def compute_thumbnails(depc, gid_list, config=None):
r"""
Computers the thumbnail for a given input image
Args:
depc (ibeis.depends_cache.DependencyCache):
gid_list (list): list of image rowids
config (dict): (default = None)
Yields:
(uri, int, int): tup
CommandLine:
ibeis --tf compute_thumbnails --show --db PZ_MTEST
Example:
>>> # ENABLE_DOCTEST
>>> from ibeis.core_images import * # NOQA
>>> import ibeis
>>> defaultdb = 'testdb1'
>>> ibs = ibeis.opendb(defaultdb=defaultdb)
>>> depc = ibs.depc_image
>>> gid_list = ibs.get_valid_gids()[0:10]
>>> thumbs = depc.get_property('thumbnails', gid_list, 'img', config={'thumbsize': 221})
>>> ut.quit_if_noshow()
>>> import plottool as pt
>>> iteract_obj = pt.interact_multi_image.MultiImageInteraction(thumbs, nPerPage=4)
>>> iteract_obj.start()
>>> pt.show_if_requested()
"""
ibs = depc.controller
draw_annots = config['draw_annots']
thumbsize = config['thumbsize']
if thumbsize is None:
cfg = ibs.cfg.other_cfg
thumbsize = cfg.thumb_size if draw_annots else cfg.thumb_bare_size
thumbsize_list = [thumbsize] * len(gid_list)
gpath_list = ibs.get_image_paths(gid_list)
orient_list = ibs.get_image_orientation(gid_list)
aids_list = ibs.get_image_aids(gid_list)
if draw_annots:
bboxes_list = ibs.unflat_map(ibs.get_annot_bboxes, aids_list)
thetas_list = ibs.unflat_map(ibs.get_annot_thetas, aids_list)
else:
bboxes_list = [ [] for aids in aids_list ]
thetas_list = [ [] for aids in aids_list ]
# Execute all tasks in parallel
args_list = zip(thumbsize_list, gpath_list, orient_list, bboxes_list, thetas_list)
genkw = {
'ordered': False,
'chunksize': 256,
'freq': 50,
#'adjust': True,
'force_serial': ibs.force_serial or config['force_serial'],
}
gen = ut.generate(draw_thumb_helper, args_list, nTasks=len(args_list), **genkw)
for val in gen:
yield val
def add_images_params_gen(gpath_list, **kwargs):
"""
generates values for add_images sqlcommands asychronously
Args:
gpath_list (list):
Kwargs:
ordered, force_serial, chunksize, prog, verbose, quiet, nTasks, freq,
adjust
Returns:
generator: params_gen
CommandLine:
python -m ibeis.algo.preproc.preproc_image --exec-add_images_params_gen
Example0:
>>> # ENABLE_DOCTEST
>>> from ibeis.algo.preproc.preproc_image import * # NOQA
>>> from vtool.tests import grabdata
>>> gpath_list = grabdata.get_test_gpaths(ndata=3) + ['doesnotexist.jpg']
>>> params_list = list(add_images_params_gen(gpath_list))
>>> assert str(params_list[0][0]) == '66ec193a-1619-b3b6-216d-1784b4833b61', 'UUID gen method changed'
>>> assert str(params_list[0][3]) == 'easy1.JPG', 'orig name is different'
>>> assert params_list[3] is None
"""
#preproc_args = [(gpath, kwargs) for gpath in gpath_list]
#print('[about to parse]: gpath_list=%r' % (gpath_list,))
params_gen = ut.generate(parse_imageinfo, gpath_list, adjust=True,
force_serial=True, **kwargs)
return params_gen
def compute_extramargin_detectchip(ibs, aid_list, config2_=None, species=None, FACTOR=4):
#from vtool import chip as ctool
#from vtool import image as gtool
arg_list, newsize_list, halfoffset_cs_list = get_extramargin_detectchip_info(
