "min": 1, "max": 999, "step": 1}), "proportional_height": ("INT", {"default": 1, "min": 1, "max": 999, "step": 1}), "scale_to_longest_side": ("BOOLEAN", {"default": True}), # 是否按长边缩放 "longest_side": ("INT", {"default": 1024, "min": 4, "max": 999999, "step": 1}), "detect": (detect_mode,), "border_reserve": ("INT", {"default": 100, "min": -9999, "max": 9999, "step": 1}), "ultra_detail_range": ("INT", {"default": 0, "min": 0, "max": 256, "step": 1}), "matting_method": (matting_method_list,), "sam_model": (list_sam_model(),), "grounding_dino_model": (list_groundingdino_model(),), "sam_threshold": ("FLOAT", {"default": 0.3, "min": 0, "max": 1.0, "step": 0.01}), "sam_prompt": ("STRING", {"default": "subject"}), }, "optional": { } } RETURN_TYPES = ("IMAGE", "IMAGE", "MASK",) RETURN_NAMES = ("cropped_image", "box_preview", "cropped_mask",) FUNCTION = 'image_auto_crop' CATEGORY = '😺dzNodes/LayerUtility' def image_auto_crop(self, image, detect, border_reserve, aspect_ratio, proportional_width, proportional_height, background_color, ultra_detail_range, scale_to_longest_side, longest_side, matting_method, sam_model, grounding_dino_model, sam_threshold, sam_prompt ): ret_images = [] ret_box_previews = [] ret_masks = [] input_images = [] input_masks = [] crop_boxs = [] for l in image: input_images.append(torch.unsqueeze(l, 0)) m = tensor2pil(l) if m.mode == 'RGBA': input_masks.append(m.split()[-1]) if len(input_masks) > 0 and len(input_masks) != len(input_images): input_masks = [] log(f"Warning, {NODE_NAME} unable align alpha to image, drop it.", message_type='warning') if aspect_ratio == 'custom': ratio = proportional_width / proportional_height elif aspect_ratio == 'detect_mask': ratio = 0 else: s = aspect_ratio.split(":") ratio = int(s[0]) / int(s[1]) side_limit = longest_side if scale_to_longest_side else 0 for i in range(len(input_images)): _image = tensor2pil(input_images[i]).convert('RGB') if len(input_masks) > 0: _mask = input_masks[i] else: if matting_method == 'SegmentAnything': sam_model = load_sam_model(sam_model) dino_model = load_groundingdino_model(grounding_dino_model) item = _image.convert('RGBA') boxes = groundingdino_predict(dino_model, item, sam_prompt, sam_threshold) (_, _mask) = sam_segment(sam_model, item, boxes) _mask = mask2image(_mask[0]) else: _mask = RMBG(_image) if ultra_detail_range: _mask = tensor2pil(mask_edge_detail(input_images[i], pil2tensor(_mask), ultra_detail_range, 0.01, 0.99)) bluredmask = gaussian_blur(_mask, 20).convert('L') x = 0 y = 0 width = 0 height = 0 x_offset = 0 y_offset = 0 if detect == "min_bounding_rect": (x, y, width, height) = min_bounding_rect(bluredmask) elif detect == "max_inscribed_rect": (x, y, width, height) = max_inscribed_rect(bluredmask) else: (x, y, width, height) = mask_area(bluredmask) canvas_width, canvas_height = _image.size x1 = x - border_reserve y1 = y - border_reserve x2 = x + width + border_reserve y2 = y + height + border_reserve if x1 < 0: canvas_width -= x1 x_offset = -x1 if y1 < 0: canvas_height -= y1 y_offset = -y1 if x2 > _image.width: canvas_width += x2 - _image.width if y2 > _image.height: canvas_height += y2 - _image.height crop_box = (x1 + x_offset, y1 + y_offset, width + border_reserve*2, height + border_reserve*2) crop_boxs.append(crop_box) if len(crop_boxs) > 0: # 批量图强制使用同一尺寸 crop_box = crop_boxs[0] if aspect_ratio == 'detect_mask': ratio = crop_box[2] / crop_box[3] target_width, target_height = calculate_side_by_ratio(crop_box[2], crop_box[3], ratio, longest_side=side_limit) _canvas = Image.new('RGB', size=(canvas_width, canvas_height), color=background_color) _mask_canvas = Image.new('L', size=(canvas_width, canvas_height), color='black') if ultra_detail_range: _image = pixel_spread(_image, _mask) _canvas.paste(_image, box=(x_offset, y_offset), mask=_mask.convert('L')) _mask_canvas.paste(_mask, box=(x_offset, y_offset)) preview_image = Image.new('RGB', size=(canvas_width, canvas_height), color='gray') preview_image.paste(_mask, box=(x_offset, y_offset)) preview_image = draw_rect(preview_image, crop_box[0], crop_box[1], crop_box[2], crop_box[3], line_color="#F00000", line_width=(canvas_width + canvas_height)//200) ret_image = _canvas.crop((crop_box[0], crop_box[1], crop_box[0]+crop_box[2], crop_box[1]+crop_box[3])) ret_image = fit_resize_image(ret_image, target_width, target_height, fit='letterbox', resize_sampler=Image.LANCZOS, background_color=background_color) ret_mask = _mask_canvas.crop((crop_box[0], crop_box[1], crop_box[0]+crop_box[2], crop_box[1]+crop_box[3])) ret_mask = fit_resize_image(ret_mask, target_width, target_height, fit='letterbox', resize_sampler=Image.LANCZOS, background_color="#000000") ret_images.append(pil2tensor(ret_image)) ret_box_previews.append(pil2tensor(preview_image)) ret_masks.append(image2mask(ret_mask)) log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish') return (torch.cat(ret_images, dim=0), torch.cat(ret_box_previews, dim=0), torch.cat(ret_masks, dim=0), ) ```