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fix : "np.asfarray was removed in the NumPy 2.0 release. Use np.asarray` with a proper dtype instead" #570

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fix : "np.asfarray was removed in the NumPy 2.0 release. Use np.asarray` with a proper dtype instead" #570

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98 changes: 49 additions & 49 deletions py/add_grain.py
100644 → 100755
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Original file line number Diff line number Diff line change
@@ -1,50 +1,50 @@
import torch
import time
from .imagefunc import log, tensor2pil, image_add_grain, pil2tensor



class AddGrain:

def __init__(self):
self.NODE_NAME = 'AddGrain'


@classmethod
def INPUT_TYPES(self):

return {
"required": {
"image": ("IMAGE", ), #
"grain_power": ("FLOAT", {"default": 0.5, "min": 0, "max": 1, "step": 0.01}),
"grain_scale": ("FLOAT", {"default": 1, "min": 0.1, "max": 10, "step": 0.1}),
"grain_sat": ("FLOAT", {"default": 1, "min": 0, "max": 1, "step": 0.01}),
},
"optional": {
}
}

RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("image",)
FUNCTION = 'add_grain'
CATEGORY = '😺dzNodes/LayerFilter'

def add_grain(self, image, grain_power, grain_scale, grain_sat):

ret_images = []

for i in range(len(image)):
_canvas = tensor2pil(torch.unsqueeze(image[i], 0)).convert('RGB')
_canvas = image_add_grain(_canvas, grain_scale, grain_power, grain_sat, toe=0, seed=int(time.time()) + i)
ret_images.append(pil2tensor(_canvas))

log(f"{self.NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
return (torch.cat(ret_images, dim=0),)

NODE_CLASS_MAPPINGS = {
"LayerFilter: AddGrain": AddGrain
}

NODE_DISPLAY_NAME_MAPPINGS = {
"LayerFilter: AddGrain": "LayerFilter: Add Grain"
import torch
import time
from .imagefunc import log, tensor2pil, image_add_grain, pil2tensor
class AddGrain:
def __init__(self):
self.NODE_NAME = 'AddGrain'
@classmethod
def INPUT_TYPES(self):
return {
"required": {
"image": ("IMAGE", ), #
"grain_power": ("FLOAT", {"default": 0.5, "min": 0, "max": 1, "step": 0.01}),
"grain_scale": ("FLOAT", {"default": 1, "min": 0.1, "max": 10, "step": 0.1}),
"grain_sat": ("FLOAT", {"default": 1, "min": 0, "max": 1, "step": 0.01}),
},
"optional": {
}
}
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("image",)
FUNCTION = 'add_grain'
CATEGORY = '😺dzNodes/LayerFilter'
def add_grain(self, image, grain_power, grain_scale, grain_sat):
ret_images = []
for i in range(len(image)):
_canvas = tensor2pil(torch.unsqueeze(image[i], 0)).convert('RGB')
_canvas = image_add_grain(_canvas, grain_scale, grain_power, grain_sat, toe=0, seed=int(time.time()) + i)
ret_images.append(pil2tensor(_canvas))
log(f"{self.NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
return (torch.cat(ret_images, dim=0),)
NODE_CLASS_MAPPINGS = {
"LayerFilter: AddGrain": AddGrain
}
NODE_DISPLAY_NAME_MAPPINGS = {
"LayerFilter: AddGrain": "LayerFilter: Add Grain"
}
68 changes: 34 additions & 34 deletions py/any_rerouter.py
100644 → 100755
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Original file line number Diff line number Diff line change
@@ -1,35 +1,35 @@
from .imagefunc import AnyType

anything = AnyType('*')

class LS_AnyRerouter():

def __init__(self):
self.NODE_NAME = 'AnyRerouter'


@classmethod
def INPUT_TYPES(self):
return {
"required": {
"any": (anything, {}),
},
"optional": { #
}
}

RETURN_TYPES = (anything,)
RETURN_NAMES = ('any',)
FUNCTION = 'any_rerouter'
CATEGORY = '😺dzNodes/LayerUtility/Data'

def any_rerouter(self, any,):
return (any,)

