Repository: Sibozhu/MotionBlur-detection-by-CNN
Branch: master
Commit: 93ee3775a087
Files: 19
Total size: 55.4 MB
Directory structure:
gitextract_1_ro849x/
├── Prediction/
│ └── prediction_start.py
├── Processing/
│ ├── blur_utils.py
│ └── processing_utils.py
├── README.md
├── Report/
│ ├── nicefrac.sty
│ ├── nips_2017.aux
│ ├── nips_2017.log
│ ├── nips_2017.out
│ ├── nips_2017.sty
│ └── nips_2017.tex
├── cnn.py
├── image_slice.py
├── mergingpatches.py
├── motionblur.h5
├── motionblur.py
├── processing_utils.py
├── slicing.py
├── test_img_generator.py
└── trainingdatacreate.py
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FILE CONTENTS
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================================================
FILE: Prediction/prediction_start.py
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import keras
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FILE: Processing/blur_utils.py
================================================
================================================
FILE: Processing/processing_utils.py
================================================
'''
utility function package for image processing
by Sibo Zhu, Kieran Xiao Wang
2017.08.24
'''
import numpy as np
import cv2
import random
from PIL import Image
import PIL
from natsort import natsorted
import os, os.path
def save_image(image_np_array, image_save_path):
'''
!!! Sibo, Please complete this function !!!
:param image_np_array: 3-D numpy array
:param image_save_path: string
:return:
'''
im = Image.fromarray(image_np_array)
im.save(image_save_path)
def patch_merge_to_one_from_folder(patch_dir):
'''
merge patches into a whole image
!!! Sibo, please complete this function !!!
!!! now you are using naming for indicating patch location and blur/no blur, which is fine
!!! the patch_dir is supposed to contain all patches of an image(no matter blured or not)
!!! you may want to use os.walk
!!! if so avoid to have any other .jpg file except image patches(e.g. do not save the whole image in .jpg in that folder)
:param patch_dir: [string] directory to the folder that contains all patches(of an image)
:return: [3-d array] whole image in np array
'''
patch_dir = patch_dir
pi_imgs = []
valid_images = [".jpg"]
for f in os.listdir(patch_dir):
ext = os.path.splitext(f)[1]
if ext.lower() not in valid_images:
continue
pi_imgs.append(Image.open(os.path.join(patch_dir, f)))
total = len(pi_imgs)
print(str(len(pi_imgs)) + " patches in total")
#######################################################
"""Loading all the images from directory"""
dir = []
valid_images = [".jpg"]
for f in os.listdir(patch_dir):
ext = os.path.splitext(f)[1]
if ext.lower() not in valid_images:
continue
dir.append(os.path.join(patch_dir, f))
#####################################################
"""concat images with numpy"""
def concat_img_horizon(list_imgs):
imgs = [PIL.Image.open(i) for i in list_imgs]
# pick the image which is the smallest, and resize the others to match it (can be arbitrary image shape here)
min_shape = sorted([(np.sum(i.size), i.size) for i in imgs])[0][1]
#
imgs_comb = np.hstack((np.asarray(i.resize(min_shape)) for i in imgs))
imgs_comb = PIL.Image.fromarray(imgs_comb)
return imgs_comb
def concat_img_vertical(list_imgs):
imgs = [PIL.Image.open(i) for i in list_imgs]
# pick the image which is the smallest, and resize the others to match it (can be arbitrary image shape here)
min_shape = sorted([(np.sum(i.size), i.size) for i in imgs])[0][1]
imgs_comb = np.hstack((np.asarray(i.resize(min_shape)) for i in imgs))
# for a vertical stacking it is simple: use vstack
imgs_comb = np.vstack((np.asarray(i.resize(min_shape)) for i in imgs))
imgs_comb = PIL.Image.fromarray(imgs_comb)
return imgs_comb
def concat_temp_horizon(list_imgs):
imgs_comb = np.hstack((np.asarray(i) for i in list_imgs))
imgs_comb = PIL.Image.fromarray(imgs_comb)
return imgs_comb
#########################################################
"""counting number of whole pictures in the folder"""
max_index = 0
for i in range(len(dir)):
flag = int(dir[i].split("/")[-1].split(",")[0].split("_")[0])
if flag > max_index:
max_index = flag
#############################################
"""placing patches to their certain picture"""
pic_index = {}
for elem in range(max_index + 1):
pic_index[elem] = []
for j in range(len(dir)):
for k in range(len(pic_index)):
if int(dir[j].split("/")[-1].split(",")[0].split("_")[0]) == k:
pic_index[k].append(dir[j])
print('the first picture contains ' + str(len(pic_index[0])) + ' patches')
##########################################
"""getting the total columns of picture"""
def get_total_column(list):
max_col = 0
for l in range(len(list)):
col_flag = int(pic_index[0][l].split("/")[-1].split(",")[0].split("_")[1])
if col_flag > max_col:
max_col = col_flag
return max_col
print ("this picture's total column is " + str(get_total_column(pic_index[0])))
#########################################
"""getting the total rows of picture"""
def get_total_row(list):
max_row = 0
for o in range(len(list)):
row_flag = int(list[o].split("/")[-1].split(",")[0].split("_")[2])
if row_flag > max_row:
max_row = row_flag
return max_row
print ("this picture's total row is " + str(get_total_row(pic_index[0])))
########################################
"""sorting this picture's patches with order of name"""
def sort_picture_patches(list):
return natsorted(list)
#####################################
"""doing global merging"""
for a in range(len(pic_index)):
max_col = get_total_column(pic_index[a])
max_row = get_total_row(pic_index[a])
pic_index[a] = sort_picture_patches(pic_index[a])
col_index = {}
for elem in range(max_col + 1):
col_index[elem] = []
for j in range(len(pic_index[a])):
for k in range(max_col):
if int(pic_index[a][j].split("/")[-1].split(",")[0].split("_")[1]) == k:
col_index[k].append(pic_index[a][j])
saver = []
for i in range(max_col - 1):
flag = concat_img_vertical(col_index[i])
saver.append(flag)
res = concat_temp_horizon(saver)
img = PIL.Image.open(res).convert("L")
arr = np.array(img)
return arr
def mass_patch_merge_to_one_from_folder(patch_dir,save_dir):
'''
After implementing the merging patches back to a whole image,
we can also do that same thing to a folder that contains several patches that
come from different images and merge and save them back to those original images (with partially
blurry) based on the naming habit of slicing images.
