- SVM算法了解
- SVM使用多项式核解决
- SVM使用核函数
- 人脸识别并在原基础上绘制68个特征点
- 绘制显示特征点，而是给人脸虚拟P上一付墨镜
import numpy as np import matplotlib.pyplot as plt from sklearn import datasets X, y = datasets.make_moons() #使用生成的数据 print(X.shape) # (100,2) print(y.shape) # (100,) X, y = datasets.make_moons(noise=0.15,random_state=777) #随机生成噪声点，random_state是随机种子，noise是方差 plt.scatter(X[y==0,0],X[y==0,1]) plt.scatter(X[y==1,0],X[y==1,1]) plt.show()
(100, 2) (100,)
我们接下来通过多项式特征的SVM来对它进行分类。这里我们引入了管道，它可以将许多算法模型串联起来，比如将特征提取、归一化、分类组织在一起形成一个典型的机器学习问题工作流。
可视化展示结果
def plot_decision_boundary(model, axis):
x0,x1 = np.meshgrid(np.linspace(axis[0],axis[1],int((axis[1]-axis[0])*100)).reshape(-1,1),np.linspace(axis[2],axis[3],int((axis[3]-axis[2])*100)).reshape(-1,1) )
X_new = np.c_[x0.ravel(), x1.ravel()]
y_predict = model.predict(X_new)
zz = y_predict.reshape(x0.shape)
from matplotlib.colors import ListedColormap
custom_cmap = ListedColormap(['#EF9A9A','#FFF59D','#90CAF9'])
plt.contourf(x0, x1, zz, cmap=custom_cmap) # 绘制决策边界
from sklearn.preprocessing import PolynomialFeatures,StandardScaler
from sklearn.svm import LinearSVC
from sklearn.pipeline import Pipeline
def PolynomialSVC(degree,C=1.0):
return Pipeline([("poly",PolynomialFeatures(degree=degree)),("std_scaler",StandardScaler()),("linearSVC",LinearSVC(C=C))])
poly_svc = PolynomialSVC(degree=3)
poly_svc.fit(X,y)
plot_decision_boundary(poly_svc,axis=[-1.5,2.5,-1.0,1.5])
plt.scatter(X[y==0,0],X[y==0,1])
plt.scatter(X[y==1,0],X[y==1,1])
plt.show()
可以看到，生成的边界不是线性的直线了。我们还可以使用核技巧来对数据进行处理，使其维度提升，使原本线性不可分的数据，在高维空间变成线性可分的。再用线性SVM来进行处理
SVM使用核函数生成数据集
import numpy as np import matplotlib.pyplot as plt from sklearn import datasets X,y = datasets.make_moons(noise=0.15,random_state=777) plt.scatter(X[y==0,0],X[y==0,1]) plt.scatter(X[y==1,0],X[y==1,1]) plt.show()
[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-JGVON9jO-1636810468141)(output_7_0.png)]
使用高斯核，同时将gamma设置为100
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
from sklearn.pipeline import Pipeline
def RBFKernelSVC(gamma=1.0):
return Pipeline([ ('std_scaler',StandardScaler()), ('svc',SVC(kernel='rbf',gamma=gamma))])
svc = RBFKernelSVC(100)
svc.fit(X,y)
plot_decision_boundary(svc,axis=[-1.5,2.5,-1.0,1.5])
plt.scatter(X[y==0,0],X[y==0,1])
plt.scatter(X[y==1,0],X[y==1,1])
plt.show()
很明显，训练出的模型过拟合。再将其设置为0.1
svc = RBFKernelSVC(0.1) svc.fit(X,y) plot_decision_boundary(svc,axis=[-1.5,2.5,-1.0,1.5]) plt.scatter(X[y==0,0],X[y==0,1]) plt.scatter(X[y==1,0],X[y==1,1]) plt.show()
这时训练出的模型欠拟合。这说明gamma变量相当于再调整模型复杂度，再实际使用时应根据具体情况调整。
人脸识别并在原基础上绘制68个特征点所需库及亮度调节：
import numpy as np
import cv2
import dlib
import os
import sys
import random
# 存储位置
output_dir = 'D:/faces'
size = 64
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# 改变图片的亮度与对比度
def relight(img, light=1, bias=0):
w = img.shape[1]
h = img.shape[0]
#image = []
for i in range(0,w):
for j in range(0,h):
for c in range(3):
tmp = int(img[j,i,c]*light + bias)
if tmp > 255:
tmp = 255
elif tmp < 0:
tmp = 0
img[j,i,c] = tmp
return img
相关特征处理及绘制
#使用dlib自带的frontal_face_detector作为我们的特征提取器
detector = dlib.get_frontal_face_detector()
# 打开摄像头 参数为输入流，可以为摄像头或视频文件
camera = cv2.VideoCapture(0)
#camera = cv2.VideoCapture('C:/Users/CUNGU/Videos/Captures/wang.mp4')
ok = True
j=0
