├── Image ├── GLCM.png ├── LBP.png ├── origin.png ├── histogram.png ├── RGB-histogram.png └── gray-histogram.png ├── 纹理特征 ├── GLCM │ ├── sample.py │ ├── plot.py │ └── fast_glcm.py └── LBP │ ├── get_uniform_map.py │ ├── get_resolve_map.py │ └── get_LBP_from_Image.py ├── 颜色特征 ├── Color Moment.py └── Color-Histograms.py ├── .gitignore ├── README.md └── LICENSE /Image/GLCM.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/1044197988/Python-Image-feature-extraction/HEAD/Image/GLCM.png -------------------------------------------------------------------------------- /Image/LBP.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/1044197988/Python-Image-feature-extraction/HEAD/Image/LBP.png -------------------------------------------------------------------------------- /Image/origin.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/1044197988/Python-Image-feature-extraction/HEAD/Image/origin.png -------------------------------------------------------------------------------- /Image/histogram.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/1044197988/Python-Image-feature-extraction/HEAD/Image/histogram.png -------------------------------------------------------------------------------- /Image/RGB-histogram.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/1044197988/Python-Image-feature-extraction/HEAD/Image/RGB-histogram.png -------------------------------------------------------------------------------- /Image/gray-histogram.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/1044197988/Python-Image-feature-extraction/HEAD/Image/gray-histogram.png -------------------------------------------------------------------------------- /纹理特征/GLCM/sample.py: -------------------------------------------------------------------------------- 1 | # coding: utf-8 2 | 3 | import numpy as np 4 | from skimage import data 5 | from matplotlib import pyplot as plt 6 | import fast_glcm 7 | 8 | def main(): 9 | pass 10 | 11 | 12 | if __name__ == '__main__': 13 | main() 14 | 15 | img = data.camera() 16 | h,w = img.shape 17 | glcm_mean = fast_glcm.fast_glcm_mean(img) 18 | 19 | plt.imshow(glcm_mean) 20 | plt.tight_layout() 21 | plt.show() 22 | -------------------------------------------------------------------------------- /颜色特征/Color Moment.py: -------------------------------------------------------------------------------- 1 | from PIL import Image 2 | import numpy as np 3 | import os 4 | 5 | """ 6 | #r通道的一阶颜色矩 7 | rd_1 = rd.mean() 8 | 9 | #r通道的二阶颜色矩 10 | rd_2 = rd.std() 11 | """ 12 | 13 | #定义一个求三阶颜色矩的函数 14 | def var(x=None): 15 | mid = np.mean(((x - x.mean()) ** 3)) 16 | return np.sign(mid) * abs(mid) ** (1/3) 17 | 18 | #批量求一个文件夹下所有图片的各阶颜色矩： 19 | def getimagedata(path): 20 | filename = os.listdir(path) 21 | n = len(filename) 22 | data = np.zeros([n,9]) 23 | for i in range(n): 24 | img = Image.open(path +'\\'+ filename[i]) 25 | M,N = img.size 26 | r,g,b = img.split() 27 | rd = np.asarray(r) 28 | gd = np.asarray(g) 29 | bd = np.asarray(b) 