:param patch_dir: [string] directory to the folder that contains all patches(of an image)
:param save_dir: [string] directory to the folder that used to save all those merged images
:return: This time there's no return
'''
patch_dir = patch_dir
result_dir = save_dir
pi_imgs = []
valid_images = [".jpg"]
for f in os.listdir(patch_dir):
ext = os.path.splitext(f)[1]
if ext.lower() not in valid_images:
continue
pi_imgs.append(Image.open(os.path.join(patch_dir, f)))
total = len(pi_imgs)
print(str(len(pi_imgs)) + " patches in total")
#######################################################
"""Loading all the images from directory"""
dir = []
valid_images = [".jpg"]
for f in os.listdir(patch_dir):
ext = os.path.splitext(f)[1]
if ext.lower() not in valid_images:
continue
dir.append(os.path.join(patch_dir, f))
#####################################################
"""concat images with numpy"""
def concat_img_horizon(list_imgs):
imgs = [PIL.Image.open(i) for i in list_imgs]
# pick the image which is the smallest, and resize the others to match it (can be arbitrary image shape here)
min_shape = sorted([(np.sum(i.size), i.size) for i in imgs])[0][1]
#
imgs_comb = np.hstack((np.asarray(i.resize(min_shape)) for i in imgs))
imgs_comb = PIL.Image.fromarray(imgs_comb)
return imgs_comb
def concat_img_vertical(list_imgs):
imgs = [PIL.Image.open(i) for i in list_imgs]
# pick the image which is the smallest, and resize the others to match it (can be arbitrary image shape here)
min_shape = sorted([(np.sum(i.size), i.size) for i in imgs])[0][1]
imgs_comb = np.hstack((np.asarray(i.resize(min_shape)) for i in imgs))
# for a vertical stacking it is simple: use vstack
imgs_comb = np.vstack((np.asarray(i.resize(min_shape)) for i in imgs))
imgs_comb = PIL.Image.fromarray(imgs_comb)
return imgs_comb
def concat_temp_horizon(list_imgs):
imgs_comb = np.hstack((np.asarray(i) for i in list_imgs))
imgs_comb = PIL.Image.fromarray(imgs_comb)
return imgs_comb
#########################################################
"""counting number of whole pictures in the folder"""
max_index = 0
for i in range(len(dir)):
flag = int(dir[i].split("/")[-1].split(",")[0].split("_")[0])
if flag > max_index:
max_index = flag
#############################################
"""placing patches to their certain picture"""
pic_index = {}
for elem in range(max_index + 1):
pic_index[elem] = []
for j in range(len(dir)):
for k in range(len(pic_index)):
if int(dir[j].split("/")[-1].split(",")[0].split("_")[0]) == k:
pic_index[k].append(dir[j])
print('the first picture contains ' + str(len(pic_index[0])) + ' patches')
##########################################
"""getting the total columns of picture"""
def get_total_column(list):
max_col = 0
for l in range(len(list)):
col_flag = int(pic_index[0][l].split("/")[-1].split(",")[0].split("_")[1])
if col_flag > max_col:
max_col = col_flag
return max_col
print ("this picture's total column is " + str(get_total_column(pic_index[0])))
#########################################
"""getting the total rows of picture"""
def get_total_row(list):
max_row = 0
for o in range(len(list)):
row_flag = int(list[o].split("/")[-1].split(",")[0].split("_")[2])
if row_flag > max_row:
max_row = row_flag
return max_row
print ("this picture's total row is " + str(get_total_row(pic_index[0])))
########################################
"""sorting this picture's patches with order of name"""
def sort_picture_patches(list):
return natsorted(list)
#####################################
"""doing global merging"""
for a in range(len(pic_index)):
max_col = get_total_column(pic_index[a])
max_row = get_total_row(pic_index[a])
pic_index[a] = sort_picture_patches(pic_index[a])
col_index = {}
for elem in range(max_col + 1):
col_index[elem] = []
for j in range(len(pic_index[a])):
for k in range(max_col):
if int(pic_index[a][j].split("/")[-1].split(",")[0].split("_")[1]) == k:
col_index[k].append(pic_index[a][j])
saver = []
for i in range(max_col - 1):
flag = concat_img_vertical(col_index[i])
saver.append(flag)
res = concat_temp_horizon(saver)
res.save(result_dir + str(a) + '.jpg')
def image_to_patch(image_path, patch_size, patch_dir):
'''
cut an image into patch with certain size
!!! Sibo, please complete this function !!!
:param image_path: [string] path to the image(e.g. ./whole_image.jpg)
:param patch_size: [tuple] i.g. (30(length),30(witch))
:param patch_dir: [string] dir where to save the patch dir.(patch dir is defined to be the folder that contains all
image patches of an image)
:return:
'''
img = Image.open(image_path)
(imageWidth, imageHeight) = img.size
gridx = patch_size
gridy = patch_size
rangex = img.width / gridx
rangey = img.height / gridy
print rangex * rangey
for x in xrange(rangex):
for y in xrange(rangey):
bbox = (x * gridx, y * gridy, x * gridx + gridx, y * gridy + gridy)
slice_bit = img.crop(bbox)
slice_bit.save(patch_dir + str(x) + '_' + str(y) + '.jpg', optimize=True,
bits=6)
print(patch_dir + str(x) + '_' + str(y) + '.jpg')
print(imageWidth)
def directory_to_patch(patch_size,original_path,no_blur_path,blur_path,all_img_path):
'''
We take a directory that contains several whole pictures and cut then into custom size of patches,
then apply 50% chance blur and non-blur to those patches, save them into blurry folder, non-blurry folder,
and a folder that contains all blurry and non-blurry patches with order.
:param patch_size: [integer] The custom patch size we want, e.g:for 30x30 patch, enter '30'
:param original_path: [string] The original path that contains all the original pictures without any modification
:param no_blur_path: [string] The destination path that contains all the non-blurry patches with order
:param blur_path: [string] The destination path that contains all the blurry patches with order
:param all_img_path: [string] The destination path that contains all the patches with order
:return: There's no return in this function, all the modified patches are saved into the destination path
'''
#motion blur preset
size = 15
gridx = patch_size
gridy = patch_size
kernel_motion_blur = np.zeros((size, size))
kernel_motion_blur[int((size - 1) / 2), :] = np.ones(size)
kernel_motion_blur = kernel_motion_blur / size
# go through every image in source folder
print('begin loading images')
pi_imgs = []
cv_imgs = []
valid_images = [".jpg"]
for f in os.listdir(original_path):
ext = os.path.splitext(f)[1]
if ext.lower() not in valid_images:
continue
pi_imgs.append(Image.open(os.path.join(original_path, f)))
cv_imgs.append(cv2.imread(os.path.join(original_path, f)))
print('finished loading images')
#
# looping to create blurry and non-blurry images in 50% chance
for i in range(len(pi_imgs)):
img = pi_imgs[i]
(imageWidth, imageHeight) = img.size
rangex = imageWidth / gridx
rangey = imageHeight / gridy
for x in xrange(rangex):
for y in xrange(rangey):
bbox = (x * gridx, y * gridy, x * gridx + gridx, y * gridy + gridy)
slice_bit = img.crop(bbox)
if random.randrange(2) == 0:
slice_bit.save(no_blur_path + str(i) + '_' + str(x) + '_' + str(y) + ',noblur.jpg', optimize=True,
bits=6)
slice_bit.save(all_img_path + str(i) + '_' + str(x) + '_' + str(y) + ',noblur.jpg', optimize=True,
bits=6)
print(str(i))
else:
slice_bit.save(blur_path + str(i) + '_' + str(x) + '_' + str(y) + ',blur.jpg', optimize=True, bits=6)
img1 = cv2.imread(blur_path + str(i) + '_' + str(x) + '_' + str(y) + ',blur.jpg')
output = cv2.filter2D(img1, -1, kernel_motion_blur)
cv2.imwrite(blur_path + str(i) + '_' + str(x) + '_' + str(y) + ',blur.jpg', output)
cv2.imwrite(all_img_path + str(i) + '_' + str(x) + '_' + str(y) + ',blur.jpg', output)
print(str(i))
================================================
FILE: README.md
================================================
# MotionBlur-detection-by-CNN
```
To run the cnn model, just enter "cnn.py" and run the code. It might take
couple hours to run it if you are using personal computers. Usinga server
or GPU to run this code would significantly lower the running time.
```
## Abstract
```
Our project aims to detect motion blur from a single, blurry image. We propose
a deep learning approach to predict the probabilistic distribution of motion blur at
the patch level using a Convolutional Neural Network (CNN).
```
## 1 Our approach
```
We approached the problem by slicing 100 images into 30x30 patches, and applied our own motion
blur algorithm to them (with a random rate of 50%). We then labeled the blurry and non-blurry
patches with 0s and 1s (0 for still, 1 for blurry), and loaded the modified images in as our training
data.
```
```
1.1 Generating the training data
```
We generated the training data using images from thePascal Visual Object Classes Challenge 2010
(VOC2010)data set. Our work was done in Python using thePIL,numpy,opency, andoslibraries.
```
Once we had the original images fromPascal, we had to modify them to fit our needs. We needed to
have 100 images, each partially blurred and with a corresponding matrix indicating which part of the
image is blurred.
We achieved this by:
```
1. Making a blurred copy of the original image.
2. Cutting both images (original and blurry) into 30 × 30 patches.
3. Creating a 2D List in Python of size 30 × 30 , to represent each image patch We initialize
each element to 0 (to represent non-blurry).
4. Picking half the patches from the list and marking them as 1 (to represent blurry).
5. Putting the final image together to get a partially-blurred, qualifying image (and its corre-
sponding matrix).
6. Saving the image as "n.jpg" (where n is the serial number of the image), and adding the
matrix to a list (to form a 3D ’list of lists’) containing the matrices of all the image.
```
Image of the original-to-blur process.
```
```
Image of the image splitting process.
```
```
The final image with its corresponding matrix.
```
We repeat the above for all 100 images, until we end up with a folder containing partially-blurred
images"0.jpg"through"100.jpg", and a 3D list (named"labels") that contains 100 matrices. This
lets us access the matrix for image"31.jpg", for example, by querying for"labels[31]".