ibs, aid_list, config2_=config2_, species=species, FACTOR=FACTOR)
# Again, it seems we cannot use warpAffine in parallel loops
extramargin_fpath_list = list(ut.generate(
gen_detectchip, arg_list, ordered=True, force_serial=True))
probchip_extramargin_fpath_list = [fpath.replace('detectchip', 'probchip')
for fpath in extramargin_fpath_list]
return extramargin_fpath_list, probchip_extramargin_fpath_list, halfoffset_cs_list
def compute_fgweights(ibs, aid_list, config2_=None):
"""
Example:
>>> # SLOW_DOCTEST
>>> from ibeis.algo.preproc.preproc_featweight import * # NOQA
>>> import ibeis
>>> ibs = ibeis.opendb('testdb1')
>>> aid_list = ibs.get_valid_aids()[1:2]
>>> config2_ = None
>>> featweight_list = compute_fgweights(ibs, aid_list)
>>> result = np.array_str(featweight_list[0][0:3], precision=3)
>>> print(result)
[ 0.125 0.061 0.053]
"""
nTasks = len(aid_list)
print('[preproc_featweight.compute_fgweights] Preparing to compute %d fgweights' % (nTasks,))
probchip_fpath_list = preproc_probchip.compute_and_write_probchip(ibs,
aid_list,
config2_=config2_)
chipsize_list = ibs.get_annot_chip_sizes(aid_list, config2_=config2_)
#if ut.DEBUG2:
# from PIL import Image
# probchip_size_list = [Image.open(fpath).size for fpath in probchip_fpath_list] # NOQA
# #with ut.embed_on_exception_context:
# # does not need to happen anymore
# assert chipsize_list == probchip_size_list, 'probably need to clear chip or probchip cache'
kpts_list = ibs.get_annot_kpts(aid_list, config2_=config2_)
# Force grayscale reading of chips
probchip_list = [vt.imread(fpath, grayscale=True) if exists(fpath) else None
for fpath in probchip_fpath_list]
print('[preproc_featweight.compute_fgweights] Computing %d fgweights' % (nTasks,))
arg_iter = zip(aid_list, kpts_list, probchip_list, chipsize_list)
featweight_gen = ut.generate(gen_featweight_worker, arg_iter,
nTasks=nTasks, ordered=True, freq=10)
featweight_param_list = list(featweight_gen)
#arg_iter = zip(aid_list, kpts_list, probchip_list)
#featweight_param_list1 = [gen_featweight_worker((aid, kpts, probchip)) for
#aid, kpts, probchip in arg_iter]
#featweight_aids = ut.get_list_column(featweight_param_list, 0)
featweight_list = ut.get_list_column(featweight_param_list, 1)
print('[preproc_featweight.compute_fgweights] Done computing %d fgweights' % (nTasks,))
return featweight_list
def add_images_params_gen(gpath_list, **kwargs):
""" generates values for add_images sqlcommands asychronously
TEST CODE:
from ibeis.dev.all_imports import *
gpath_list = grabdata.get_test_gpaths(ndata=3) + ['doesnotexist.jpg']
params_list = list(preproc_image.add_images_params_gen(gpath_list))
<CYTH: yeilds=tup>
cdef:
list gpath_list
dict kwargs
</CYTH>
"""
#preproc_args = [(gpath, kwargs) for gpath in gpath_list]
#print('[about to parse]: gpath_list=%r' % (gpath_list,))
params_gen = utool.generate(parse_imageinfo, gpath_list, **kwargs)
return params_gen
def _test_buffered_generator_general2(bgfunc, bgargs, fgfunc,
target_looptime=1.0, serial_cheat=1,
buffer_size=2, show_serial=True):
"""
