fix bugs, ImageAutoCrop add cropped_mask output.
This commit is contained in:
chflame163
+157
-146
@@ -1,147 +1,158 @@
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from .imagefunc import *
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from .segment_anything_func import *
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NODE_NAME = 'ImageAutoCrop'
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class ImageAutoCrop:
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def __init__(self):
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pass
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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']
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ratio_list = ['1:1', '3:2', '4:3', '16:9', '2:3', '3:4', '9:16', 'custom']
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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",)
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RETURN_NAMES = ("cropped_image", "box_preview")
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FUNCTION = 'image_auto_crop'
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CATEGORY = '😺dzNodes/LayerUtility'
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OUTPUT_NODE = True
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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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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, {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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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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if detect == "max_inscribed_rect":
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(x, y, width, height) = max_inscribed_rect(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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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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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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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_images.append(pil2tensor(ret_image))
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ret_box_previews.append(pil2tensor(preview_image))
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log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
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return (torch.cat(ret_images, dim=0), torch.cat(ret_box_previews, dim=0),)
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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"
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from .imagefunc import *
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from .segment_anything_func import *
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NODE_NAME = 'ImageAutoCrop'
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class ImageAutoCrop:
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def __init__(self):
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pass
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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']
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ratio_list = ['1:1', '3:2', '4:3', '16:9', '2:3', '3:4', '9:16', 'custom']
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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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OUTPUT_NODE = True
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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, {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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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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if detect == "max_inscribed_rect":
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(x, y, width, height) = max_inscribed_rect(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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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"{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"
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}
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@@ -0,0 +1,158 @@
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import torch
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from .imagefunc import *
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NODE_NAME = 'ImageScaleByAspectRatio'
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class ImageScaleByAspectRatio:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(self):
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ratio_list = ['original', 'custom', '1:1', '3:2', '4:3', '16:9', '2:3', '3:4', '9:16']
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fit_mode = ['letterbox', 'crop', 'fill']
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method_mode = ['lanczos', 'bicubic', 'hamming', 'bilinear', 'box', 'nearest']
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multiple_list = ['8', '16', 'None']
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return {
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"required": {
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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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"fit": (fit_mode,),
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"method": (method_mode,),
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"round_to_multiple": (multiple_list,),
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"scale_to_longest_side": ("BOOLEAN", {"default": False}), # 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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},
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"optional": {
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"image": ("IMAGE",), #
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"mask": ("MASK",), #
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}
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}
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RETURN_TYPES = ("IMAGE", "MASK", "BOX",)
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RETURN_NAMES = ("image", "mask", "original_size")
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FUNCTION = 'image_scale_by_aspect_ratio'
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CATEGORY = '😺dzNodes/LayerUtility'
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OUTPUT_NODE = True
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def image_scale_by_aspect_ratio(self, aspect_ratio, proportional_width, proportional_height,