## 2 Learning the Convolutional Neural Network (CNN)
Once we had the prepared images,we loaded them into our training set.We ran into a prob-
lem loading the images into anumpyarray, where our images were of the form (30,30,3), while
theKeras.Conv2Dlayer required input to be of the form (3,30,30). We solved this by using the
numpy.swapexes()function to alter the images’ shape in order to fit the convolutional layer.
We then apply the CNN learning model. First, we apply a Convolution2D layer with 7 × 7 filters,
followed by aReLUfunction. TheConvlayer’s parameters consiste of a set of learnable filters. Each
filter is small spatially, but extends through the depth of the input volume.
During the forward pass, we slide each filter across the width and height of the input volume and
compute the dot products between the entries of the filter and the input at any position.ReLUis the
rectifier function- an activation function that can be used by neurons, just like any other activation
function. A node using the rectifier activation function is called aReLU node.ReLUsets all negative
values in the matrix x to 0, and all other values are kept constant. ReLU us computed after the
convolution, and thus a nonlinear activation function (liketanhorsigmoid).
After that,we add aMaxPooling2Dlayerwith a pool size of 2 × 2. MaxPooling is a sample-
based discretization process. The objective is to down-sample an input representation, reducing
its dimensionality and allowing for assumptions to be made about features contained in the binned
sub-regions.
We thenadd aDropoutlayerwith dropout rate of 0.2, which makes our learning process faster.
Dropout randomly ignoring nodes is useful in CNN models because it prevents interdependencies
from emerging between nodes. This allows the network to learn more and form a more robust
relationship. We then do the’Conv2D, ReLU, MaxPooling2D, Dropout’circle again. Finally, we add
a fully-connected layer withReLU, and thensoftmaxthe result.Softmaxis a classifier at the end of
the neural network — a logistic regression to regularize outputs to a value between 0 and 1.
We set our model’s learning rate to be 0. 01. This might generally be too big, but we made this
decision for the sake of brevity - it was the fastest way to show a result. We chose a batch size of 126
(because we had large training data). We also choseAdamas our optimizer as it’s the most efficient
optimizer for our model.
After training with 100 epochs,we had testing accuracy of 92%, which is a very optimal rate for
our model. Our training model is saved in an HDF5 file,"motionblur.h5".
## 3 Conclusion
In this report, we have proposed a novel CNN-based motion blur detection apporach. We learn an
effective CNN for estimating motion blur from local patches. In the future, we are interested in
designing a CNN for eastimating motion kernels. We are also interested in design a CNN non-uniform
motion deblurring method.
## Acknowledgement
This report has been prepared for the Boston University Machine Learning course (CS 542), taken
over the Summer 2, 2017 semester by the listed authors. It is intended to be used in compliance of
the requirements of the course.
## References
[1] Jian Sun, Wenfei Cao, Zongben Xu, Jean Ponce. Learning a convolutional neural network for non-uniform
motion blur removal. CVPR 2015 - IEEE Conference on Computer Vision and Pattern Recognition 2015, Jun
2015, Boston, United States. IEEE, 2015,.
[2] “Visual Object Classes Challenge 2010 (VOC2010).” The PASCAL Visual Object Classes Challenge 2010
(VOC2010), PASCAL, 2010, host.robots.ox.ac.uk/pascal/VOC/voc2010/.
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FILE: Report/nicefrac.sty
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================================================
FILE: Report/nips_2017.out
================================================
\BOOKMARK [1][-]{section.1}{Our approach}{}% 1
\BOOKMARK [2][-]{subsection.1.1}{Generating the training data}{section.1}% 2
\BOOKMARK [1][-]{section.2}{Learning the Convolutional Neural Network \(CNN\)}{}% 3
\BOOKMARK [1][-]{section.3}{Conclusion}{}% 4
================================================
FILE: Report/nips_2017.sty
================================================
% partial rewrite of the LaTeX2e package for submissions to the
% Conference on Neural Information Processing Systems (NIPS):
%
% - uses more LaTeX conventions
% - line numbers at submission time replaced with aligned numbers from
% lineno package
% - \nipsfinalcopy replaced with [final] package option
% - automatically loads times package for authors
% - loads natbib automatically; this can be suppressed with the
% [nonatbib] package option
% - adds foot line to first page identifying the conference
%
% Roman Garnett (garnett@wustl.edu) and the many authors of
% nips15submit_e.sty, including MK and drstrip@sandia
%
% last revision: March 2017
\NeedsTeXFormat{LaTeX2e}
\ProvidesPackage{nips_2017}[2017/03/20 NIPS 2017 submission/camera-ready style file]
% declare final option, which creates camera-ready copy
\newif\if@nipsfinal\@nipsfinalfalse
\DeclareOption{final}{
\@nipsfinaltrue
}
% declare nonatbib option, which does not load natbib in case of
% package clash (users can pass options to natbib via
% \PassOptionsToPackage)
\newif\if@natbib\@natbibtrue
\DeclareOption{nonatbib}{
\@natbibfalse
}
\ProcessOptions\relax
% fonts
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\renewcommand{\sfdefault}{phv}
% change this every year for notice string at bottom
\newcommand{\@nipsordinal}{31st}
\newcommand{\@nipsyear}{2017}
\newcommand{\@nipslocation}{Long Beach, CA, USA}
% handle tweaks for camera-ready copy vs. submission copy
\if@nipsfinal
\newcommand{\@noticestring}{%
\@nipsordinal\/ Conference on Neural Information Processing Systems
(NIPS \@nipsyear), \@nipslocation.%
}
\else
\newcommand{\@noticestring}{%
Submitted to \@nipsordinal\/ Conference on Neural Information
Processing Systems (NIPS \@nipsyear). Do not distribute.%
}
% line numbers for submission
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% for perfect author name centering
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================================================
FILE: Report/nips_2017.tex
================================================
\documentclass{article}
\usepackage[final]{nips_2017}
% to compile a camera-ready version, add the [final] option, e.g.:
% \usepackage[final]{nips_2017}
\usepackage[utf8]{inputenc} % allow utf-8 input
\usepackage[T1]{fontenc} % use 8-bit T1 fonts
\usepackage{hyperref} % hyperlinks
\usepackage{url} % simple URL typesetting
\usepackage{booktabs} % professional-quality tables
\usepackage{amsfonts} % blackboard math symbols
\usepackage{nicefrac} % compact symbols for 1/2, etc.
\usepackage{microtype} % microtypography
\usepackage{graphicx} % to allow images
\graphicspath{ {images/} }
\title{Detecting Motion Blur in Images}
\author{
Karan Varindani \\
\And
Wenyang Zhang \\
\And
Sibo Zhu \\
}
\begin{document}
\maketitle
\begin{abstract}
Our project aims to estimate motion blur from a single, blurry image.
We propose a deep learning approach to predict the probabilistic distribution
of motion blur at the patch level using a Convolutional Neural Network (CNN).
\end{abstract}
\section{Our approach}
We approached the problem by slicing 100 images into 30x30 patches, and applied
our own motion blur algorithm to them (with a random rate of 50\%). We then labeled
the blurry and non-blurry patches with 0s and 1s (0 for still, 1 for blurry), and
loaded the modified images in as our training data.
\subsection{Generating the training data}
We generated the training data using images from the
\textit{\href{http://host.robots.ox.ac.uk/pascal/VOC/voc2010/}{Pascal Visual Object
Classes Challenge 2010 (VOC2010)}} data set. Our work was done in Python using the
\textit{PIL}, \textit{numpy}, \textit{opency}, and \textit{os} libraries.
Once we had the original images from \textit{Pascal}, we had to modify them to fit our
needs. We needed to have 100 images, each partially blurred and with a corresponding
matrix indicating which part of the image is blurred.
We achieved this by:
\begin{enumerate}
\item Making a blurred copy of the original image.
\item Cutting both images (original and blurry) into $30\times30$ patches.
\item Creating a 2D List in Python of size $30\times30$, to represent each image patch
We initialize each element to 0 (to represent non-blurry).
\item Picking half the patches from the list and marking them as 1 (to represent
blurry).
\item Putting the final image together to get a partially-blurred, qualifying image
(and its corresponding matrix).
\item Saving the image as "n.jpg" (where n is the serial number of the image), and
adding the matrix to a list (to form a 3D 'list of lists') containing the matrices
of all the image.
