Table(grid) detector

Table(grid) detector source code.
The code find table row & col lines in document image.

Output

Table(Grid) Detector
buy : https://www.cvlecture.marearts.com/product-page/table-grid-detector
test : http://www.marearts.com/webapp/dtable/
github : https://github.com/MareArts/DTable

Usage source code

#include "DTable.h"

//for local computer
void main()
{

    CDTable cDT;

    //test image
    char str[255];
    for (int i = 0; i < 10; ++i)
    {
        
        //make file string
        sprintf_s(str, "%d.jpg", i + 1);
        printf("%s open \n", str);

        //read
        Mat oimg = imread(str);

        //check
        if (oimg.empty())
            continue;

        //get rect vector
        vector< GridV> vRect;
        //dtable
        if (cDT.FindGrid(oimg, vRect, 0, 0) == false)
        {
            printf("Couldn't Find \n");
            break;
        }


        //show table
        namedWindow("oimg", 0);
        int idx = 0;
        for (auto it : vRect)
        {

            printf("%d grid :  (x:%d, y:%d, width:%d, height:%d \n", idx, it.rect.x, it.rect.y, it.rect.width, it.rect.height);

            if (it.pure)
                cv::rectangle(oimg, it.rect, CV_RGB(0, 255, 0), 2);
            else
                cv::rectangle(oimg, it.rect, CV_RGB(255, 0, 0), 2);

            idx++;
        }

        vRect.clear();

        //exit
        imshow("oimg", oimg);
        if (waitKey(0) == 'q')
        {
            break;
        }
    }

    destroyAllWindows();
    
}

Elastic image effect, python opencv example source code.

Elastic effect source code.
It can be useful when you want to augment image dataset.
and it is also good to make image effect.

Please refer code:

import numpy as np

import cv2

from scipy.ndimage.interpolation import map_coordinates

from scipy.ndimage.filters import gaussian_filter

def elastic(image, alpha, sigma, random_state=None):

"""Elastic deformation of images as described in [Simard2003]_.

.. [Simard2003] Simard, Steinkraus and Platt, "Best Practices for

Convolutional Neural Networks applied to Visual Document Analysis", in

Proc. of the International Conference on Document Analysis and

Recognition, 2003.

"""

if random_state is None:

random_state = np.random.RandomState(None)

#print(random_state)

shape = image.shape

dx = gaussian_filter((random_state.rand(*shape) * 2 - 1), sigma, mode="constant", cval=0) * alpha

dy = gaussian_filter((random_state.rand(*shape) * 2 - 1), sigma, mode="constant", cval=0) * alpha

dz = np.zeros_like(dx)

x, y, z = np.meshgrid(np.arange(shape[1]), np.arange(shape[0]), np.arange(shape[2]))

indices = np.reshape(y+dy, (-1, 1)), np.reshape(x+dx, (-1, 1)), np.reshape(z, (-1, 1))

distored_image = map_coordinates(image, indices, order=1, mode='nearest') #wrap,reflect, nearest

return distored_image.reshape(image.shape)

#main

#file read

o_img = cv2.imread('izone_oy.png')

elMat = elastic(o_img, alpha=5000, sigma=8, random_state=None)

cv2.namedWindow('origin',0)

cv2.imshow('origin', o_img)

cv2.namedWindow('elastic',0)

cv2.imshow('elastic', elMat)

cv2.waitKey(0)

GitHub url :

https://github.com/MareArts/Elastic_Effect.git

Test it here:
http://www.marearts.com/webapp/elastic/

python OpenCV, draw grid example source code

make well divided linear coordinate
And make pair coordinate

Please see code for detail explanation.

import numpy as np

import cv2

import sys

rows = 500

cols = 500

newMat_3ch = np.zeros((rows, cols, 3), dtype = "uint8") #3channel

step = 20

x = np.linspace(start=0, stop=rows, num=step)

y = np.linspace(start=0, stop=cols, num=step)

v_xy = []

h_xy = []

for i in range(step):

v_xy.append( [int(x[i]), 0, int(x[i]), rows-1] )

h_xy.append( [0, int(y[i]), cols-1, int(y[i])] )

for i in range(step):

