Dubhe/dubhe_data_process/common/augment_utils/ACE.py

"""
/**
* Copyright 2020 Zhejiang Lab. All Rights Reserved.
*
* 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.
* =============================================================
*/
"""

import cv2
import numpy as np
import math
para = {}

def ACE(img, ratio=4, radius=300):
    """The implementation of ACE"""
    global para
    para_mat = para.get(radius)
    if para_mat is not None:
        pass
    else:
        size = radius * 2 + 1
        para_mat = np.zeros((size, size))
        for h in range(-radius, radius + 1):
            for w in range(-radius, radius + 1):
                if not h and not w:
                    continue
                para_mat[radius + h, radius + w] = 1.0 / \
                    math.sqrt(h ** 2 + w ** 2)
        para_mat /= para_mat.sum()
        para[radius] = para_mat
    h, w = img.shape[:2]
    p_h, p_w = [0] * radius + list(range(h)) + [h - 1] * radius,\
             [0] * radius + list(range(w)) + [w - 1] * radius
    temp = img[np.ix_(p_h, p_w)]
    res = np.zeros(img.shape)
    for i in range(radius * 2 + 1):
        for j in range(radius * 2 + 1):
            if para_mat[i][j] == 0:
                continue
            res += (para_mat[i][j] *
                    np.clip((img - temp[i:i + h, j:j + w]) * ratio, -1, 1))
    return res

def ACE_channel(img, ratio, radius):
    """The implementation of ACE through individual channel"""
    h, w = img.shape[:2]
    if min(h, w) <= 2:
        return np.zeros(img.shape) + 0.5
    down_ori = cv2.pyrDown(img, ((w + 1) // 2, (h + 1) // 2))
    temp = ACE_channel(down_ori, ratio, radius)
    up_temp = cv2.resize(temp, (w, h))
    up_ori = cv2.resize(down_ori, (w, h))
    re = up_temp + ACE(img, ratio, radius) - ACE(up_ori, ratio, radius)
    return re

def ACE_color(img, ratio=4, radius=3):
    """Enhance the image through RGB channels"""
    re = np.zeros(img.shape)
    for c in range(3):
        re[:, :, c] = reprocessImage(ACE_channel(img[:, :, c], ratio, radius))
    return re

def reprocessImage(img):
    """Reprocess and map the image to [0,1]"""
    ht = np.histogram(img, 2000)
    d = np.cumsum(ht[0]) / float(img.size)
    try:
        left = next(x for x in range(len(d)) if d[x] >= 0.005)
    except:
        left = 1999
    try:
        right = next(y for y in range(len(d)-1,0,-1) if d[y] <= 0.995)
    except:
        right = 1
    return np.clip((img - ht[1][left]) / (ht[1][right] - ht[1][left]), 0, 1)