\end{enumerate}
\includegraphics[width=\textwidth]{blur} \\
\textit{Image of the original-to-blur process.} \\
\includegraphics[width=\textwidth]{patches} \\
\textit{Image of the image splitting process.} \\
\includegraphics[width=\textwidth]{matrix} \\
\textit{The final image with its corresponding matrix.} \\
We repeat the above for all 100 images, until we end up with a folder containing
partially-blurred images \textit{"0.jpg"} through \textit{"100.jpg"}, and a 3D list
(named \textit{"labels"}) that contains 100 matrices. This lets us access the matrix for
image \textit{"31.jpg"}, for example, by querying for \textit{"labels[31]"}.
\section{Learning the Convolutional Neural Network (CNN)}
Once we had the prepared images, \textbf{we loaded them into our training set.} We ran
into a problem loading the images into a \textit{numpy} array, where our images were of
the form (30,30,3), while the \textit{Keras.Conv2D} layer required input to be of the form
(3,30,30). We solved this by using the \textit{numpy.swapexes()} function to alter the
images' shape in order to fit the convolutional layer.
\paragraph{We then apply the CNN learning model.}
First, we apply a Convolution2D layer with $7\times7$ filters, followed by a \textit{ReLU}
function. The \textit{Conv} layer's parameters consiste of a set of learnable filters.
Each filter is small spatially, but extends through the depth of the input volume.
\paragraph{During the forward pass,}
we slide each filter across the width and height of the input
volume and compute the dot products between the entries of the filter and the input at
any position. \textit{ReLU} is the rectifier function- an activation function that can be
used by neurons, just like any other activation function. A node using the rectifier
activation function is called a \textit{ReLU node}. \textit{ReLU} sets all negative values
in the matrix x to 0, and all other values are kept constant. ReLU us computed after the
convolution, and thus a nonlinear activation function (like \textit{tanh} or
\textit{sigmoid}).
After that, \textbf{we add a \textit{MaxPooling2D} layer} with a pool size of $2\times2$.
MaxPooling is a sample-based discretization process. The objective is to down-sample an
input representation, reducing its dimensionality and allowing for assumptions to be made
about features contained in the binned sub-regions.
We then \textbf{add a \textit{Dropout} layer} with dropout rate of 0.2, which makes our
learning process faster. Dropout randomly ignoring nodes is useful in CNN models because
it prevents interdependencies from emerging between nodes. This allows the network to learn
more and form a more robust relationship. We then do the \textit{'Conv2D, ReLU,
MaxPooling2D, Dropout'} circle again. Finally, we add a fully-connected layer with
\textit{ReLU}, and then \textit{softmax} the result. \textit{Softmax} is a classifier at
the end of the neural network — a logistic regression to regularize outputs to a value
between 0 and 1.
\paragraph{We set our model's learning rate to be $0.01$.} This might generally be too
big, but we made this decision for the sake of brevity - it was the fastest way to show
a result. We chose a batch size of 126 (because we had large training data). We also
chose \textit{Adam} as our optimizer as it's the most efficient optimizer for our model.
After training with 100 epochs, \textbf{we had testing accuracy of 92\%}, which is a
very optimal rate for our model. Our training model is saved in an HDF5 file,
\textit{"motionblur.h5"}.
\includegraphics[width=\textwidth]{accuracy} \\
\section{Conclusion}
In this report, we have proposed a novel CNN-based motion blur detection apporach. We
learn an effective CNN for estimating motion blur from local patches. In the future,
we are interested in designing a CNN for eastimating motion kernels. We are also interested
in design a CNN non-uniform motion deblurring method.
\section*{Acknowledgement}
This report has been prepared for the Boston University Machine Learning course (CS 542),
taken over the Summer 2, 2017 semester by the listed authors. It is intended to be used
in compliance of the requirements of the course.
\section*{References}
\medskip
\small
[1] Jian Sun, Wenfei Cao, Zongben Xu, Jean Ponce. Learning a convolutional neural network
for non-uniform motion blur removal. CVPR 2015 - IEEE Conference on Computer Vision and
Pattern Recognition 2015, Jun 2015, Boston, United States. IEEE, 2015, .
[2] “Visual Object Classes Challenge 2010 (VOC2010).” The PASCAL Visual Object Classes
Challenge 2010 (VOC2010), PASCAL, 2010, host.robots.ox.ac.uk/pascal/VOC/voc2010/.
\end{document}
================================================
FILE: cnn.py
================================================
import numpy
import cv2
import PIL
import os, os.path
import matplotlib.pyplot as plt
from PIL import Image
from keras.callbacks import EarlyStopping
from sklearn.utils import shuffle
from sklearn.model_selection import train_test_split
from keras.constraints import maxnorm
from keras.preprocessing.image import ImageDataGenerator, load_img, img_to_array
from matplotlib import pyplot
from scipy.misc import toimage
from keras.models import Sequential
from keras.layers import Dropout
from keras import callbacks
from keras.layers import Dense, Flatten
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.optimizers import RMSprop, SGD,Adam
from keras.layers.convolutional import MaxPooling2D
from keras.layers.convolutional import Convolution2D
from keras.utils import np_utils
from keras import backend as k
k.set_image_dim_ordering('th')
seed = 7
numpy.random.seed(seed)
trainblur_directory = './s_cnn/train/blur/'
trainnoblur_directory = './s_cnn/train/no_blur/'
testblur_directory = './s_cnn/test/0/blur/'
testnoblur_directory = './s_cnn/test/0/no_blur/'
filepath = "./s_cnn/models/"
num_classes = 2
#########################################################
#loading blurry images
img_data_list1=[]
data_dir_list1 = os.listdir(trainblur_directory)
img_list1=os.listdir(trainblur_directory)
for img in img_list1:
input_img=cv2.imread(trainblur_directory + img )
input_img=numpy.swapaxes(input_img,0,2)
img_data_list1.append(input_img)
img_data1 = numpy.array(img_data_list1)
img_data1 = img_data1.astype('float32')
img_data1 /= 255
print(img_data1.shape)
num_of_samples1 = img_data1.shape[0]
labels1 = numpy.ones((num_of_samples1,),dtype='int64')
print("length of labels1 is "+str(len(labels1)))
print("labels1 are all "+str(labels1[10]))
##########################################################
#laoding none blurry images
img_data_list2=[]
data_dir_list2 = os.listdir(trainnoblur_directory)
img_list2=os.listdir(trainnoblur_directory)
for img in img_list2:
input_img=cv2.imread(trainnoblur_directory + img )
input_img = numpy.swapaxes(input_img, 0, 2)
img_data_list2.append(input_img)
img_data2 = numpy.array(img_data_list2)
img_data2 = img_data2.astype('float32')
img_data2 /= 255
print(img_data2.shape)
num_of_samples2 = img_data2.shape[0]
labels2 = numpy.ones((num_of_samples2,),dtype='int64')
labels2[:]=0
print("length of labels2 is "+str(len(labels2)))
print("labels1 are all "+str(labels2[10]))
#######################################################
# Combine the two numpy arrays and shuffle
labels=numpy.concatenate((labels1,labels2),axis=0)
img_data = numpy.concatenate((img_data1,img_data2),axis=0)
Y = np_utils.to_categorical(labels, num_classes)
#Shuffle the dataset
x,y = shuffle(img_data,Y, random_state=2)
# Split the dataset
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=2)
###########################################################
# Defining the model
input_shape=img_data[0].shape
model = Sequential()
model.add(Convolution2D(96, 7,7,input_shape=input_shape))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))
model.add(Convolution2D(256, 5, 5))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))
model.add(Flatten())
model.add(Dense(1024))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Dense(num_classes))
model.add(Activation('softmax'))
###########################
epochs = 100
learning_rate = 0.01
decay = learning_rate / epochs
adam = Adam(lr=learning_rate)
model.compile(loss='categorical_crossentropy', optimizer='adam',metrics=["accuracy"])
model.summary()
model.get_config()
model.layers[0].get_config()
model.layers[0].input_shape
model.layers[0].output_shape
model.layers[0].get_weights()
