def grab_selenium_chromedriver(redownload=False):
r"""
Automatically download selenium chrome driver if needed
CommandLine:
python -m utool.util_grabdata --test-grab_selenium_chromedriver:1
Example:
>>> # DISABLE_DOCTEST
>>> ut.grab_selenium_chromedriver()
>>> import selenium.webdriver
>>> driver = selenium.webdriver.Chrome()
>>> driver.get('http://www.google.com')
>>> search_field = driver.find_element_by_name('q')
>>> search_field.send_keys('puppies')
>>> search_field.send_keys(selenium.webdriver.common.keys.Keys.ENTER)
Example1:
>>> # DISABLE_DOCTEST
>>> import selenium.webdriver
>>> driver = selenium.webdriver.Firefox()
>>> driver.get('http://www.google.com')
>>> search_field = driver.find_element_by_name('q')
>>> search_field.send_keys('puppies')
>>> search_field.send_keys(selenium.webdriver.common.keys.Keys.ENTER)
"""
import utool as ut
import os
import stat
# TODO: use a better download dir (but it must be in the PATh or selenium freaks out)
chromedriver_dpath = ut.ensuredir(ut.truepath('~/bin'))
chromedriver_fpath = join(chromedriver_dpath, 'chromedriver')
if not ut.checkpath(chromedriver_fpath) or redownload:
assert chromedriver_dpath in os.environ['PATH'].split(os.pathsep)
# TODO: make this work for windows as well
if ut.LINUX and ut.util_cplat.is64bit_python():
import requests
rsp = requests.get('http://chromedriver.storage.googleapis.com/LATEST_RELEASE')
assert rsp.status_code == 200
url = 'http://chromedriver.storage.googleapis.com/' + rsp.text.strip() + '/chromedriver_linux64.zip'
ut.grab_zipped_url(url, download_dir=chromedriver_dpath, redownload=True)
else:
raise AssertionError('unsupported chrome driver getter script')
if not ut.WIN32:
st = os.stat(chromedriver_fpath)
os.chmod(chromedriver_fpath, st.st_mode | stat.S_IEXEC)
ut.assert_exists(chromedriver_fpath)
os.environ['webdriver.chrome.driver'] = chromedriver_fpath
return chromedriver_fpath
def ensure_ctags_win32():
import utool as ut
from os.path import join
dpath = ut.grab_zipped_url('http://prdownloads.sourceforge.net/ctags/ctags58.zip')
"""
TODO: Download the zipfile, then unzip and take ONLY the
file ctags58/ctags58/ctags.exe and move it somewhere in the path
the best place might be C;\ProgFiles\Git\mingw64\bin
ALSO:
make a win setup file
Downloads fonts from https://www.dropbox.com/sh/49h1ht1e2t7dlbj/AACzVIDrfn1GkImP5l_C3Vtia?dl=1
"""
ctags_fname = 'ctags.exe'
ctags_src = join(dpath, ctags_fname)
def find_mingw_bin():
pathdirs = ut.get_path_dirs()
copydir = None
# hueristic for finding mingw bin
for pathdir in pathdirs:
pathdir_ = pathdir.lower()
ismingwbin = (pathdir_.find('mingw') > -1 and pathdir_.endswith('bin'))
if ismingwbin:
issmaller = (copydir is None or len(pathdir) < len(copydir))
if issmaller:
copydir = pathdir
return copydir
copydir = find_mingw_bin()
ctags_dst = join(copydir, ctags_fname)
ut.copy(ctags_src, ctags_dst, overwrite=False)
def ensure_db_from_url(zipped_db_url):
""" SeeAlso ibeis.init.sysres """
from ibeis import sysres
workdir = sysres.get_workdir()
dbdir = ut.grab_zipped_url(zipped_url=zipped_db_url, ensure=True, download_dir=workdir)
print('have %s=%r' % (zipped_db_url, dbdir,))
return dbdir
def init_console2():
assert ut.WIN32, 'win32 only script'
url = 'http://downloads.sourceforge.net/project/console/console-devel/2.00/Console-2.00b148-Beta_32bit.zip'
unzipped_fpath = ut.grab_zipped_url(url)
# FIXME: bugged
unzipped_fpath2 = join(dirname(unzipped_fpath), 'Console2')
win32_bin = ut.truepath('~/local/PATH')
ut.copy(ut.ls(unzipped_fpath2), win32_bin)
def ensure_db_from_url(zipped_db_url):
""" SeeAlso ibeis.init.sysres """
from ibeis import sysres
dbname = 'testdb2'
workdir = sysres.get_workdir()
zipped_db_url = 'https://dl.dropboxusercontent.com/s/or2ngpaodrb42gd/testdb2.tar.gz'
dbdir = ut.grab_zipped_url(zipped_db_url, ensure=True, download_dir=workdir)
print('have %s=%r' % (dbname, dbdir,))
def ensure_pz_mtest():
"""
Ensures that you have the PZ_MTEST dataset
CommandLine:
python -m ibeis.init.sysres --exec-ensure_pz_mtest
python -m ibeis --tf ensure_pz_mtest
Example:
>>> # SCRIPT
>>> from ibeis.init.sysres import * # NOQA
>>> ensure_pz_mtest()
"""
print('ensure_pz_mtest')
from ibeis import sysres
workdir = sysres.get_workdir()
mtest_zipped_url = const.ZIPPED_URLS.PZ_MTEST
mtest_dir = ut.grab_zipped_url(mtest_zipped_url, ensure=True, download_dir=workdir)
print('have mtest_dir=%r' % (mtest_dir,))
# update the the newest database version
import ibeis
ibs = ibeis.opendb('PZ_MTEST')
print('cleaning up old database and ensureing everything is properly computed')
ibs.db.vacuum()
valid_aids = ibs.get_valid_aids()
assert len(valid_aids) == 119
ibs.update_annot_semantic_uuids(valid_aids)
if ut.VERYVERBOSE:
ibs.print_annotation_table()
nid = ibs.get_name_rowids_from_text('', ensure=False)
if nid is not None:
ibs.set_name_texts([nid], ['lostname'])
# Remove old imagesets and update to new special ones
all_imgset_ids = ibs.get_valid_imgsetids()
special_imgset_ids = ibs.get_special_imgsetids()
other_imgset_ids = ut.setdiff(all_imgset_ids, special_imgset_ids)
ibs.delete_imagesets(other_imgset_ids)
ibs.set_exemplars_from_quality_and_viewpoint()
ibs.update_all_image_special_imageset()
occurrence_gids = [2, 9, 12, 16, 25, 26, 29, 30, 32, 33, 35, 46, 47, 52,
57, 61, 66, 70, 71, 73, 74, 76, 77, 78, 79, 87, 88, 90,
96, 97, 103, 106, 108, 110, 112, 113]
other_gids = ut.setdiff(ibs.get_valid_gids(), occurrence_gids)
other_gids1 = other_gids[0::2]
other_gids2 = other_gids[1::2]