30 | data[i,0] = rd.mean() 31 | data[i,1] = gd.mean() 32 | data[i,2] = bd.mean() 33 | data[i,3] = rd.std() 34 | data[i,4] = gd.std() 35 | data[i,5] = bd.std() 36 | data[i,6] = var(rd) 37 | data[i,7] = var(gd) 38 | data[i,8] = var(bd) 39 | return data 40 | 41 | if __name__=='__main__': 42 | a = getimagedata("C:\\Users\\Admin\\Desktop\\666\\Project\\segmention\\train") 43 | -------------------------------------------------------------------------------- /纹理特征/LBP/get_uniform_map.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | # get_uniform_map.py 3 | # 2015-7-7 4 | # github: https://github.com/michael92ht 5 | #__author__ = 'huangtao' 6 | 7 | #求等价模式的58种特征值从小到大进行序列化编号得到的字典 8 | 9 | #求其二进制表示旋转一周的8个值 10 | def circle(arr,values): 11 | for i in range(0,8): 12 | b=0 13 | sum=0 14 | for j in range(i,8): 15 | sum+=arr[j]<1): #构造二进制表示的列表 36 | arr.append(string.atoi(b[j])) 37 | j-=1 38 | for s in range(0,8-len(arr)): 39 | arr.append(0) 40 | get_min_for_revolve(arr,values) 41 | 42 | values.sort() #构造字典 43 | map={} 44 | num=0 45 | f=open(r"d:/cv/test.txt",'w') 46 | for v in values: 47 | if not map.has_key(v): 48 | map[v]=num 49 | num+=1 50 | f.write(str(v)+':'+str(map[v])+',') 51 | 52 | 53 | 54 | 55 | 56 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | *.egg-info/ 24 | .installed.cfg 25 | *.egg 26 | MANIFEST 27 | 28 | # PyInstaller 29 | # Usually these files are written by a python script from a template 30 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 31 | *.manifest 32 | *.spec 33 | 34 | # Installer logs 35 | pip-log.txt 36 | pip-delete-this-directory.txt 37 | 38 | # Unit test / coverage reports 39 | htmlcov/ 40 | .tox/ 41 | .coverage 42 | .coverage.* 43 | .cache 44 | nosetests.xml 45 | coverage.xml 46 | *.cover 47 | .hypothesis/ 48 | .pytest_cache/ 49 | 50 | # Translations 51 | *.mo 52 | *.pot 53 | 54 | # Django stuff: 55 | *.log 56 | local_settings.py 57 | db.sqlite3 58 | 59 | # Flask stuff: 60 | instance/ 61 | .webassets-cache 62 | 63 | # Scrapy stuff: 64 | .scrapy 65 | 66 | # Sphinx documentation 67 | docs/_build/ 68 | 69 | # PyBuilder 70 | target/ 71 | 72 | # Jupyter Notebook 73 | .ipynb_checkpoints 74 | 75 | # pyenv 76 | .python-version 77 | 78 | # celery beat schedule file 79 | celerybeat-schedule 80 | 81 | # SageMath parsed files 82 | *.sage.py 83 | 84 | # Environments 85 | .env 86 | .venv 87 | env/ 88 | venv/ 89 | ENV/ 90 | env.bak/ 91 | venv.bak/ 92 | 93 | # Spyder project settings 94 | .spyderproject 95 | .spyproject 96 | 97 | # Rope project settings 98 | .ropeproject 99 | 100 | # mkdocs documentation 101 | /site 102 | 103 | # mypy 104 | .mypy_cache/ 105 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Python-Image-feature-extraction 2 | Python实现提取图像的纹理、颜色特征，包含快速灰度共现矩阵（GLCM）、LBP特征、颜色矩、颜色直方图。 3 | 4 | # 原始图片 5 | ![image](https://github.com/1044197988/Python-Image-feature-extraction/blob/master/Image/origin.png) 6 | 7 | ## 纹理特征 8 | ### GLCM 9 | numpy的快速灰度共现矩阵（GLCM）。该脚本在没有每个像素For循环的情况下计算GLCM，并且在scikit-image上比GLCM更快地工作。 10 | ```python 11 | import fast_glcm 12 | from skimage import data 13 | 14 | if __name__ == '__main__': 15 | img = data.camera() 16 | glcm_mean = fast_glcm.fast_glcm_mean(img) 17 | ``` 18 | ![GLCM](https://github.com/1044197988/Python-Image-feature-extraction/blob/master/Image/GLCM.png) 19 | 20 | ### LBP 21 | 获取图像的LBP特征：对图像的原始LBP模式、等价LBP模式、旋转不变LBP模式，以及等价旋转不变LBP模式的LBP特征进行提取以及显示。