numpy.shape(model.layers[0].get_weights()[0])
model.layers[0].trainable
# Training
hist = model.fit(X_train, y_train, batch_size=128, nb_epoch=100, verbose=1, validation_data=(X_test, y_test))
filename='model_train_new.csv'
csv_log=callbacks.CSVLogger(filename, separator=',', append=False)
early_stopping=callbacks.EarlyStopping(monitor='val_loss', min_delta=0, patience=0, verbose=0, mode='min')
checkpoint = callbacks.ModelCheckpoint(filepath, monitor='val_loss', verbose=1, save_best_only=True, mode='min')
# tensorboard callback
tensorboard_callback = k.callbacks.TensorBoard(log_dir='./logs', histogram_freq=0, batch_size=32, write_graph=True, write_grads=False, write_images=False, embeddings_freq=0, embeddings_layer_names=None, embeddings_metadata=None)
callbacks_list = [csv_log,early_stopping,checkpoint, tensorboard_callback]
# Evaluating the model
score = model.evaluate(X_test, y_test, show_accuracy=True, verbose=0)
print('Test Loss:', score[0])
print('Test accuracy:', score[1])
test_image = X_test[0:1]
print (test_image.shape)
print(model.predict(test_image))
print(model.predict_classes(test_image))
print(y_test[0:1])
# Save our model here
file = open(filepath+"motionblur.h5", 'a')
model.save(filepath+"motionblur.h5")
file.close()
######################################################
================================================
FILE: image_slice.py
================================================
from PIL import Image
img = Image.open("/Users/sibozhu/DeepLearning/testing/JR_blur.jpg")
print(img)
(imageWidth, imageHeight)=img.size
gridx=60
gridy=60
rangex=img.width/gridx
rangey=img.height/gridy
print rangex*rangey
for x in xrange(rangex):
for y in xrange(rangey):
bbox=(x*gridx, y*gridy, x*gridx+gridx, y*gridy+gridy)
slice_bit=img.crop(bbox)
slice_bit.save('/Users/sibozhu/DeepLearning/testing/ppt/'+str(x)+','+str(y)+'.jpg', optimize=True, bits=6)
================================================
FILE: mergingpatches.py
================================================
import numpy as np
import cv2
import random
from PIL import Image
import PIL
import os, os.path
import numpy as np
from natsort import natsorted
import matplotlib.pyplot as plt
patch_dir = "./testing/"
temp_dir = "./testing/temp/"
result_dir = "./testing/result/"
pi_imgs = []
# cv_imgs = []
valid_images = [".jpg"]
for f in os.listdir(patch_dir):
ext = os.path.splitext(f)[1]
if ext.lower() not in valid_images:
continue
pi_imgs.append(Image.open(os.path.join(patch_dir,f)))
# cv_imgs.append(cv2.imread(os.path.join(patch_dir,f)))
total = len(pi_imgs)
print(str(len(pi_imgs))+" patches in total")
#######################################################
"""Loading all the images from directory"""
dir=[]
valid_images = [".jpg"]
for f in os.listdir(patch_dir):
ext = os.path.splitext(f)[1]
if ext.lower() not in valid_images:
continue
dir.append(os.path.join(patch_dir,f))
##############################################################
'''
for testing here, no influence on global algorithm
'''
# flg = './testing/0_5_9,blur.jpg'
#
# im_index = flg.split("/")[-1].split(",")[0].split("_")[0]
# print("the image's index: "+str(im_index))
#
# im_width = flg.split("/")[-1].split(",")[0].split("_")[1]
# print("the image's width's code: "+str(im_width))
#
# im_height = flg.split("/")[-1].split(",")[0].split("_")[2]
# print("the image's height's code: "+str(im_height))
#######################################
"""concat images with numpy"""
def concat_img_horizon(list_imgs):
imgs = [PIL.Image.open(i) for i in list_imgs]
# pick the image which is the smallest, and resize the others to match it (can be arbitrary image shape here)
min_shape = sorted([(np.sum(i.size), i.size) for i in imgs])[0][1]
#
imgs_comb = np.hstack((np.asarray(i.resize(min_shape)) for i in imgs))
imgs_comb = PIL.Image.fromarray( imgs_comb)
# imgs_comb.save( './testing/temp/'+str(save_name) +',horizon.jpg' )
return imgs_comb
def concat_img_vertical(list_imgs):
imgs = [PIL.Image.open(i) for i in list_imgs]
# pick the image which is the smallest, and resize the others to match it (can be arbitrary image shape here)
min_shape = sorted([(np.sum(i.size), i.size) for i in imgs])[0][1]
imgs_comb = np.hstack((np.asarray(i.resize(min_shape)) for i in imgs))
# for a vertical stacking it is simple: use vstack
imgs_comb = np.vstack((np.asarray(i.resize(min_shape)) for i in imgs))
imgs_comb = PIL.Image.fromarray(imgs_comb)
# imgs_comb.save( './testing/temp/'+str(save_name) +',vertical.jpg' )
return imgs_comb
def concat_temp_horizon(list_imgs):
imgs_comb = np.hstack((np.asarray(i) for i in list_imgs))
imgs_comb = PIL.Image.fromarray(imgs_comb)
return imgs_comb
#########################################################
'''
for testing here, no influence on global algorithm
'''
#
# list_im1 = pic_index[0][:3]
# list_im2 = pic_index[0][3:6]
# test1 = concat_img_vertical(list_im1)
# test2 = concat_img_vertical(list_im2)
# test3 = concat_temp_horizon(test1,test2)
# test4 = concat_temp_horizon(test2,test3)
# plt.imshow(test4)
# plt.show()
##############################################################
"""counting number of whole pictures in the folder"""
max_index=0
for i in range(len(dir)):
flag = int(dir[i].split("/")[-1].split(",")[0].split("_")[0])
if flag > max_index:
max_index = flag
# im_width = dir[i].split("/")[-1].split(",")[0].split("_")[1]
# im_height = dir[i].split("/")[-1].split(",")[0].split("_")[2]
#############################################
"""placing patches to their certain picture"""
pic_index={}
for elem in range(max_index+1):
pic_index[elem]=[]
for j in range(len(dir)):
for k in range(len(pic_index)):
if int(dir[j].split("/")[-1].split(",")[0].split("_")[0]) == k:
pic_index[k].append(dir[j])
print('the first picture contains '+str(len(pic_index[0]))+' patches')
##########################################
"""getting the total columns of picture"""
def get_total_column(list):
max_col = 0
for l in range(len(list)):
col_flag = int(pic_index[0][l].split("/")[-1].split(",")[0].split("_")[1])
if col_flag > max_col:
max_col = col_flag
return max_col
print ("this picture's total column is "+str(get_total_column(pic_index[0])))
#########################################
"""getting the total rows of picture"""
def get_total_row(list):
max_row = 0
for o in range(len(list)):
row_flag = int(list[o].split("/")[-1].split(",")[0].split("_")[2])
if row_flag > max_row:
max_row = row_flag
return max_row
print ("this picture's total row is " + str(get_total_row(pic_index[0])))
########################################
"""sorting this picture's patches with order of name"""
def sort_picture_patches(list):
return natsorted(list)
#####################################
"""doing global merging"""
for a in range(len(pic_index)):
max_col = get_total_column(pic_index[a])
max_row = get_total_row(pic_index[a])
pic_index[a] = sort_picture_patches(pic_index[a])
col_index = {}
for elem in range(max_col + 1):
col_index[elem] = []
for j in range(len(pic_index[a])):
for k in range(max_col):
if int(pic_index[a][j].split("/")[-1].split(",")[0].split("_")[1]) == k:
col_index[k].append(pic_index[a][j])
saver = []
for i in range(max_col - 1):
flag = concat_img_vertical(col_index[i])
saver.append(flag)
res = concat_temp_horizon(saver)
res.save(result_dir + str(a)+ '.jpg')
================================================
FILE: motionblur.h5
================================================
[File too large to display: 55.3 MB]
================================================
FILE: motionblur.py
================================================
import cv2
import numpy as np
img = cv2.imread('/Users/sibozhu/DeepLearning/testing/JR.jpg')
size = 15
# generating the kernel
kernel_motion_blur = np.zeros((size, size))
kernel_motion_blur[int((size-1)/2), :] = np.ones(size)
kernel_motion_blur = kernel_motion_blur / size
# applying the kernel to the input image
output = cv2.filter2D(img, -1, kernel_motion_blur)
cv2.imwrite('/Users/sibozhu/DeepLearning/testing/JR_blur.jpg',output)
================================================
FILE: processing_utils.py
================================================
'''
utility function package for image processing
by Sibo Zhu, Kieran Xiao Wang
2017.08.24
'''
import numpy as np
import cv2
import random
from PIL import Image
import PIL
from natsort import natsorted
import os, os.path
def save_image(image_np_array, image_save_path):
'''
!!! Sibo, Please complete this function !!!
