图像特征提取_Hog图像特征提取算法,HOG

    from skimage import feature as ftfeatures = ft.hog(image,  # input image                  orientations=ori,  # number of bins                  pixels_per_cell=ppc, # pixel per cell                  cells_per_block=cpb, # cells per blcok                  block_norm = 'L1', #  block norm : str {‘L1’, ‘L1-sqrt’, ‘L2’, ‘L2-Hys’}                  transform_sqrt = True, # power law compression (also known as gamma correction)                  feature_vector=True, # flatten the final vectors                  visualise=False) # return HOG map

    from skimage.feature import hoggray = rgb2gray(image) / 255.0fd = hog(gray, orientations=12, block_norm='L1', pixels_per_cell=[10, 10], cells_per_block=[4, 4], visualize=False, transform_sqrt=True)

    import cv2import numpy as npimport mathimport matplotlib.pyplot as pltclass Hog_descriptor():    def __init__(self, img, cell_size=16, bin_size=8):        self.img = img        self.img = np.sqrt(img / np.max(img))        self.img = img * 255        self.cell_size = cell_size        self.bin_size = bin_size        self.angle_unit = 360 / self.bin_size    def extract(self):        height, width = self.img.shape        # 计算图像的梯度大小和方向        gradient_magnitude, gradient_angle = self.global_gradient()        gradient_magnitude = abs(gradient_magnitude)        cell_gradient_vector = np.zeros((int(height / self.cell_size), int(width / self.cell_size), self.bin_size))for i in range(cell_gradient_vector.shape[0]):for j in range(cell_gradient_vector.shape[1]):                # cell内的梯度大小                cell_magnitude = gradient_magnitude[i * self.cell_size:(i + 1) * self.cell_size,                                 j * self.cell_size:(j + 1) * self.cell_size]                # cell内的梯度方向                cell_angle = gradient_angle[i * self.cell_size:(i + 1) * self.cell_size,                             j * self.cell_size:(j + 1) * self.cell_size]                # 转化为梯度直方图格式                cell_gradient_vector[i][j] = self.cell_gradient(cell_magnitude, cell_angle)        # 绘制梯度直方图        hog_image = self.render_gradient(np.zeros([height, width]), cell_gradient_vector)        # block组合、归一化        hog_vector = []for i in range(cell_gradient_vector.shape[0] - 1):for j in range(cell_gradient_vector.shape[1] - 1):                block_vector = []                block_vector.extend(cell_gradient_vector[i][j])                block_vector.extend(cell_gradient_vector[i][j + 1])                block_vector.extend(cell_gradient_vector[i + 1][j])                block_vector.extend(cell_gradient_vector[i + 1][j + 1])                mag = lambda vector: math.sqrt(sum(i ** 2 for i in vector))                magnitude = mag(block_vector)if magnitude != 0:                    normalize = lambda block_vector, magnitude: [element / magnitude for element in block_vector]                    block_vector = normalize(block_vector, magnitude)                hog_vector.append(block_vector)return hog_vector, hog_image    def global_gradient(self):        gradient_values_x = cv2.Sobel(self.img, cv2.CV_64F, 1, 0, ksize=5)        gradient_values_y = cv2.Sobel(self.img, cv2.CV_64F, 0, 1, ksize=5)        gradient_magnitude = cv2.addWeighted(gradient_values_x, 0.5, gradient_values_y, 0.5, 0)        gradient_angle = cv2.phase(gradient_values_x, gradient_values_y, angleInDegrees=True)return gradient_magnitude, gradient_angle    def cell_gradient(self, cell_magnitude, cell_angle):        orientation_centers = [0] * self.bin_sizefor i in range(cell_magnitude.shape[0]):for j in range(cell_magnitude.shape[1]):                gradient_strength = cell_magnitude[i][j]                gradient_angle = cell_angle[i][j]                min_angle, max_angle, mod = self.get_closest_bins(gradient_angle)                orientation_centers[min_angle] += (gradient_strength * (1 - (mod / self.angle_unit)))                orientation_centers[max_angle] += (gradient_strength * (mod / self.angle_unit))return orientation_centers    def get_closest_bins(self, gradient_angle):        idx = int(gradient_angle / self.angle_unit)        mod = gradient_angle % self.angle_unitreturn idx, (idx + 1) % self.bin_size, mod    def render_gradient(self, image, cell_gradient):        cell_width = self.cell_size / 2        max_mag = np.array(cell_gradient).max()for x in range(cell_gradient.shape[0]):for y in range(cell_gradient.shape[1]):                cell_grad = cell_gradient[x][y]                cell_grad /= max_mag                angle = 0                angle_gap = self.angle_unitfor magnitude in cell_grad:                    angle_radian = math.radians(angle)                    x1 = int(x * self.cell_size + magnitude * cell_width * math.cos(angle_radian))                    y1 = int(y * self.cell_size + magnitude * cell_width * math.sin(angle_radian))                    x2 = int(x * self.cell_size - magnitude * cell_width * math.cos(angle_radian))                    y2 = int(y * self.cell_size - magnitude * cell_width * math.sin(angle_radian))                    cv2.line(image, (y1, x1), (y2, x2), int(255 * math.sqrt(magnitude)))                    angle += angle_gapreturn imageimg = cv2.imread('0.jpg', cv2.IMREAD_GRAYSCALE)hog = Hog_descriptor(img, cell_size=8, bin_size=9)vector, image = hog.extract()# 输出图像的特征向量shapeprint(np.array(vector).shape)plt.imshow(image, cmap=plt.cm.gray)plt.show()