22 | get_LBP_from_Image.py 主要文件 获取图像的LBP特征。
23 | get_resolve_map.py和get_uniform_map.py主要是做降维后新的像素值的映射。已经将求出的结果写入了get_LBP_from_Image.py中，这两个主要是帮助理解算法降维后新的像素值怎么得到的。 24 | ![LBP](https://github.com/1044197988/Python-Image-feature-extraction/blob/master/Image/LBP.png) 25 | ## 颜色特征 26 | ### 颜色矩 27 | 颜色是彩色图像最重要的内容之一，被广泛用于图像检索中。但从图像中提取颜色特征时，很多算法都先要对图像进行量化处理。量化处理容易导致误检，并且产生的图像特征维数较高，不利于检索。AMA Stricker和M Orengo提出了颜色矩的方法，颜色矩是一种简单有效的颜色特征表示方法，有一阶矩(均值,mean)、二阶矩(方差, variance)和三阶矩(斜度,skewness)等，由于颜色信息主要分布于低阶矩中，所以用一阶矩，二阶矩和三阶矩足以表达图像的颜色分布，颜色矩已证明可有效地表示图像中的颜色分布，该方法的优点在于：不需要颜色空间量化，特征向量维数低；但实验发现该方法的检索效率比较低，因而在实际应用中往往用来过滤图像以缩小检索范围。
28 | ### 颜色直方图 29 | ![RGB-histogram](https://github.com/1044197988/Python-Image-feature-extraction/blob/master/Image/RGB-histogram.png) 30 | ![gray-histogram](https://github.com/1044197988/Python-Image-feature-extraction/blob/master/Image/gray-histogram.png) 31 | ![histogram](https://github.com/1044197988/Python-Image-feature-extraction/blob/master/Image/histogram.png) 32 | # 参考 33 | * [michael92ht/LBP](https://github.com/michael92ht/LBP) 34 | * [tzm030329/GLCM](https://github.com/tzm030329/GLCM) 35 | 36 | -------------------------------------------------------------------------------- /纹理特征/GLCM/plot.py: -------------------------------------------------------------------------------- 1 | # coding: utf-8 2 | 3 | import numpy as np 4 | from skimage import data 5 | from matplotlib import pyplot as plt 6 | import fast_glcm 7 | from PIL import Image 8 | 9 | def main(): 10 | pass 11 | 12 | 13 | if __name__ == '__main__': 14 | #main() 15 | image = r"C:\\Users\\Admin\\Desktop\\666\\Project\\segmention\\train\\4_3_0.tif"; 16 | img=np.array(Image.open(image).convert('L')) 17 | #img = data.camera() 18 | h,w = img.shape 19 | 20 | mean = fast_glcm.fast_glcm_mean(img) 21 | std = fast_glcm.fast_glcm_std(img) 22 | cont = fast_glcm.fast_glcm_contrast(img) 23 | diss = fast_glcm.fast_glcm_dissimilarity(img) 24 | homo = fast_glcm.fast_glcm_homogeneity(img) 25 | asm, ene = fast_glcm.fast_glcm_ASM(img) 26 | ma = fast_glcm.fast_glcm_max(img) 27 | ent = fast_glcm.fast_glcm_entropy(img) 28 | 29 | plt.figure(figsize=(10,4.5)) 30 | fs = 15 31 | plt.subplot(2,5,1) 32 | plt.tick_params(labelbottom=False, labelleft=False) 33 | plt.imshow(img) 34 | plt.title('original', fontsize=fs) 35 | 36 | plt.subplot(2,5,2) 37 | plt.tick_params(labelbottom=False, labelleft=False) 38 | plt.imshow(mean) 39 | plt.title('mean', fontsize=fs) 40 | 41 | plt.subplot(2,5,3) 42 | plt.tick_params(labelbottom=False, labelleft=False) 43 | plt.imshow(std) 44 | plt.title('std', fontsize=fs) 