ibs.set_image_imagesettext(occurrence_gids, ['Occurrence 1'] * len(occurrence_gids))
ibs.set_image_imagesettext(other_gids1, ['Occurrence 2'] * len(other_gids1))
ibs.set_image_imagesettext(other_gids2, ['Occurrence 3'] * len(other_gids2))
# hack in some tags
print('Hacking in some tags')
foal_aids = [4, 8, 15, 21, 28, 34, 38, 41, 45, 49, 51, 56, 60, 66, 69, 74, 80, 83, 91, 97, 103, 107, 109, 119]
mother_aids = [9, 16, 35, 42, 52, 57, 61, 67, 75, 84, 98, 104, 108, 114]
ibs.append_annot_case_tags(foal_aids, ['foal'] * len(foal_aids))
ibs.append_annot_case_tags(mother_aids, ['mother'] * len(mother_aids))
def grab_mnist1():
# This is the same mnist data used in the lasange script
train_imgs_fpath = ut.grab_zipped_url(
'http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz')
train_lbls_fpath = ut.grab_zipped_url(
'http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz')
test_imgs_fpath = ut.grab_zipped_url(
'http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz')
test_lbls_fpath = ut.grab_zipped_url(
'http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz')
train_images, train_labels = open_mnist_files(train_imgs_fpath,
train_lbls_fpath)
test_images, test_labels = open_mnist_files(test_imgs_fpath,
test_lbls_fpath)
data = np.vstack((train_images, test_images))
labels = np.append(train_labels, test_labels)
metadata = None
return data, labels, metadata
def ensure_db_from_url(zipped_db_url):
""" SeeAlso wbia.init.sysres """
from wbia import sysres
workdir = sysres.get_workdir()
dbdir = ut.grab_zipped_url(
zipped_url=zipped_db_url, ensure=True, download_dir=workdir
)
logger.info('have %s=%r' % (zipped_db_url, dbdir,))
return dbdir
def get_testdata_dir(ensure=True, key='testdb1'):
"""
Gets test img directory and downloads it if it doesn't exist
"""
testdata_map = {
'testdb1': 'https://dl.dropboxusercontent.com/s/of2s82ed4xf86m6/testdata.zip',
}
zipped_testdata_url = testdata_map[key]
testdata_dir = ut.grab_zipped_url(zipped_testdata_url, ensure=ensure)
return testdata_dir
def get_testdata_dir(ensure=True, key='testdb1'):
"""
Gets test img directory and downloads it if it doesn't exist
"""
testdata_map = {
'testdb1': 'https://cthulhu.dyn.wildme.io/public/data/testdata.zip'
}
zipped_testdata_url = testdata_map[key]
testdata_dir = utool.grab_zipped_url(zipped_testdata_url, ensure=ensure)
return testdata_dir
def get_testdata_dir(ensure=True, key='testdb1'):
"""
Gets test img directory and downloads it if it doesn't exist
"""
testdata_map = {
'testdb1': 'https://lev.cs.rpi.edu/public/data/testdata.zip'
}
zipped_testdata_url = testdata_map[key]
testdata_dir = utool.grab_zipped_url(zipped_testdata_url, ensure=ensure)
return testdata_dir
def get_testdata_dir(ensure=True, key='testdb1'):
"""
Gets test img directory and downloads it if it doesn't exist
"""
testdata_map = {
'testdb1': 'https://lev.cs.rpi.edu/public/data/testdata.zip',
}
zipped_testdata_url = testdata_map[key]
testdata_dir = ut.grab_zipped_url(zipped_testdata_url, ensure=ensure)
return testdata_dir
def __init__(bing,
default=True,
verbose=VERBOSE_BING,
quiet=QUIET_BING,
**kwargs):
'''
Create the C object for the PyBING detector.
Args:
verbose (bool, optional): verbose flag; defaults to --verbbing flag
Kwargs:
base (int)
W (int)
NNS (int)
Returns:
detector (object): the BING Detector object
'''
bing.verbose = verbose
bing.quiet = quiet
# Default values
params = odict([
('base', 2.0),
('W', 8),
('NNS', 2),
('verbose', verbose),
('quiet', quiet),
])
params.update(kwargs)
params_list = list(params.values())
if bing.verbose and not bing.quiet:
""" debug with dmesg | tail -n 200 """
print('[pybing.py] Start Create New BING Object')
ut.print_dict(params)
print('[pybing.py] params_list = %r' % (params_list, ))
print('[pybing.py] type of params = %r' %
(list(map(type, params_list)), ))
pass
bing.detector_c_obj = BING_CLIB.init(*params_list)
if bing.verbose and not bing.quiet:
print('[pybing.py] Finished Create New BING Object')
if default:
model_path = ut.grab_zipped_url(VOC2007_MODEL_URL,
appname='pybing')
model_path = join(model_path, 'model')
print('Loading models: %r' % (model_path, ))
bing.model(model_path)
def ensure_nongit_plugins():
import utool as ut
import REPOS1
BUNDLE_DPATH = util_git1.BUNDLE_DPATH
for url in REPOS1.VIM_NONGIT_PLUGINS:
fpath = ut.grab_zipped_url(url, download_dir=BUNDLE_DPATH)
if fpath.endswith(".vba"):
cmd_ = "vim " + fpath + ' -c "so % | q"'
ut.cmd(cmd_)
print("url = %r" % (url,))
pass
def build_command():
""" Build command run by utool.util_setup """
if util_cplat.WIN32:
fftw_win32_url = 'ftp://ftp.fftw.org/pub/fftw/fftw-3.3.4-dll32.zip'
#fftw_win64_url = 'ftp://ftp.fftw.org/pub/fftw/fftw-3.3.4-dll64.zip'
# Ensure you have everything to build on windows
setup_dir = utool.dirname(__file__)
fftw_dir = utool.grab_zipped_url(fftw_win32_url, download_dir=setup_dir)
print(fftw_dir)
util_cplat.shell('mingw_build.bat')
else:
util_cplat.shell('./unix_build.sh')
def ensure_nongit_plugins():
try:
import utool as ut
import REPOS1
BUNDLE_DPATH = util_git1.BUNDLE_DPATH
for url in REPOS1.VIM_NONGIT_PLUGINS:
fpath = ut.grab_zipped_url(url, download_dir=BUNDLE_DPATH)
if fpath.endswith('.vba'):
cmd_ = 'vim ' + fpath + ' -c "so % | q"'
ut.cmd(cmd_)
print('url = %r' % (url,))
except ImportError:
print('Cant do nongit plugins without utool')
def __init__(bing, default=True, verbose=VERBOSE_BING, quiet=QUIET_BING, **kwargs):
'''
Create the C object for the PyBING detector.
