Perceptual Losses

Extracting Facial Features

Verifeyed

docker run -it klokoy/pdf2htmlex bash

pdf2htmlEX --zoom 1.3 test.pdf

Removed watermark

后来网上看到应该是显卡驱动的问题，于是在这一步，光标移动到Install Ubuntu-按e-修改

'quiet splash ---” 为“quiet splash nomodeset”

然后F10保存就能进入安装界面了，但是分辨率非常感人(→_→)

于是我只能参考别人按照步骤的截图，数着Tab，一步一步安装，

one_class_svm

mendeley

eps 图像

jigsaw puzzle

pip install keras== 2.1 . 2 -i https: //pypi.tuna.tsinghua.edu.cn/simple

pip install tensorflow-gpu== 1.2 . 1 -i https: //pypi.tuna.tsinghua.edu.cn/simple

gan  PSNR ssim srgan

IJCAI

I

CTR预估模型

配音阁

CMU Multi-PIE人脸数据库介绍

http://yongfeng.me/

唇语识别 

Artificial muscle

ta = tf.zeros((2,2))

print(ta)

Tensor("zeros_1:0", shape=(2, 2), dtype=float32)

print(ta.eval())

numpy

a = np.zeros((2,2))

print(a)

https://github.com/hzy46/fast-neural-style-tensorflow

动力外骨骼机器人

python tensorflow 生成对抗网络 差异比较

Image Inpainting

梯形 矫正

立体定向

Semantic Image Segmentation via Deep Parsing Network

Perceptual Losses

okay, i found the solution to solve it， i tried to python3 setup.py build and install in research folder, after done that, i executed the code,and worked

• 先下载你要安装的包，并解压到磁盘下；
• 进入到该文件的setup.py 目录下 ，打开cmd，并切换到该目录下；
• 先执行 python setup.py build
• 然后执行 python setup.py install

pip install --upgrade https://mirrors.tuna.tsinghua.edu.cn/tensorflow/windows/gpu/tensorflow_gpu-1.3.0rc0-cp35-cp35m-win_amd64.whl

exit(0)

MIAS标准数据集

Windows Driver Kit

Image Completion 

aiimooc

1

https://baijia.baidu.com/s?id=1575151965430656

[email protected]

http://www.lfd.uci.edu/~gohlke/pythonlibs/#scikit-image

ceil

https://orcid.org/

0000-0003-2698-8507下载数据库

 DDSM

下载数据库  转换 泡一个模型 后改进 。3个月

traffic sign detection

https://luna16.grand-challenge.org/

https://www.kaggle.com/arnavkj95/candidate-generation-and-luna16-preprocessing

Image tamper detection

“贴心小棉袄” iTBra智能文胸检测女性乳房癌症

pip install  - - upgrade

 这个是乳腺癌的，RIDER Breast 乳腺癌MRI影像数据，400M左右，http://dataju.cn/Dataju/web/datasetInstanceDetail/415 

点配准算法

75 16

# 最小化方差
loss = tf.reduce_mean(tf.square(y - y_data))

0615  目标之后添加 高斯 噪声 ？？或许 测试 均匀噪声 ？？

super resolution

def load_model(session,netmodel_path,param_path):

   new_saver = tf.train.import_meta_graph(netmodel_path)
   new_saver.restore(session, param_path)
   x= tf.get_collection('real_A')[0]#在训练阶段需要调用tf.add_to_collection('test_images',test_images),保存之
   y = tf.get_collection("fake_B")[0]
   return  x,y

y_np=sess.run(tensorB,feed_dict = {tensorA:images})

zxing  zba

码湿度，解码 ，二维码以外

神经外科导航定位机器人

https://gist.github.com/33c758ad77e6e6531392

SimGAN.

https://gist.github.com/33c758ad77e6e6531392

output_list = []
for j in range(0,16):
 show_img1 = features[j, :, :, :]

 #lab = show_img1.reshape([1] + list(show_img1.shape)).astype(np.float32)
 #show_img2 = sess.run(show_img1)
 #show_img2 = tf.reshape(show_img1, [64, 64, 3])
 show_img2 = tf.cast(show_img1, tf.float32)/255.0
 #show_img = tf.image.resize_images(show_img, [16, 16])
 #show_img3 = show_img2
 show_img3 = salt(show_img2, 7)
 show_img4 = show_img3
 show_img5 = ops.convert_to_tensor(show_img4, dtype=tf.float32,name='labels_and_features')
 #show_img3 = tf.reshape(show_img2, [64, 64, 3])
 #lab = show_img.reshape([1] + list(show_img.shape)).astype(np.float32)
 #jab = show_img3
 #train_features[i, :, :, :] = lab
 #lab = tf.reshape(lab, [64, 64, 3])
 output_list.append(show_img5)


 #lab2 = tf.reshape(output_list, [16, 64, 64, 3])

 #lab = tf.reshape(lab, [16,64, 64, 3])
 #lab=tf.convert_to_tensor(output_list)
jab2 = ops.convert_to_tensor(output_list, dtype=tf.float32, name='labels_and_features')

train_features2 = train_features
test_features2 = test_features

train_features1 = sess.run(train_features)
test_features1 = sess.run(test_features)

php  imagefilter  imagecolorstotal

yum install python-imaging

随机过程论（三节连上）｛1-16周[教师:孙洪祥,地点:3-235]｝星期四 前三节

import cv2
import numpy as np


def salt(img, img2,n):
 noff=int((n-1)/2)
   for i in range(img.shape[1]):
       for j in range(img.shape[1]):
        # img.shape[0] -- 取得img 的列（图片的高）
        # img.shape[1] -- 取得img 的行（图片的宽）
        #i = int(np.random.random() * img.shape[1]);
        #j = int(np.random.random() * img.shape[0]);


       
            img[j, i, 0] = 255
            img[j, i, 1] = 255
            img[j, i, 2] = 255
       return img




img = cv2.imread("000001.jpg")
img2 = img.copy()
saltImage = salt(img, img2,11)


cv2.imshow("Salt", saltImage)
cv2.waitKey(0)
cv2.destroyAllWindows()

http://blog.csdn.net/chinamming?viewmode=contents

https://public.kitware.com/IGSTKWIKI/index.php/How_to_Build_IGSTK(old)