45 | 46 | plt.subplot(2,5,4) 47 | plt.tick_params(labelbottom=False, labelleft=False) 48 | plt.imshow(cont) 49 | plt.title('contrast', fontsize=fs) 50 | 51 | plt.subplot(2,5,5) 52 | plt.tick_params(labelbottom=False, labelleft=False) 53 | plt.imshow(diss) 54 | plt.title('dissimilarity', fontsize=fs) 55 | 56 | plt.subplot(2,5,6) 57 | plt.tick_params(labelbottom=False, labelleft=False) 58 | plt.imshow(homo) 59 | plt.title('homogeneity', fontsize=fs) 60 | 61 | plt.subplot(2,5,7) 62 | plt.tick_params(labelbottom=False, labelleft=False) 63 | plt.imshow(asm) 64 | plt.title('ASM', fontsize=fs) 65 | 66 | plt.subplot(2,5,8) 67 | plt.tick_params(labelbottom=False, labelleft=False) 68 | plt.imshow(ene) 69 | plt.title('energy', fontsize=fs) 70 | 71 | plt.subplot(2,5,9) 72 | plt.tick_params(labelbottom=False, labelleft=False) 73 | plt.imshow(ma) 74 | plt.title('max', fontsize=fs) 75 | 76 | plt.subplot(2,5,10) 77 | plt.tick_params(labelbottom=False, labelleft=False) 78 | plt.imshow(ent) 79 | plt.title('entropy', fontsize=fs) 80 | 81 | plt.tight_layout(pad=0.5) 82 | plt.savefig('img/output.jpg') 83 | plt.show() 84 | -------------------------------------------------------------------------------- /颜色特征/Color-Histograms.py: -------------------------------------------------------------------------------- 1 | # author: Adrian Rosebrock 2 | # date: 22 January 2014 3 | # website: http://www.pyimagesearch.com 4 | 5 | # USAGE 6 | # python histograms.py --image grant.jpg 7 | 8 | # import the necessary packages 9 | from matplotlib import pyplot as plt 10 | import numpy as np 11 | import argparse 12 | import cv2 13 | """ 14 | # construct the argument parser and parse the arguments 15 | ap = argparse.ArgumentParser() 16 | ap.add_argument("-i", "--image", required = True, help = "Path to the image") 17 | args = vars(ap.parse_args()) 18 | """ 19 | # load the image and show it args["image"] 20 | image = cv2.imread("C:\\Users\\Admin\\Desktop\\666\\Project\\segmention\\3_6_0.tif") 21 | cv2.imshow("image", image) 22 | 23 | # convert the image to grayscale and create a histogram 24 | gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) 25 | cv2.imshow("gray", gray) 26 | hist = cv2.calcHist([gray], [0], None, [256], [0, 256]) 27 | plt.figure() 28 | plt.title("Grayscale Histogram") 29 | plt.xlabel("Bins") 30 | plt.ylabel("# of Pixels") 31 | plt.plot(hist) 32 | plt.xlim([0, 256]) 33 | 34 | # grab the image channels, initialize the tuple of colors, 35 | # the figure and the flattened feature vector 36 | chans = cv2.split(image) 37 | colors = ("b", "g", "r") 38 | plt.figure() 39 | plt.title("'Flattened' Color Histogram") 40 | plt.xlabel("Bins") 41 | plt.ylabel("# of Pixels") 42 | features = [] 43 | 44 | # loop over the image channels 45 | for (chan, color) in zip(chans, colors): 46 | # create a histogram for the current channel and 47 | # concatenate the resulting histograms for each 48 | # channel 49 | hist = cv2.calcHist([chan], [0], None, [256], [0, 