NODE_CLASS_MAPPINGS = {
"LayerUtility: AnyRerouter": LS_AnyRerouter
}

NODE_DISPLAY_NAME_MAPPINGS = {
"LayerUtility: AnyRerouter": "LayerUtility: Any Rerouter"
from .imagefunc import AnyType
anything = AnyType('*')
class LS_AnyRerouter():
def __init__(self):
self.NODE_NAME = 'AnyRerouter'
@classmethod
def INPUT_TYPES(self):
return {
"required": {
"any": (anything, {}),
},
"optional": { #
}
}
RETURN_TYPES = (anything,)
RETURN_NAMES = ('any',)
FUNCTION = 'any_rerouter'
CATEGORY = '😺dzNodes/LayerUtility/Data'
def any_rerouter(self, any,):
return (any,)
NODE_CLASS_MAPPINGS = {
"LayerUtility: AnyRerouter": LS_AnyRerouter
}
NODE_DISPLAY_NAME_MAPPINGS = {
"LayerUtility: AnyRerouter": "LayerUtility: Any Rerouter"
}
128 changes: 64 additions & 64 deletions py/batch_selector.py
100644 → 100755
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Original file line number Diff line number Diff line change
@@ -1,65 +1,65 @@
import torch
from .imagefunc import log, pil2tensor,image2mask, extract_numbers
from PIL import Image



class BatchSelector:

def __init__(self):
self.NODE_NAME = 'BatchSelector'
pass

@classmethod
def INPUT_TYPES(self):

return {
"required": {
"select": ("STRING", {"default": "0,"},),
},
"optional": {
"images": ("IMAGE",), #
"masks": ("MASK",), #
}
}

RETURN_TYPES = ("IMAGE", "MASK",)
RETURN_NAMES = ("image", "mask",)
FUNCTION = 'batch_selector'
CATEGORY = '😺dzNodes/LayerUtility/SystemIO'

def batch_selector(self, select, images=None, masks=None
):
ret_images = []
ret_masks = []
empty_image = pil2tensor(Image.new("RGBA", (64, 64), (0, 0, 0, 0)))
empty_mask = image2mask(Image.new("L", (64, 64), color="black"))

indexs = extract_numbers(select)
for i in indexs:
if images is not None:
if i < len(images):
ret_images.append(images[i].unsqueeze(0))
else:
ret_images.append(images[-1].unsqueeze(0))
if masks is not None:
if i < len(masks):
ret_masks.append(masks[i].unsqueeze(0))
else:
ret_masks.append(masks[-1].unsqueeze(0))

if len(ret_images) == 0:
ret_images.append(empty_image)
if len(ret_masks) == 0:
ret_masks.append(empty_mask)

log(f"{self.NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
return (torch.cat(ret_images, dim=0), torch.cat(ret_masks, dim=0),)

NODE_CLASS_MAPPINGS = {
"LayerUtility: BatchSelector": BatchSelector
}

NODE_DISPLAY_NAME_MAPPINGS = {
"LayerUtility: BatchSelector": "LayerUtility: Batch Selector"
import torch
from .imagefunc import log, pil2tensor,image2mask, extract_numbers
from PIL import Image
class BatchSelector:
def __init__(self):
self.NODE_NAME = 'BatchSelector'
pass
@classmethod
def INPUT_TYPES(self):
return {
"required": {
"select": ("STRING", {"default": "0,"},),
},
"optional": {
"images": ("IMAGE",), #
"masks": ("MASK",), #
}
}
RETURN_TYPES = ("IMAGE", "MASK",)
RETURN_NAMES = ("image", "mask",)
FUNCTION = 'batch_selector'
CATEGORY = '😺dzNodes/LayerUtility/SystemIO'
def batch_selector(self, select, images=None, masks=None
):
ret_images = []
ret_masks = []
empty_image = pil2tensor(Image.new("RGBA", (64, 64), (0, 0, 0, 0)))
empty_mask = image2mask(Image.new("L", (64, 64), color="black"))
indexs = extract_numbers(select)
for i in indexs:
if images is not None:
if i < len(images):
ret_images.append(images[i].unsqueeze(0))
else:
ret_images.append(images[-1].unsqueeze(0))
if masks is not None:
if i < len(masks):
ret_masks.append(masks[i].unsqueeze(0))
else:
ret_masks.append(masks[-1].unsqueeze(0))
if len(ret_images) == 0:
ret_images.append(empty_image)
if len(ret_masks) == 0:
ret_masks.append(empty_mask)
log(f"{self.NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
return (torch.cat(ret_images, dim=0), torch.cat(ret_masks, dim=0),)
NODE_CLASS_MAPPINGS = {
"LayerUtility: BatchSelector": BatchSelector
}
NODE_DISPLAY_NAME_MAPPINGS = {
"LayerUtility: BatchSelector": "LayerUtility: Batch Selector"
}
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