Args:
verbose (bool, optional): verbose flag; defaults to --verbbing flag
Kwargs:
base (int)
W (int)
NNS (int)
Returns:
detector (object): the BING Detector object
'''
bing.verbose = verbose
bing.quiet = quiet
# Default values
params = odict([
('base', 2.0),
('W', 8),
('NNS', 2),
('verbose', verbose),
('quiet', quiet),
])
params.update(kwargs)
params_list = list(params.values())
if bing.verbose and not bing.quiet:
""" debug with dmesg | tail -n 200 """
print('[pybing.py] Start Create New BING Object')
ut.print_dict(params)
print('[pybing.py] params_list = %r' % (params_list,))
print('[pybing.py] type of params = %r' % (list(map(type, params_list)),))
pass
bing.detector_c_obj = BING_CLIB.init(*params_list)
if bing.verbose and not bing.quiet:
print('[pybing.py] Finished Create New BING Object')
if default:
model_path = ut.grab_zipped_url(VOC2007_MODEL_URL, appname='pybing')
model_path = join(model_path, 'model')
print('Loading models: %r' % (model_path, ))
bing.model(model_path)
def test_pygist():
print('[pygist] Testing pygist')
# Ensure you have test data
print('[pygist] Ensuring testdata')
datafile = utool.grab_file_url(TEST_MODEL_URL, appname='utool')
test_image_dir = utool.grab_zipped_url(TEST_IMAGES_URL, appname='utool')
imgpaths = utool.list_images(test_image_dir, fullpath=True) # test image paths
outdir = utool.get_app_resource_dir('pygist') # where to put results
# Run pygist on test images
print('[pygist] Running tests')
test_results = pygist.test(imgpaths, outdir=outdir, datafile=datafile)
# Print results
target_results = [-1, -1, 1, -1, 1, -1, -1, -1, 1, 1, -1, 1, 1]
assert target_results == target_results, 'results do not match'
print('test_results = %r' % (test_results,))
print(utool.list_str(list(izip(imgpaths, test_results))))
return locals()
def test_pygist():
print('[pygist] Testing pygist')
# Ensure you have test data
print('[pygist] Ensuring testdata')
datafile = utool.grab_file_url(TEST_MODEL_URL, appname='utool')
test_image_dir = utool.grab_zipped_url(TEST_IMAGES_URL, appname='utool')
imgpaths = utool.list_images(test_image_dir,
fullpath=True) # test image paths
outdir = utool.get_app_resource_dir('pygist') # where to put results
# Run pygist on test images
print('[pygist] Running tests')
test_results = pygist.test(imgpaths, outdir=outdir, datafile=datafile)
# Print results
target_results = [-1, -1, 1, -1, 1, -1, -1, -1, 1, 1, -1, 1, 1]
assert target_results == target_results, 'results do not match'
print('test_results = %r' % (test_results, ))
print(utool.list_str(list(izip(imgpaths, test_results))))
return locals()
def ensure_db_from_url(zipped_db_url):
""" SeeAlso wbia.init.sysres """
from wbia import sysres
workdir = sysres.get_workdir()
dbdir = ut.grab_zipped_url(
zipped_url=zipped_db_url, ensure=True, download_dir=workdir
)
# Determine if the implementation is using a URI for database connection.
# This is confusing, sorry. If the URI is set we are using a non-sqlite
# database connection. As such, we most translate the sqlite db.
uri = get_wbia_db_uri(dbdir)
if uri:
logger.info(f"Copying '{dbdir}' databases to the database at: {uri}")
for _, future, _, _ in copy_sqlite_to_postgres(Path(dbdir), uri):
future.result() # will raise if there is a problem
logger.info('have %s=%r' % (zipped_db_url, dbdir))
return dbdir
def ensure_pz_mtest():
"""
Ensures that you have the PZ_MTEST dataset
CommandLine:
python -m ibeis.init.sysres --exec-ensure_pz_mtest
python -m ibeis --tf ensure_pz_mtest
Example:
>>> # SCRIPT
>>> from ibeis.init.sysres import * # NOQA
>>> ensure_pz_mtest()
"""
print('ensure_pz_mtest')
from ibeis import sysres
workdir = sysres.get_workdir()
mtest_zipped_url = const.ZIPPED_URLS.PZ_MTEST
mtest_dir = ut.grab_zipped_url(mtest_zipped_url, ensure=True, download_dir=workdir)
print('have mtest_dir=%r' % (mtest_dir,))
# update the the newest database version
import ibeis
ibs = ibeis.opendb('PZ_MTEST')
print('cleaning up old database and ensureing everything is properly computed')
ibs.db.vacuum()
valid_aids = ibs.get_valid_aids()
assert len(valid_aids) == 119
ibs.update_annot_semantic_uuids(valid_aids)
if ut.VERYVERBOSE:
ibs.print_annotation_table()
nid = ibs.get_name_rowids_from_text('', ensure=False)
if nid is not None:
ibs.set_name_texts([nid], ['lostname'])
# hack in some tags
print('Hacking in some tags')
foal_aids = [4, 8, 15, 21, 28, 34, 38, 41, 45, 49, 51, 56, 60, 66, 69, 74, 80, 83, 91, 97, 103, 107, 109, 119]
mother_aids = [9, 16, 35, 42, 52, 57, 61, 67, 75, 84, 98, 104, 108, 114]
ibs.append_annot_case_tags(foal_aids, ['foal'] * len(foal_aids))
ibs.append_annot_case_tags(mother_aids, ['mother'] * len(mother_aids))
def ensure_ctags_win32():
import utool as ut
from os.path import join
dpath = ut.grab_zipped_url("http://prdownloads.sourceforge.net/ctags/ctags58.zip")
ctags_fname = "ctags.exe"
ctags_src = join(dpath, ctags_fname)
def find_mingw_bin():
pathdirs = ut.get_path_dirs()
copydir = None
# hueristic for finding mingw bin
for pathdir in pathdirs:
pathdir_ = pathdir.lower()
ismingwbin = pathdir_.find("mingw") > -1 and pathdir_.endswith("bin")
if ismingwbin:
issmaller = copydir is None or len(pathdir) < len(copydir)
if issmaller:
copydir = pathdir
return copydir
copydir = find_mingw_bin()
ctags_dst = join(copydir, ctags_fname)
ut.copy(ctags_src, ctags_dst, overwrite=False)
def download_baseline_distinctiveness_normalizer(cachedir, species):
zipped_url = BASELINE_DISTINCTIVNESS_URLS[species]
ut.grab_zipped_url(zipped_url, ensure=True, download_dir=cachedir)
#!/usr/bin/env python
from six.moves import input
import sys
import random
import numpy
from yael import ynumpy
# ensure the test data
import utool
utool.ensuredir('selective_match_kernel_v289')
url = 'https://gforge.inria.fr/frs/download.php/file/33650/yael_mini_demo_v0.2.tgz'
utool.grab_zipped_url(url, download_dir='.')