[x1, y1, x2, y2] = v_xy[i]

[x1_, y1_, x2_, y2_] = h_xy[i]

cv2.line(newMat_3ch, (x1,y1), (x2, y2), (0,0,255),1 )

cv2.line(newMat_3ch, (x1_,y1_), (x2_, y2_), (255,0,0),1 )

cv2.namedWindow('newMat_3ch',0)

cv2.imshow('newMat_3ch', newMat_3ch)

cv2.waitKey(0)

data dump by pickle in python

Data dump by pickle

see below example:

data dump to file 

import pickle

f = open("any_file.dmp', "wb")

f.write(pickle.dumps(data))

f.close()

data load by dump

data = pickle.loads(open("any_file.dmp", "rb").read())

_mask.so : undefined symbol: _Py_ZeroStruct

>git clone https://github.com/cocodataset/cocoapi.git

>cd cocoapi/PythonAPI

>sudo make

*** python 2.7

>python setup.py build_ext install

***python 3.x

>python3 setup.py build_ext install

>cp -r pycocotools <path_to_tensorflow>/models/research/




How to mount extra hdd permanently

> sudo -I blkid

/dev/sdb1: UUID="0687f976-6efe-4ac5-88dd-8fd863f8b8bf" TYPE="ext4" PARTUUID="1569f982-01"/dev/sda1: LABEL="cloudimg-rootfs" UUID="8ae3d910-2d2a-492d-8667-d0fa24e4d357" TYPE="ext4" PARTUUID="05ae307a-01"/dev/sdc1: UUID="e8abbf34-2cb2-471f-bac6-2a5caec81ad8" TYPE="ext4" PARTUUID="4c4db30a-01"

check uuid that you want to mount
>sudo nano /etc/fstab

Add this string end of file, but uuid must be your value, and ext4 also should be same with blkid information

UUID=e8abbf34-2cb2-471f-bac6-2a5caec81ad8 /media/datadisk ext4 defults,nofail    0       2

Thank you.

OpenCV python, SuperPixel example source code. (usage of createSuperpixelSEEDS)

input image



output image

 
 


source code

import cv2 as cv

import numpy as np

import sys

import random

#read image

img = cv.imread('izone_oy.png')

converted_img = cv.cvtColor(img, cv.COLOR_BGR2HSV)

height,width,channels = converted_img.shape

num_iterations = 6

prior = 2

double_step = False

num_superpixels = 200

num_levels = 4

num_histogram_bins = 5

seeds = cv.ximgproc.createSuperpixelSEEDS(width, height, channels, num_superpixels, num_levels, prior, num_histogram_bins)

color_img = np.zeros((height,width,3), np.uint8)

color_img[:] = (0, 0, 255)

seeds.iterate(converted_img, num_iterations)

# retrieve the segmentation result

labels = seeds.getLabels()

# labels output: use the last x bits to determine the color

num_label_bits = 2

labels &= (1<<num_label_bits)-1

labels *= 1<<(16-num_label_bits)

mask = seeds.getLabelContourMask(False)

# stitch foreground & background together

mask_inv = cv.bitwise_not(mask)

result_bg = cv.bitwise_and(img, img, mask=mask_inv)

result_fg = cv.bitwise_and(color_img, color_img, mask=mask)

result = cv.add(result_bg, result_fg)

cv.namedWindow('mask',0)

cv.namedWindow('result_bg',0)

cv.namedWindow('result_fg',0)

cv.namedWindow('result',0)

cv.imshow('mask',mask_inv)

cv.imshow('result_bg',result_bg)

cv.imshow('result_fg',result_fg)

cv.imshow('result',result)

cv.imwrite('mask.jpg',mask_inv)

cv.imwrite('result_bg.jpg',result_bg)

cv.imwrite('result_fg.jpg',result_fg)

cv.imwrite('result.jpg',result)

cv.waitKey(0)

reference
https://docs.opencv.org/3.0-beta/modules/ximgproc/doc/superpixels.html
https://github.com/opencv/opencv_contrib/blob/master/samples/python2/seeds.py

TypeError: Incorrect type of self (must be 'Feature2D' or its derivative), FastFeatureDetector

This is because of opencv version difference.
Some function is changed as XX_create().
Refer to below code. This is case of FastFeatureDetector.
Thank you.

fast = cv2.FastFeatureDetector_create() # <- FastFeatureDetector()

kp = fast.detect(blurGrayMat)

E: Unable to locate package nvidia-docker

go to this page and run the properly command.

https://nvidia.github.io/nvidia-docker/

How to increase the font size of the bounding box in Tensorflow object detection module?

