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# 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
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