I am extracting prosody features from an audio file while using Opensmile using Windows version of Opensmile. It runs successful and an output csv is generated. But when I open csv, it shows some rows that are not readable. I used this command to extract prosody feature:
SMILEXtract -C \opensmile-3.0-win-x64\config\prosody\prosodyShs.conf -I audio_sample_01.wav -O prosody_sample1.csv
And the output of csv looks like this:
[
Even I tried to use the sample wave file given in Example audio folder given in opensmile directory and the output is same (not readable). Can someone help me in identifying where the problem is actually? and how can I fix it?
You need to enable the csvSink component in the configuration file to make it work. The file config\prosody\prosodyShs.conf that you are using does not have this component defined and always writes binary output.
You can verify that it is the standart binary output in this way: omit the -O parameter from your command so it becomesSMILEXtract -C \opensmile-3.0-win-x64\config\prosody\prosodyShs.conf -I audio_sample_01.wav and execute it. You will get a output.htk file which is exactly the same as the prosody_sample1.csv.
How output csv? You can take a look at the example configuration in opensmile-3.0-win-x64\config\demo\demo1_energy.conf where a csvSink component is defined.
You can find more information in the official documentation:
Get started page of the openSMILE documentation
The section on configuration files
Documentation for cCsvSink
This is how I solved the issue. First I added the csvSink component to the list of the component instances. instance[csvSink].type = cCsvSink
Next I added the configuration parameters for this instance.
[csvSink:cCsvSink]
reader.dmLevel = energy
filename = \cm[outputfile(O){output.csv}:file name of the output CSV
file]
delimChar = ;
append = 0
timestamp = 1
number = 1
printHeader = 1
\{../shared/standard_data_output_lldonly.conf.inc}`
Now if you run this file it will throw you errors because reader.dmLevel = energy is dependent on waveframes. So the final changes would be:
[energy:cEnergy]
reader.dmLevel = waveframes
writer.dmLevel = energy
[int:cIntensity]
reader.dmLevel = waveframes
[framer:cFramer]
reader.dmLevel=wave
writer.dmLevel=waveframes
Further reference on how to configure opensmile configuration files can be found here
I am trying to train Tesseract 4 with particular pictures (to read multimeters with 7 segments),
please note that I am aware of the allready trained data from Arthur Augusto at https://github.com/arturaugusto/display_ocr but I need to train Tesseract over my own data.
In order to train tess, I followed differents tutorials (as https://robipritrznik.medium.com/recognizing-vehicle-license-plates-on-images-using-tesseract-4-ocr-with-custom-trained-models-4ba9861595e7 or https://pretius.com/how-to-prepare-training-files-for-tesseract-ocr-and-improve-characters-recognition/)
but i allways get problem when running the shapeclustering command with my own data
(With example data as https://github.com/tesseract-ocr/tesseract/issues/1174#issuecomment-338448972, every things is working fine)
Indeed when I try to do the shapeclusturing command it have this output screenshot
Then my shape_table is empty and the trainig could'nt be efficient...
With example data it's working fine and the shape_table is well filled
I am guessing that I have issue with box file generation, here is my process to create box file :
I use the
tesseract imageFileName.tif imageFileName batch.nochop makebox
command to generate box file and then i edit it with JtessboxEditor.
So I can't see where I'am wrong with my .box/.tif data couple.
Have a good day & thanks for helping me
\n
Adrien
Here is my full batch script for training after having generated and edited box files.
set name=sev7.exp0
set shortName=sev7
echo Run Tesseract for Training..
tesseract.exe %name%.tif %name% nobatch box.train
echo Compute the Character Set..
unicharset_extractor.exe %name%.box
shapeclustering -F font_properties -U unicharset -O %shortName%.unicharset %name%.tr
mftraining -F font_properties -U unicharset -O %shortName%.unicharset %name%.tr
echo Clustering..
cntraining.exe %name%.tr
echo Rename Files..
rename normproto %shortName%.normproto
rename inttemp %shortName%.inttemp
rename pffmtable %shortName%.pffmtable
rename shapetable %shortName%.shapetable
echo Create Tessdata..
combine_tessdata.exe %shortName%.
echo. & pause
Ok so finally I achieved to train tesseract.
