0

** 我想要做的是使用 Python 3.0x 快速计算 GLCM 特征，并且应该在每个图像上绘制整个图像组的特征平均值以及图像的单个特征值。没有平均值的示例图像是这样的。并且特征值包括熵、对比度、角二阶矩等。任何加速这个过程的想法都值得赞赏。**

gray_level = 64    

def maxGrayLevel(img):
    max_gray_level = 0
    (height, width) = img.shape
    print(height, width)

    for y in range(height):
        for x in range(width):
            if img[y][x] > max_gray_level:
                max_gray_level = img[y][x]
    return max_gray_level + 1


def getGlcm(input, d_x, d_y):
    srcdata = input.copy()
    ret = [[0.0 for i in range(gray_level)] for j in range(gray_level)]
    (height, width) = input.shape
    max_gray_level = maxGrayLevel(input)

    # 若灰度级数大于gray_level，则将图像的灰度级缩小至gray_level，减小灰度共生矩阵的大小
    if max_gray_level > gray_level:
        for j in range(height):
            for i in range(width):
                srcdata[j][i] = srcdata[j][i] * gray_level / max_gray_level

    for j in range(height - d_y):
        for i in range(width - d_x):
            rows = srcdata[j][i]
            cols = srcdata[j + d_y][i + d_x]
            ret[rows][cols] += 1.0

    for i in range(gray_level):
        for j in range(gray_level):
            ret[i][j] /= float(height * width)

    return ret


def feature_computer(p):
    '''
    :param p: 单张灰度图片矩阵
    :return: 四个 GLCM 特征值
    '''
    Con = 0.0
    Ent = 0.0
    Asm = 0.0
    Idm = 0.0
    for i in range(gray_level):
        for j in range(gray_level):
            Con += (i - j) * (i - j) * p[i][j]
            Asm += p[i][j] * p[i][j]
            Idm += p[i][j] / (1 + (i - j) * (i - j))
            if p[i][j] > 0.0:
                Ent += p[i][j] * math.log(p[i][j])
    return Asm, Con, -Ent, Idm
def Put_text(im_path):
    # for im_path in files:
        im_name = im_path.split('\\')[-1]
        suffix_path = im_path.split('\\')[-2]
        new_path_head = r'I:\DATA\Texture-based-data\annot_texture'
        new_path = os.path.join(new_path_head, suffix_path)
        new_root_im_name = os.path.join(new_path, im_name)
        if not os.path.exists(new_path):
            os.makedirs(new_path)
        if not os.path.exists(new_root_im_name):
            im = cv2.imread(im_path)
            try:
                img_shape = im.shape
            except:
                print('imread error')
                return

            img = cv2.resize(im, (int(img_shape[1] / 2), int(img_shape[0] / 2)), interpolation=cv2.INTER_CUBIC)
            img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
            glcm_0 = getGlcm(img_gray, 1, 0)
            asm, con, ent, idm = feature_computer(glcm_0)



            imgzi = New_put_text('asm'+str(asm)+'\n'+'con'+str(con)+'\n'+'ent'+str(ent)+'\n'+'idm'+str(idm), img)


            cv2.imwrite(new_root_im_name, img)
            print(new_root_im_name, '\n', getImageVar(im), asm, con, ent, idm)
 if __name__ == '__main__':
        subfiles = [f for f in glob.glob(os.path.join(sub_files, r'*bmp'))]
        p = Pool(4)
        p.map(Put_text, subfiles)
4

1 回答 1

0

嘿，我在我的项目中也使用了 GLCM，你可以在https://github.com/ellengiacometti/LemonCV?files=1上查看它

我使用了 graycoprops，它的工作速度非常快。这是一个 Trata Imagem.py 例如：

glcm = greycomatrix(gray_BoundingBox, [5], [0], 256, 
symmetric=True, normed=True)
dissimilarity= greycoprops(glcm, 'dissimilarity')[0, 0]
correlation= greycoprops(glcm, 'correlation')[0, 0]
homogeneity = greycoprops(glcm,'homogeneity')[0, 0]
energy = greycoprops(glcm, 'energy')[0, 0]
contrast= greycoprops(glcm, 'contrast')[0, 0]
ASM = greycoprops(glcm,'ASM')[0,0]

我希望这个对你有用 ：）

于 2019-12-27T04:31:22.483 回答