Find "visualization_utils.py"
This exist in "research/object_detection/utils"

And find below code, modify font size.

If it is not working, change path to absolute path.

Or copy font file to project folder.

Thank you.

try:

#font = ImageFont.truetype('arial.ttf', 24)

font = ImageFont.truetype('/Library/Fonts/Arial.ttf', 100) #leon modify

except IOError:

font = ImageFont.load_default()

Font size up

Korean car number plate 822 images data share

Korean car number plate 822 images,
refer to this link:
https://www.amazon.com/clouddrive/share/zT8bgXMGB4m7ex8VMI8Mgibhi6qJYZRcOtnOwYArMgi

Thank you.

pedestrian 29,286 images data

I share pedestrian 29,286 images data.
https://www.amazon.com/clouddrive/share/bp055ddwIp8DSCgTJL9AWeZvqin6gkpoTg8YrM6K4rt

Thank you.

frontal face 4,429 images (size 24 * 24)

Frontal face 4,429 images

Size is 24 x 24

refer to this link:

https://www.amazon.com/clouddrive/share/YEBEF5klqZHytohUkUoPXDSGdfpqcwrM6H9bMItzyRz

*If this data have copyright, please inform to me, I will delete ASAP.

Face 2166 image data

I share face image 2,166 data.

I got these data from an website at very long time ago.

So, I don't remember which site.

If this data have copyright, please inform to me. 

I will delete ASAP.

share link here :

https://www.amazon.com/clouddrive/share/7f6ge3SqL73qImKyGdHLTuUfcphFeF756J8B7Tazmgk

Thank you.

 

Image data : background images

This is background images those are 12,439.
I used this images for negative data when I train face detection model.

This is random images and there is no specific object.
So it will be use for background image or negative image.

Share link is here:
https://www.amazon.com/clouddrive/share/vYnNKcwkK987A7fAUuasX0aPmaMt5wKl6ekSk1v6QAR

I hope it help to anyone.
Thank you.

google object detection api error: ModuleNotFoundError: No module named 'pycocotools’

You might see this error : No module named 'pycocotools’, when you try to start train.
I refer to my solution.
Good luck!

Error:

ModuleNotFoundError: No module named 'pycocotools’

Solution :

git clone https://github.com/cocodataset/cocoapi.git

cd cocoapi/PythonAPI

make

cp -r pycocotools <path_to_object_detection_api>/models/research/

google object detection api error : object_detection/protos/model.proto:12:5: "Ssd" is not defined.

You might meet this error : "object_detection/protos/model.proto:12:5: "Ssd" is not defined.", when you try below command.

# From tensorflow/models/research/

protoc object_detection/protos/*.proto --python_out=.

I solved this error to use below work.

# Make sure you grab the latest version

curl -OL https://github.com/google/protobuf/releases/download/v3.2.0/protoc-3.2.0-linux-x86_64.zip 

# Unzip 

unzip protoc-3.2.0-linux-x86_64.zip -d protoc3 

# Move protoc to /usr/local/bin/ 

sudo mv protoc3/bin/* /usr/local/bin/ 

# Move protoc3/include to /usr/local/include/ 

sudo mv protoc3/include/* /usr/local/include/ 

# Optional: change owner 

sudo chwon [user] /usr/local/bin/protoc 

sudo chwon -R [user] /usr/local/include/google 

refer to this url:

https://gist.github.com/sofyanhadia/37787e5ed098c97919b8c593f0ec44d8

https://github.com/tensorflow/models/issues/4223

python make folder (create directory)

import os

makeFolder = 'folderName'

if not os.path.exists(makeFolder): #if not exist

os.makedirs(makeFolder) #make folder

vs code auto indentations

On Windows Shift + Alt + F
On Mac Shift + Option + F
On Ubuntu Ctrl + Shift +

python pandas, shuffle

refer to example code:

from sklearn.utils import shuffle

import pandas as pd

df = pd.read_csv('test.csv')

df = shuffle(df) #suffle

df.reset_index(drop=True) #index reset

df.to_csv('rfine_table_shuffle.csv', index=False)