# change directory to test data
import os
os.chdir('yael_mini_demo_v0.2')
# prepare for plotting
try:
from plottool.__MPL_INIT__ import init_matplotlib
init_matplotlib()
#import matplotlib as mpl
#mpl.use('Qt4Agg')
import matplotlib.pyplot as plt
# Python Imaging Library (used to load images)
from PIL import Image
show = True
plt.ion()
except ImportError as RuntimeError:
print("Cannot import matplotlib or PIL. Not showing graphics!")
show = False
#!/usr/bin/env python2.7
# -*- coding: utf-8 -*-
"""
downloads an archive file and then unzips it into a directory with the same name
as the archive (sans the .zip or .tar.gz)
grabzippedurl.py "https://download.zotero.org/standalone/4.0.26.3/Zotero-4.0.26.3_linux-x86_64.tar.bz2"
"""
from __future__ import absolute_import, division, print_function
import sys
import utool
if __name__ == '__main__':
url = sys.argv[1]
download_dir = '.'
utool.grab_zipped_url(url,
download_dir=download_dir,
cleanup=False,
spoof=True)
# Create a directory for the demo database
workdir = ibeis.get_workdir()
demodir = join(workdir, 'demo')
if utool.get_arg('--reset'):
# Remove the previous demo if it exists
utool.delete(demodir)
# Start a new database there
main_locals = ibeis.main(dbdir=demodir)
# Get a handle to the GUIBackend Control
back = main_locals['back']
# Get a directory with some images in it
testurl = 'https://www.dropbox.com/s/s4gkjyxjgghr18c/testdata_detect.zip'
testdir = utool.grab_zipped_url(testurl)
execstr = ibeis.main_loop(main_locals)
exec(execstr)
script = """
back.import_images_from_dir(testdir)
back.detect_grevys_quick()
back.compute_encounters()
"""
#execstr = ibeis.main_loop(main_locals)
#exec(execstr)
def ensure_db_from_url(zipped_db_url):
""" SeeAlso ibeis.init.sysres """
from ibeis import sysres
workdir = sysres.get_workdir()
dbdir = ut.grab_zipped_url(zipped_url=zipped_db_url, ensure=True, download_dir=workdir)
print('have %s=%r' % (zipped_db_url, dbdir,))
def grab_liberty_siam_dataset(pairs=250000):
"""
References:
http://www.cs.ubc.ca/~mbrown/patchdata/patchdata.html
https://github.com/osdf/datasets/blob/master/patchdata/dataset.py
Notes:
"info.txt" contains the match information Each row of info.txt
corresponds corresponds to a separate patch, with the patches ordered
from left to right and top to bottom in each bitmap image.
3 types of metadata files
info.txt - contains patch ids that correspond with the order of patches
in the bmp images
In the format:
pointid, unused
interest.txt -
interest points corresponding to patches with patchids
has same number of rows as info.txt
In the format:
reference image id, x, y, orientation, scale (in log2 units)
m50_<d>_<d>_0.txt -
matches files
patchID1 3DpointID1 unused1 patchID2 3DpointID2 unused2
CommandLine:
python -m ibeis_cnn.ingest_data --test-grab_liberty_siam_dataset --show
Example:
>>> # ENABLE_DOCTEST
>>> from ibeis_cnn.ingest_data import * # NOQA
>>> pairs = 500
>>> dataset = grab_liberty_siam_dataset(pairs)
>>> ut.quit_if_noshow()
>>> from ibeis_cnn import draw_results
>>> #ibsplugin.rrr()
>>> flat_metadata = {}
>>> data, labels = dataset.subset('full')
>>> ut.quit_if_noshow()
>>> warped_patch1_list = data[::2]
>>> warped_patch2_list = data[1::2]
>>> dataset.interact()
>>> ut.show_if_requested()
"""
datakw = {
'detector': 'dog',
'pairs': pairs,
}
assert datakw['detector'] in ['dog', 'harris']
assert pairs in [500, 50000, 100000, 250000]
liberty_urls = {
'dog': 'http://www.cs.ubc.ca/~mbrown/patchdata/liberty.zip',
'harris': 'http://www.cs.ubc.ca/~mbrown/patchdata/liberty_harris.zip',
}
url = liberty_urls[datakw['detector']]
ds_path = ut.grab_zipped_url(url)
ds_name = splitext(basename(ds_path))[0]
alias_key = 'liberty;' + ut.dict_str(datakw, nl=False, explicit=True)
cfgstr = ','.join([str(val) for key, val in ut.iteritems_sorted(datakw)])
# TODO: allow a move of the base data prefix
training_dpath = ut.ensure_app_resource_dir('ibeis_cnn', 'training',
ds_name)
if ut.get_argflag('--vtd'):
ut.vd(training_dpath)
ut.ensuredir(training_dpath)
data_fpath = join(training_dpath, 'liberty_data_' + cfgstr + '.pkl')
labels_fpath = join(training_dpath, 'liberty_labels_' + cfgstr + '.pkl')
if not ut.checkpath(data_fpath, verbose=True):
data, labels = ingest_helpers.extract_liberty_style_patches(
ds_path, pairs)
ut.save_data(data_fpath, data)
ut.save_data(labels_fpath, labels)
# hack for caching num_labels
labels = ut.load_data(labels_fpath)
num_labels = len(labels)
dataset = DataSet.new_training_set(
alias_key=alias_key,
data_fpath=data_fpath,
labels_fpath=labels_fpath,
metadata_fpath=None,
training_dpath=training_dpath,
data_shape=(64, 64, 1),
data_per_label=2,
output_dims=1,
num_labels=num_labels,
)
return dataset
# DUPLICATE CODE, DELETE
from __future__ import absolute_import, division, print_function
from plottool import interact_multi_image
from plottool import draw_func2 as df2
import utool
#import ibeis
def test_interact_multimage(imgpaths):
print("len: ", len(imgpaths))
bboxes_list = [[]] * len(imgpaths)
bboxes_list[0] = [(-200, -100, 400, 400)]
print(bboxes_list)
iteract_obj = interact_multi_image.MultiImageInteraction(imgpaths, nPerPage=4, bboxes_list=bboxes_list)
# def test_interact_multimage(imgpaths, gid_list=None, aids_list=None, bboxes_list=None):
# img_list = imread_many(imgpaths)
# iteract_obj = interact_multi_image.MultiImageInteraction(img_list +
# img_list,
# gid_list, aids_list, bboxes_list,
# nPerPage=6)
return iteract_obj
if __name__ == '__main__':
TEST_IMAGES_URL = 'https://lev.cs.rpi.edu/public/data/testdata.zip'
test_image_dir = utool.grab_zipped_url(TEST_IMAGES_URL, appname='utool')
imgpaths = utool.list_images(test_image_dir, fullpath=True, recursive=False) # test image paths
iteract_obj = test_interact_multimage(imgpaths)
exec(df2.present())
page = urllib2.urlopen(req)
page_str = page.read()
page_str = ut.ensure_unicode(page_str)
next = False
lines = page_str.split('\n')
for index, x in enumerate(lines):
if next:
print(x)
import parse
url_suffix = parse.parse('{foo}href="{href}"{other}', x)['href']
url = r'https://cmake.org' + url_suffix
break
if 'Linux x86_64' in x:
next = True
url = url.replace('.sh', '.tar.gz')
cmake_unzipped_fpath = ut.grab_zipped_url(url)
install_prefix = ut.unixpath('~')
for dname in ['bin', 'doc', 'man', 'share']:
install_dst = join(install_prefix, dname)
install_src = join(cmake_unzipped_fpath, dname)
# FIXME: this broke
#ut.util_path.copy(install_src, install_dst)
# HACK AROUND IT
from os.path import dirname
cmd = str('cp -r "' + install_src + '" "' + dirname(install_dst) + '"')
print(cmd)
ut.cmd(cmd)
#os.system(cmd)
print(cmake_unzipped_fpath)
#!/usr/bin/env python
from six.moves import input
import sys
import random
import numpy
from yael import ynumpy
# ensure the test data
import utool
utool.ensuredir('selective_match_kernel_v289')
url = 'https://gforge.inria.fr/frs/download.php/file/33650/yael_mini_demo_v0.2.tgz'
utool.grab_zipped_url(url, download_dir='.')
