熟练掌握CV中最基础的概念：图像特征，看这篇万字的长文就够了（二）-阿里云开发者社区

Shi-Tomasi 角点检测

·Harris角点检测有一个角点选择准则。

·为每个像素计算一个分数，如果分数高于某个值，则将该像素标记为角点。

·Shi和Tomasi建议取消这项功能。只有特征值可以用来检查像素是否是角点。

·这些角点是对harris角点的一次升级。

%matplotlib inline  import numpy as np  import cv2   from matplotlib import pyplot as plt  img = cv2.imread("imgs/chapter9/shape.jpg", 1)  gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)  ################################FOCUS###############################  corners = cv2.goodFeaturesToTrack(gray,25,0.01,10)  # Self-study: Parameters  corners = np.int0(corners)  ####################################################################  ################################FOCUS###############################  for i in corners:       x,y = i.ravel()       cv2.circle(img,(x,y), 5,(0, 125, 125),-1)  ################################################################  plt.figure(figsize=(8, 8))  plt.imshow(img[:,:,::-1])  plt.show()

OpenCV快速角点检测

·选择一个像素，让它的强度为i。

·选择阈值t。

·如上图所示，在像素周围画一个16像素的圆。

·现在，如果圆中存在一组n个连续像素（16个像素），且这些像素都比i+t强度更大，或者都比i-t强度更小，则像素p是一个角点。

import cv2  from matplotlib import pyplot as plt  img = cv2.imread('imgs/chapter9/tessellate.png',1)  img2 = img.copy()  img3 = img.copy()  ###############################FOCUS################################   # Initiate FAST object with default values  fast = cv2.FastFeatureDetector_create()  # find and draw the keypoints  kp = fast.detect(img,None)  ####################################################################  for i in kp:      x,y = int(i.pt[0]), int(i.pt[1])      cv2.circle(img2,(x,y), 5,(0, 255, 0),-1)  # Print all default params  print ("Threshold: ", fast.getThreshold())  print ("nonmaxSuppression: ", fast.getNonmaxSuppression())  print ("neighborhood: ", fast.getType())  print ("Total Keypoints with nonmaxSuppression: ", len(kp))  # Disable nonmaxSuppression  fast.setNonmaxSuppression(0)  kp = fast.detect(img,None)  print ("Total Keypoints without nonmaxSuppression: ", len(kp))  for i in kp:      x,y = int(i.pt[0]), int(i.pt[1])      cv2.circle(img3,(x,y), 5,(0, 255, 0),-1)  f = plt.figure(figsize=(15,15))  f.add_subplot(2, 1, 1).set_title('Corners with non-maximal-suppression')  plt.imshow(img2[:, :,::-1])  f.add_subplot(2, 1, 2).set_title('Corners without non-maximal-suppression')  plt.imshow(img3[:, :,::-1])  plt.show()

输出

Threshold:  10   nonmaxSuppression:  True   neighborhood:  2   Total Keypoints with nonmaxSuppression:  225   Total Keypoints without nonmaxSuppression:  1633

使用OpenCV和PIL进行特征提取和可视化

A级特征

HoG features（方向梯度直方图特征）

梯度直方图
用于目标检测

步骤

查找x和y方向上的梯度
使用梯度大小和方向将梯度归类为直方图。

HoG特征对图像中物体的旋转很敏感。

使用Skimage来实现HoG

%matplotlib inlineimport numpy as npimport skimageimport skimage.featureimport cv2from matplotlib import pyplot as pltimg = cv2.imread("imgs/chapter9/plant.jpg", 1)features, output = skimage.feature.hog(img, orientations=9, pixels_per_cell=(8,8),cells_per_block=(3, 3), block_norm='L2-Hys',visualize=True,transform_sqrt=False, feature_vector=True,multichannel=None)print(features.shape)# Rescale histogram for betterdisplayoutput = skimage.exposure.rescale_intensity(output, in_range=(0, 10))f =plt.figure(figsize=(15,15))f.add_subplot(2, 1, 1).set_title('Original Image')plt.imshow(img[:, :,::-1])f.add_subplot(2, 1, 2).set_title('Features')plt.imshow(output)plt.show()

输出

(322218,)

Daisy Features（面向稠密特征提取的可快速计算的局部图像特征描述子）

升级版的HOG特征
创建一个不适合可视化的稠密特征向量

步骤

T块->计算梯度或梯度直方图
S块->使用高斯加权加法（轮廓）组合T块特征
N块->归一化添加的特征（使范围在0-1之间）
D块->缩小特征维数（PCA算法）
Q块->压缩特征用于存储

