比较python中的LBP [英] Compare the LBP in python
问题描述
我生成了这样的纹理图像
I generated a texture image like this
我必须比较两个纹理.我使用了直方图比较方法.
I have to compare two textures. I have used histogram comparison method.
image_file = 'output_ori.png'
img_bgr = cv2.imread(image_file)
height, width, channel = img_bgr.shape
hist_lbp = cv2.calcHist([img_bgr], [0], None, [256], [0, 256])
print("second started")
image_fileNew = 'output_scan.png'
img_bgr_new = cv2.imread(image_fileNew)
height_new, width_new, channel_new = img_bgr_new.shape
print("second lbp")
hist_lbp_new = cv2.calcHist([img_bgr_new], [0], None, [256], [0, 256])
print("compar started")
compare = cv2.compareHist(hist_lbp, hist_lbp_new, cv2.HISTCMP_CORREL)
print(compare)
但是这种方法无效.对于两种不同的图像纹理，它显示出相似的结果.同样，它也没有显示太多变化来识别打印和打印.扫描效果.如何比较纹理?我想分析GLCM特性.
But this method is not effective. It shows similar results for two different image textures. Also it is not showing too much of variation to identify Print & Scan effect. How do I compare the textures? I thought of analysing the GLCM characteristics.
import cv2
import numpy as np
from skimage.feature import greycomatrix
img = cv2.imread('images/noised_img1.jpg', 0)
image = np.array(img, dtype=np.uint8)
g = greycomatrix(image, [1, 2], [0, np.pi/2], levels=4, normed=True, symmetric=True)
contrast = greycoprops(g, 'contrast')
print(contrast)
在这种方法中，我得到的输出为2 * 2矩阵.如何比较两个具有对比度，相似性，同质性，ASM，能量和相关性的矩阵?
In this method, I am getting the output as 2*2 matrix. How do I compare two matrices of several features like contrast, similarity, homogeneity, ASM, energy and correlation?
评论说明
import numpy as np
from PIL import Image
class LBP:
def __init__(self, input, num_processes, output):
# Convert the image to grayscale
self.image = Image.open(input).convert("L")
self.width = self.image.size[0]
self.height = self.image.size[1]
self.patterns = []
self.num_processes = num_processes
self.output = output
def execute(self):
self._process()
if self.output:
self._output()
def _process(self):
pixels = list(self.image.getdata())
pixels = [pixels[i * self.width:(i + 1) * self.width] for i in range(self.height)]
# Calculate LBP for each non-edge pixel
for i in range(1, self.height - 1):
# Cache only the rows we need (within the neighborhood)
previous_row = pixels[i - 1]
current_row = pixels[i]
next_row = pixels[i + 1]
for j in range(1, self.width - 1):
# Compare this pixel to its neighbors, starting at the top-left pixel and moving
# clockwise, and use bit operations to efficiently update the feature vector
pixel = current_row[j]
pattern = 0
pattern = pattern | (1 << 0) if pixel < previous_row[j-1] else pattern
pattern = pattern | (1 << 1) if pixel < previous_row[j] else pattern
pattern = pattern | (1 << 2) if pixel < previous_row[j+1] else pattern
pattern = pattern | (1 << 3) if pixel < current_row[j+1] else pattern
pattern = pattern | (1 << 4) if pixel < next_row[j+1] else pattern
pattern = pattern | (1 << 5) if pixel < next_row[j] else pattern
pattern = pattern | (1 << 6) if pixel < next_row[j-1] else pattern
pattern = pattern | (1 << 7) if pixel < current_row[j-1] else pattern
self.patterns.append(pattern)
def _output(self):
# Write the result to an image file
result_image = Image.new(self.image.mode, (self.width - 2, self.height - 2))
result_image.putdata(self.patterns)
result_image.save("output.png")
我用此代码生成了纹理.我有纹理，也有计算纹理属性的方法，但是问题是如何识别两个纹理之间的相似性.
I generated texture with this code. I have texture and I have methods to calculate the texture properties, but the question is how to identify the similarity between two textures.
推荐答案
假设您有两个类，例如 couscous 和针织品，并且您希望对<强烈，未知的彩色图像，例如蒸粗麦粉或针织品.一种可能的方法是:
Suppose you have two classes, for example couscous and knitwear, and you wish to classify an unknown color image as either couscous or knitwear. One possible method would be:
- 将彩色图像转换为灰度图像.
- 计算本地二进制模式.
- 计算局部二进制模式的归一化直方图.
以下代码段实现了这种方法:
The following snippet implements this approach:
import numpy as np
from skimage import io, color
from skimage.feature import local_binary_pattern
def lbp_histogram(color_image):
img = color.rgb2gray(color_image)
patterns = local_binary_pattern(img, 8, 1)
hist, _ = np.histogram(patterns, bins=np.arange(2**8 + 1), density=True)
return hist
couscous = io.imread('https://i.stack.imgur.com/u3xLI.png')
knitwear = io.imread('https://i.stack.imgur.com/Zj14J.png')
unknown = io.imread('https://i.stack.imgur.com/JwP3j.png')
couscous_feats = lbp_histogram(couscous)
knitwear_feats = lbp_histogram(knitwear)
unknown_feats = lbp_histogram(unknown)
然后，您需要测量未知图像的LBP直方图和代表两个已考虑类别的图像的直方图之间的相似度(或相异度).直方图之间的欧式距离是一种流行的差异度量.
Then you need to measure the similarity (or dissimilarity) between the LBP histogram of the unknown image and the histograms of the images that represent the two considered classes. Euclidean distance between histograms is a popular dissimilarity measure.
In [63]: from scipy.spatial.distance import euclidean
In [64]: euclidean(unknown_feats, couscous_feats)
Out[64]: 0.10165884804845844
In [65]: euclidean(unknown_feats, knitwear_feats)
Out[65]: 0.0887492936776889
在此示例中，未知图像将被归类为针织品，因为相异度 unknown-couscous 大于相异度 unknown-knitwear .这与未知图像实际上是另一种针织品的事实完全吻合.
In this example the unknown image will be classified as knitwear because the dissimilarity unknown-couscous is greater than the dissimilarity unknown-knitwear. This is in good agreement with the fact that the unknown image is actually a different type of knitwear.
import matplotlib.pyplot as plt
hmax = max([couscous_feats.max(), knitwear_feats.max(), unknown_feats.max()])
fig, ax = plt.subplots(2, 3)
ax[0, 0].imshow(couscous)
ax[0, 0].axis('off')
ax[0, 0].set_title('Cous cous')
ax[1, 0].plot(couscous_feats)
ax[1, 0].set_ylim([0, hmax])
ax[0, 1].imshow(knitwear)
ax[0, 1].axis('off')
ax[0, 1].set_title('Knitwear')
ax[1, 1].plot(knitwear_feats)
ax[1, 1].set_ylim([0, hmax])
ax[1, 1].axes.yaxis.set_ticklabels([])
ax[0, 2].imshow(unknown)
ax[0, 2].axis('off')
ax[0, 2].set_title('Unknown (knitwear)')
ax[1, 2].plot(unknown_feats)
ax[1, 1].set_ylim([0, hmax])
ax[1, 2].axes.yaxis.set_ticklabels([])
plt.show(fig)
这篇关于比较python中的LBP的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