# change directory to test data
import os
os.chdir('yael_mini_demo_v0.2')
# prepare for plotting
try:
from plottool.__MPL_INIT__ import init_matplotlib
init_matplotlib()
#import matplotlib as mpl
#mpl.use('Qt4Agg')
import matplotlib.pyplot as plt
# Python Imaging Library (used to load images)
from PIL import Image
show = True
plt.ion()
except ImportError as RuntimeError:
print("Cannot import matplotlib or PIL. Not showing graphics!")
show = False
fp = browser.firefox_profile
fp.set_preference("browser.helperApps.neverAsk.saveToDisk", "application/exe")
fp.set_preference("browser.helperApps.neverAsk.saveToDisk", "application/exe")
fp.set_preference("browser.download.manager.showWhenStarting", False)
fp.set_preference("browser.helperApps.neverAsk.saveToDisk", "application/binary")
fp.set_preference('browser.download.folderList', 2)
fp.set_preference('browser.download.dir', dldir)
else:
#install32 = normpath(r'C:\Program Files (x86)')
#chromepath = normpath(r'\Google\Chrome\Application')
#chromeexe = join(install32, chromepath, 'chrome.exe')
import utool
DRIVER_URL = 'http://chromedriver.storage.googleapis.com/2.9/chromedriver_win32.zip'
chromedriverexe = utool.get_app_resource_dir('utool') + '/chromedriver.exe'
if not utool.checkpath(chromedriverexe):
utool.grab_zipped_url(DRIVER_URL, appname='utool')
print(type(chromedriverexe))
print(chromedriverexe)
#chromedriverexe = normpath(r'C:\Users\joncrall\local\PATH\chromedriver.exe')
browser = webdriver.Chrome(executable_path=chromedriverexe)
weburl = 'http://www.lfd.uci.edu/~gohlke/pythonlibs'
browser.get(weburl)
source = browser.page_source
def clean_package_names(pkgname_list):
clean_list = []
unsuported_list = []
for pkgname in pkgname_list:
def download_baseline_distinctiveness_normalizer(cachedir, species):
zipped_url = BASELINE_DISTINCTIVNESS_URLS[species]
ut.grab_zipped_url(zipped_url, ensure=True, download_dir=cachedir)
def ensure_pz_mtest():
"""
Ensures that you have the PZ_MTEST dataset
CommandLine:
python -m ibeis.init.sysres --exec-ensure_pz_mtest
python -m ibeis --tf ensure_pz_mtest
Example:
>>> # SCRIPT
>>> from ibeis.init.sysres import * # NOQA
>>> ensure_pz_mtest()
"""
print('ensure_pz_mtest')
from ibeis import sysres
workdir = sysres.get_workdir()
mtest_zipped_url = const.ZIPPED_URLS.PZ_MTEST
mtest_dir = ut.grab_zipped_url(mtest_zipped_url,
ensure=True,
download_dir=workdir)
print('have mtest_dir=%r' % (mtest_dir, ))
# update the the newest database version
import ibeis
ibs = ibeis.opendb('PZ_MTEST')
print(
'cleaning up old database and ensureing everything is properly computed'
)
ibs.db.vacuum()
valid_aids = ibs.get_valid_aids()
assert len(valid_aids) == 119
ibs.update_annot_semantic_uuids(valid_aids)
if ut.VERYVERBOSE:
ibs.print_annotation_table()
nid = ibs.get_name_rowids_from_text('', ensure=False)
if nid is not None:
ibs.set_name_texts([nid], ['lostname'])
# Remove old imagesets and update to new special ones
all_imgset_ids = ibs.get_valid_imgsetids()
special_imgset_ids = ibs.get_special_imgsetids()
other_imgset_ids = ut.setdiff(all_imgset_ids, special_imgset_ids)
ibs.delete_imagesets(other_imgset_ids)
ibs.set_exemplars_from_quality_and_viewpoint()
ibs.update_all_image_special_imageset()
occurrence_gids = [
2, 9, 12, 16, 25, 26, 29, 30, 32, 33, 35, 46, 47, 52, 57, 61, 66, 70,
71, 73, 74, 76, 77, 78, 79, 87, 88, 90, 96, 97, 103, 106, 108, 110,
112, 113
]
other_gids = ut.setdiff(ibs.get_valid_gids(), occurrence_gids)
other_gids1 = other_gids[0::2]
other_gids2 = other_gids[1::2]
ibs.set_image_imagesettext(occurrence_gids,
['Occurrence 1'] * len(occurrence_gids))
ibs.set_image_imagesettext(other_gids1,
['Occurrence 2'] * len(other_gids1))
ibs.set_image_imagesettext(other_gids2,
['Occurrence 3'] * len(other_gids2))
# hack in some tags
print('Hacking in some tags')
foal_aids = [
4, 8, 15, 21, 28, 34, 38, 41, 45, 49, 51, 56, 60, 66, 69, 74, 80, 83,
91, 97, 103, 107, 109, 119
]
mother_aids = [9, 16, 35, 42, 52, 57, 61, 67, 75, 84, 98, 104, 108, 114]
ibs.append_annot_case_tags(foal_aids, ['foal'] * len(foal_aids))
ibs.append_annot_case_tags(mother_aids, ['mother'] * len(mother_aids))
def test_pyrf():
category = 'zebra_plains'
detect_config = {
'save_detection_images': True,
'save_scales': True,
'percentage_top': 0.40,
}
#=================================
# Train / Detect Initialization
#=================================
testdata_dir = utool.unixpath('~/code/pyrf/results')
# assert utool.checkpath(testdata_dir)
if utool.get_argflag('--vd'):
print(utool.ls(testdata_dir))
# Create detector
detector = Random_Forest_Detector()
test_path = utool.grab_zipped_url(TEST_DATA_DETECT_URL, appname='utool')
models_path = utool.grab_zipped_url(TEST_DATA_MODEL_URL, appname='utool')
trees_path = join(models_path, category)
results_path = join(utool.unixpath('~/code/pyrf/results'), category)
# detect_path = join(results_path, 'detect')
trees_path = join(results_path, 'trees')
detect_path = join(test_path, category, 'detect')
utool.ensuredir(detect_path)
utool.ensuredir(test_path)
utool.ensuredir(trees_path)
#=================================
# Detect using Random Forest
#=================================
# Get input images
from vtool import image
big_gpath_list = utool.list_images(test_path, fullpath=True, recursive=False)
print(big_gpath_list)
# Resize images to standard size
if utool.get_argflag('--small'):
big_gpath_list = big_gpath_list[0:8]
#big_gpath_list = big_gpath_list[0:8]
output_dir = join(test_path, 'resized')
std_gpath_list = image.resize_imagelist_to_sqrtarea(big_gpath_list,
sqrt_area=800,
output_dir=output_dir,
checkexists=True)
dst_gpath_list = [join(detect_path, split(gpath)[1]) for gpath in std_gpath_list]
#utool.view_directory(test_path)
#utool.view_directory('.')
print(std_gpath_list)
num_images = len(std_gpath_list)
#assert num_images == 16, 'the test has diverged!'
