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

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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

2024-05-22 16:31| 来源: 网络整理| 查看: 265

目录 1，图像特征 2，角点特征 3，使用OpenCV和PIL进行特征提取和可视化 4，特征匹配 5，图像拼接

图像特征

什么是图像特征？

| 特征

·对图像进行描述。

·指出图像中的相关信息。

·将一组图像与其他图像区分开来。

| 类别

·全局特征：将图像视为一个整体。

·局部特征：图像中的部分区域。

| 正式定义

在计算机视觉和图像处理中，特征指的是为解决与某一应用有关的计算任务的一段信息。

·所有的机器学习和深度学习算法都依赖于特征。

什么能作为特征？

·单独的像素点强度不能作为特征

| 特征

·总体强度测量平均值，直方图，调色板等。

·边缘和山脊梯度和轮廓。

·特殊的角点特征和曲率。

·斑点和纹理。

·使用过滤器获得的特征。

例子

1，像素点强度

像素点强度组合作为特征

2，边缘

边缘作为特征

3，关键点

关键点作为特征

什么是好的特征？

那些受外部因素影响不大的特征是好的特征。

| 特征不变性

·伸缩

·旋转

·转换

·透视法

·仿射

·颜色

·照度

角点特征

·角：定义为两条边的交点。·关键点：图像中具有明确位置且可以可靠检测的点。

| 适用范围

·运动检测和视频跟踪。·图像注册。·图像拼接和全景拼接。·3D建模和对象识别。

例子

1，关键点

关键点识别

2，角

角识别

Harris角点检测

| Harris角点检测算法可以分为5个步骤

·转化为灰度图

·计算空间导数

·设置结构向量

·计算Harris响应

·抑制非最大值

| 使用OpenCV实现Harris角点检测

代码语言：javascript复制 ''''' Harris Corners using OpenCV ''' %matplotlib inline import numpy as np import cv2 from matplotlib import pyplot as plt img = cv2.imread("imgs/chapter9/chess_slant.jpg", 1) #img = cv2.resize(img, (96, 96)) gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) gray = np.float32(gray) ################################FOCUS############################### dst = cv2.cornerHarris(gray,2,3,0.04) #################################################################### # Self-study: Parameters plt.figure(figsize=(8, 8)) plt.imshow(dst, cmap="gray") plt.show()

代码语言：javascript复制''''' result is dilated for marking the corners ''' dst = cv2.dilate(dst,None) plt.figure(figsize=(8, 8)) plt.imshow(dst, cmap="gray") plt.show()

代码语言：javascript复制 ''''' Threshold for an optimal value, it may vary depending on the image. We first calculate what is the maximum and minimum value of pixel in this image ''' max_val = np.uint8(dst).max() min_val = np.uint8(dst).min() print("max_val = {}".format(max_val)) print("min_val = {}".format(min_val))

输出

代码语言：javascript复制max_val = 255 min_val = 0 img = cv2.imread("imgs/chapter9/chess_slant.jpg", 1) img[dst>0.1*dst.max()]=[0,0,255] plt.figure(figsize=(8, 8)) plt.imshow(img[:,:,::-1]) plt.show()

|利用OpenCV-harris角点求角点坐标

代码语言：javascript复制%matplotlib inline import numpy as np import cv2 from matplotlib import pyplot as plt img = cv2.imread("imgs/chapter9/chess_slant.jpg", 1); #img = cv2.resize(img, (96, 96)) gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) # find Harris corners gray = np.float32(gray) dst = cv2.cornerHarris(gray,2,3,0.04) dst = cv2.dilate(dst,None) ret, dst = cv2.threshold(dst,0.01*dst.max(),255,0) dst = np.uint8(dst) # find centroids ret, labels, stats, centroids = cv2.connectedComponentsWithStats(dst) # define the criteria to stop and refine the corners # Explain Criteria criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 100, 0.001) corners = cv2.cornerSubPix(gray,np.float32(centroids),(5,5),(-1,-1),criteria) # Now draw them res = np.hstack((centroids,corners)) res = np.int0(res) for x1, y1, x2, y2 in res: #cv2.circle(img,(x1, y1), 5, (0,255,0), -1) # Point Centroids cv2.circle(img,(x2, y2), 10, (0,0,255), -1) # Point corners #img[res[:,1],res[:,0]]=[0,0,255] #img[res[:,3],res[:,2]] = [0,255,0] plt.figure(figsize=(8, 8)) plt.imshow(img[:,:,::-1]) plt.show()