Python String handling tip, remove specific character forward and backward in string

So, this is example code for this processing

input string
__A b c_D___

output string
A b c_D

'_' character is removed in forward and backward.

Refer to code. 🤘
Thank you.

testStr = [' Abc ', ' ', ' ', 'A bc', 'A bc ', ' ', ' a 12 43', ' da ', ' ', 'a2 34', ' 1 2 ']

testStr2 = ['___Abc__', '_', '__', 'A_bc', 'A_bc_', '_____', '_a_12_43', '_da____', '__', 'a2_34', '_1_2_']

def removeTFoward(str, T):

if len(str) == 0:

return str

if str[0] == T:

return removeTFoward(str[1:],T)

else:

return str

def removeTBackword(str, T):

if len(str) == 0:

return str

if str[-1] == T:

return removeTBackword(str[:-1],T)

else:

return str

for n, v in enumerate(testStr2):

str = removeTFoward(v, '_')

str = removeTBackword(str, '_')

if len(str) == 0:

print('nothing')

else:

print(str)

output :
Abc
nothing
nothing
A_bc
A_bc
nothing
a_12_43
da
nothing
a2_34
1_2

Dithering python opencv source code (Floyd–Steinberg dithering)

This is dithering example, it make image like a stippling effect.

I referenced to blew website.
wiki page:
https://en.wikipedia.org/wiki/Floyd–Steinberg_dithering 

youtube tutorial (thank you, good job!) :
https://www.youtube.com/watch?v=0L2n8Tg2FwI&t=0s&list=WL&index=151

In the source code, there are two functions those are :

dithering_gray, dithering_color

One is for Gary image (1 channel), other is for color (3 channel).

It's very easy to use, just call function~ ^^

import cv2

import numpy as np

def minmax(v):

if v > 255:

v = 255

if v < 0:

v = 0

return v

def dithering_gray(inMat, samplingF):

#https://en.wikipedia.org/wiki/Floyd–Steinberg_dithering

#https://www.youtube.com/watch?v=0L2n8Tg2FwI&t=0s&list=WL&index=151

#input is supposed as color

# grab the image dimensions

h = inMat.shape[0]

w = inMat.shape[1]

# loop over the image

for y in range(0, h-1):

for x in range(1, w-1):

# threshold the pixel

old_p = inMat[y, x]

new_p = np.round(samplingF * old_p/255.0) * (255/samplingF)

inMat[y, x] = new_p

quant_error_p = old_p - new_p

# inMat[y, x+1] = minmax(inMat[y, x+1] + quant_error_p * 7 / 16.0)

# inMat[y+1, x-1] = minmax(inMat[y+1, x-1] + quant_error_p * 3 / 16.0)

# inMat[y+1, x] = minmax(inMat[y+1, x] + quant_error_p * 5 / 16.0)

# inMat[y+1, x+1] = minmax(inMat[y+1, x+1] + quant_error_p * 1 / 16.0)

inMat[y, x+1] = minmax(inMat[y, x+1] + quant_error_p * 7 / 16.0)

inMat[y+1, x-1] = minmax(inMat[y+1, x-1] + quant_error_p * 3 / 16.0)

inMat[y+1, x] = minmax(inMat[y+1, x] + quant_error_p * 5 / 16.0)

inMat[y+1, x+1] = minmax(inMat[y+1, x+1] + quant_error_p * 1 / 16.0)

