Source code for wbia.algo.detect.selectivesearch

# -*- 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