使用Sklearn实现面向稠密特征提取的可快速计算的局部图像特征描述子

import numpyasnpimport skimageimport skimage.featureimport cv2
from matplotlibimport pyplot as pltimg = cv2.imread("imgs/chapter9/plant.jpg", 0)#img = cv2.resize(img, (img.shape[0]//4,img.shape[1]//4))features, output = skimage.feature.daisy(img, step=180, radius=58, rings=2, histograms=8, orientations=9, visualize=True)print(features.shape)f = plt.figure(figsize=(15,15))f.add_subplot(2, 1, 1).set_title('Original Image');plt.imshow(img, cmap="gray")f.add_subplot(2, 1, 2).set_title('Features');plt.imshow(output);plt.show()

输出

(2, 3, 153)

GLCM Features（灰度共生矩阵特征）

灰度协方差矩阵
计算各像素在不同方面(同质性、均匀性)的整体平均相关程度。
灰度共生矩阵(GLCM)通过计算灰度像素i与j在特定空间关系中出现的频率。

使用Skimage实现GLCM

'''

Source: https://scikitimage.org/docs/dev/auto_examples/features_detection/plot_glcm.html'''importmatplotlib.pyplotasplt

from skimage.featureimport greycomatrix, greycoprops
from skimageimport data
PATCH_SIZE = 21
# open the camera image
image = data.camera()
# select some patches from grassy areas of the image
grass_locations = [(474, 291), (440, 433), (466, 18), (462, 236)]
grass_patches = []
for loc in grass_locations:
    grass_patches.append(image[loc[0]:loc[0] + PATCH_SIZE,
                               loc[1]:loc[1] + PATCH_SIZE])
# select some patches from sky areas of the image
sky_locations = [(54, 48), (21, 233), (90, 380), (195, 330)]
sky_patches = []
for loc in sky_locations:
    sky_patches.append(image[loc[0]:loc[0] + PATCH_SIZE,
                             loc[1]:loc[1] + PATCH_SIZE])
# compute some GLCM properties each patch
xs = []
ys = []
for patch in (grass_patches + sky_patches):
    glcm = greycomatrix(patch, [5], [0], 256, symmetric=True, normed=True)
    xs.append(greycoprops(glcm, 'dissimilarity')[0, 0])
    ys.append(greycoprops(glcm, 'correlation')[0, 0])
# create the figure
fig = plt.figure(figsize=(8, 8))
# display original image with locations of patches
ax = fig.add_subplot(3, 2, 1)
ax.imshow(image, cmap=plt.cm.gray, interpolation='nearest',
          vmin=0, vmax=255)
for (y, x) in grass_locations:
    ax.plot(x + PATCH_SIZE / 2, y + PATCH_SIZE / 2, 'gs')
for (y, x) in sky_locations:
    ax.plot(x + PATCH_SIZE / 2, y + PATCH_SIZE / 2, 'bs')
ax.set_xlabel('Original Image')
ax.set_xticks([])
ax.set_yticks([])
ax.axis('image')
# for each patch, plot (dissimilarity, correlation)
ax = fig.add_subplot(3, 2, 2)
ax.plot(xs[:len(grass_patches)], ys[:len(grass_patches)], 'go',
        label='Grass')
ax.plot(xs[len(grass_patches):], ys[len(grass_patches):], 'bo',
        label='Sky')
ax.set_xlabel('GLCM Dissimilarity')
ax.set_ylabel('GLCM Correlation')
ax.legend()
# display the image patchesfor i, patch in enumerate(grass_patches):
    ax = fig.add_subplot(3, len(grass_patches), len(grass_patches)*1 + i + 1)
    ax.imshow(patch, cmap=plt.cm.gray, interpolation='nearest',
              vmin=0, vmax=255)
    ax.set_xlabel('Grass %d' % (i + 1))
for i, patch in enumerate(sky_patches):
    ax = fig.add_subplot(3, len(sky_patches), len(sky_patches)*2 + i + 1)
    ax.imshow(patch, cmap=plt.cm.gray, interpolation='nearest',
              vmin=0, vmax=255)
    ax.set_xlabel('Sky %d' % (i + 1))
# display the patches and plotfig.suptitle('Grey level co-occurrence matrix features', fontsize=14)
plt.show()

