def testImg(infile='../show/pic_with_noise/lena_7.png',
infile_raw='../pic_raw/lena.png',
filename='lena'):
im_test = np.array(Image.open(infile).convert('L')).reshape(1, pixelNum)
scipy.misc.imsave(infile, im_test.reshape(picSize[0], picSize[1]))
im_test = im_test.astype('float32') / 255.0
im_label = np.array(Image.open(infile_raw).convert('L')).reshape(
1, pixelNum)
'''开始预测'''
pred = sess.run(y, feed_dict={x: im_test, keep_prob: 1.0})
im_out = im_test - pred
im_out = im_out * 255.0
im_out = im_out.astype(int)
for i in range(im_out.shape[0]):
for j in range(im_out.shape[1]):
if im_out[i][j] < 0:
im_out[i][j] = 0
elif im_out[i][j] > 255:
im_out[i][j] = 255
pred = pred * 255.0
pred = pred.astype(int)
for i in range(pred.shape[0]):
for j in range(pred.shape[1]):
if pred[i][j] < 0:
pred[i][j] = -pred[i][j]
elif pred[i][j] > 255:
pred[i][j] = 255
scipy.misc.imsave('../vis/0' + filename + '_test.jpg',
im_test.reshape(picSize[0], picSize[1]))
scipy.misc.imsave('../vis/1' + filename + '.jpg',
im_out.reshape(picSize[0], picSize[1]))
scipy.misc.imsave('../vis/3' + filename + '_noise.jpg',
pred.reshape(picSize[0], picSize[1]))
scipy.misc.imsave('../vis/2' + filename + '_label.jpg',
im_label.reshape(picSize[0], picSize[1]))
print("Before ", end=':')
getSNR.getSNR(
Image.open(r'../vis/0lena_test.jpg').convert('L'),
Image.open(r'../vis/2lena_label.jpg').convert('L'))
print("After ", end=':')
getSNR.getSNR(
Image.open(r'../vis/1lena.jpg').convert('L'),
Image.open(r'../vis/2lena_label.jpg').convert('L'))
from src import test_rgb_big
import os
from PIL import Image
from src import getSNR
pic_with_noise_path = r'../show/Test12_with_noise/'
pic_save_path = r'../show/Test12_after/'
noise_path = r'../show/noise/'
pic_raw_path = r'../show/Test12/'
infile = '09.png'
print(infile)
#test_rgb_big.testImg(Image.open(pic_with_noise_path + infile).convert('RGB'), pic_save_path, infile, noise_path)
print(getSNR.getSNR(Image.open(pic_save_path + infile), Image.open(pic_raw_path + infile)))
for infile in os.listdir(pic_save_path):
print(infile)
print(getSNR.getSNR(Image.open(pic_save_path + infile), Image.open(pic_raw_path + infile)))
print("After ", end=':')
getSNR.getSNR(
Image.open(r'../vis/1lena.jpg').convert('L'),
Image.open(r'../vis/2lena_label.jpg').convert('L'))
scipy.misc.imsave('../vis/1lena.jpg', im_out.reshape(256, 256))
scipy.misc.imsave('../vis/3lena_noise.jpg', pred.reshape(256, 256))
scipy.misc.imsave('../vis/2lena_label.jpg', im_label.reshape(256, 256))
print(pred)
print(im_out)
print(im_label)
print("Before ", end=':')
getSNR.getSNR(
Image.open(r'../vis/0lena_test.jpg').convert('L'),
Image.open(r'../vis/2lena_label.jpg').convert('L'))
print("After ", end=':')
getSNR.getSNR(
Image.open(r'../vis/1lena.jpg').convert('L'),
Image.open(r'../vis/2lena_label.jpg').convert('L'))
print("Ps ", end=':')
getSNR.getSNR(
Image.open(r'../vis/lena_ps.jpg').convert('L'),
Image.open(r'../vis/2lena_label.jpg').convert('L'))
print("----------------------------------")
testImg(infile=r'../pic_gauss/279.png',
infile_raw=r'../pic_raw/279.png',
filename='new')