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor('D:/myworks/shape_predictor_68_face_landmarks.dat')
while ok:
# 读取摄像头中的图像，ok为是否读取成功的判断参数
ok, img = camera.read()
# 转换成灰度图像
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
rects = detector(img_gray, 0)
for i in range(len(rects)):
landmarks = np.matrix([[p.x, p.y] for p in predictor(img,rects[i]).parts()])
for idx, point in enumerate(landmarks):
# 68点的坐标
pos = (point[0, 0], point[0, 1])
if j<68:
print(idx,pos)
# 利用cv2.circle给每个特征点画一个圈，共68个
cv2.circle(img, pos, 2, color=(0, 255, 0))
# 利用cv2.putText输出1-68
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(img, str(idx+1), pos, font, 0.2, (0, 0, 255), 1,cv2.LINE_AA)
j=j+1
cv2.imshow('video', img)
k = cv2.waitKey(1)
if k == 27: # press 'ESC' to quit
break
camera.release()
cv2.destroyAllWindows()
提取所得特征值如下 0 (253, 125) 1 (256, 150) 2 (259, 174) 3 (263, 197) 4 (273, 218) 5 (288, 235) 6 (307, 249) 7 (329, 261) 8 (352, 264) 9 (374, 262) 10 (394, 250) 11 (411, 235) 12 (425, 218) 13 (434, 198) 14 (438, 176) 15 (442, 153) 16 (443, 130) 17 (277, 102) 18 (289, 91) 19 (306, 87) 20 (324, 90) 21 (340, 97) 22 (373, 98) 23 (388, 91) 24 (405, 90) 25 (420, 94) 26 (431, 105) 27 (357, 122) 28 (357, 135) 29 (358, 148) 30 (358, 162) 31 (338, 177) 32 (347, 179) 33 (357, 182) 34 (365, 180) 35 (374, 177) 36 (296, 125) 37 (306, 120) 38 (319, 120) 39 (329, 127) 40 (318, 129) 41 (306, 129) 42 (381, 127) 43 (392, 121) 44 (403, 121) 45 (413, 127) 46 (404, 131) 47 (392, 130) 48 (320, 212) 49 (333, 203) 50 (347, 198) 51 (356, 200) 52 (364, 198) 53 (376, 202) 54 (387, 211) 55 (376, 221) 56 (364, 225) 57 (355, 226) 58 (346, 226) 59 (332, 222) 60 (326, 212) 61 (347, 209) 62 (356, 209) 63 (364, 209) 64 (381, 212) 65 (364, 212) 66 (356, 213) 67 (346, 213)
描绘得到图像如下所示
导入所需库，以及图象亮度调节
import numpy as np
import cv2
import dlib
import os
import sys
import random
# 存储位置
output_dir = 'D:/faces'
size = 64
if not os.path.exists(output_dir):
os.makedirs(output_dir)
def relight(img, light=1, bias=0):
w = img.shape[1]
h = img.shape[0]
#image = []
for i in range(0,w):
for j in range(0,h):
for c in range(3):
tmp = int(img[j,i,c]*light + bias)
if tmp > 255:
tmp = 255
elif tmp < 0:
tmp = 0
img[j,i,c] = tmp
return img
相关图像处理（加’墨镜’）
detector = dlib.get_frontal_face_detector()
camera = cv2.VideoCapture(0)
ok = True
j=0
detector = dlib.get_frontal_face_detector()
#使用5点特征点，方便找出所需特征点
predictor = dlib.shape_predictor('D:/myworks/shape_predictor_5_face_landmarks.dat')
while ok:
ok, img = camera.read()
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
pot1 = (0,0)
pot2 = (0,0)
t = (0,0)
rects = detector(img_gray, 0)
for i in range(len(rects)):
landmarks = np.matrix([[p.x, p.y] for p in predictor(img,rects[i]).parts()])
r=0
for idx, point in enumerate(landmarks):
pos = (point[0, 0], point[0, 1])
if idx in (0,2):
pot1 = pos
if idx in (1,3):
if idx == 1:
t = pos
pot2 = pos
x = abs(pot1[0] + pot2[0])
y = abs(pot1[1] + pot2[1])
if idx == 1:
r = np.sqrt(abs(np.square(x)-np.square(y)))
temp = (int(x/2),int(y/2))
cv2.circle(img,temp,int(r/20), color=(0, 0, 0),thickness=-1)
cv2.line(img,pot2,t,color=(0,0,0), thickness=5)
j=j+1
cv2.imshow('video', img)
k = cv2.waitKey(1)
if k == 27: # press 'ESC' to quit
break
camera.release()
cv2.destroyAllWindows()
效果如下
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