import cv2
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
def get_LBP(gray_image, ratio=3):
img_lbp = np.zeros_like(gray_image)
img_h, img_w = gray_image.shape
center_p = ratio // 2
for ih in range(0,img_h - ratio):
for iw in range(0,img_w - ratio):
img = gray_image[ih:ih+ratio,iw:iw+ratio]
center = img[center_p,center_p]
img_binary = (img >= center)*1.0
binary_vector = img_binary.flatten()
binary_vector = np.delete(binary_vector, 4)
where_binary_one = np.where(binary_vector)[0]
if len(where_binary_one) >= 1:
num = np.sum(2**where_binary_one)
else:
num = 0
img_lbp[ih+1,iw+1] = num
return img_lbp
img = cv2.imread('trump.jpg')
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img_lbp = get_LBP(img_gray)
```python
vecimg_lbp = img_lbp.flatten()
fig = plt.figure(figsize=(20,8))
ax = fig.add_subplot(1,3,1)
ax.imshow(img_gray, 'gray')
ax.set_title('gray')
ax = fig.add_subplot(1,3,2)
ax.imshow(img_lbp, 'gray')
ax.set_title('LBP')
ax = fig.add_subplot(1,3,3)
freq,lbp, _ = ax.hist(vecimg_lbp,bins=255)
ax.set_title('LBP histogram')
def binary_min(binary): #为了让LBP具有旋转不变性，将二进制串进行最小化处理
length = len(binary)
min_b = []
for i in range(length)[::-1]: # [::-1]代表翻转
if binary[i] == 0:
binary = binary[:i] # [:i]代表切片，i为停止位置
min_b = np.append(min_b, 0)
else:
min_b = np.append(min_b, binary)
return np.array(min_b, dtype=np.uint8)
demo = np.array([0, 1, 1, 1, 0, 1, 0, 0])
binary_min(demo)
Processed:
0.030, SQL:
9