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。

代码语言：javascript复制%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。

代码语言：javascript复制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

代码语言：javascript复制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

代码语言：javascript复制%matplotlib inline import numpy as np import skimage import skimage.feature import cv2 from matplotlib import pyplot as plt img = 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

代码语言：javascript复制代码语言：javascript复制import numpyasnp import skimage import skimage.feature import cv2 from matplotlibimport pyplot as plt img = 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()代码语言：javascript复制

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

代码语言：javascript复制''' Source: https://scikitimage.org/docs/dev/auto_examples/features_detection/plot_glcm.html'''importmatplotlib.pyplotasplt代码语言：javascript复制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()代码语言：javascript复制

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。代码语言：javascript复制代码语言：javascript复制''' Source: https://scikitimage.org/docs/dev/auto_examples/features_detection/plot_haar.html ''' # Haar like feature Descriptors代码语言：javascript复制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()代码语言：javascript复制

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

代码语言：javascript复制import numpyas np import skimage import skimage.feature import cv2 from matplotlib import pyplot as plt img = 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

代码语言：javascript复制import numpyas np import skimage import skimage.feature import cv2 import math from 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

'''

代码语言：javascript复制from mathimport sqrt fromskimage import data fromskimage.feature import blob_dog, blob_log, blob_doh fromskimage.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==3.4.2.16 2.pip install opencv-python==3.4.2.16 '''

代码语言：javascript复制import numpy as np import cv2 print(cv2.__version__) from matplotlib import pyplot as plt img = 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：

使用Skimage实现CENSURE

代码语言：javascript复制import numpyas np import cv2 import skimage.feature from matplotlib import pyplot as plt img = 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）代码语言：javascript复制代码语言：javascript复制''' 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代码语言：javascript复制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()代码语言：javascript复制

BRIEF Features（二进制鲁棒独立的基本特征）

二进制鲁棒的独立基本特征在很多情况下，在速度和识别率方面都优于其他快速描述子，如SURF和SIFT。

步骤：

使用高斯核平滑图像转换为二进制特征向量

使用Opencv实现BRIEF

代码语言：javascript复制import numpyas np import cv2 from matplotlib import pyplot as plt img = cv2.imread("imgs/chapter9/indoor.jpg", 1); gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) # Initiate FAST detector star = cv2.xfeatures2d.StarDetector_create() kp = star.detect(gray,None) brief = cv2.xfeatures2d.BriefDescriptorExtractor_create() keypoints, descriptors = brief.compute(gray, kp) 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()

BRISK Features（二进制鲁棒不变可扩展关键点特征）

（Binary RobustIndependent Elementary Features.）原文给的这个但BRISK全称应该是Binary Robust Invariant Scalable Keypoints

由三部分组成

采样模式：在描述子周围的位置采样

方向补偿:对关键点的方向和旋转进行某种机制的补偿。抽样对:构建最终描述符时要比较哪些对。代码语言：javascript复制import numpy as np import cv2 from matplotlib import pyplot as plt img = cv2.imread("imgs/chapter9/indoor.jpg", 1); gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) brisk = cv2.BRISK_create() keypoints, descriptors = brisk.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()

KAZE and Accelerated-KAZE features（KAZE是日语风的谐音）

使用OpenCV实现KAZE

代码语言：javascript复制import numpyas np import cv2 from matplotlib import pyplot as plt img = cv2.imread("imgs/chapter9/indoor.jpg", 1); gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) kaze = cv2.KAZE_create() keypoints, descriptors = kaze.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()

AKAZE Features

使用OpenCV实现AKAZE

代码语言：javascript复制import numpyas np import cv2 from matplotlib import pyplot as plt img = cv2.imread("imgs/chapter9/indoor.jpg", 1); gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) akaze = cv2.AKAZE_create() keypoints, descriptors = akaze.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()

ORB Features

面向快速鲁棒的BRIEF特征

使用OpenCV实现Orb

代码语言：javascript复制import numpyas np import cv2 from matplotlib import pyplot as plt img = cv2.imread("imgs/chapter9/indoor.jpg", 1) gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) orb = cv2.ORB_create(500) keypoints, descriptors = orb.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()

特征匹配

用于识别多幅图像中的相似特征。用于目标检测。

方法

1.暴力算法

将图像1中的每个特征与图像2中的每个特征逐个进行匹配

2.基于FLANN（快速最近邻开源库）的匹配

快速最近邻开源库它包含一组算法，这些算法针对大型数据集中的快速最近邻搜索和高维特征进行了优化。

使用Opencv实现特征匹配

''' Using Brute-Force matching '''