The solution is to add a --psm parameter when using the command
tesseract.exe %name%.tif %name% nobatch box.train
as
tesseract.exe %name%.%typeFile% %name% --psm %psm% nobatch box.train
note that all the psm value are :
REM pagesegmode values are:
REM 0 = Orientation and script detection (OSD) only.
REM 1 = Automatic page segmentation with OSD.
REM 2 = Automatic page segmentation, but no OSD, or OCR
REM 3 = Fully automatic page segmentation, but no OSD. (Default)
REM 4 = Assume a single column of text of variable sizes.
REM 5 = Assume a single uniform block of vertically aligned text.
REM 6 = Assume a single uniform block of text.
REM 7 = Treat the image as a single text line.
REM 8 = Treat the image as a single word.
REM 9 = Treat the image as a single word in a circle.
REM 10 = Treat the image as a single character.
REM 11 = Sparse text. Find as much text as possible in no particular order.
REM 12 Sparse text with OSD.
REM 13 Raw line. Treat the image as a single text line bypassing hacks that are Tesseract-specific.
founded on https://github.com/tesseract-ocr/tesseract/issues/434
I am relatively new to machine learning/python/ubuntu.
I have a set of images in .jpg format where half contain a feature I want caffe to learn and half don't. I'm having trouble in finding a way to convert them to the required lmdb format.
I have the necessary text input files.
My question is can anyone provide a step by step guide on how to use convert_imageset.cpp in the ubuntu terminal?
Thanks
A quick guide to Caffe's convert_imageset
Build
First thing you must do is build caffe and caffe's tools (convert_imageset is one of these tools).
After installing caffe and makeing it make sure you ran make tools as well.
Verify that a binary file convert_imageset is created in $CAFFE_ROOT/build/tools.
Prepare your data
Images: put all images in a folder (I'll call it here /path/to/jpegs/).
Labels: create a text file (e.g., /path/to/labels/train.txt) with a line per input image . For example:
img_0000.jpeg 1
img_0001.jpeg 0
img_0002.jpeg 0
In this example the first image is labeled 1 while the other two are labeled 0.
Convert the dataset
Run the binary in shell
~$ GLOG_logtostderr=1 $CAFFE_ROOT/build/tools/convert_imageset \
--resize_height=200 --resize_width=200 --shuffle \
/path/to/jpegs/ \
/path/to/labels/train.txt \
/path/to/lmdb/train_lmdb
Command line explained:
GLOG_logtostderr flag is set to 1 before calling convert_imageset indicates the logging mechanism to redirect log messages to stderr.
--resize_height and --resize_width resize all input images to same size 200x200.
--shuffle randomly change the order of images and does not preserve the order in the /path/to/labels/train.txt file.
Following are the path to the images folder, the labels text file and the output name. Note that the output name should not exist prior to calling convert_imageset otherwise you'll get a scary error message.
Other flags that might be useful:
--backend - allows you to choose between an lmdb dataset or levelDB.
--gray - convert all images to gray scale.
--encoded and --encoded_type - keep image data in encoded (jpg/png) compressed form in the database.
--help - shows some help, see all relevant flags under Flags from tools/convert_imageset.cpp
You can check out $CAFFE_ROOT/examples/imagenet/convert_imagenet.sh
for an example how to use convert_imageset.
