颜色矩
#计算图像的颜色一二三阶矩
def color_moments(img):
n_images = img.shape[0]
all_color_feature = np.zeros((n_images, 9))
for i in range(n_images):
#img = cv2.imread(filename)
if img is None:
return
# Convert BGR to HSV colorspace
hsv = cv2.cvtColor(img[i], cv2.COLOR_BGR2HSV)
# Split the channels - h,s,v
h,s,v = cv2.split(hsv)
# Initialize the color feature
color_feature = []
# N = h.shape[0] * h.shape[1]
# The first central moment - average
h_mean = np.mean(h) # np.sum(h)/float(N)
s_mean = np.mean(s) # np.sum(s)/float(N)
v_mean = np.mean(v) # np.sum(v)/float(N)
color_feature.extend([h_mean, s_mean, v_mean])
# The second central moment - standard deviation
h_std = np.std(h) # np.sqrt(np.mean(abs(h - h.mean())**2))
s_std = np.std(s) # np.sqrt(np.mean(abs(s - s.mean())**2))
v_std = np.std(v) # np.sqrt(np.mean(abs(v - v.mean())**2))
color_feature.extend([h_std, s_std, v_std])
# The third central moment - the third root of the skewness
h_skewness = np.mean(abs(h - h.mean())**3)
s_skewness = np.mean(abs(s - s.mean())**3)
v_skewness = np.mean(abs(v - v.mean())**3)
h_thirdMoment = h_skewness**(1./3)
s_thirdMoment = s_skewness**(1./3)
v_thirdMoment = v_skewness**(1./3)
color_feature.extend([h_thirdMoment, s_thirdMoment, v_thirdMoment])
all_color_feature[i]= color_feature
all_color_feature=np.delete(all_color_feature,errorline,axis=0)
return all_color_feature
HOG
def get_hog(img):
n_images = img.shape[0]
all_hog_feature = np.zeros((n_images, 144))
for i in range(n_images):
normalised_blocks, hog_image = hog(img[i], orientations=9, pixels_per_cell=(8, 8), cells_per_block=(2, 2), block_norm='L2-Hys',visualize=True)
all_hog_feature[i]= normalised_blocks
all_hog_feature=np.delete(all_hog_feature,errorline,axis=0)
return all_hog_feature
LBP 纹理特征
#使用LBP方法提取图像的纹理特征.
def get_lbp_data(images_data, hist_size, lbp_radius, lbp_point):
n_images = images_data.shape[0]
hist = np.zeros((n_images, 256))
global errorline
errorline=[]
for i in range(n_images):
#rgb图像转换为灰度图像
rgb_data=images_data[i]
#这个计算结果是float64
#b, g, r = rgb_data[:,:,0], rgb_data[:,:,1], rgb_data[:,:,2]
#gray_data = 0.2989 * r + 0.5870 * g + 0.1140 * b
gray_data=cv2.cvtColor(rgb_data, cv2.COLOR_BGR2GRAY)
lbp = local_binary_pattern(gray_data, 8, 1, 'default')
# 统计图像的直方图
max_bins = int(lbp.max() + 1)
#print(max_bins)
#print('hhhhhhhh')
#print(i)
#删除不符合条件的行
if max_bins!=256:
print(i)
errorline.append(i)
continue
hist[i], _ = np.histogram(lbp, normed=True, bins=max_bins, range=(0, max_bins))
print(errorline)
hist=np.delete(hist,errorline,axis=0)
return hist