HAAR Features（哈尔特征）

用于物体识别

哈尔矩形特征

矩形内区域像素之和的差值。
每个特征都是一个单独的值，由白色矩形下的像素之和减去黑色矩形下的像素之和得到。

'''

Source:

https://scikitimage.org/docs/dev/auto_examples/features_detection/plot_haar.html

''' # Haar like feature Descriptors

importnumpyasnp
importmatplotlib.pyplotasplt
importskimage.feature
fromskimage.featureimport haar_like_feature_coord, draw_haar_like_feature 
images = [np.zeros((2, 2)), np.zeros((2, 2)),np.zeros((3, 3)), np.zeros((3, 3)),np.zeros((2, 2))] 
feature_types = ['type-2-x', 'type-2-y','type-3-x', 'type-3-y',                  'type-4'] 
fig, axs = plt.subplots(3, 2)
for ax, img, feat_t in zip(np.ravel(axs), images, feature_types):
    coord, _ = haar_like_feature_coord(img.shape[0],
                                       img.shape[1],
                                       feat_t)    
    haar_feature = draw_haar_like_feature(img, 0, 0,       img.shape[0],img.shape[1],coord,max_n_features=1,                       random_state=0)    
    ax.imshow(haar_feature)    
    ax.set_title(feat_t)    
    ax.set_xticks([])    
    ax.set_yticks([])  fig.suptitle('The different Haar-like feature descriptors') plt.axis('off')
plt.show()

LBP Features（局部二值模式特征）

局部二值模式

要素

LBP阈值
特征求和

import numpyas npimport skimageimport skimage.featureimport cv2from matplotlib import pyplot as pltimg = cv2.imread("imgs/chapter9/plant.jpg", 0);#img = cv2.resize(img,(img.shape[0]//4,img.shape[1]//4));
output = skimage.feature.local_binary_pattern(img, 3, 8, method='default')print(features.shape);
# Rescale histogram for betterdisplay#output = skimage.exposure.rescale_intensity(output,in_range=(0, 10))
f = plt.figure(figsize=(15,15))f.add_subplot(2, 1, 1).set_title('Original Image');plt.imshow(img, cmap="gray")f.add_subplot(2, 1, 2).set_title('Features');plt.imshow(output);plt.show()

输出

(2,3, 153)

Blobs as features（斑点作为特征）

Blob检测方法的目的是检测数字图像中与周围区域相比具有不同属性(如亮度或颜色)的区域。
通俗地说，blob是图像的一个区域，其中一些属性是常量或近似常量，可以认为blob中的所有点在某种意义上是相似的。

使用Skimage来实现Blobs

import numpyas npimport skimageimport skimage.featureimport cv2import mathfrom matplotlib import pyplot as plt
img = cv2.imread("imgs/chapter9/shape.jpg", 0)#img =skimage.data.hubble_deep_field()[0:500, 0:500]#image_gray =skimage.color.rgb2gray(image)
blobs = skimage.feature.blob_dog(img, max_sigma=5, threshold=0.05)
blobs[:, 2] = blobs[:, 2]print(blobs.shape)
for y , x, r in blobs:cv2.circle(img,(int(x), int(y)), int(r), (0,255,0), 1)
f = plt.figure(figsize=(15,15))f.add_subplot(2, 1, 1).set_title('Original Image');plt.imshow(img, cmap="gray")plt.show()

输出

(91, 3)

使用Blobs检测外太空星系

'''

Source:

https://scikitimage.org/docs/dev/auto_examples/features_detection/plot_blob.html

'''

from mathimport sqrtfromskimage import datafromskimage.feature import blob_dog, blob_log, blob_dohfromskimage.color import rgb2gray importmatplotlib.pyplot as plt  image =data.hubble_deep_field()[0:500, 0:500]image_gray= rgb2gray(image) blobs_log= blob_log(image_gray, max_sigma=30, num_sigma=10, threshold=.1) # Computeradii in the 3rd column.blobs_log[:,2] = blobs_log[:, 2] * sqrt(2) blobs_dog= blob_dog(image_gray, max_sigma=30, threshold=.1)blobs_dog[:,2] = blobs_dog[:, 2] * sqrt(2) blobs_doh= blob_doh(image_gray, max_sigma=30, threshold=.01) blobs_list= [blobs_log, blobs_dog, blobs_doh]colors =['yellow', 'lime', 'red']titles =['Laplacian of Gaussian', 'Difference of Gaussian',          'Determinant of Hessian']sequence= zip(blobs_list, colors, titles) fig, axes= plt.subplots(3, 1, figsize=(15, 15), sharex=True, sharey=True)ax =axes.ravel()for idx,(blobs, color, title) in enumerate(sequence):    ax[idx].set_title(title)    ax[idx].imshow(image,interpolation='nearest')    for blob in blobs:        y, x, r = blob        c = plt.Circle((x, y), r, color=color,linewidth=2, fill=False)        ax[idx].add_patch(c)    ax[idx].set_axis_off() plt.tight_layout()plt.show()