256]) 50 | features.extend(hist) 51 | 52 | # plot the histogram 53 | plt.plot(hist, color = color) 54 | plt.xlim([0, 256]) 55 | 56 | # here we are simply showing the dimensionality of the 57 | # flattened color histogram 256 bins for each channel 58 | # x 3 channels = 768 total values -- in practice, we would 59 | # normally not use 256 bins for each channel, a choice 60 | # between 32-96 bins are normally used, but this tends 61 | # to be application dependent 62 | print("flattened feature vector size: %d" % (np.array(features).flatten().shape)) 63 | 64 | # let's move on to 2D histograms -- I am reducing the 65 | # number of bins in the histogram from 256 to 32 so we 66 | # can better visualize the results 67 | fig = plt.figure() 68 | 69 | # plot a 2D color histogram for green and blue 70 | ax = fig.add_subplot(131) 71 | hist = cv2.calcHist([chans[1], chans[0]], [0, 1], None, 72 | [32, 32], [0, 256, 0, 256]) 73 | p = ax.imshow(hist, interpolation = "nearest") 74 | ax.set_title("2D Color Histogram for Green and Blue") 75 | plt.colorbar(p) 76 | 77 | # plot a 2D color histogram for green and red 78 | ax = fig.add_subplot(132) 79 | hist = cv2.calcHist([chans[1], chans[2]], [0, 1], None, 80 | [32, 32], [0, 256, 0, 256]) 81 | p = ax.imshow(hist, interpolation = "nearest") 82 | ax.set_title("2D Color Histogram for Green and Red") 83 | plt.colorbar(p) 84 | 85 | # plot a 2D color histogram for blue and red 86 | ax = fig.add_subplot(133) 87 | hist = cv2.calcHist([chans[0], chans[2]], [0, 1], None, 88 | [32, 32], [0, 256, 0, 256]) 89 | p = ax.imshow(hist, interpolation = "nearest") 90 | ax.set_title("2D Color Histogram for Blue and Red") 91 | plt.colorbar(p) 92 | 93 | # finally, let's examine the dimensionality of one of 94 | # the 2D histograms 95 | print ("2D histogram shape: %s, with %d values" % ( 96 | hist.shape, hist.flatten().shape[0])) 97 | 98 | # our 2D histogram could only take into account 2 out 99 | # of the 3 channels in the image so now let's build a 100 | # 3D color histogram (utilizing all channels) with 8 bins 101 | # in each direction -- we can't plot the 3D histogram, but 102 | # the theory is exactly like that of a 2D histogram, so 103 | # we'll just show the shape of the histogram 104 | hist = cv2.calcHist([image], [0, 1, 2], 105 | None, [8, 8, 8], [0, 256, 0, 256, 0, 256]) 106 | print ("3D histogram shape: %s, with %d values" % ( 107 | hist.shape, hist.flatten().shape[0])) 108 | 109 | # show the figures and wait for a keypress 110 | plt.show() 111 | cv2.waitKey(0) 112 | -------------------------------------------------------------------------------- /纹理特征/GLCM/fast_glcm.py: -------------------------------------------------------------------------------- 1 | # coding: utf-8 2 | 3 | import numpy as np 4 | import matplotlib.pyplot as plt 5 | import cv2 6 | from skimage import data 7 | 8 | 9 | def main(): 10 | pass 11 | 12 | 13 | def fast_glcm(img, vmin=0, vmax=255, nbit=8, kernel_size=5): 14 | mi, ma = vmin, vmax 