def pic_handle(img_data, sd, ori_size):
"""
做PCA处理之后，在还原到原来的维度，然后显示，之后输出信噪比
"""
Pca = PCA(sd, img_data)
c_data, w_star = Pca.pca() # 进行pca降维,获取投影矩阵
w_star = np.real(w_star)
print(w_star)
new_data = w_star * w_star.T * c_data + Pca.mean # 还原到原来的维度
total_img = []
# 图片混合
for i in range(Pca.data_size):
if len(total_img) == 0:
total_img = new_data[:, i].T.reshape(ori_size)
else:
total_img = np.hstack(
[total_img, new_data[:, i].T.reshape(ori_size)])
# 计算信噪比
print('信噪比:')
for i in range(Pca.data_size):
a = psnr(np.array(data[:, i].T), np.array(new_data[:, i].T))
print('图', i, '的信噪比为:', a, 'dB')
# 处理图片
total_img = np.array(total_img).astype(np.uint8)
cv2.imwrite('pca image.jpg', total_img) # 图片显示
cv2.imshow('pca image', total_img)
cv2.waitKey(0)
# ================== Load Example Dataset ===================
data = sio.loadmat('data\\ex7data1.mat')
X = data['X']
m, n = X.shape
fig = plt.figure()
ax = fig.add_subplot(1, 2, 1)
ax.plot(X[:, 0], X[:, 1], 'bo')
# =============== Principal Component Analysis ===============
pca = PCA()
X_norm, mu, sigma = pca.featureNormalize(X)
U, S = pca.pca(X_norm)
p1 = mu
p2 = mu + 1.5 * S[0] * U[:, 0].T
data = np.r_[p1, p2].reshape([-1, 2])
ax.plot(data[:, 0], data[:, 1], '-k', linewidth=2)
p2 = mu + 1.5 * S[1] * U[:, 1].T
data = np.r_[p1, p2].reshape([-1, 2])
ax.plot(data[:, 0], data[:, 1], '-k', linewidth=2)
# =================== Dimension Reduction ===================
ax = fig.add_subplot(1, 2, 2)
ax.plot(X_norm[:, 0], X_norm[:, 1], 'bo')
K = 1
imlist = glob.glob("data/a_thumbs/*.jpg")
#print imlist
im = array(Image.open(imlist[0]))
#imshow(im)
#show()
m, n = im.shape[0:2]
imageCount = len(imlist)
#create matrix to stor all flattened images
imageMatrix = array([array(Image.open(im)).flatten()
for im in imlist], 'f')
#perform pca
V, S, immean = PCA.pca(imageMatrix)
#show some images (mean and 7 first modes
figure()
gray()
subplot(2,4,1)
imshow(immean.reshape(m,n))
for i in range(7):
subplot(2,4,i+2)
#convert images back to one dimension
imshow(V[i].reshape(m,n))
#imshow(imageMatrix[i].reshape(m,n))
#show()
f = open('font_pca_modes.pkl', 'wb')