# quant_error := oldpixel - newpixel

# pixel[x + 1][y ] := pixel[x + 1][y ] + quant_error * 7 / 16

# pixel[x - 1][y + 1] := pixel[x - 1][y + 1] + quant_error * 3 / 16

# pixel[x ][y + 1] := pixel[x ][y + 1] + quant_error * 5 / 16

# pixel[x + 1][y + 1] := pixel[x + 1][y + 1] + quant_error * 1 / 16

# return the thresholded image

return inMat

def dithering_color(inMat, samplingF):

#https://en.wikipedia.org/wiki/Floyd–Steinberg_dithering

#https://www.youtube.com/watch?v=0L2n8Tg2FwI&t=0s&list=WL&index=151

#input is supposed as color

# grab the image dimensions

h = inMat.shape[0]

w = inMat.shape[1]

# loop over the image

for y in range(0, h-1):

for x in range(1, w-1):

# threshold the pixel

old_b = inMat[y, x, 0]

old_g = inMat[y, x, 1]

old_r = inMat[y, x, 2]

new_b = np.round(samplingF * old_b/255.0) * (255/samplingF)

new_g = np.round(samplingF * old_g/255.0) * (255/samplingF)

new_r = np.round(samplingF * old_r/255.0) * (255/samplingF)

inMat[y, x, 0] = new_b

inMat[y, x, 1] = new_g

inMat[y, x, 2] = new_r

quant_error_b = old_b - new_b

quant_error_g = old_g - new_g

quant_error_r = old_r - new_r

inMat[y, x+1, 0] = minmax(inMat[y, x+1, 0] + quant_error_b * 7 / 16.0)

inMat[y, x+1, 1] = minmax(inMat[y, x+1, 1] + quant_error_g * 7 / 16.0)

inMat[y, x+1, 2] = minmax(inMat[y, x+1, 2] + quant_error_r * 7 / 16.0)

inMat[y+1, x-1, 0] = minmax(inMat[y+1, x-1, 0] + quant_error_b * 3 / 16.0)

inMat[y+1, x-1, 1] = minmax(inMat[y+1, x-1, 1] + quant_error_g * 3 / 16.0)

inMat[y+1, x-1, 2] = minmax(inMat[y+1, x-1, 2] + quant_error_r * 3 / 16.0)

inMat[y+1, x, 0] = minmax(inMat[y+1, x, 0] + quant_error_b * 5 / 16.0)

inMat[y+1, x, 1] = minmax(inMat[y+1, x, 1] + quant_error_g * 5 / 16.0)

inMat[y+1, x, 2] = minmax(inMat[y+1, x, 2] + quant_error_r * 5 / 16.0)

inMat[y+1, x+1, 0] = minmax(inMat[y+1, x+1, 0] + quant_error_b * 1 / 16.0)

inMat[y+1, x+1, 1] = minmax(inMat[y+1, x+1, 1] + quant_error_g * 1 / 16.0)

inMat[y+1, x+1, 2] = minmax(inMat[y+1, x+1, 2] + quant_error_r * 1 / 16.0)

# quant_error := oldpixel - newpixel

# pixel[x + 1][y ] := pixel[x + 1][y ] + quant_error * 7 / 16

# pixel[x - 1][y + 1] := pixel[x - 1][y + 1] + quant_error * 3 / 16

# pixel[x ][y + 1] := pixel[x ][y + 1] + quant_error * 5 / 16

# pixel[x + 1][y + 1] := pixel[x + 1][y + 1] + quant_error * 1 / 16

# return the thresholded image

return inMat

#read image

inMat = cv2.imread('iu.jpg') #lena.png')

#color ditering

outMat_color = dithering_color(inMat.copy(), 1)

cv2.imwrite('out_color.jpg', outMat_color)

#gray ditering

grayMat = cv2.cvtColor(inMat, cv2.COLOR_BGR2GRAY)

outMat_gray = dithering_gray(grayMat.copy(), 1)

cv2.imwrite('out_gray.jpg', outMat_gray)

python opencv, example code for image pixel access

The code is example to access every pixel in opencv python.
More detail, just see below code.
Thank you.

import cv2

def OpenCV_Access_RGBValue(inMat):

#input is supposed as color

# grab the image dimensions

h = inMat.shape[0]

w = inMat.shape[1]

# loop over the image

for y in range(0, h):

for x in range(0, w):