15 | ks = kernel_size 16 | h,w = img.shape 17 | 18 | # digitize 19 | bins = np.linspace(mi, ma+1, nbit+1) 20 | gl1 = np.digitize(img, bins) - 1 21 | gl2 = np.append(gl1[:,1:], gl1[:,-1:], axis=1) 22 | 23 | # make glcm 24 | glcm = np.zeros((nbit, nbit, h, w), dtype=np.uint8) 25 | for i in range(nbit): 26 | for j in range(nbit): 27 | mask = ((gl1==i) & (gl2==j)) 28 | glcm[i,j, mask] = 1 29 | 30 | kernel = np.ones((ks, ks), dtype=np.uint8) 31 | for i in range(nbit): 32 | for j in range(nbit): 33 | glcm[i,j] = cv2.filter2D(glcm[i,j], -1, kernel) 34 | 35 | glcm = glcm.astype(np.float32) 36 | return glcm 37 | 38 | 39 | def fast_glcm_mean(img, vmin=0, vmax=255, nbit=8, ks=5): 40 | ''' 41 | calc glcm mean 42 | ''' 43 | h,w = img.shape 44 | glcm = fast_glcm(img, vmin, vmax, nbit, ks) 45 | mean = np.zeros((h,w), dtype=np.float32) 46 | for i in range(nbit): 47 | for j in range(nbit): 48 | mean += glcm[i,j] * i / (nbit)**2 49 | 50 | return mean 51 | 52 | 53 | def fast_glcm_std(img, vmin=0, vmax=255, nbit=8, ks=5): 54 | ''' 55 | calc glcm std 56 | ''' 57 | h,w = img.shape 58 | glcm = fast_glcm(img, vmin, vmax, nbit, ks) 59 | mean = np.zeros((h,w), dtype=np.float32) 60 | for i in range(nbit): 61 | for j in range(nbit): 62 | mean += glcm[i,j] * i / (nbit)**2 63 | 64 | std2 = np.zeros((h,w), dtype=np.float32) 65 | for i in range(nbit): 66 | for j in range(nbit): 67 | std2 += (glcm[i,j] * i - mean)**2 68 | 69 | std = np.sqrt(std2) 70 | return std 71 | 72 | 73 | def fast_glcm_contrast(img, vmin=0, vmax=255, nbit=8, ks=5): 74 | ''' 75 | calc glcm contrast 76 | ''' 77 | h,w = img.shape 78 | glcm = fast_glcm(img, vmin, vmax, nbit, ks) 79 | cont = np.zeros((h,w), dtype=np.float32) 80 | for i in range(nbit): 81 | for j in range(nbit): 82 | cont += glcm[i,j] * (i-j)**2 83 | 84 | return cont 85 | 86 | 87 | def fast_glcm_dissimilarity(img, vmin=0, vmax=255, nbit=8, ks=5): 88 | ''' 89 | calc glcm dissimilarity 90 | ''' 91 | h,w = img.shape 92 | glcm = fast_glcm(img, vmin, vmax, nbit, ks) 93 | diss = np.zeros((h,w), dtype=np.float32) 94 | for i in range(nbit): 95 | for j in range(nbit): 96 | diss += glcm[i,j] * np.abs(i-j) 97 | 98 | return diss 99 | 100 | 101 | def fast_glcm_homogeneity(img, vmin=0, vmax=255, nbit=8, ks=5): 102 | ''' 103 | calc glcm homogeneity 104 | ''' 105 | h,w = img.shape 106 | glcm = fast_glcm(img, vmin, vmax, nbit, ks) 107 | homo = np.zeros((h,w), dtype=np.float32) 108 | for i in range(nbit): 109 | for j in range(nbit): 110 | homo += glcm[i,j] / (1.+(i-j)**2) 111 | 112 | return homo 113 | 114 | 115 | def fast_glcm_ASM(img, vmin=0, vmax=255, nbit=8, ks=5): 116 | ''' 117 | calc glcm asm, energy 118 | ''' 119 | h,w = img.shape 120 | glcm = fast_glcm(img, vmin, vmax, nbit, ks) 121 | asm = np.zeros((h,w), dtype=np.float32) 122 | for i in range(nbit): 123 | for j in range(nbit): 124 | asm += glcm[i,j]**2 125 | 126 | ene = np.sqrt(asm) 127 | return asm, ene 128 | 129 | 130 | def fast_glcm_max(img, vmin=0, vmax=255, nbit=8, ks=5): 