# -*- coding: utf-8 -*-
"""
Interface to Selective Search object proposals.
"""
from __future__ import absolute_import, division, print_function
import utool as ut
import vtool as vt
from six.moves import zip
import tempfile
import subprocess
import shlex
import os
from os.path import abspath, dirname, expanduser, join, exists # NOQA
import numpy as np
import scipy.io
(print, rrr, profile) = ut.inject2(__name__, '[selective search]')
# SCRIPT_PATH = abspath(dirname(__file__))
SCRIPT_PATH = abspath(expanduser(join('~', 'code', 'selective_search_ijcv_with_python')))
if not ut.get_argflag('--no-selective-search'):
try:
assert exists(SCRIPT_PATH)
except AssertionError as ex:
print(
'WARNING Failed to find selective_search_ijcv_with_python. '
'Selective Search is unavailable'
)
# if ut.SUPER_STRICT:
# raise
VERBOSE_SS = ut.get_argflag('--verbdss') or ut.VERBOSE
[docs]def detect_gid_list(ibs, gid_list, downsample=True, verbose=VERBOSE_SS, **kwargs):
"""
Args:
gid_list (list of int): the list of IBEIS image_rowids that need detection
downsample (bool, optional): a flag to indicate if the original image
sizes should be used; defaults to True
True: ibs.get_image_detectpaths() is used
False: ibs.get_image_paths() is used
Kwargs (optional): refer to the Selective Search documentation for configuration settings
Args:
ibs (wbia.IBEISController): image analysis api
gid_list (list of int): the list of IBEIS image_rowids that need detection
downsample (bool, optional): a flag to indicate if the original image
sizes should be used; defaults to True
Kwargs:
detector, config_filepath, weights_filepath, verbose
Yields:
tuple: (gid, gpath, result_list)
CommandLine:
python -m wbia.algo.detect.selectivesearch detect_gid_list --show
Example:
>>> # DISABLE_DOCTEST
>>> from wbia.algo.detect.selectivesearch import * # NOQA
>>> from wbia.core_images import LocalizerConfig
>>> import wbia
>>> ibs = wbia.opendb('testdb1')
>>> gid_list = ibs.get_valid_gids()
>>> config = {'matlab_command': 'selective_search', 'verbose': True}
>>> downsample = False
>>> results_list = detect_gid_list(ibs, gid_list, downsample, **config)
>>> results_list = list(results_list)
>>> print('result lens = %r' % (map(len, list(results_list))))
>>> print('result[0] = %r' % (len(list(results_list[0][2]))))
>>> config = {'matlab_command': 'selective_search_rcnn', 'verbose': True}
>>> downsample = False
>>> results_list = detect_gid_list(ibs, gid_list, downsample, **config)
>>> results_list = list(results_list)
>>> print('result lens = %r' % (map(len, list(results_list))))
>>> print('result[0] = %r' % (len(list(results_list[0][2]))))
>>> ut.quit_if_noshow()
>>> import wbia.plottool as pt
>>> ut.show_if_requested()
Yields:
results (list of dict)
"""
# Get new gpaths if downsampling
if downsample:
gpath_list = ibs.get_image_detectpaths(gid_list)
neww_list = [vt.open_image_size(gpath)[0] for gpath in gpath_list]
oldw_list = [oldw for (oldw, oldh) in ibs.get_image_sizes(gid_list)]
downsample_list = [oldw / neww for oldw, neww in zip(oldw_list, neww_list)]
orient_list = [1] * len(gid_list)
else:
gpath_list = ibs.get_image_paths(gid_list)
downsample_list = [None] * len(gpath_list)
orient_list = ibs.get_image_orientation(gid_list)
# Run detection
results_iter = detect(gpath_list, verbose=verbose, **kwargs)
# Upscale the results
_iter = zip(downsample_list, gid_list, orient_list, results_iter)
for downsample, gid, orient, (gpath, result_list) in _iter:
# Upscale the results back up to the original image size
for result in result_list:
if downsample is not None and downsample != 1.0:
for key in ['xtl', 'ytl', 'width', 'height']:
result[key] = int(result[key] * downsample)
bbox = (
result['xtl'],
result['ytl'],
result['width'],
result['height'],
)
bbox_list = [bbox]
bbox = bbox_list[0]
result['xtl'], result['ytl'], result['width'], result['height'] = bbox
yield (gid, gpath, result_list)
[docs]def detect(gpath_list, matlab_command='selective_search', verbose=VERBOSE_SS, **kwargs):
"""
Args:
gpath_list (list of str): the list of image paths that need proposal candidates
Kwargs (optional): refer to the Selective Search documentation for configuration settings
Returns:
iter
"""
# Form the MATLAB script command that processes images and write to
# temporary results file.
temp_file, temp_filepath = tempfile.mkstemp(suffix='.mat')
os.close(temp_file)
gpath_str = '{%s}' % (','.join(["'%s'" % (gpath,) for gpath in gpath_list]))
matlab_command_str = "%s(%s, '%s')" % (matlab_command, gpath_str, temp_filepath)
if verbose:
print('Calling: %s' % (matlab_command_str,))
# Execute command in MATLAB.
bash_command = 'matlab -nojvm -r "try; %s; catch; exit; end; exit"'
bash_str = bash_command % (matlab_command_str,)
bash_list = shlex.split(bash_str)
with open('/dev/null', 'w') as null:
process_id = subprocess.Popen(bash_list, stdout=null, cwd=SCRIPT_PATH)
process_return_code = process_id.wait()
if process_return_code != 0:
raise RuntimeError('Matlab selective search did not exit successfully')
# Read the results and undo Matlab's 1-based indexing.
boxes_list = list(scipy.io.loadmat(temp_filepath)['all_boxes'][0])
subtractor = np.array((1, 1, 0, 0))[np.newaxis, :]
results_list = [boxes - subtractor for boxes in boxes_list]
if len(results_list) != len(gpath_list):
raise ValueError('Matlab selective search did not return valid data')
# Remove temporary file, and return.
os.remove(temp_filepath)
# Pack results
results_list_ = []
for result_list in results_list:
result_list_ = []
for result in result_list:
xtl = int(np.around(result[0]))
ytl = int(np.around(result[1]))
xbr = int(np.around(result[2]))
ybr = int(np.around(result[3]))
result_dict = {
'xtl': xtl,
'ytl': ytl,
'width': xbr - xtl,
'height': ybr - ytl,
'class': None,
'confidence': 1.0,
}
result_list_.append(result_dict)
results_list_.append(result_list_)
results_list = zip(gpath_list, results_list_)
return results_list