:param image_np_array: 3-D numpy array
:param image_save_path: string
:return:
'''
im = Image.fromarray(image_np_array)
im.save(image_save_path)
def patch_merge_to_one_from_folder(patch_dir):
'''
merge patches into a whole image
!!! Sibo, please complete this function !!!
!!! now you are using naming for indicating patch location and blur/no blur, which is fine
!!! the patch_dir is supposed to contain all patches of an image(no matter blured or not)
!!! you may want to use os.walk
!!! if so avoid to have any other .jpg file except image patches(e.g. do not save the whole image in .jpg in that folder)
:param patch_dir: [string] directory to the folder that contains all patches(of an image)
:return: [3-d array] whole image in np array
'''
patch_dir = patch_dir
pi_imgs = []
valid_images = [".jpg"]
for f in os.listdir(patch_dir):
ext = os.path.splitext(f)[1]
if ext.lower() not in valid_images:
continue
pi_imgs.append(Image.open(os.path.join(patch_dir, f)))
total = len(pi_imgs)
print(str(len(pi_imgs)) + " patches in total")
#######################################################
"""Loading all the images from directory"""
dir = []
valid_images = [".jpg"]
for f in os.listdir(patch_dir):
ext = os.path.splitext(f)[1]
if ext.lower() not in valid_images:
continue
dir.append(os.path.join(patch_dir, f))
#####################################################
"""concat images with numpy"""
def concat_img_horizon(list_imgs):
imgs = [PIL.Image.open(i) for i in list_imgs]
# pick the image which is the smallest, and resize the others to match it (can be arbitrary image shape here)
min_shape = sorted([(np.sum(i.size), i.size) for i in imgs])[0][1]
#
imgs_comb = np.hstack((np.asarray(i.resize(min_shape)) for i in imgs))
imgs_comb = PIL.Image.fromarray(imgs_comb)
return imgs_comb
def concat_img_vertical(list_imgs):
imgs = [PIL.Image.open(i) for i in list_imgs]
# pick the image which is the smallest, and resize the others to match it (can be arbitrary image shape here)
min_shape = sorted([(np.sum(i.size), i.size) for i in imgs])[0][1]
imgs_comb = np.hstack((np.asarray(i.resize(min_shape)) for i in imgs))
# for a vertical stacking it is simple: use vstack
imgs_comb = np.vstack((np.asarray(i.resize(min_shape)) for i in imgs))
imgs_comb = PIL.Image.fromarray(imgs_comb)
return imgs_comb
def concat_temp_horizon(list_imgs):
imgs_comb = np.hstack((np.asarray(i) for i in list_imgs))
imgs_comb = PIL.Image.fromarray(imgs_comb)
return imgs_comb
#########################################################
"""counting number of whole pictures in the folder"""
max_index = 0
for i in range(len(dir)):
flag = int(dir[i].split("/")[-1].split(",")[0].split("_")[0])
if flag > max_index:
max_index = flag
#############################################
"""placing patches to their certain picture"""
pic_index = {}
for elem in range(max_index + 1):
pic_index[elem] = []
for j in range(len(dir)):
for k in range(len(pic_index)):
if int(dir[j].split("/")[-1].split(",")[0].split("_")[0]) == k:
pic_index[k].append(dir[j])
print('the first picture contains ' + str(len(pic_index[0])) + ' patches')
##########################################
"""getting the total columns of picture"""
def get_total_column(list):
max_col = 0
for l in range(len(list)):
col_flag = int(pic_index[0][l].split("/")[-1].split(",")[0].split("_")[1])
if col_flag > max_col:
max_col = col_flag
return max_col
print ("this picture's total column is " + str(get_total_column(pic_index[0])))
#########################################
"""getting the total rows of picture"""
def get_total_row(list):
max_row = 0
for o in range(len(list)):
row_flag = int(list[o].split("/")[-1].split(",")[0].split("_")[2])
if row_flag > max_row:
max_row = row_flag
return max_row
print ("this picture's total row is " + str(get_total_row(pic_index[0])))
########################################
"""sorting this picture's patches with order of name"""
def sort_picture_patches(list):
return natsorted(list)
#####################################
"""doing global merging"""
for a in range(len(pic_index)):
max_col = get_total_column(pic_index[a])
max_row = get_total_row(pic_index[a])
pic_index[a] = sort_picture_patches(pic_index[a])
col_index = {}
for elem in range(max_col + 1):
col_index[elem] = []
for j in range(len(pic_index[a])):
for k in range(max_col):
if int(pic_index[a][j].split("/")[-1].split(",")[0].split("_")[1]) == k:
col_index[k].append(pic_index[a][j])
saver = []
for i in range(max_col - 1):
flag = concat_img_vertical(col_index[i])
saver.append(flag)
res = concat_temp_horizon(saver)
img = PIL.Image.open(res).convert("L")
arr = np.array(img)
return arr
def mass_patch_merge_to_one_from_folder(patch_dir,save_dir):
'''
After implementing the merging patches back to a whole image,
we can also do that same thing to a folder that contains several patches that
come from different images and merge and save them back to those original images (with partially
blurry) based on the naming habit of slicing images.
:param patch_dir: [string] directory to the folder that contains all patches(of an image)
:param save_dir: [string] directory to the folder that used to save all those merged images
:return: This time there's no return
'''
patch_dir = patch_dir
result_dir = save_dir
pi_imgs = []
valid_images = [".jpg"]
for f in os.listdir(patch_dir):
ext = os.path.splitext(f)[1]
if ext.lower() not in valid_images:
continue
pi_imgs.append(Image.open(os.path.join(patch_dir, f)))
total = len(pi_imgs)
print(str(len(pi_imgs)) + " patches in total")
#######################################################
"""Loading all the images from directory"""
dir = []
valid_images = [".jpg"]
for f in os.listdir(patch_dir):
ext = os.path.splitext(f)[1]
if ext.lower() not in valid_images:
continue
dir.append(os.path.join(patch_dir, f))
#####################################################
"""concat images with numpy"""
def concat_img_horizon(list_imgs):
imgs = [PIL.Image.open(i) for i in list_imgs]
# pick the image which is the smallest, and resize the others to match it (can be arbitrary image shape here)
min_shape = sorted([(np.sum(i.size), i.size) for i in imgs])[0][1]
#
imgs_comb = np.hstack((np.asarray(i.resize(min_shape)) for i in imgs))
imgs_comb = PIL.Image.fromarray(imgs_comb)
return imgs_comb
def concat_img_vertical(list_imgs):
imgs = [PIL.Image.open(i) for i in list_imgs]
# pick the image which is the smallest, and resize the others to match it (can be arbitrary image shape here)
min_shape = sorted([(np.sum(i.size), i.size) for i in imgs])[0][1]
imgs_comb = np.hstack((np.asarray(i.resize(min_shape)) for i in imgs))
# for a vertical stacking it is simple: use vstack
imgs_comb = np.vstack((np.asarray(i.resize(min_shape)) for i in imgs))
imgs_comb = PIL.Image.fromarray(imgs_comb)
return imgs_comb
def concat_temp_horizon(list_imgs):
imgs_comb = np.hstack((np.asarray(i) for i in list_imgs))
imgs_comb = PIL.Image.fromarray(imgs_comb)
return imgs_comb
#########################################################
"""counting number of whole pictures in the folder"""
max_index = 0
for i in range(len(dir)):
flag = int(dir[i].split("/")[-1].split(",")[0].split("_")[0])
if flag > max_index:
max_index = flag
#############################################
"""placing patches to their certain picture"""
pic_index = {}
for elem in range(max_index + 1):
pic_index[elem] = []
for j in range(len(dir)):
for k in range(len(pic_index)):
if int(dir[j].split("/")[-1].split(",")[0].split("_")[0]) == k:
pic_index[k].append(dir[j])
print('the first picture contains ' + str(len(pic_index[0])) + ' patches')
##########################################
"""getting the total columns of picture"""
def get_total_column(list):
max_col = 0
for l in range(len(list)):
col_flag = int(pic_index[0][l].split("/")[-1].split(",")[0].split("_")[1])
if col_flag > max_col:
max_col = col_flag
return max_col
print ("this picture's total column is " + str(get_total_column(pic_index[0])))
#########################################
"""getting the total rows of picture"""
def get_total_row(list):
max_row = 0
for o in range(len(list)):
row_flag = int(list[o].split("/")[-1].split(",")[0].split("_")[2])
if row_flag > max_row:
max_row = row_flag
return max_row
print ("this picture's total row is " + str(get_total_row(pic_index[0])))
########################################
"""sorting this picture's patches with order of name"""
def sort_picture_patches(list):
return natsorted(list)
#####################################
"""doing global merging"""
for a in range(len(pic_index)):
max_col = get_total_column(pic_index[a])
max_row = get_total_row(pic_index[a])
pic_index[a] = sort_picture_patches(pic_index[a])
col_index = {}
for elem in range(max_col + 1):
col_index[elem] = []
for j in range(len(pic_index[a])):
for k in range(max_col):
if int(pic_index[a][j].split("/")[-1].split(",")[0].split("_")[1]) == k:
col_index[k].append(pic_index[a][j])
saver = []
for i in range(max_col - 1):
flag = concat_img_vertical(col_index[i])
saver.append(flag)
res = concat_temp_horizon(saver)
res.save(result_dir + str(a) + '.jpg')
def image_to_patch(image_path, patch_size, patch_dir):
'''
cut an image into patch with certain size
!!! Sibo, please complete this function !!!