# threshold the pixel

b = inMat[y, x, 0]

g = inMat[y, x, 1]

r = inMat[y, x, 2]

b = 255 - b

g = 255 - g

r = 255 - r

inMat[y, x, 0] = b

inMat[y, x, 1] = g

inMat[y, x, 2] = r

#image[y, x] = 255 if image[y, x] >= 128T else 0

# return the thresholded image

return inMat

#read image

inMat = cv2.imread('iu.jpg')

#test value access

OpenCV_Access_RGBValue(inMat)

#display

cv2.namedWindow('test',0)

cv2.imwrite('output.jpg', inMat)

cv2.imshow('test',inMat)

cv2.waitKey(0)

search column name and modify data, python pandas usages

Above all,
Let's make initial column head.

import pandas as pd

#init

col_names = ['product', 'count']

word_pd = pd.DataFrame(columns = col_names)

There is no data yet.
So, let's add initial data

#add produce list

word_pd.loc[len(word_pd)] = ['apple', 4]

word_pd.loc[len(word_pd)] = ['orange', 7]

word_pd.loc[len(word_pd)] = ['beer', 10]

word_pd.loc[len(word_pd)] = ['cola', 7]

word_pd.loc[len(word_pd)] = ['beer', 8]

#check

print('origin data', word_pd)

>
origin data   product count
0   apple     4
1  orange     7
2    beer    10
3    cola     7
4    beer     8


OK, then let's find specific product name and increase count.

#find product

list_pd = word_pd.loc[word_pd['product'] == 'apple']

list_f = list_pd.index.tolist()

#add count

if len(list_f)>0:

for index in list_f:

word_pd.iloc[index][1] = word_pd.iloc[index, word_pd.columns.get_loc('count')] +1

print('result')

print('data', word_pd)

>
result
data   product count
0   apple     5
1  orange     7
2    beer    10
3    cola     7
4    beer     8



OK, at this time, let's find beer product and add count.
Note, there are 2 rows of beer product, so all beer product's count are increased.

#one more test

#find product

list_pd = word_pd.loc[word_pd['product'] == 'beer']

list_f = list_pd.index.tolist()

#add count

if len(list_f)>0:

for index in list_f:

word_pd.iloc[index][1] = word_pd.iloc[index, word_pd.columns.get_loc('count')] +1

print('result')

print('data', word_pd)

result
data   product count
0   apple     5
1  orange     7
2    beer    11
3    cola     7
4    beer     9




This is whole source code.

import pandas as pd

#init

col_names = ['product', 'count']

word_pd = pd.DataFrame(columns = col_names)

#add produce list

word_pd.loc[len(word_pd)] = ['apple', 4]

word_pd.loc[len(word_pd)] = ['orange', 7]

word_pd.loc[len(word_pd)] = ['beer', 10]

word_pd.loc[len(word_pd)] = ['cola', 7]

word_pd.loc[len(word_pd)] = ['beer', 8]

#check

print('origin data', word_pd)

#find product

list_pd = word_pd.loc[word_pd['product'] == 'apple']

list_f = list_pd.index.tolist()

#add count

if len(list_f)>0:

for index in list_f:

word_pd.iloc[index][1] = word_pd.iloc[index, word_pd.columns.get_loc('count')] +1

print('result')

print('data', word_pd)

#one more test

#find product

list_pd = word_pd.loc[word_pd['product'] == 'beer']

list_f = list_pd.index.tolist()

#add count

if len(list_f)>0:

for index in list_f:

word_pd.iloc[index][1] = word_pd.iloc[index, word_pd.columns.get_loc('count')] +1

print('result')

print('data', word_pd)

Thank you.

ValueError: too many values to unpack (expected 2), when you use "cv2.findContours"

check this tip!

before (error)
contours, _ = cv2.findContours(***, ***, ***)


after (solved)
_, contours, _ = cv2.findContours(***, ***, ***)

Mac error : missing xcrun at: /Library/Developer/CommandLineTools/usr/bin/xcrun (tip)

It seems to occur after osx updating.
In my case, I solved using below method.

xcode-select --install

^^.