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CorpByMask node add crop_box input

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chflame
2024-03-07 19:16:39 +08:00
parent a429917568
commit 356fe57983
2 changed files with 125 additions and 30 deletions
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py/crop_by_mask.py
+24 -30
View File
@@ -22,7 +22,6 @@ class CropByMask:
"right_reserve": ("INT", {"default": 20, "min": -9999, "max": 9999, "step": 1}),
},
"optional": {
"crop_box": ("BOX",),
}
}
@@ -33,16 +32,15 @@ class CropByMask:
OUTPUT_NODE = True
def crop_by_mask(self, image, mask_for_crop, invert_mask, detect,
top_reserve, bottom_reserve,
left_reserve, right_reserve,
crop_box=None
):
top_reserve, bottom_reserve, left_reserve, right_reserve
):
ret_images = []
ret_masks = []
l_images = []
l_masks = []
for l in image:
l_images.append(torch.unsqueeze(l, 0))
if mask_for_crop.dim() == 2:
@@ -56,32 +54,28 @@ class CropByMask:
l_masks.append(tensor2pil(torch.unsqueeze(mask_for_crop, 0)).convert('L'))
_mask = mask2image(mask_for_crop)
bluredmask = gaussian_blur(_mask, 20).convert('L')
x = 0
y = 0
width = 0
height = 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(_mask)
log(f"{NODE_NAME}: Box detected. x={x},y={y},width={width},height={height}")
canvas_width, canvas_height = tensor2pil(torch.unsqueeze(image[0], 0)).convert('RGB').size
x1 = x - left_reserve if x - left_reserve > 0 else 0
y1 = y - top_reserve if y - top_reserve > 0 else 0
x2 = x + width + right_reserve if x + width + right_reserve < canvas_width else canvas_width
y2 = y + height + bottom_reserve if y + height + bottom_reserve < canvas_height else canvas_height
preview_image = tensor2pil(mask_for_crop).convert('RGB')
if crop_box is None:
bluredmask = gaussian_blur(_mask, 20).convert('L')
x = 0
y = 0
width = 0
height = 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(_mask)
log(f"{NODE_NAME}: Box detected. x={x},y={y},width={width},height={height}")
canvas_width, canvas_height = tensor2pil(torch.unsqueeze(image[0], 0)).convert('RGB').size
x1 = x - left_reserve if x - left_reserve > 0 else 0
y1 = y - top_reserve if y - top_reserve > 0 else 0
x2 = x + width + right_reserve if x + width + right_reserve < canvas_width else canvas_width
y2 = y + height + bottom_reserve if y + height + bottom_reserve < canvas_height else canvas_height
crop_box = (x1, y1, x2, y2)
preview_image = draw_rect(preview_image, x, y, width, height, line_color="#F00000",
line_width=(width + height) // 100)
preview_image = draw_rect(preview_image, crop_box[0], crop_box[1],
crop_box[2] - crop_box[0], crop_box[3] - crop_box[1],
line_color="#00F000",
line_width=(crop_box[2] - crop_box[0] + crop_box[3] - crop_box[1]) // 200)
preview_image = draw_rect(preview_image, x, y, width, height, line_color="#F00000", line_width=(width+height)//100)
preview_image = draw_rect(preview_image, x1, y1, x2 - x1, y2 - y1,
line_color="#00F000", line_width=(width+height)//200)
crop_box = (x1, y1, x2, y2)
for i in range(len(l_images)):
_canvas = tensor2pil(l_images[i]).convert('RGB')
_mask = l_masks[0]
py/crop_by_mask_v2.py
+101
View File
@@ -0,0 +1,101 @@
from .imagefunc import *
NODE_NAME = 'CropByMask V2'
class CropByMaskV2:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(self):
detect_mode = ['min_bounding_rect', 'max_inscribed_rect', 'mask_area']
return {
"required": {
"image": ("IMAGE", ), #
"mask": ("MASK",),
"invert_mask": ("BOOLEAN", {"default": False}), # 反转mask#
"detect": (detect_mode,),
"top_reserve": ("INT", {"default": 20, "min": -9999, "max": 9999, "step": 1}),
"bottom_reserve": ("INT", {"default": 20, "min": -9999, "max": 9999, "step": 1}),
"left_reserve": ("INT", {"default": 20, "min": -9999, "max": 9999, "step": 1}),
"right_reserve": ("INT", {"default": 20, "min": -9999, "max": 9999, "step": 1}),
},
"optional": {
"crop_box": ("BOX",),
}
}
RETURN_TYPES = ("IMAGE", "MASK", "BOX", "IMAGE",)
RETURN_NAMES = ("croped_image", "croped_mask", "crop_box", "box_preview")
FUNCTION = 'crop_by_mask_v2'
CATEGORY = '😺dzNodes/LayerUtility'
OUTPUT_NODE = True
def crop_by_mask_v2(self, image, mask, invert_mask, detect,
top_reserve, bottom_reserve,
left_reserve, right_reserve,
crop_box=None
):
ret_images = []
ret_masks = []
l_images = []
l_masks = []
for l in image:
l_images.append(torch.unsqueeze(l, 0))
if mask.dim() == 2:
mask = torch.unsqueeze(mask_for_crop, 0)
# 如果有多张mask输入，使用第一张
if mask.shape[0] > 1:
log(f"Warning: Multiple mask inputs, using the first.", message_type='warning')
mask = torch.unsqueeze(mask_for_crop[0], 0)
if invert_mask:
mask = 1 - mask
l_masks.append(tensor2pil(torch.unsqueeze(mask, 0)).convert('L'))
_mask = mask2image(mask)
preview_image = tensor2pil(mask).convert('RGB')
if crop_box is None:
bluredmask = gaussian_blur(_mask, 20).convert('L')
x = 0
y = 0
width = 0
height = 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(_mask)
log(f"{NODE_NAME}: Box detected. x={x},y={y},width={width},height={height}")
canvas_width, canvas_height = tensor2pil(torch.unsqueeze(image[0], 0)).convert('RGB').size
x1 = x - left_reserve if x - left_reserve > 0 else 0
y1 = y - top_reserve if y - top_reserve > 0 else 0
x2 = x + width + right_reserve if x + width + right_reserve < canvas_width else canvas_width
y2 = y + height + bottom_reserve if y + height + bottom_reserve < canvas_height else canvas_height
crop_box = (x1, y1, x2, y2)
preview_image = draw_rect(preview_image, x, y, width, height, line_color="#F00000",
line_width=(width + height) // 100)
preview_image = draw_rect(preview_image, crop_box[0], crop_box[1],
crop_box[2] - crop_box[0], crop_box[3] - crop_box[1],
line_color="#00F000",
line_width=(crop_box[2] - crop_box[0] + crop_box[3] - crop_box[1]) // 200)
for i in range(len(l_images)):
_canvas = tensor2pil(l_images[i]).convert('RGB')
_mask = l_masks[0]
ret_images.append(pil2tensor(_canvas.crop(crop_box)))
ret_masks.append(image2mask(_mask.crop(crop_box)))
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), list(crop_box), pil2tensor(preview_image),)
NODE_CLASS_MAPPINGS = {
"LayerUtility: CropByMask V2": CropByMaskV2
}
NODE_DISPLAY_NAME_MAPPINGS = {
"LayerUtility: CropByMask V2": "LayerUtility: CropByMask V2"
}