131 | ''' 132 | calc glcm max 133 | ''' 134 | glcm = fast_glcm(img, vmin, vmax, nbit, ks) 135 | max_ = np.max(glcm, axis=(0,1)) 136 | return max_ 137 | 138 | 139 | def fast_glcm_entropy(img, vmin=0, vmax=255, nbit=8, ks=5): 140 | ''' 141 | calc glcm entropy 142 | ''' 143 | glcm = fast_glcm(img, vmin, vmax, nbit, ks) 144 | pnorm = glcm / np.sum(glcm, axis=(0,1)) + 1./ks**2 145 | ent = np.sum(-pnorm * np.log(pnorm), axis=(0,1)) 146 | return ent 147 | 148 | 149 | if __name__ == '__main__': 150 | main() 151 | 152 | nbit = 8 153 | ks = 5 154 | mi, ma = 0, 255 155 | 156 | img = data.camera() 157 | h,w = img.shape 158 | 159 | img[:,:w//2] = img[:,:w//2]//2+127 160 | glcm_mean = fast_glcm_mean(img, mi, ma, nbit, ks) 161 | -------------------------------------------------------------------------------- /纹理特征/LBP/get_LBP_from_Image.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | # get_LBP_from_Image.py 3 | # 2015-7-7 4 | # github: https://github.com/michael92ht 5 | #__author__ = 'huangtao' 6 | 7 | # import the necessary packages 8 | import numpy as np 9 | import cv2 10 | from PIL import Image 11 | from pylab import* 12 | 13 | class LBP: 14 | def __init__(self): 15 | #revolve_map为旋转不变模式的36种特征值从小到大进行序列化编号得到的字典 16 | self.revolve_map={0:0,1:1,3:2,5:3,7:4,9:5,11:6,13:7,15:8,17:9,19:10,21:11,23:12, 17 | 25:13,27:14,29:15,31:16,37:17,39:18,43:19,45:20,47:21,51:22,53:23,55:24, 18 | 59:25,61:26,63:27,85:28,87:29,91:30,95:31,111:32,119:33,127:34,255:35} 19 | #uniform_map为等价模式的58种特征值从小到大进行序列化编号得到的字典 20 | self.uniform_map={0:0,1:1,2:2,3:3,4:4,6:5,7:6,8:7,12:8, 21 | 14:9,15:10,16:11,24:12,28:13,30:14,31:15,32:16, 22 | 48:17,56:18,60:19,62:20,63:21,64:22,96:23,112:24, 23 | 120:25,124:26,126:27,127:28,128:29,129:30,131:31,135:32, 24 | 143:33,159:34,191:35,192:36,193:37,195:38,199:39,207:40, 25 | 223:41,224:42,225:43,227:44,231:45,239:46,240:47,241:48, 26 | 243:49,247:50,248:51,249:52,251:53,252:54,253:55,254:56, 27 | 255:57} 28 | 29 | 30 | #将图像载入，并转化为灰度图，获取图像灰度图的像素信息 31 | def describe(self,image): 32 | image_array=np.array(Image.open(image).convert('L')) 33 | return image_array 34 | 35 | #图像的LBP原始特征计算算法：将图像指定位置的像素与周围8个像素比较 36 | #比中心像素大的点赋值为1，比中心像素小的赋值为0，返回得到的二进制序列 37 | def calute_basic_lbp(self,image_array,i,j): 38 | sum=[] 39 | if image_array[i-1,j-1]>image_array[i,j]: 40 | sum.append(1) 41 | else: 42 | sum.append(0) 43 | if image_array[i-1,j]>image_array[i,j]: 44 | sum.append(1) 45 | else: 46 | sum.append(0) 47 | if image_array[i-1,j+1]>image_array[i,j]: 48 | sum.append(1) 49 | else: 50 | sum.append(0) 51 | if image_array[i,j-1]>image_array[i,j]: 52 | sum.append(1) 53 | else: 54 | sum.append(0) 55 | if image_array[i,j+1]>image_array[i,j]: 56 | sum.append(1) 57 | else: 58 | sum.append(0) 59 | if image_array[i+1,j-1]>image_array[i,j]: 60 | sum.append(1) 61 | else: 62 | sum.append(0) 63 | if image_array[i+1,j]>image_array[i,j]: 64 | sum.append(1) 65 | else: 66 | sum.append(0) 67 | if image_array[i+1,j+1]>image_array[i,j]: 68 | sum.append(1) 