I have a really huge CSV files. There are about 1700 columns and 40000 rows like below:
x,y,z,x1,x2,x3,x4,x5,x6,x7,x8,x9,...(about 1700 more)...,x1700
0,0,0,a1,a2,a3,a4,a5,a6,a7,a8,a9,...(about 1700 more)...,a1700
1,1,1,b1,b2,b3,b4,b5,b6,b7,b8,b9,...(about 1700 more)...,b1700
// (about 40000 more rows below)
I need to split this CSV file into multiple files which contain a less number of columns like:
# file1.csv
x,y,z
0,0,0
1,1,1
... (about 40000 more rows below)
# file2.csv
x1,x2,x3,x4,x5,x6,x7,x8,x9,...(about 1000 more)...,x1000
a1,a2,a3,a4,a5,a6,a7,a8,a9,...(about 1000 more)...,a1000
b1,b2,b3,b4,b5,b6,b7,b8,b9,...(about 1000 more)...,b1700
// (about 40000 more rows below)
#file3.csv
x1001,x1002,x1003,x1004,x1005,...(about 700 more)...,x1700
a1001,a1002,a1003,a1004,a1005,...(about 700 more)...,a1700
b1001,b1002,b1003,b1004,b1005,...(about 700 more)...,b1700
// (about 40000 more rows below)
Is there any program or library doing this?
I've googled for it , but programs that I found only split a file by rows not by columns.
Or which language could I use to do this efficiently?
I can use R, shell script, Python, C/C++, Java
A one-line solution for your example data and desired output:
cut -d, -f -3 huge.csv > file1.csv
cut -d, -f 4-1004 huge.csv > file2.csv
cut -d, -f 1005- huge.csv > file3.csv
The cut program is available on most POSIX platforms and is part of GNU Core Utilities. There is also a Windows version.
update in python, since the OP asked for a program in an acceptable language:
# python 3 (or python 2, if you must)
import csv
import fileinput
output_specifications = ( # csv file name, selector function
('file1.csv', slice(3)),
('file2.csv', slice(3, 1003)),
('file3.csv', slice(1003, 1703)),
)
output_row_writers = [
(
csv.writer(open(file_name, 'wb'), quoting=csv.QUOTE_MINIMAL).writerow,
selector,
) for file_name, selector in output_specifications
]
reader = csv.reader(fileinput.input())
for row in reader:
for row_writer, selector in output_row_writers:
row_writer(row[selector])
This works with the sample data given and can be called with the input.csv as an argument or by piping from stdin.
Use a small python script like:
fin = 'file_in.csv'
fout1 = 'file_out1.csv'
fout1_fd = open(fout1,'w')
...
lines = []
with open(fin) as fin_fd:
lines = fin_fd.read().split('\n')
for l in lines:
l_arr = l.split(',')
fout1_fd.write(','.join(l_arr[0:3]))
fout1_fd.write('\n')
...
...
fout1_fd.close()
...
You can open the file in Microsoft Excel, delete the extra columns, save as csv for file #1. Repeat the same procedure for the other 2 tables.
I usually use open office ( or microsof excel in case you are using windows) to do that without writing any program and change the file and save it. Following are two useful links showing how to do that.
https://superuser.com/questions/407082/easiest-way-to-open-csv-with-commas-in-excel
http://office.microsoft.com/en-us/excel-help/import-or-export-text-txt-or-csv-files-HP010099725.aspx
I have a csv file which has 5 entries on every row. Every entry is whether a network packet is triggered or not. The last entry in every row is the size of packet. Every row = time elapsed in ms.
e.g. row
1 , 0 , 1 , 2 , 117
How do I plot a graph for e.g. where x axis is the row number and y is the value for e.g. 1st entry in every row?
This should get you started:
set datafile separator ","
plot 'infile' using 0:1
You can also plot to a png file using gnuplot (which is free):
terminal commands
gnuplot> set title '<title>'
gnuplot> set ylabel '<yLabel>'
gnuplot> set xlabel '<xLabel>'
gnuplot> set grid
gnuplot> set term png
gnuplot> set output '<Output file name>.png'
gnuplot> plot '<fromfile.csv>'
note: you always need to give the right extension (.png here) at set output
Then it is also possible that the ouput is not lines, because your data is not continues. To fix this simply change the 'plot' line to:
plot '<Fromfile.csv>' with line lt -1 lw 2
More line editing options (dashes and line color ect.) at:
http://gnuplot.sourceforge.net/demo_canvas/dashcolor.html
gnuplot is available in most linux distros via the package manager (e.g. on an apt based distro, run apt-get install gnuplot)
gnuplot is available in windows via Cygwin
gnuplot is available on macOS via homebrew (run brew install gnuplot)