:param image_path: [string] path to the image(e.g. ./whole_image.jpg)
:param patch_size: [tuple] i.g. (30(length),30(witch))
:param patch_dir: [string] dir where to save the patch dir.(patch dir is defined to be the folder that contains all
image patches of an image)
:return:
'''
img = Image.open(image_path)
(imageWidth, imageHeight) = img.size
gridx = patch_size
gridy = patch_size
rangex = img.width / gridx
rangey = img.height / gridy
print rangex * rangey
for x in xrange(rangex):
for y in xrange(rangey):
bbox = (x * gridx, y * gridy, x * gridx + gridx, y * gridy + gridy)
slice_bit = img.crop(bbox)
slice_bit.save(patch_dir + str(x) + '_' + str(y) + '.jpg', optimize=True,
bits=6)
print(patch_dir + str(x) + '_' + str(y) + '.jpg')
print(imageWidth)
def directory_to_patch(patch_size,original_path,no_blur_path,blur_path,all_img_path):
'''
We take a directory that contains several whole pictures and cut then into custom size of patches,
then apply 50% chance blur and non-blur to those patches, save them into blurry folder, non-blurry folder,
and a folder that contains all blurry and non-blurry patches with order.
:param patch_size: [integer] The custom patch size we want, e.g:for 30x30 patch, enter '30'
:param original_path: [string] The original path that contains all the original pictures without any modification
:param no_blur_path: [string] The destination path that contains all the non-blurry patches with order
:param blur_path: [string] The destination path that contains all the blurry patches with order
:param all_img_path: [string] The destination path that contains all the patches with order
:return: There's no return in this function, all the modified patches are saved into the destination path
'''
#motion blur preset
size = 15
gridx = patch_size
gridy = patch_size
kernel_motion_blur = np.zeros((size, size))
kernel_motion_blur[int((size - 1) / 2), :] = np.ones(size)
kernel_motion_blur = kernel_motion_blur / size
# go through every image in source folder
print('begin loading images')
pi_imgs = []
cv_imgs = []
valid_images = [".jpg"]
for f in os.listdir(original_path):
ext = os.path.splitext(f)[1]
if ext.lower() not in valid_images:
continue
pi_imgs.append(Image.open(os.path.join(original_path, f)))
cv_imgs.append(cv2.imread(os.path.join(original_path, f)))
print('finished loading images')
#
# looping to create blurry and non-blurry images in 50% chance
for i in range(len(pi_imgs)):
img = pi_imgs[i]
(imageWidth, imageHeight) = img.size
rangex = imageWidth / gridx
rangey = imageHeight / gridy
for x in xrange(rangex):
for y in xrange(rangey):
bbox = (x * gridx, y * gridy, x * gridx + gridx, y * gridy + gridy)
slice_bit = img.crop(bbox)
if random.randrange(2) == 0:
slice_bit.save(no_blur_path + str(i) + '_' + str(x) + '_' + str(y) + ',noblur.jpg', optimize=True,
bits=6)
slice_bit.save(all_img_path + str(i) + '_' + str(x) + '_' + str(y) + ',noblur.jpg', optimize=True,
bits=6)
print(str(i))
else:
slice_bit.save(blur_path + str(i) + '_' + str(x) + '_' + str(y) + ',blur.jpg', optimize=True, bits=6)
img1 = cv2.imread(blur_path + str(i) + '_' + str(x) + '_' + str(y) + ',blur.jpg')
output = cv2.filter2D(img1, -1, kernel_motion_blur)
cv2.imwrite(blur_path + str(i) + '_' + str(x) + '_' + str(y) + ',blur.jpg', output)
cv2.imwrite(all_img_path + str(i) + '_' + str(x) + '_' + str(y) + ',blur.jpg', output)
print(str(i))
================================================
FILE: slicing.py
================================================
from PIL import Image
save_path = "./testing/"
img = Image.open("/Users/sibozhu/DeepLearning/testing/JR.jpg")
(imageWidth, imageHeight)=img.size
gridx=30
gridy=30
rangex=imageWidth/gridx
print('Width: '+str(rangex))
rangey=imageHeight/gridy
print('Height: '+str(rangey))
print str(rangex*rangey) + " patches in total"
for x in xrange(rangex):
for y in xrange(rangey):
bbox=(x*gridx, y*gridy, x*gridx+gridx, y*gridy+gridy)
slice_bit=img.crop(bbox)
slice_bit.save(save_path+str(x)+'_'+str(y)+'.jpg', optimize=True, bits=6)