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fit, method, round_to_multiple, scale_to_longest_side, longest_side,
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image=None, mask = None,
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):
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orig_images = []
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orig_masks = []
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orig_width = 0
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orig_height = 0
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target_width = 0
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target_height = 0
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ratio = 1.0
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ret_images = []
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ret_masks = []
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if image is not None:
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for i in image:
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i = torch.unsqueeze(i, 0)
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orig_images.append(i)
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orig_width, orig_height = tensor2pil(orig_images[0]).size
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if mask is not None:
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if mask.dim() == 2:
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mask = torch.unsqueeze(mask, 0)
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for m in mask:
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m = torch.unsqueeze(m, 0)
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orig_masks.append(m)
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_width, _height = tensor2pil(orig_masks[0]).size
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if (orig_width > 0 and orig_width != _width) or (orig_height > 0 and orig_height != _height):
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log(f"Error: {NODE_NAME} skipped, because the mask is does'nt match image.", message_type='error')
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return (None, None,)
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elif orig_width + orig_height == 0:
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orig_width = _width
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orig_height = _height
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if orig_width + orig_height == 0:
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log(f"Error: {NODE_NAME} skipped, because the image or mask at least one must be input.", message_type='error')
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return (None, None,)
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if aspect_ratio == 'original':
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ratio = orig_width / orig_height
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elif aspect_ratio == 'custom':
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ratio = proportional_width / proportional_height
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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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# calculate target width and height
|
||||
if orig_width > orig_height:
|
||||
if scale_to_longest_side:
|
||||
target_width = longest_side
|
||||
else:
|
||||
target_width = orig_width
|
||||
target_height = int(target_width / ratio)
|
||||
else:
|
||||
if scale_to_longest_side:
|
||||
target_height = longest_side
|
||||
else:
|
||||
target_height = orig_height
|
||||
target_width = int(target_height * ratio)
|
||||
|
||||
if ratio < 1:
|
||||
if scale_to_longest_side:
|
||||
_r = longest_side / target_height
|
||||
target_height = longest_side
|
||||
else:
|
||||
_r = orig_height / target_height
|
||||
target_height = orig_height
|
||||
target_width = int(target_width * _r)
|
||||
|
||||
if round_to_multiple != 'None':
|
||||
multiple = int(round_to_multiple)
|
||||
target_width = num_round_to_multiple(target_width, multiple)
|
||||
target_height = num_round_to_multiple(target_height, multiple)
|
||||
|
||||
_mask = Image.new('L', size=(target_width, target_height), color='black')
|
||||
_image = Image.new('RGB', size=(target_width, target_height), color='black')
|
||||
|
||||
resize_sampler = Image.LANCZOS
|
||||
if method == "bicubic":
|
||||
resize_sampler = Image.BICUBIC
|
||||
elif method == "hamming":
|
||||
resize_sampler = Image.HAMMING
|
||||
elif method == "bilinear":
|
||||
resize_sampler = Image.BILINEAR
|
||||
elif method == "box":
|
||||
resize_sampler = Image.BOX
|
||||
elif method == "nearest":
|
||||
resize_sampler = Image.NEAREST
|
||||
|
||||
if len(orig_images) > 0:
|
||||
for i in orig_images:
|
||||
_image = tensor2pil(i).convert('RGB')
|
||||
_image = fit_resize_image(_image, target_width, target_height, fit, resize_sampler)
|
||||
ret_images.append(pil2tensor(_image))
|
||||
if len(orig_masks) > 0:
|
||||
for m in orig_masks:
|
||||
_mask = tensor2pil(m).convert('L')
|
||||
_mask = fit_resize_image(_mask, target_width, target_height, fit, resize_sampler).convert('L')
|
||||
ret_masks.append(image2mask(_mask))
|
||||
if len(ret_images) > 0 and len(ret_masks) >0:
|
||||
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
|
||||
return (torch.cat(ret_images, dim=0), torch.cat(ret_masks, dim=0),)
|
||||
elif len(ret_images) > 0 and len(ret_masks) == 0:
|
||||
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
|
||||
return (torch.cat(ret_images, dim=0), None,)
|
||||
elif len(ret_images) == 0 and len(ret_masks) > 0:
|
||||
log(f"{NODE_NAME} Processed {len(ret_masks)} image(s).", message_type='finish')
|
||||
return (None, torch.cat(ret_masks, dim=0),)
|
||||
else:
|
||||
log(f"Error: {NODE_NAME} skipped, because the available image or mask is not found.", message_type='error')
|
||||
return (None, None,)
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"LayerUtility: ImageScaleByAspectRatio": ImageScaleByAspectRatio
|
||||
}
|
||||
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"LayerUtility: ImageScaleByAspectRatio": "LayerUtility: ImageScaleByAspectRatio"
|
||||
}
|
||||
+2
-1
@@ -41,7 +41,8 @@ def log(message:str, message_type:str='info'):
|
||||
|
||||
try:
|
||||
from cv2.ximgproc import guidedFilter
|
||||
except ImportError:
|
||||
except ImportError as e:
|
||||
print(e)
|
||||
log(f'Dependency package error, unable import "cv2.ximgproc".'
|
||||
f'\nPlease REINSTALL package "opencv-contrib-python".'
|
||||
f'\nFor detail refer to \033[4mhttps://github.com/chflame163/ComfyUI_LayerStyle/issues/5\033[0m',
|
||||
|
||||
Reference in New Issue
Block a user