From 624da2ea835d5b013ff3dada778131ea5afa3411 Mon Sep 17 00:00:00 2001
From: akatz-ai <katzfeyalex+akatzgh@gmail.com>
Date: Tue, 5 Nov 2024 15:49:44 -0800
Subject: [PATCH] Updated audioreactive dynamic dilation mask node with
quality_factor
---
.gitignore | 3 +-
modules/__pycache__/easing.cpython-310.pyc | Bin 7754 -> 7754 bytes
src/ak_audioreactive_dynamic_dilation_mask.py | 40 +++++++++++++-----
3 files changed, 31 insertions(+), 12 deletions(-)
diff --git a/.gitignore b/.gitignore
index e2da6af..efd697e 100644
--- a/.gitignore
+++ b/.gitignore
@@ -2,4 +2,5 @@
/models/*.bin
/models/*.safetensors
.directory
-src/__pycache__/
\ No newline at end of file
+modules/__pycache__/
+src/__pycache__/
diff --git a/modules/__pycache__/easing.cpython-310.pyc b/modules/__pycache__/easing.cpython-310.pyc
index b45baa8927a9034a374f2adf0046c3d068c56d03..854455341754c7c53daed22e7371b97b1699577c 100644
GIT binary patch
delta 49
zcmX?QbIOJ%pO=@50SMUFX{B%EVHRb)HCaG(Es)$N8Vn?b#OhdXF>2h}JVk69I{;vZ
B52^qF
delta 49
zcmX?QbIOJ%pO=@50SIj0Jxtrk!z{}9YqEgoS|GVkG#E$<iPf?EV$}Gxd5YLJb^wyh
B5q1Co
diff --git a/src/ak_audioreactive_dynamic_dilation_mask.py b/src/ak_audioreactive_dynamic_dilation_mask.py
index e5cffa3..42a48b5 100644
--- a/src/ak_audioreactive_dynamic_dilation_mask.py
+++ b/src/ak_audioreactive_dynamic_dilation_mask.py
@@ -8,7 +8,7 @@
class AK_AudioreactiveDynamicDilationMask:
def __init__(self):
- pass
+ pass
@classmethod
def INPUT_TYPES(s):
@@ -23,6 +23,13 @@ def INPUT_TYPES(s):
"min_radius": ("INT",{
"default": 0
}),
+ "quality_factor": ("FLOAT", {
+ "default": 0.25,
+ "min": 0.0,
+ "max": 1.0,
+ "step": 0.01,
+ "display": "number",
+ }),
},
}
@@ -44,6 +51,7 @@ def VALIDATE_INPUTS(cls, input_types):
- shape: The shape of the dilation
- max_radius: The maximum radius of the dilation
- min_radius: The minimum radius of the dilation
+ - quality_factor: The quality factor of the dilation
"""
def create_circular_kernel(self, radius):
@@ -54,30 +62,40 @@ def create_circular_kernel(self, radius):
kernel[mask] = 1
return kernel
- def dilate_mask_with_amplitude(self, mask, normalized_amp, shape="circle", max_radius=25, min_radius=0):
+ def dilate_mask_with_amplitude(self, mask, normalized_amp, shape="circle", max_radius=25, min_radius=0, quality_factor=0.25):
dup = copy.deepcopy(mask.cpu().numpy())
# Convert normalize_amp into a float list from numpy array if it is not already a list
if not isinstance(normalized_amp, list):
normalized_amp = normalized_amp.tolist()
- # Pre-compute circular kernels if shape is "circle"
- if shape == "circle":
- circular_kernels = [self.create_circular_kernel(r) for r in range(max_radius+1)]
+ epsilon = 1e-6
+ if quality_factor < epsilon:
+ shape = "square"
- for index, (mask, amp) in enumerate(zip(dup, normalized_amp)):
+ for index, (mask_frame, amp) in enumerate(zip(dup, normalized_amp)):
# Scale the amplitude to fluctuate between min_radius and max_radius
- current_radius = min_radius + amp * (max_radius - min_radius)
+ radius = min_radius + amp * (max_radius - min_radius)
- if current_radius <= 0:
+ if radius <= 0:
continue
+ s = abs(int(radius * quality_factor if shape == "circle" else radius))
+ d = s * 2 + 1
+
if shape == "circle":
- k = circular_kernels[int(current_radius)]
+ k = np.zeros((d, d), np.uint8)
+ k = cv2.circle(k, (s, s), s, 1, -1)
else:
- d = 2 * int(current_radius) + 1
k = np.ones((d, d), np.uint8)
- dup[index] = cv2.dilate(mask, k, iterations=1)
+ iterations = int(1 / quality_factor if quality_factor >= epsilon else 1)
+
+ if radius > 0:
+ dilated_mask = cv2.dilate(mask_frame, k, iterations=iterations)
+ else:
+ dilated_mask = cv2.erode(mask_frame, k, iterations=iterations)
+
+ dup[index] = dilated_mask
return (torch.from_numpy(dup),)
\ No newline at end of file