代码语言：javascript复制import numpy as np import cv2 from matplotlib import pyplot as plt orb = cv2.ORB_create(500) img1 = cv2.imread("imgs/chapter9/indoor_lamp.jpg", 1); img1 = cv2.resize(img1, (256, 256)); gray1 = cv2.cvtColor(img1,cv2.COLOR_BGR2GRAY); img2 = cv2.imread("imgs/chapter9/indoor.jpg", 1); img2 = cv2.resize(img2, (640, 480)); gray2 = cv2.cvtColor(img2,cv2.COLOR_BGR2GRAY); # find the keypoints anddescriptors with SIFT kp1, des1 = orb.detectAndCompute(img1,None) kp2, des2 = orb.detectAndCompute(img2,None) # BFMatcher with default params bf = cv2.BFMatcher() matches = bf.knnMatch(des1,des2,k=2) # Apply ratio test good = [] for m,n in matches: if m.distance< 0.75*n.distance: good.append([m]) # cv.drawMatchesKnn expects list oflists as matches. img3 = cv2.drawMatchesKnn(img1,kp1,img2,kp2,good,None,flags=cv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS) plt.figure(figsize=(15, 15)) plt.imshow(img3[:,:,::-1]) plt.show()

使用Opencv实现基于Flann特征匹配

代码语言：javascript复制''' Using Flann-based matching on ORB features代码语言：javascript复制import numpy as np import cv2 from matplotlibimport pyplot as plt import imutils orb = cv2.ORB_create(500) img1 = cv2.imread("imgs/chapter9/indoor_lamp.jpg", 1); img1 = cv2.resize(img1, (256, 256)); gray1 = cv2.cvtColor(img1,cv2.COLOR_BGR2GRAY); img2 = cv2.imread("imgs/chapter9/indoor.jpg", 1); img2 = cv2.resize(img2, (640, 480)); gray2 = cv2.cvtColor(img2,cv2.COLOR_BGR2GRAY); # find the keypoints and descriptors with SIFT kp1, des1 = orb.detectAndCompute(img1,None) kp2, des2 = orb.detectAndCompute(img2,None) # FLANN parameters FLANN_INDEX_LSH = 6 index_params= dict(algorithm = FLANN_INDEX_LSH, table_number = 6, # 12 key_size = 12, # 20 multi_probe_level = 1) #2 search_params = dict(checks=50) # or pass empty dictionary flann = cv2.FlannBasedMatcher(index_params,search_params) matches = flann.knnMatch(des1,des2,k=2) # Need to draw only good matches, so create a mask matchesMask = [[0,0] for i in range(len(matches))] # ratio test as per Lowe's paperfor i,(m,n) in enumerate(matches): if m.distance < 0.7*n.distance: matchesMask[i]=[1,0] draw_params = dict(matchColor = (0,255,0), singlePointColor = (255,0,0), matchesMask = matchesMask, flags = cv2.DrawMatchesFlags_DEFAULT) img3 = cv2.drawMatchesKnn(img1,kp1,img2,kp2,matches,None,**draw_params) plt.figure(figsize=(15, 15)) plt.imshow(img3[:,:,::-1]) plt.show()代码语言：javascript复制

图像拼接

图像拼接或照片拼接是将多个摄影图像中重叠的视野相结合，产生一个分段的全景图或高分辨率图像的过程。

使用OpenCV来实现图像拼接

代码语言：javascript复制代码语言：javascript复制from matplotlib import pyplot as plt %matplotlib inline import cv2 import numpy as np import argparse import sys modes = (cv2.Stitcher_PANORAMA, cv2.Stitcher_SCANS)# read input imagesimgs = [cv2.imread("imgs/chapter9/left.jpeg", 1),cv2.imread("imgs/chapter9/right.jpeg", 1)]stitcher = cv2.Stitcher.create(cv2.Stitcher_PANORAMA) status, pano = stitcher.stitch(imgs)f = plt.figure(figsize=(15,15)) f.add_subplot(1, 2, 1).set_title('Left Image') plt.imshow(imgs[0][:,:,::-1]) f.add_subplot(1, 2, 2).set_title('Right Image') plt.imshow(imgs[1][:,:,::-1]) plt.show()

代码语言：javascript复制plt.figure(figsize=(15, 15)) plt.imshow(pano[:,:,::-1]) plt.show()代码语言：javascript复制

作者：Abhishek和Akash

deephub翻译组：tensor-zhang，gkkkkkk

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