# pylint: skip-file
"""
Copyright 2023 Lvmin Zhang

   Licensed under the Apache License, Version 2.0 (the "License");
   you may not use this file except in compliance with the License.
   You may obtain a copy of the License at

       http://www.apache.org/licenses/LICENSE-2.0

   Unless required by applicable law or agreed to in writing, software
   distributed under the License is distributed on an "AS IS" BASIS,
   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   See the License for the specific language governing permissions and
   limitations under the License.
"""

# 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