Open Source Computer Vision Library https://opencv.org/
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#!/usr/bin/env python
def substitute(cv, output_dir):
# setup the template engine
template_dir = os.path.join(os.path.dirname(__file__), 'templates')
jtemplate = Environment(loader=FileSystemLoader(template_dir), trim_blocks=True, lstrip_blocks=True)
# add the filters
jtemplate.filters['cellarray'] = cellarray
jtemplate.filters['split'] = split
jtemplate.filters['csv'] = csv
# load the template
template = jtemplate.get_template('template_cvmex_base.m')
# create the build directory
output_dir = output_dir+'/+cv'
if not os.path.isdir(output_dir):
os.mkdir(output_dir)
# populate template
populated = template.render(cv=cv, time=time)
with open(os.path.join(output_dir, 'mex.m'), 'wb') as f:
f.write(populated.encode('utf-8'))
if __name__ == "__main__":
"""
Usage: python cvmex.py --jinja2 /path/to/jinja2/engine
--opts [-list -of -opts]
--include_dirs [-list -of -opencv_include_directories]
--lib_dir opencv_lib_directory
--libs [-lopencv_core -lopencv_imgproc ...]
--flags [-Wall -opencv_build_flags ...]
--outdir /path/to/generated/output
cvmex.py generates a custom mex compiler that automatically links OpenCV
libraries to built sources where appropriate. The calling syntax is the
same as the builtin mex compiler, with added cv qualification:
>> cv.mex(..., ...);
"""
# parse the input options
import sys, re, os, time
from argparse import ArgumentParser
parser = ArgumentParser()
parser.add_argument('--jinja2')
parser.add_argument('--opts')
parser.add_argument('--include_dirs')
parser.add_argument('--lib_dir')
parser.add_argument('--libs')
parser.add_argument('--flags')
parser.add_argument('--outdir')
cv = parser.parse_args()
# add jinja to the path
sys.path.append(cv.jinja2)
from filters import *
from jinja2 import Environment, FileSystemLoader
# populate the mex base template
substitute(cv, cv.outdir)