jaidaken
f09734b0ee
Some checks failed
Python Linting / Run Ruff (push) Has been cancelled
Python Linting / Run Pylint (push) Has been cancelled
Full Comfy CI Workflow Runs / test-stable (12.1, , linux, 3.10, [self-hosted Linux], stable) (push) Has been cancelled
Full Comfy CI Workflow Runs / test-stable (12.1, , linux, 3.11, [self-hosted Linux], stable) (push) Has been cancelled
Full Comfy CI Workflow Runs / test-stable (12.1, , linux, 3.12, [self-hosted Linux], stable) (push) Has been cancelled
Full Comfy CI Workflow Runs / test-unix-nightly (12.1, , linux, 3.11, [self-hosted Linux], nightly) (push) Has been cancelled
Execution Tests / test (macos-latest) (push) Has been cancelled
Execution Tests / test (ubuntu-latest) (push) Has been cancelled
Execution Tests / test (windows-latest) (push) Has been cancelled
Test server launches without errors / test (push) Has been cancelled
Unit Tests / test (macos-latest) (push) Has been cancelled
Unit Tests / test (ubuntu-latest) (push) Has been cancelled
Unit Tests / test (windows-2022) (push) Has been cancelled
Includes 30 custom nodes committed directly, 7 Civitai-exclusive loras stored via Git LFS, and a setup script that installs all dependencies and downloads HuggingFace-hosted models on vast.ai. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
88 lines
2.4 KiB
Python
88 lines
2.4 KiB
Python
# High Quality Edge Thinning using Pure Python
|
|
# Written by Lvmin Zhang
|
|
# 2023 April
|
|
# Stanford University
|
|
# If you use this, please Cite "High Quality Edge Thinning using Pure Python", Lvmin Zhang, In Mikubill/sd-webui-controlnet.
|
|
|
|
|
|
import cv2
|
|
import numpy as np
|
|
|
|
|
|
lvmin_kernels_raw = [
|
|
np.array([
|
|
[-1, -1, -1],
|
|
[0, 1, 0],
|
|
[1, 1, 1]
|
|
], dtype=np.int32),
|
|
np.array([
|
|
[0, -1, -1],
|
|
[1, 1, -1],
|
|
[0, 1, 0]
|
|
], dtype=np.int32)
|
|
]
|
|
|
|
lvmin_kernels = []
|
|
lvmin_kernels += [np.rot90(x, k=0, axes=(0, 1)) for x in lvmin_kernels_raw]
|
|
lvmin_kernels += [np.rot90(x, k=1, axes=(0, 1)) for x in lvmin_kernels_raw]
|
|
lvmin_kernels += [np.rot90(x, k=2, axes=(0, 1)) for x in lvmin_kernels_raw]
|
|
lvmin_kernels += [np.rot90(x, k=3, axes=(0, 1)) for x in lvmin_kernels_raw]
|
|
|
|
lvmin_prunings_raw = [
|
|
np.array([
|
|
[-1, -1, -1],
|
|
[-1, 1, -1],
|
|
[0, 0, -1]
|
|
], dtype=np.int32),
|
|
np.array([
|
|
[-1, -1, -1],
|
|
[-1, 1, -1],
|
|
[-1, 0, 0]
|
|
], dtype=np.int32)
|
|
]
|
|
|
|
lvmin_prunings = []
|
|
lvmin_prunings += [np.rot90(x, k=0, axes=(0, 1)) for x in lvmin_prunings_raw]
|
|
lvmin_prunings += [np.rot90(x, k=1, axes=(0, 1)) for x in lvmin_prunings_raw]
|
|
lvmin_prunings += [np.rot90(x, k=2, axes=(0, 1)) for x in lvmin_prunings_raw]
|
|
lvmin_prunings += [np.rot90(x, k=3, axes=(0, 1)) for x in lvmin_prunings_raw]
|
|
|
|
|
|
def remove_pattern(x, kernel):
|
|
objects = cv2.morphologyEx(x, cv2.MORPH_HITMISS, kernel)
|
|
objects = np.where(objects > 127)
|
|
x[objects] = 0
|
|
return x, objects[0].shape[0] > 0
|
|
|
|
|
|
def thin_one_time(x, kernels):
|
|
y = x
|
|
is_done = True
|
|
for k in kernels:
|
|
y, has_update = remove_pattern(y, k)
|
|
if has_update:
|
|
is_done = False
|
|
return y, is_done
|
|
|
|
|
|
def lvmin_thin(x, prunings=True):
|
|
y = x
|
|
for i in range(32):
|
|
y, is_done = thin_one_time(y, lvmin_kernels)
|
|
if is_done:
|
|
break
|
|
if prunings:
|
|
y, _ = thin_one_time(y, lvmin_prunings)
|
|
return y
|
|
|
|
|
|
def nake_nms(x):
|
|
f1 = np.array([[0, 0, 0], [1, 1, 1], [0, 0, 0]], dtype=np.uint8)
|
|
f2 = np.array([[0, 1, 0], [0, 1, 0], [0, 1, 0]], dtype=np.uint8)
|
|
f3 = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]], dtype=np.uint8)
|
|
f4 = np.array([[0, 0, 1], [0, 1, 0], [1, 0, 0]], dtype=np.uint8)
|
|
y = np.zeros_like(x)
|
|
for f in [f1, f2, f3, f4]:
|
|
np.putmask(y, cv2.dilate(x, kernel=f) == x, x)
|
|
return y
|