print('Testing on %r images' % num_images)
# Load forest, so we don't have to reload every time
forest = detector.load(trees_path, category + '-', num_trees=25)
detector.set_detect_params(**detect_config)
results_list1 = []
with utool.Timer('[test_pyrf] for loop detector.detect') as t1:
if not utool.get_argflag('--skip1'):
for ix, (img_fpath, dst_fpath) in enumerate(zip(std_gpath_list, dst_gpath_list)):
#img_fname = split(img_fpath)[1]
#dst_fpath = join(detect_path, img_fname)
#print(' * img_fpath = %r' % img_fpath)
#print(' * dst_fpath = %r' % dst_fpath)
with utool.Timer('[test_pyrf] detector.detect ix=%r' % (ix,)):
results = detector.detect(forest, img_fpath, dst_fpath)
results_list1.append(results)
print('num results = %r' % len(results))
else:
print('...skipped')
# with utool.Timer('[test_pyrf] detector.detect_many') as t2:
# results_list2 = detector.detect_many(forest, std_gpath_list,
# dst_gpath_list, use_openmp=True)
print('')
print('+ --------------')
print('| total time1: %r' % t1.ellapsed)
# print('| total time2: %r' % t2.ellapsed)
print('|')
print('| num results1 = %r' % (list(map(len, results_list1))))
# print('| num results2 = %r' % (list(map(len, results_list2))))
#assert results_list2 == results_list1
return locals()
#!/usr/bin/env python2.7
"""
downloads an archive file and then unzips it into a directory with the same name
as the archive (sans the .zip or .tar.gz)
grabzippedurl.py "https://download.zotero.org/standalone/4.0.26.3/Zotero-4.0.26.3_linux-x86_64.tar.bz2"
"""
from __future__ import absolute_import, division, print_function
import sys
import utool
if __name__ == '__main__':
url = sys.argv[1]
download_dir = '.'
utool.grab_zipped_url(url, download_dir=download_dir, cleanup=False, spoof=True)
def test_pyrf():
r"""
CommandLine:
python run_tests.py --test-test_pyrf
Example:
>>> # ENABLE_DOCTEST
>>> from run_tests import * # NOQA
>>> result = test_pyrf()
>>> print(result)
"""
#=================================
# Initialization
#=================================
category = 'zebra_plains'
#detect_config = {
# 'save_detection_images': True,
# 'percentage_top': 0.40,
#}
testdata_dir = ut.unixpath('~/code/pyrf/results')
# assert ut.checkpath(testdata_dir)
if ut.get_argflag('--vd'):
print(ut.ls(testdata_dir))
# Create detector
detector = Random_Forest_Detector()
test_path = ut.grab_zipped_url(TEST_DATA_DETECT_URL, appname='utool')
models_path = ut.grab_zipped_url(TEST_DATA_MODEL_URL, appname='utool')
trees_path = join(models_path, category)
detect_path = join(test_path, category, 'detect')
ut.ensuredir(detect_path)
ut.ensuredir(test_path)
ut.ensuredir(trees_path)
#=================================
# Load Input Images
#=================================
# Get input images
big_gpath_list = ut.list_images(test_path, fullpath=True, recursive=False)
print(big_gpath_list)
# Resize images to standard size
if ut.get_argflag('--small'):
big_gpath_list = big_gpath_list[0:8]
#big_gpath_list = big_gpath_list[0:8]
output_dir = join(test_path, 'resized')
std_gpath_list = resize_imagelist_to_sqrtarea(big_gpath_list,
sqrt_area=800,
output_dir=output_dir,
checkexists=True)
dst_gpath_list = [join(detect_path, split(gpath)[1]) for gpath in std_gpath_list]
#ut.view_directory(test_path)
#ut.view_directory('.')
print(std_gpath_list)
num_images = len(std_gpath_list)
#assert num_images == 16, 'the test has diverged!'
print('Testing on %r images' % num_images)
#=================================
# Load Pretrained Forests
#=================================
# Load forest, so we don't have to reload every time
trees_fpath_list = ut.ls(trees_path, '*.txt')
#forest = detector.load(trees_path, category + '-')
forest = detector.forest(trees_fpath_list)
#detector.set_detect_params(**detect_config)
results_list1 = []
#=================================
# Detect using Random Forest
#=================================
with ut.Timer('[test_pyrf] for loop detector.detect') as t1:
if not ut.get_argflag('--skip1'):
results_list1 = detector.detect(forest, std_gpath_list, output_gpath_list=dst_gpath_list)
#for ix, (img_fpath, dst_fpath) in enumerate(zip(std_gpath_list, dst_gpath_list)):
# #img_fname = split(img_fpath)[1]
# #dst_fpath = join(detect_path, img_fname)
# #print(' * img_fpath = %r' % img_fpath)
# #print(' * dst_fpath = %r' % dst_fpath)
# with ut.Timer('[test_pyrf] detector.detect ix=%r' % (ix,)):
# results = detector.detect(forest, img_fpath, dst_fpath)
# results_list1.append(results)
# print('num results = %r' % len(results))
#else:
# print('...skipped')
#with ut.Timer('[test_pyrf] detector.detect_many') as t2:
# results_list2 = detector.detect_many(forest, std_gpath_list,
# dst_gpath_list, use_openmp=True)
detector.free_forest(forest)
print('')
print('+ --------------')
print('| total time1: %r' % t1.ellapsed)
#print('| total time2: %r' % t2.ellapsed)
print('|')
print('| num results1 = %r' % (list(map(len, results_list1))))
#print('| num results2 = %r' % (list(map(len, results_list2))))
#assert results_list2 == results_list1
return locals()
def classify(vector_list, weight_filepath, verbose=VERBOSE_SVM, **kwargs):
"""
Args:
thumbail_list (list of str): the list of image thumbnails that need classifying
Returns:
iter
"""
import multiprocessing
import numpy as np
# Get correct weight if specified with shorthand
if weight_filepath in CONFIG_URL_DICT:
weight_url = CONFIG_URL_DICT[weight_filepath]
if weight_url.endswith('.zip'):
weight_filepath = ut.grab_zipped_url(weight_url, appname='ibeis')
else:
weight_filepath = ut.grab_file_url(weight_url, appname='ibeis',
check_hash=True)
# Get ensemble
is_ensemble = isdir(weight_filepath)
if is_ensemble:
weight_filepath_list = sorted([
join(weight_filepath, filename) for filename in listdir(weight_filepath)
if isfile(join(weight_filepath, filename))
])
else:
weight_filepath_list = [weight_filepath]
num_weights = len(weight_filepath_list)
assert num_weights > 0
# Form dictionaries
num_vectors = len(vector_list)
index_list = list(range(num_vectors))
# Generate parallelized wrapper
OLD = False
if is_ensemble and OLD:
vectors_list = [ vector_list for _ in range(num_weights) ]
args_list = zip(weight_filepath_list, vectors_list)
nTasks = num_weights
print('Processing ensembles in parallel using %d ensembles' % (num_weights, ))
else:
num_cpus = multiprocessing.cpu_count()
vector_batch = int(np.ceil(float(num_vectors) / num_cpus))
vector_rounds = int(np.ceil(float(num_vectors) / vector_batch))
args_list = []
for vector_round in range(vector_rounds):
start_index = vector_round * vector_batch
stop_index = (vector_round + 1) * vector_batch
assert start_index < num_vectors
stop_index = min(stop_index, num_vectors)
# print('Slicing index range: [%r, %r)' % (start_index, stop_index, ))
# Slice gids and get feature data
index_list_ = list(range(start_index, stop_index))
vector_list_ = vector_list[start_index: stop_index]
assert len(index_list_) == len(vector_list_)
for weight_filepath in weight_filepath_list:
args = (weight_filepath, vector_list_, index_list_)
args_list.append(args)
nTasks = len(args_list)
print('Processing vectors in parallel using vector_batch = %r' % (vector_batch, ))
# Perform inference
classify_iter = ut.generate2(classify_helper, args_list, nTasks=nTasks,
ordered=True, force_serial=False)
# Classify with SVM for each image vector
score_dict = { index: [] for index in index_list }
class_dict = { index: [] for index in index_list }
for score_dict_, class_dict_ in classify_iter:
for index in index_list:
if index in score_dict_:
score_dict[index] += score_dict_[index]
if index in class_dict_:
class_dict[index] += class_dict_[index]
