HOG称为方向梯度直方图(Histogram of Oriented Gradient),主要是为了对图像进行特征提取。所以在传统目标检测算法中经常与SVM结合用于行人识别任务(当前都是基于深度学习来做了,毕竟效果不要太好了,并且省去了对框的预选问题)。
HOG主要是计算图像中每个像素的梯度值和梯度方向,从而来获得梯度特征,是一种特征描述子[1]。
HOG特点
1.由于计算局部直方图和归一化,所以它对图像几何的和光学的形变都能保持很好的不变性;
2.细微的动作可以被忽略而不影响检测效果。
HOG计算步骤
1.对输入图像进行灰度化
2.利用gamma校正法对图像进行颜色空间归一化;(伽玛校正就是对图像的伽玛曲线进行编辑,以对图像进行非线性色调编辑的方法,检出图像信号中的深色部分和浅色部分,并使两者比例增大,从而提高图像对比度效果。主要是为了降低图像局部的阴影和光照变化所造成的影响,同时可以抑制噪音的干扰;)
3.计算图像中每个像素的梯度大小和方向;
4.将图像划分cells,计算每个cell内的梯度直方图;
5.将每几个cell组成一个block,计算每个block内的梯度特征;
6.将图像中所有block的梯度特征组合起来就得到了图像的特征描述子;
7.将图像特征输入分类器进行分类。
算法实现#coding:utf-8 import cv2 import numpy as np import math import matplotlib.pyplot as plt class Hog_descriptor(): def __init__(self, img, cell_size=16, bin_size=8): self.img = img self.img = np.sqrt(img / np.max(img)) # 做完归一化取根号,取值范围[0,1] 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_size for 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_unit return 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_unit for 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_gap return image img = cv2.imread('qiao.jpg', cv2.IMREAD_GRAYSCALE) # v2.IMREAD_COLOR:读取一副彩色图片,图片的透明度会被忽略,默认为该值,实际取值为1; # cv2.IMREAD_GRAYSCALE:以灰度模式读取一张图片,实际取值为0 # cv2.IMREAD_UNCHANGED:加载一副彩色图像,透明度不会被忽略。 hog = Hog_descriptor(img, cell_size=8, bin_size=9) # vector, image = hog.extract() # # # 输出图像的特征向量shape # print(np.array(vector).shape) # plt.imshow(image, cmap=plt.cm.gray) # plt.show()
结果:
原图 | 特征 |
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标签:HOG,angle,python,self,cell,gradient,算法,vector,size From: https://www.cnblogs.com/peixu/p/16905225.html