gpt4 book ai didi

python - 比较python中的LBP

转载 作者:太空狗 更新时间:2023-10-30 01:31:24 27 4
gpt4 key购买 nike

我生成了这样的纹理图像

我必须比较两个纹理。我使用了直方图比较方法。

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)

但是这个方法并不奏效。它显示了两种不同图像纹理的相似结果。它也没有显示出太多的变化来识别打印和扫描效果。我如何比较纹理?我想到了分析 GLCM 的特性。

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,我得到的输出是 2*2 矩阵。我如何比较具有对比度、相似性、同质性、ASM、能量和相关性等几个特征的两个矩阵?

评论澄清

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")

我用这段代码生成了纹理。我有纹理,也有计算纹理属性的方法,但问题是如何识别两个纹理之间的相似性。

最佳答案

假设您有两个类别,例如 couscousknitwear,并且您希望将一张未知 颜色图像分类为 couscous 或 knitwear .一种可能的方法是:

  1. 将彩色图像转换为灰度图像。
  2. 计算局部二进制模式。
  3. 计算局部二进制模式的归一化直方图。

以下代码片段实现了这种方法:

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('/image/u3xLI.png')
knitwear = io.imread('/image/Zj14J.png')
unknown = io.imread('/image/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 直方图与表示所考虑的两个类别的图像直方图之间的相似性(或相异性)。直方图之间的欧氏距离 是一种流行的相异性度量。

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 unknown-couscous 的差异性大于 unknown-knitwear 的差异性。这与未知图像实际上是不同类型的针织品这一事实非常吻合。

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)

Plots

关于python - 比较python中的LBP,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/51239715/

27 4 0
Copyright 2021 - 2024 cfsdn All Rights Reserved 蜀ICP备2022000587号
广告合作:1813099741@qq.com 6ren.com