# Organize and compute mode and average for class and score
for index in index_list:
score_list_ = score_dict[index]
class_list_ = class_dict[index]
score_ = sum(score_list_) / len(score_list_)
class_ = max(set(class_list_), key=class_list_.count)
class_ = 'positive' if int(class_) == 1 else 'negative'
yield score_, class_
def _download_training_kit():
training_kit_url = CONFIG_URL_DICT['training_kit']
training_kit_path = ut.grab_zipped_url(training_kit_url,
appname='lightnet')
return training_kit_path
def train_pyrf():
boosting = 3
num_trees = 5
category = 'giraffe'
#=================================
# Train / Detect Configurations
#=================================
train_config = {
'object_min_width': 32,
'object_min_height': 32,
'mine_negatives': True,
'mine_max_keep': 1,
'mine_exclude_categories': [category],
'mine_width_min': 128,
'mine_width_max': 512,
'mine_height_min': 128,
'mine_height_max': 512,
'neg_exclude_categories': [category],
'max_rois_pos': 325,
'max_rois_neg': 600,
'num_trees': num_trees,
}
detect_config = {
'save_detection_images': True,
'detection_height': 208,
}
#=================================
# Train / Detect Initialization
#=================================
# Create detector
detector = Random_Forest_Detector()
# Gather Dataset
dataset_path = utool.unixpath('~/code/IBEIS2014/')
dataset = IBEIS_Data(dataset_path, **train_config)
results_path = join(utool.unixpath('~/code/pyrf/results'), category)
pos_path = join(results_path, 'train-positives')
neg_path = join(results_path, 'train-negatives')
val_path = join(results_path, 'val')
test_path = join(results_path, 'test')
test_pos_path = join(results_path, 'test-positives')
test_neg_path = join(results_path, 'test-negatives')
detect_path = join(results_path, 'detect')
trees_path = join(results_path, 'trees')
# Ensure result path for the category
# rmtreedir(results_path)
ensuredir(results_path)
for phase in range(1, boosting + 1):
print("*********************")
print("Phase: %s" % phase)
print("*********************")
# raw_input()
# =================================
# Train Random Forest
#=================================
detector.train(dataset, category, pos_path, neg_path, val_path,
test_path, test_pos_path, test_neg_path,
trees_path, reshuffle=(phase == 1), **train_config)
if phase < boosting:
#=================================
# Detect using Random Forest
#=================================
# Load forest, so we don't have to reload every time
forest = detector.load(trees_path, category + '-', num_trees=(phase * num_trees))
detector.set_detect_params(**detect_config)
# Ensure output detection paths
rmtreedir(detect_path)
ensuredir(detect_path)
# Calculate error on test set
direct = Directory(test_path, include_file_extensions=["jpg"])
accuracy_list = []
image_filepath_list = direct.files()
dst_filepath_list = [ join(detect_path, split(image_filepath)[1]) for image_filepath in image_filepath_list ]
predictions_list = detector.detect_many(forest, image_filepath_list, dst_filepath_list, use_openmp=True)
for index, (predictions, image_filepath) in enumerate(zip(predictions_list, image_filepath_list)):
image_path, image_filename = split(image_filepath)
image = dataset[image_filename]
accuracy, true_pos, false_pos, false_neg = image.accuracy(predictions, category)
accuracy_list.append(accuracy)
progress = "%0.2f" % (float(index) / len(image_filepath_list))
print("TEST %s %0.4f %s" % (image, accuracy, progress), end='\r')
sys.stdout.flush()
# image.show(prediction_list=predictions, category=category)
print(' ' * 100, end='\r')
print("TEST ERROR: %0.4f" % (1.0 - (float(sum(accuracy_list)) / len(accuracy_list))))
#=================================
# Eval and prep boosting train set
#=================================
detector.boosting(phase, forest, dataset, category, pos_path, neg_path,
test_pos_path, test_neg_path, detect_path)
####################################
# New FAST
####################################
detector = Random_Forest_Detector(
scales='6 1.0 0.75 0.55 0.40 0.30 0.20'
)
# Ensure output detection paths
detect_path_temp = detect_path + "_1"
rmtreedir(detect_path_temp)
ensuredir(detect_path_temp)
# Load forest, so we don't have to reload every time
forest = detector.load(trees_path, category + '-', num_trees=15)
detector.set_detect_params(**detect_config)
# Calculate error on test set
direct = Directory(test_path, include_file_extensions=["jpg"])
accuracy_list = []
true_pos_list = []
false_pos_list = []
false_neg_list = []
image_filepath_list = direct.files()
dst_filepath_list = [ join(detect_path_temp, split(image_filepath)[1]) for image_filepath in image_filepath_list ]
predictions_list = detector.detect_many(forest, image_filepath_list, dst_filepath_list, use_openmp=True)
for index, (predictions, image_filepath) in enumerate(zip(predictions_list, image_filepath_list)):
image_path, image_filename = split(image_filepath)
image = dataset[image_filename]
accuracy, true_pos, false_pos, false_neg = image.accuracy(predictions, category)
accuracy_list.append(accuracy)
true_pos_list.append(true_pos)
false_pos_list.append(false_pos)
false_neg_list.append(false_neg)
progress = "%0.2f" % (float(index) / len(image_filepath_list))
print("TEST %s %0.4f %s" % (image, accuracy, progress), end='\r')
sys.stdout.flush()
# image.show(prediction_list=predictions, category=category)
print(' ' * 100, end='\r')
print("1 TEST ERROR : %0.4f" % (1.0 - (float(sum(accuracy_list)) / len(accuracy_list))))
print("1 TEST TRUE POS : %d" % (sum(true_pos_list)))
print("1 TEST FALSE POS : %d" % (sum(false_pos_list)))
print("1 TEST FALSE NEG : %d" % (sum(false_neg_list)))
####################################
# New SLOW
####################################
detector = Random_Forest_Detector(
scales='11 1.5 1.25 1.0 0.8 0.64 0.51 0.41 0.33 0.26 0.21 0.17'
)
# Ensure output detection paths
detect_path_temp = detect_path + "_2"
rmtreedir(detect_path_temp)
ensuredir(detect_path_temp)
# Load forest, so we don't have to reload every time
forest = detector.load(trees_path, category + '-', num_trees=15)
detector.set_detect_params(**detect_config)
# Calculate error on test set
direct = Directory(test_path, include_file_extensions=["jpg"])
accuracy_list = []
true_pos_list = []
false_pos_list = []
false_neg_list = []
image_filepath_list = direct.files()
dst_filepath_list = [ join(detect_path_temp, split(image_filepath)[1]) for image_filepath in image_filepath_list ]
predictions_list = detector.detect_many(forest, image_filepath_list, dst_filepath_list, use_openmp=True)
for index, (predictions, image_filepath) in enumerate(zip(predictions_list, image_filepath_list)):
image_path, image_filename = split(image_filepath)
image = dataset[image_filename]
accuracy, true_pos, false_pos, false_neg = image.accuracy(predictions, category)
accuracy_list.append(accuracy)
true_pos_list.append(true_pos)
false_pos_list.append(false_pos)
false_neg_list.append(false_neg)
progress = "%0.2f" % (float(index) / len(image_filepath_list))
print("TEST %s %0.4f %s" % (image, accuracy, progress), end='\r')
sys.stdout.flush()
# image.show(prediction_list=predictions, category=category)
print(' ' * 100, end='\r')
print("2 TEST ERROR : %0.4f" % (1.0 - (float(sum(accuracy_list)) / len(accuracy_list))))
print("2 TEST TRUE POS : %d" % (sum(true_pos_list)))
print("2 TEST FALSE POS : %d" % (sum(false_pos_list)))
print("2 TEST FALSE NEG : %d" % (sum(false_neg_list)))
####################################
# Current FAST
####################################
detector = Random_Forest_Detector(
scales='6 1.0 0.75 0.55 0.40 0.30 0.20'
)