B级特征

SIFT Features（尺度不变特征变换特征）

尺度不变特征变换
专利持有人是加拿大的UBC大学

SIFT具有特征如下：

尺度不变性（图像尺度变化，提取的特征不变）
旋转不变性（图像旋转，提取的特征不变）
亮度不变性（亮度发生改变，提取的特征不变）
视角不变性（视角变化，提取的特征不变）

SIFT提取步骤

构建规模空间金字塔
使用近似LOG算子处理特征和梯度
在高斯图像的差分中找出最大和最小的关键点。
剔除非关键点
给关键点分配一个方向。
生成最终的SIFT特征—为缩放和旋转不变性生成一个新的表示。

使用OpenCV实现SIFT

'''
NOTE: Patented work. Cannot be used for commercial purposes1.pip installopencv-contrib-python==3.4.2.162.pip install opencv-python==3.4.2.16
'''

import numpy as npimport cv2print(cv2.__version__)from matplotlib import pyplot as pltimg = cv2.imread("imgs/chapter9/indoor.jpg", 1);
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
sift = cv2.xfeatures2d.SIFT_create()keypoints, descriptors = sift.detectAndCompute(gray, None)
for i in keypoints:    x,y = int(i.pt[0]), int(i.pt[1])    cv2.circle(img,(x,y), 5,(0, 125,125),-1)
plt.figure(figsize=(8, 8))plt.imshow(img[:,:,::-1]);plt.show()

CenSurE Features（中心环绕特征）

中心环绕用于实时特征检测
胜过许多其他关键点检测器和特征提取器。

CenSureE具有特征如下：

尺度不变性（图像尺度变化，提取的特征不变）
旋转不变性（图像旋转，提取的特征不变）
亮度不变性（亮度发生改变，提取的特征不变）
视角不变性（视角变化，提取的特征不变）

使用Skimage实现CENSURE

import numpyas npimport cv2import skimage.featurefrom matplotlib import pyplot as pltimg = cv2.imread("imgs/chapter9/indoor.jpg", 1);
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
detector = skimage.feature.CENSURE(min_scale=1, max_scale=7, mode='Star',non_max_threshold=0.05, line_threshold=10)
detector.detect(gray)
for i in detector.keypoints:x,y = int(i[1]), int(i[0])cv2.circle(img,(x,y), 5,(0, 125, 125),-1)
plt.figure(figsize=(8, 8))plt.imshow(img[:,:,::-1]);plt.show()

SURFFeatures（快速鲁棒特征）

快速鲁棒特征
已获得专利的局部特征检测器和特征描述子
标准版本的SURF比SIFT要快好几倍

SURF使用的算法：

Hessian斑点检测器行列式的整数近似值
Haar小波响应之和
多分辨率金字塔

SUFR特性有：

尺度不变性（图像尺度变化，提取的特征不变）
旋转不变性（图像旋转，提取的特征不变）
视角不变性（视角变化，提取的特征不变）

'''

NOTE: Patented work. Cannot be used for commercial purposes

1.pip install opencv-contrib-python==3.4.2.16

2.pip install opencv-python==3.4.2.16

import numpy as np
importcv2
frommatplotlibimport pyplot as plt
img = cv2.imread("imgs/chapter9/indoor.jpg", 1);
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
surf = cv2.xfeatures2d.SURF_create(1000)
keypoints, descriptors = surf.detectAndCompute(gray, None)
for i in keypoints:
    x,y = int(i.pt[0]), int(i.pt[1])
    cv2.circle(img,(x,y), 5,(0, 125, 125),-1)
plt.figure(figsize=(8, 8))
plt.imshow(img[:,:,::-1]);
plt.show()

熟练掌握CV中最基础的概念：图像特征，看这篇万字的长文就够了（二）

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