# print (save_path+str(x)+'_'+str(y)+'.jpg')
print imageWidth
================================================
FILE: test_img_generator.py
================================================
import numpy as np
import cv2
import random
from PIL import Image
import os, os.path
#
# NOTE: this function creates new partly blured images to tarpath folder
# and return a 3D-list labels which indicates blured parts in each image
#
# NOTE: please enter your correct folder path
# NOTE: transpath is just a temp path that stores the blured copies of images
# NOTE: Oripath is the source path and tarpath is where all the outputs are.
oripath = "./s_oridata/"
transpath = "./transimg/"
tarpath = "./tarimg/"
# global variables
size = 15
labels = []
gridx = 30
gridy = 30
kernel_motion_blur = np.zeros((size, size))
kernel_motion_blur[int((size-1)/2), :] = np.ones(size)
kernel_motion_blur = kernel_motion_blur / size
# go through every image in source folder
pi_imgs = []
cv_imgs = []
valid_images = [".jpg"]
for f in os.listdir(oripath):
ext = os.path.splitext(f)[1]
if ext.lower() not in valid_images:
continue
pi_imgs.append(Image.open(os.path.join(oripath,f)))
cv_imgs.append(cv2.imread(os.path.join(oripath,f)))
# looping to create blured images
for i in range(len(pi_imgs)):
# creating a blured copy of the original image
temp = cv_imgs[i]
blured = cv2.filter2D(temp, -1, kernel_motion_blur)
cv2.imwrite(transpath+str(i)+".jpg",blured)
# reload both original and blured version
img1 = pi_imgs[i]
img2 = Image.open(transpath+str(i)+".jpg")
# cut the image into 30*30 pieces
# and randomly choose half of the pieces to blur
(imageWidth, imageHeight)=img1.size
rangex=int(img1.width/gridx)
rangey=int(img1.height/gridy)
t_parts = rangex*rangey
# creating labels that identifies which part is blured
label = []
for j in range(rangex):
label += [[0]*rangey]
for k in range(int(t_parts/2)):
x = random.randint(0,rangex-1)
y = random.randint(0,rangey-1)
label[x][y] = 1
box = (x*gridx,y*gridy,x*gridx+gridx,y*gridy+gridy)
sliced = img2.crop(box)
img1.paste(sliced, (x*gridx,y*gridy))
img1.save(tarpath + str(i)+".jpg")
labels += [label]
================================================
FILE: trainingdatacreate.py
================================================
import numpy as np
import cv2
import random
from PIL import Image
import os, os.path
oripath = "./s_oridata/"
noblurpath = "./s_cnn/train/no_blur/"
blurpath = "./s_cnn/train/blur/"
allimgpath = "./s_cnn/train/inputdata/"
size = 15
gridx=30
gridy=30
kernel_motion_blur = np.zeros((size, size))
kernel_motion_blur[int((size-1)/2), :] = np.ones(size)
kernel_motion_blur = kernel_motion_blur / size
# img = Image.open(oripath+"JR.jpg")
#go through every image in source folder
print('begin loading images')
pi_imgs = []
cv_imgs = []
valid_images = [".jpg"]
for f in os.listdir(oripath):
ext = os.path.splitext(f)[1]
if ext.lower() not in valid_images:
continue
pi_imgs.append(Image.open(os.path.join(oripath,f)))
cv_imgs.append(cv2.imread(os.path.join(oripath,f)))
print('finished loading images')
#
#
# looping to create blurry and non-blurry images in 50% chance
for i in range(len(pi_imgs)):
img = pi_imgs[i]
(imageWidth, imageHeight) = img.size
rangex = imageWidth / gridx
rangey = imageHeight / gridy
for x in xrange(rangex):
for y in xrange(rangey):
bbox = (x * gridx, y * gridy, x * gridx + gridx, y * gridy + gridy)
slice_bit = img.crop(bbox)
if random.randrange(2) == 0:
slice_bit.save(noblurpath + str(i)+'_'+ str(x) + '_' + str(y) + ',noblur.jpg', optimize=True, bits=6)
slice_bit.save(allimgpath + str(i)+'_'+ str(x) + '_' + str(y) + ',noblur.jpg', optimize=True,bits=6)
print(str(i))
else:
slice_bit.save(blurpath + str(i)+'_'+ str(x) + '_' + str(y) + ',blur.jpg', optimize=True, bits=6)
img1 = cv2.imread(blurpath + str(i)+'_'+ str(x) + '_' + str(y) + ',blur.jpg')
output = cv2.filter2D(img1, -1, kernel_motion_blur)
cv2.imwrite(blurpath + str(i)+'_'+ str(x) + '_' + str(y) + ',blur.jpg', output)
cv2.imwrite(allimgpath + str(i)+'_'+ str(x) + '_' + str(y) + ',blur.jpg', output)
print(str(i))
gitextract_1_ro849x/ ├── Prediction/ │ └── prediction_start.py ├── Processing/ │ ├── blur_utils.py │ └── processing_utils.py ├── README.md ├── Report/ │ ├── nicefrac.sty │ ├── nips_2017.aux │ ├── nips_2017.log │ ├── nips_2017.out │ ├── nips_2017.sty │ └── nips_2017.tex ├── cnn.py ├── image_slice.py ├── mergingpatches.py ├── motionblur.h5 ├── motionblur.py ├── processing_utils.py ├── slicing.py ├── test_img_generator.py └── trainingdatacreate.py
SYMBOL INDEX (16 symbols across 3 files) FILE: Processing/processing_utils.py function save_image (line 14) | def save_image(image_np_array, image_save_path): function patch_merge_to_one_from_folder (line 25) | def patch_merge_to_one_from_folder(patch_dir): function mass_patch_merge_to_one_from_folder (line 168) | def mass_patch_merge_to_one_from_folder(patch_dir,save_dir): function image_to_patch (line 312) | def image_to_patch(image_path, patch_size, patch_dir): function directory_to_patch (line 340) | def directory_to_patch(patch_size,original_path,no_blur_path,blur_path,a... FILE: mergingpatches.py function concat_img_horizon (line 59) | def concat_img_horizon(list_imgs): function concat_img_vertical (line 69) | def concat_img_vertical(list_imgs): function concat_temp_horizon (line 80) | def concat_temp_horizon(list_imgs): function get_total_column (line 126) | def get_total_column(list): function get_total_row (line 137) | def get_total_row(list): function sort_picture_patches (line 149) | def sort_picture_patches(list): FILE: processing_utils.py function save_image (line 14) | def save_image(image_np_array, image_save_path): function patch_merge_to_one_from_folder (line 25) | def patch_merge_to_one_from_folder(patch_dir): function mass_patch_merge_to_one_from_folder (line 168) | def mass_patch_merge_to_one_from_folder(patch_dir,save_dir): function image_to_patch (line 312) | def image_to_patch(image_path, patch_size, patch_dir): function directory_to_patch (line 340) | def directory_to_patch(patch_size,original_path,no_blur_path,blur_path,a...
Condensed preview — 19 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (118K chars).
[
{
"path": "Prediction/prediction_start.py",
"chars": 14,
"preview": "import keras\n\n"
},
{
"path": "Processing/blur_utils.py",
"chars": 0,
"preview": ""
},
{
"path": "Processing/processing_utils.py",
"chars": 15267,
"preview": "'''\nutility function package for image processing\nby Sibo Zhu, Kieran Xiao Wang\n2017.08.24\n'''\nimport numpy as np\nimport"
},
{
"path": "README.md",
"chars": 6477,
"preview": "# MotionBlur-detection-by-CNN\n\n\n```\nTo run the cnn model, just enter \"cnn.py\" and run the code. It might take \ncouple ho"
},
{
"path": "Report/nicefrac.sty",
"chars": 4029,
"preview": "%%\n%% This is file `nicefrac.sty',\n%% generated with the docstrip utility.\n%%\n%% The original source files were:\n%%\n%% u"
},
{
"path": "Report/nips_2017.aux",
"chars": 1351,
"preview": "\\relax \n\\providecommand\\hyper@newdestlabel[2]{}\n\\providecommand\\HyperFirstAtBeginDocument{\\AtBeginDocument}\n\\HyperFirstA"
},
{
"path": "Report/nips_2017.log",
"chars": 34715,
"preview": "This is pdfTeX, Version 3.14159265-2.6-1.40.17 (TeX Live 2016/Debian) (preloaded format=pdflatex 2017.7.8) 12 AUG 2017 "
},
{
"path": "Report/nips_2017.out",
"chars": 253,
"preview": "\\BOOKMARK [1][-]{section.1}{Our approach}{}% 1\n\\BOOKMARK [2][-]{subsection.1.1}{Generating the training data}{section.1}"
},
{
"path": "Report/nips_2017.sty",
"chars": 10348,
"preview": "% partial rewrite of the LaTeX2e package for submissions to the\n% Conference on Neural Information Processing Systems (N"
},
{
"path": "Report/nips_2017.tex",
"chars": 7395,
"preview": "\\documentclass{article}\n\n\\usepackage[final]{nips_2017}\n\n% to compile a camera-ready version, add the [final] option, e.g"
},
{
"path": "cnn.py",
"chars": 5151,
"preview": "import numpy\nimport cv2\nimport PIL\nimport os, os.path\nimport matplotlib.pyplot as plt\nfrom PIL import Image\nfrom keras.c"
},
{
"path": "image_slice.py",
"chars": 485,
"preview": "from PIL import Image\nimg = Image.open(\"/Users/sibozhu/DeepLearning/testing/JR_blur.jpg\")\nprint(img)\n(imageWidth, imageH"
},
{
"path": "mergingpatches.py",
"chars": 5659,
"preview": "import numpy as np\nimport cv2\nimport random\nfrom PIL import Image\nimport PIL\nimport os, os.path\nimport numpy as np\nfrom "
},
{
"path": "motionblur.py",
"chars": 443,
"preview": "import cv2\nimport numpy as np\n\nimg = cv2.imread('/Users/sibozhu/DeepLearning/testing/JR.jpg')\n\n\nsize = 15\n\n# generating "
},
{
"path": "processing_utils.py",
"chars": 15267,
"preview": "'''\nutility function package for image processing\nby Sibo Zhu, Kieran Xiao Wang\n2017.08.24\n'''\nimport numpy as np\nimport"
},
{
"path": "slicing.py",
"chars": 621,
"preview": "from PIL import Image\nsave_path = \"./testing/\"\n\nimg = Image.open(\"/Users/sibozhu/DeepLearning/testing/JR.jpg\")\n(imageWid"
},
{
"path": "test_img_generator.py",
"chars": 2289,
"preview": "import numpy as np\r\nimport cv2\r\nimport random\r\nfrom PIL import Image\r\nimport os, os.path\r\n\r\n#\r\n# NOTE: this function cre"
},
{
"path": "trainingdatacreate.py",
"chars": 2062,
"preview": "import numpy as np\nimport cv2\nimport random\nfrom PIL import Image\nimport os, os.path\n\noripath = \"./s_oridata/\"\nnoblurpat"
}
]
// ... and 1 more files (download for full content)
About this extraction
This page contains the full source code of the Sibozhu/MotionBlur-detection-by-CNN GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 19 files (55.4 MB), approximately 32.5k tokens, and a symbol index with 16 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
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