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* allow numpy>=1.25.0

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This commit is contained in:
Alexander G. Morano
2025-07-13 14:16:39 -04:00
parent af484c87db
commit 25cfed0cc4
5 changed files with 32 additions and 32 deletions
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README.md
+3
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@@ -138,6 +138,9 @@ Nodes that have been migrated:
[Migrated to Jovi_GLSL](https://github.com/Amorano/Jovi_GLSL)
**2025/07/13** @2.1.18:
* allow numpy>=1.25.0
**2025/07/07** @2.1.17:
* updated to cozy_comfyui 0.0.39
core/compose.py
+26 -29
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@@ -37,9 +37,6 @@ from cozy_comfyui.image.convert import \
from cozy_comfyui.image.misc import \
image_by_size, image_minmax, image_stack
from cozy_comfyui.image.pixel import \
pixel_eval
# ==============================================================================
# === GLOBAL ===
# ==============================================================================
@@ -90,8 +87,8 @@ Combine two input images using various blending modes, such as normal, screen, m
return Lexicon._parse(d)
def run(self, **kw) -> RGBAMaskType:
pA = parse_param(kw, Lexicon.IMAGE_BACK, EnumConvertType.IMAGE, None)
pB = parse_param(kw, Lexicon.IMAGE_FORE, EnumConvertType.IMAGE, None)
back = parse_param(kw, Lexicon.IMAGE_BACK, EnumConvertType.IMAGE, None)
fore = parse_param(kw, Lexicon.IMAGE_FORE, EnumConvertType.IMAGE, None)
mask = parse_param(kw, Lexicon.MASK, EnumConvertType.MASK, None)
func = parse_param(kw, Lexicon.FUNCTION, EnumBlendType, EnumBlendType.NORMAL.name)
alpha = parse_param(kw, Lexicon.ALPHA, EnumConvertType.FLOAT, 1)
@@ -102,41 +99,41 @@ Combine two input images using various blending modes, such as normal, screen, m
sample = parse_param(kw, Lexicon.SAMPLE, EnumInterpolation, EnumInterpolation.LANCZOS4.name)
matte = parse_param(kw, Lexicon.MATTE, EnumConvertType.VEC4INT, (0, 0, 0, 255), 0, 255)
inputMode = parse_param(kw, Lexicon.INPUT, EnumScaleInputMode, EnumScaleInputMode.NONE.name)
params = list(zip_longest_fill(pA, pB, mask, func, alpha, swap, invert, mode, wihi, sample, matte, inputMode))
params = list(zip_longest_fill(back, fore, mask, func, alpha, swap, invert, mode, wihi, sample, matte, inputMode))
images = []
pbar = ProgressBar(len(params))
for idx, (pA, pB, mask, func, alpha, swap, invert, mode, wihi, sample, matte, inputMode) in enumerate(params):
for idx, (back, fore, mask, func, alpha, swap, invert, mode, wihi, sample, matte, inputMode) in enumerate(params):
if swap:
pA, pB = pB, pA
back, fore = fore, back
width, height = IMAGE_SIZE_MIN, IMAGE_SIZE_MIN
if pA is None:
if pB is None:
if back is None:
if fore is None:
if mask is None:
if mode != EnumScaleMode.MATTE:
width, height = wihi
else:
height, width = mask.shape[:2]
else:
height, width = pB.shape[:2]
height, width = fore.shape[:2]
else:
height, width = pA.shape[:2]
height, width = back.shape[:2]
if pA is None:
pA = channel_solid(width, height, matte)
if back is None:
back = channel_solid(width, height, matte)
else:
pA = tensor_to_cv(pA)
matted = pixel_eval(matte)
pA = image_matte(pA, matted)
back = tensor_to_cv(back)
#matted = pixel_eval(matte)
#back = image_matte(back, matted)
if pB is None:
if fore is None:
clear = list(matte[:3]) + [0]
pB = channel_solid(width, height, clear)
fore = channel_solid(width, height, clear)
else:
pB = tensor_to_cv(pB)
fore = tensor_to_cv(fore)
if mask is None:
mask = image_mask(pB, 255)
mask = image_mask(fore, 255)
else:
mask = tensor_to_cv(mask, 1)
@@ -144,18 +141,18 @@ Combine two input images using various blending modes, such as normal, screen, m
mask = 255 - mask
if inputMode != EnumScaleInputMode.NONE:
# get the min/max of pA, pB; and mask?
imgs = [pA, pB]
# get the min/max of back, fore; and mask?
imgs = [back, fore]
_, w, h = image_by_size(imgs)
pA = image_scalefit(pA, w, h, inputMode, sample, matte)
pB = image_scalefit(pB, w, h, inputMode, sample, matte)
back = image_scalefit(back, w, h, inputMode, sample, matte)
fore = image_scalefit(fore, w, h, inputMode, sample, matte)
mask = image_scalefit(mask, w, h, inputMode, sample)
pA = image_scalefit(pA, w, h, EnumScaleMode.RESIZE_MATTE, sample, matte)
pB = image_scalefit(pB, w, h, EnumScaleMode.RESIZE_MATTE, sample, (0,0,0,255))
back = image_scalefit(back, w, h, EnumScaleMode.RESIZE_MATTE, sample, matte)
fore = image_scalefit(fore, w, h, EnumScaleMode.RESIZE_MATTE, sample, (0,0,0,255))
mask = image_scalefit(mask, w, h, EnumScaleMode.RESIZE_MATTE, sample, (255,255,255,255))
img = image_blend(pA, pB, mask, func, alpha)
img = image_blend(back, fore, mask, func, alpha)
mask = image_mask(img)
if mode != EnumScaleMode.MATTE:
@@ -163,7 +160,7 @@ Combine two input images using various blending modes, such as normal, screen, m
img = image_scalefit(img, width, height, mode, sample, matte)
img = cv_to_tensor_full(img, matte)
#img = [cv_to_tensor(pA), cv_to_tensor(pB), cv_to_tensor(mask, True)]
#img = [cv_to_tensor(back), cv_to_tensor(fore), cv_to_tensor(mask, True)]
images.append(img)
pbar.update_absolute(idx)
pyproject.toml
+2 -2
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@@ -1,7 +1,7 @@
[project]
name = "jovimetrix"
description = "Animation via tick. Parameter manipulation with wave generator. Unary and Binary math support. Value convert int/float/bool, VectorN and Image, Mask types. Shape mask generator. Stack images, do channel ops, split, merge and randomize arrays and batches. Load images & video from anywhere. Dynamic bus routing. Save output anywhere! Flatten, crop, transform; check colorblindness or linear interpolate values."
version = "2.1.17"
version = "2.1.18"
license = { file = "LICENSE" }
readme = "README.md"
authors = [{ name = "Alexander G. Morano", email = "amorano@gmail.com" }]
@@ -20,7 +20,7 @@ dependencies = [
"aenum",
"git+https://github.com/cozy-comfyui/cozy_comfyui@main#egg=cozy_comfyui",
"matplotlib",
"numpy<2",
"numpy>=1.25.0",
"opencv-contrib-python",
"Pillow"
]
requirements.txt
+1 -1
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@@ -1,6 +1,6 @@
aenum
git+https://github.com/cozy-comfyui/cozy_comfyui@main#egg=cozy_comfyui
matplotlib
numpy<2
numpy>=1.25.0
opencv-contrib-python
Pillow
res/img/color-h.png
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