直方图匹配又称为直方图规定化，是指将一幅图像的直方图变成规定形状的直方图而进行的图像增强方法。即将某幅影像或某一区域的直方图匹配到另一幅影像上。将图像的直方图分布规律与待匹配图像的脂肪分布规律近似。下面介绍两种方法：

1. 直方图匹配

def style_transfer(image, ref):

out = np.zeros_like(ref)

_, _, ch = image.shape

for i in range(ch):

print(i)

hist_img, _ = np.histogram(image[:, :, i], 256)

hist_ref, _ = np.histogram(ref[:, :, i], 256)

cdf_img = np.cumsum(hist_img)

cdf_ref = np.cumsum(hist_ref)

for j in range(256):

tmp = abs(cdf_img[j] - cdf_ref)

tmp = tmp.tolist()

idx = tmp.index(min(tmp)) # 找出tmp中最小的数，得到这个数的索引

out[:, :, i][ref[:, :, i] == j] = idx

return out

out = style_transfer(img, ref)

hist_img = cv2.calcHist([img], [0], None, [255], [0, 255])

hist_ref = cv2.calcHist([ref], [0], None, [255], [0, 255])

hist_out = cv2.calcHist([out], [0], None, [255], [0, 255])

plt.subplot(231)

plt.title("img")

plt.imshow(img)

plt.subplot(234)

plt.plot(hist_img)

plt.subplot(232)

plt.title("ref")

plt.imshow(ref)

plt.subplot(235)

plt.plot(hist_ref)

plt.subplot(233)

plt.title("out")

plt.imshow(out)

plt.subplot(236)

plt.plot(hist_out)

plt.show()

2. 针对灰度图优化

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

ref_gray = cv2.cvtColor(ref, cv2.COLOR_BGR2GRAY)

hist = cv2.calcHist([gray], [0], None, [256], [0, 256])

hist_ref = cv2.calcHist([ref_gray], [0], None, [256], [0, 256])

# 计算累计直方图

out = np.zeros_like(img)

tmp_ref = 0.0

h_ref = hist_ref.copy()

for i in range(256):

tmp_ref += h_ref[i]

h_ref[i] = tmp_ref

tmp = 0.0

h_acc = hist.copy()

for i in range(256):

tmp += hist[i]

h_acc[i] = tmp

# 计算映射

diff = np.zeros([256, 256])

for i in range(256):

for j in range(256):

diff[i][j] = np.fabs(h_ref[j] - h_acc[i])

M = np.zeros(256)

for i in range(256):

index = 0

min = diff[i][0] # min = 1.

for j in range(256):

if (diff[i][j] < min):

min = diff[i][j]

index = int(j)

M[i] = index

out = M[gray].astype(np.float32)

hist_img = cv2.calcHist([img], [0], None, [255], [0, 255])

hist_ref = cv2.calcHist([ref], [0], None, [255], [0, 255])

hist_out = cv2.calcHist([out], [0], None, [255], [0, 255])

plt.subplot(231)

plt.title("img")

plt.imshow(img)

plt.subplot(234)

plt.plot(hist_img)

plt.subplot(232)

plt.title("ref")

plt.imshow(ref)

plt.subplot(235)

plt.plot(hist_ref)

plt.subplot(233)

plt.title("out")

plt.imshow(out)

plt.subplot(236)

plt.plot(hist_out)

plt.show()