# Use pre-trained trees?
TEST_DATA_MODEL_URL = 'https://lev.cs.rpi.edu/public/models/rf.zip'
models_path = utool.grab_zipped_url(TEST_DATA_MODEL_URL, appname='utool')
trees_path = join(models_path, category)
# Ensure output detection paths
detect_path_temp = detect_path + "_3"
rmtreedir(detect_path_temp)
ensuredir(detect_path_temp)
# Load forest, so we don't have to reload every time
forest = detector.load(trees_path, category + '-', num_trees=10)
detector.set_detect_params(**detect_config)
# Calculate error on test set
direct = Directory(test_path, include_file_extensions=["jpg"])
accuracy_list = []
true_pos_list = []
false_pos_list = []
false_neg_list = []
image_filepath_list = direct.files()
dst_filepath_list = [ join(detect_path_temp, split(image_filepath)[1]) for image_filepath in image_filepath_list ]
predictions_list = detector.detect_many(forest, image_filepath_list, dst_filepath_list, use_openmp=True)
for index, (predictions, image_filepath) in enumerate(zip(predictions_list, image_filepath_list)):
image_path, image_filename = split(image_filepath)
image = dataset[image_filename]
accuracy, true_pos, false_pos, false_neg = image.accuracy(predictions, category)
accuracy_list.append(accuracy)
true_pos_list.append(true_pos)
false_pos_list.append(false_pos)
false_neg_list.append(false_neg)
progress = "%0.2f" % (float(index) / len(image_filepath_list))
print("TEST %s %0.4f %s" % (image, accuracy, progress), end='\r')
sys.stdout.flush()
# image.show(prediction_list=predictions, category=category)
print(' ' * 100, end='\r')
print("3 TEST ERROR : %0.4f" % (1.0 - (float(sum(accuracy_list)) / len(accuracy_list))))
print("3 TEST TRUE POS : %d" % (sum(true_pos_list)))
print("3 TEST FALSE POS : %d" % (sum(false_pos_list)))
print("3 TEST FALSE NEG : %d" % (sum(false_neg_list)))
####################################
# Current SLOW
####################################
detector = Random_Forest_Detector(
scales='11 1.5 1.25 1.0 0.8 0.64 0.51 0.41 0.33 0.26 0.21 0.17'
)
# Ensure output detection paths
detect_path_temp = detect_path + "_4"
rmtreedir(detect_path_temp)
ensuredir(detect_path_temp)
# Load forest, so we don't have to reload every time
forest = detector.load(trees_path, category + '-', num_trees=10)
detector.set_detect_params(**detect_config)
# Calculate error on test set
direct = Directory(test_path, include_file_extensions=["jpg"])
accuracy_list = []
true_pos_list = []
false_pos_list = []
false_neg_list = []
image_filepath_list = direct.files()
dst_filepath_list = [ join(detect_path_temp, split(image_filepath)[1]) for image_filepath in image_filepath_list ]
predictions_list = detector.detect_many(forest, image_filepath_list, dst_filepath_list, use_openmp=True)
for index, (predictions, image_filepath) in enumerate(zip(predictions_list, image_filepath_list)):
image_path, image_filename = split(image_filepath)
image = dataset[image_filename]
accuracy, true_pos, false_pos, false_neg = image.accuracy(predictions, category)
accuracy_list.append(accuracy)
true_pos_list.append(true_pos)
false_pos_list.append(false_pos)
false_neg_list.append(false_neg)
progress = "%0.2f" % (float(index) / len(image_filepath_list))
print("TEST %s %0.4f %s" % (image, accuracy, progress), end='\r')
sys.stdout.flush()
# image.show(prediction_list=predictions, category=category)
print(' ' * 100, end='\r')
print("4 TEST ERROR : %0.4f" % (1.0 - (float(sum(accuracy_list)) / len(accuracy_list))))
print("4 TEST TRUE POS : %d" % (sum(true_pos_list)))
print("4 TEST FALSE POS : %d" % (sum(false_pos_list)))
print("4 TEST FALSE NEG : %d" % (sum(false_neg_list)))
# Build parameters
bbox_list = [dummy_bbox(img), dummy_bbox(img, (-.25, -.25), .1)]
showkw = {
'title': 'test axis title',
# The list of bounding boxes to be drawn on the image
'bbox_list': bbox_list,
'theta_list': [tau * .7, tau * .9],
'sel_list': [True, False],
'label_list': ['test label', 'lbl2'],
}
# Print the keyword arguments to illustrate their format
print('showkw = ' + utool.dict_str(showkw))
# Display the image in figure-num 42, using a 1x1 axis grid in the first
# axis. Pass showkw as keyword arguments.
viz_image2.show_image(img, fnum=42, pnum=(1, 1, 1), **showkw)
df2.set_figtitle('Test figure title')
if __name__ == '__main__':
TEST_IMAGES_URL = 'https://lev.cs.rpi.edu/public/data/testdata.zip'
test_image_dir = utool.grab_zipped_url(TEST_IMAGES_URL, appname='utool')
imgpaths = utool.list_images(test_image_dir,
fullpath=True,
recursive=False) # test image paths
# Get one image filepath to load and display
img_fpath = imgpaths[0]
# Run Test
test_viz_image(img_fpath)
# Magic exec which displays or puts you into IPython with --cmd flag
exec(df2.present())