69 | else: 70 | sum.append(0) 71 | return sum 72 | 73 | #获取二进制序列进行不断环形旋转得到新的二进制序列的最小十进制值 74 | def get_min_for_revolve(self,arr): 75 | values=[] 76 | circle=arr 77 | circle.extend(arr) 78 | for i in range(0,8): 79 | j=0 80 | sum=0 81 | bit_num=0 82 | while j<8: 83 | sum+=circle[i+j]<255: 136 | k=k-255 137 | xor=basic_array[i,j]^k 138 | num=self.calc_sum(xor) 139 | if num<=2: 140 | uniform_array[i,j]=self.uniform_map[basic_array[i,j]] 141 | else: 142 | uniform_array[i,j]=58 143 | return uniform_array 144 | 145 | #获取图像的LBP旋转不变等价模式特征 146 | def lbp_revolve_uniform(self,image_array): 147 | uniform_revolve_array=np.zeros(image_array.shape, np.uint8) 148 | basic_array=self.lbp_basic(image_array) 149 | width=image_array.shape[0] 150 | height=image_array.shape[1] 151 | for i in range(1,width-1): 152 | for j in range(1,height-1): 153 | k= basic_array[i,j]<<1 154 | if k>255: 155 | k=k-255 156 | xor=basic_array[i,j]^k 157 | num=self.calc_sum(xor) 158 | if num<=2: 159 | uniform_revolve_array[i,j]=self.calc_sum(basic_array[i,j]) 160 | else: 161 | uniform_revolve_array[i,j]=9 162 | return uniform_revolve_array 163 | 164 | #绘制指定维数和范围的图像灰度归一化统计直方图 165 | def show_hist(self,img_array,im_bins,im_range): 166 | hist = cv2.calcHist([img_array],[0],None,im_bins,im_range) 167 | hist = cv2.normalize(hist,None).flatten() 168 | plt.plot(hist,color = 'r') 169 | plt.xlim(im_range) 170 | plt.show() 171 | 172 | #绘制图像原始LBP特征的归一化统计直方图 173 | def show_basic_hist(self,img_array): 174 | self.show_hist(img_array,[256],[0,256]) 175 | 176 | #绘制图像旋转不变LBP特征的归一化统计直方图 177 | def show_revolve_hist(self,img_array): 178 | self.show_hist(img_array,[36],[0,36]) 179 | 180 | #绘制图像等价模式LBP特征的归一化统计直方图 181 | def show_uniform_hist(self,img_array): 182 | self.show_hist(img_array,[60],[0,60]) 183 | 184 | #绘制图像旋转不变等价模式LBP特征的归一化统计直方图 185 | def show_revolve_uniform_hist(self,img_array): 186 | self.show_hist(img_array,[10],[0,10]) 187 | 188 | #显示图像 189 | def show_image(self,image_array): 190 | cv2.imshow('Image',image_array) 191 | cv2.waitKey(0) 192 | 193 | if __name__ == '__main__': 194 | image = r"C:\\Users\\Admin\\Desktop\\666\\Project\\segmention\\train\\4_3_0.tif"; 195 | lbp=LBP() 196 | image_array=lbp.describe(image) 197 | 198 | #获取图像原始LBP特征，并显示其统计直方图与特征图像 199 | basic_array=lbp.lbp_basic(image_array) 200 | lbp.show_basic_hist(basic_array) 201 | lbp.show_image(basic_array) 202 | 203 | #获取图像旋转不变LBP特征，并显示其统计直方图与特征图像 204 | #revolve_array=lbp.lbp_revolve(image_array) 205 | #lbp.show_revolve_hist(revolve_array) 206 | #lbp.show_image(revolve_array) 207 | 208 | #获取图像等价模式LBP特征，并显示其统计直方图与特征图像 209 | #uniform_array=lbp.lbp_uniform(image_array) 210 | #lbp.show_uniform_hist(uniform_array) 211 | #lbp.show_image(uniform_array) 212 | 213 | #获取图像等价模式LBP特征，并显示其统计直方图与特征图像 214 | #resolve_uniform_array=lbp.lbp_revolve_uniform(image_array) 215 | #lbp.show_revolve_uniform_hist(resolve_uniform_array) 216 | #lbp.show_image(resolve_uniform_array) 217 | 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