动物识别样本模型训练

[python]代码库

import cv2 
#环境python3、opencv3

#导入需要使用的库
import numpy as np
import matplotlib.pyplot as plt

#读取npy文件
samples = np.load("animals.npy")
labels = np.load("labels.npy")

#将numpy数组转换为图像并显示出来
def show_image_plt(img_data):
    plt.imshow(img_data)
    plt.show()

#显示出前10个图像
for i in range(10):
    show_image_plt(samples[i])

#定义样本特征工程并处理
samples_feature = []
for sample in samples:
    #用高斯滤波处理
    sample = cv2.GaussianBlur(sample,(3,3),0,0)
    #用sobel算子进行边缘检测
    sobel_x=cv2.Sobel(sample,cv2.CV_64F,1,0,ksize=3)#(图像，深度，x方向的阶数，y方向的阶数，卷积核大小)
    sobel_y=cv2.Sobel(sample,cv2.CV_64F,0,1,ksize=3)
    sobel_xy= np.sqrt(np.square(sobel_x)+np.square(sobel_y))
    #将处理后的图像展平
    sample_feature = np.reshape(sobel_xy,(-1)).tolist()
    samples_feature.append(sample_feature)

#数据标准化
from sklearn.preprocessing import StandardScaler 
sc = StandardScaler()
sc.fit_transform(samples_feature)

#利用支持向量机构建分类模型
from sklearn import svm 
clf = svm.SVC()
#用所有的样本数据进行模型训练
clf.fit(samples_feature,labels)

#查看训练好的模型预测准确率
print("准确率:",clf.score(samples_feature,labels))

#预测新图像
test_sample = cv2.imread("test_animals.jpg")
#用高斯滤波处理
test_sample = cv2.GaussianBlur(test_sample,(3,3),0,0)
#用sobel算子进行边缘检测
sobel_x=cv2.Sobel(test_sample,cv2.CV_64F,1,0,ksize=3)
sobel_y=cv2.Sobel(test_sample,cv2.CV_64F,0,1,ksize=3)
sobel_xy= np.sqrt(np.square(sobel_x)+np.square(sobel_y))
#将处理后的图像展平
test_sample_feature = np.reshape(sobel_xy,(-1))

#用训练好的模型预测新图像
prediction = clf.predict([test_sample_feature])
print("预测结果：",prediction)