# 圖像增強算法，圖像銳化算法
# 1）基于直方圖均衡化 2）基于拉普拉斯算子 3）基于對數變換 4）基于伽馬變換 5)CLAHE 6)retinex-SSR 7)retinex-MSR
# 其中，基于拉普拉斯算子的圖像增強為利用空域卷積運算實現濾波
# 基于同一圖像對比增強效果
# 直方圖均衡化:對比度較低的圖像適合使用直方圖均衡化方法來增強圖像細節(jié)
# 拉普拉斯算子可以增強局部的圖像對比度
# log對數變換對于整體對比度偏低并且灰度值偏低的圖像增強效果較好
# 伽馬變換對于圖像對比度偏低，并且整體亮度值偏高（對于相機過曝）情況下的圖像增強效果明顯

import cv2
import numpy as np
import matplotlib.pyplot as plt


# 直方圖均衡增強
def hist(image):
  r, g, b = cv2.split(image)
  r1 = cv2.equalizeHist(r)
  g1 = cv2.equalizeHist(g)
  b1 = cv2.equalizeHist(b)
  image_equal_clo = cv2.merge([r1, g1, b1])
  return image_equal_clo


# 拉普拉斯算子
def laplacian(image):
  kernel = np.array([[0, -1, 0], [-1, 5, -1], [0, -1, 0]])
  image_lap = cv2.filter2D(image, cv2.CV_8UC3, kernel)
  return image_lap


# 對數變換
def log(image):
  image_log = np.uint8(np.log(np.array(image) + 1))
  cv2.normalize(image_log, image_log, 0, 255, cv2.NORM_MINMAX)
  # 轉換成8bit圖像顯示
  cv2.convertScaleAbs(image_log, image_log)
  return image_log


# 伽馬變換
def gamma(image):
  fgamma = 2
  image_gamma = np.uint8(np.power((np.array(image) / 255.0), fgamma) * 255.0)
  cv2.normalize(image_gamma, image_gamma, 0, 255, cv2.NORM_MINMAX)
  cv2.convertScaleAbs(image_gamma, image_gamma)
  return image_gamma


# 限制對比度自適應直方圖均衡化CLAHE
def clahe(image):
  b, g, r = cv2.split(image)
  clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
  b = clahe.apply(b)
  g = clahe.apply(g)
  r = clahe.apply(r)
  image_clahe = cv2.merge([b, g, r])
  return image_clahe


def replaceZeroes(data):
  min_nonzero = min(data[np.nonzero(data)])
  data[data == 0] = min_nonzero
  return data


# retinex SSR
def SSR(src_img, size):
  L_blur = cv2.GaussianBlur(src_img, (size, size), 0)
  img = replaceZeroes(src_img)
  L_blur = replaceZeroes(L_blur)

  dst_Img = cv2.log(img/255.0)
  dst_Lblur = cv2.log(L_blur/255.0)
  dst_IxL = cv2.multiply(dst_Img, dst_Lblur)
  log_R = cv2.subtract(dst_Img, dst_IxL)

  dst_R = cv2.normalize(log_R,None, 0, 255, cv2.NORM_MINMAX)
  log_uint8 = cv2.convertScaleAbs(dst_R)
  return log_uint8


def SSR_image(image):
  size = 3
  b_gray, g_gray, r_gray = cv2.split(image)
  b_gray = SSR(b_gray, size)
  g_gray = SSR(g_gray, size)
  r_gray = SSR(r_gray, size)
  result = cv2.merge([b_gray, g_gray, r_gray])
  return result


# retinex MMR
def MSR(img, scales):
  weight = 1 / 3.0
  scales_size = len(scales)
  h, w = img.shape[:2]
  log_R = np.zeros((h, w), dtype=np.float32)

  for i in range(scales_size):
    img = replaceZeroes(img)
    L_blur = cv2.GaussianBlur(img, (scales[i], scales[i]), 0)
    L_blur = replaceZeroes(L_blur)
    dst_Img = cv2.log(img/255.0)
    dst_Lblur = cv2.log(L_blur/255.0)
    dst_Ixl = cv2.multiply(dst_Img, dst_Lblur)
    log_R += weight * cv2.subtract(dst_Img, dst_Ixl)

  dst_R = cv2.normalize(log_R,None, 0, 255, cv2.NORM_MINMAX)
  log_uint8 = cv2.convertScaleAbs(dst_R)
  return log_uint8


def MSR_image(image):
  scales = [15, 101, 301] # [3,5,9]
  b_gray, g_gray, r_gray = cv2.split(image)
  b_gray = MSR(b_gray, scales)
  g_gray = MSR(g_gray, scales)
  r_gray = MSR(r_gray, scales)
  result = cv2.merge([b_gray, g_gray, r_gray])
  return result


if __name__ == "__main__":
  image = cv2.imread("example.jpg")
  image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

  plt.subplot(4, 2, 1)
  plt.imshow(image)
  plt.axis('off')
  plt.title('Offical')

  # 直方圖均衡增強
  image_equal_clo = hist(image)

  plt.subplot(4, 2, 2)
  plt.imshow(image_equal_clo)
  plt.axis('off')
  plt.title('equal_enhance')

  # 拉普拉斯算法增強
  image_lap = laplacian(image)

  plt.subplot(4, 2, 3)
  plt.imshow(image_lap)
  plt.axis('off')
  plt.title('laplacian_enhance')

  # LoG對象算法增強
  image_log = log(image)

  plt.subplot(4, 2, 4)
  plt.imshow(image_log)
  plt.axis('off')
  plt.title('log_enhance')

  # 伽馬變換
  image_gamma = gamma(image)

  plt.subplot(4, 2, 5)
  plt.imshow(image_gamma)
  plt.axis('off')
  plt.title('gamma_enhance')

  # CLAHE
  image_clahe = clahe(image)

  plt.subplot(4, 2, 6)
  plt.imshow(image_clahe)
  plt.axis('off')
  plt.title('CLAHE')

  # retinex_ssr
  image_ssr = SSR_image(image)

  plt.subplot(4, 2, 7)
  plt.imshow(image_ssr)
  plt.axis('off')
  plt.title('SSR')

  # retinex_msr
  image_msr = MSR_image(image)

  plt.subplot(4, 2, 8)
  plt.imshow(image_msr)
  plt.axis('off')
  plt.title('MSR')

  plt.show()