# We are going to generate output of bgfunc in the background while
# fgfunc is running in the foreground. fgfunc takes results of bffunc as
# args.
# --- Hyperparams
target_looptime = 1.5 # maximum time to run all loops
"""
import utool as ut
with ut.Timer('One* call to bgfunc') as t_bgfunc:
results = [bgfunc(arg) for arg in bgargs]
bgfunctime = t_bgfunc.ellapsed / len(bgargs)
#fgfunc = ut.is_prime
with ut.Timer('One* call to fgfunc') as t_fgfunc:
[fgfunc(x) for x in results]
fgfunctime = t_fgfunc.ellapsed / len(bgargs)
# compute amount of loops to run
est_looptime = (bgfunctime + fgfunctime)
_num_loops = round(target_looptime // est_looptime)
num_data = int(_num_loops // len(bgargs))
num_loops = int(num_data * len(bgargs))
serial_cheat = min(serial_cheat, num_data)
data = ut.flatten([bgargs] * num_data)
est_tfg = fgfunctime * num_loops
est_tbg = bgfunctime * num_loops
est_needed_buffers = fgfunctime / bgfunctime
print('Estimated stats' + ut.repr4(ut.dict_subset(locals(), [
'num_loops',
'bgfunctime', 'fgfunctime', 'est_tfg', 'est_tbg', 'serial_cheat',
'buffer_size', 'est_needed_buffers',
])))
if show_serial:
with ut.Timer('serial') as t1:
# cheat for serial to make it go faster
for x in map(bgfunc, data[:len(data) // serial_cheat]):
fgfunc(x)
t_serial = serial_cheat * t1.ellapsed
print('...toc(\'adjusted_serial\') = %r' % (t_serial))
with ut.Timer('ut.buffered_generator') as t2:
gen_ = ut.buffered_generator(map(bgfunc, data), buffer_size=buffer_size)
for x in gen_:
fgfunc(x)
with ut.Timer('ut.generate') as t3:
gen_ = ut.generate(bgfunc, data, chunksize=buffer_size, quiet=1, verbose=0)
for x in gen_:
fgfunc(x)
# Compare theoretical vs practical efficiency
print('\n Theoretical Results')
def parallel_efficiency(ellapsed, est_tfg, est_tbg):
return (1 - ((ellapsed - est_tfg) / est_tbg)) * 100
if show_serial:
print('Theoretical gain (serial) = %.3f%%' % (
parallel_efficiency(t_serial, est_tfg, est_tbg),))
print('Theoretical gain (ut.buffered_generator) = %.3f%%' % (
parallel_efficiency(t2.ellapsed, est_tfg, est_tbg),))
print('Theoretical gain (ut.generate) = %.2f%%' % (
parallel_efficiency(t3.ellapsed, est_tfg, est_tbg),))
if show_serial:
prac_tbg = t_serial - est_tfg
print('\n Practical Results')
print('Practical gain (serial) = %.3f%%' % (
parallel_efficiency(t1.ellapsed, est_tfg, prac_tbg),))
print('Practical gain (ut.buffered_generator) = %.3f%%' % (
parallel_efficiency(t2.ellapsed, est_tfg, prac_tbg),))
print('Practical gain (ut.generate) = %.2f%%' % (
parallel_efficiency(t3.ellapsed, est_tfg, prac_tbg),))
def _test_buffered_generator_general(func, args, sleepfunc,
target_looptime=1.0,
serial_cheat=1, argmode=False,
buffer_size=2):
"""
# We are going to generate output of func in the background while sleep
# func is running in the foreground
# --- Hyperparams
target_looptime = 1.5 # maximum time to run all loops
"""
import utool as ut
#serial_cheat = 1 # approx division factor to run serial less times
show_serial = True # target_looptime < 10. # 3.0
with ut.Timer('One* call to func') as t_fgfunc:
results = [func(arg) for arg in args]
functime = t_fgfunc.ellapsed / len(args)
#sleepfunc = ut.is_prime
with ut.Timer('One* call to sleep func') as t_sleep:
if argmode:
[sleepfunc(x) for x in results]
else:
[sleepfunc() for x in results]
sleeptime = t_sleep.ellapsed / len(args)
# compute amount of loops to run
_num_loops = round(target_looptime // (functime + sleeptime))
num_data = int(_num_loops // len(args))
num_loops = int(num_data * len(args))
serial_cheat = min(serial_cheat, num_data)
data = ut.flatten([args] * num_data)
est_tsleep = sleeptime * num_loops
est_tfunc = functime * num_loops
est_needed_buffers = sleeptime / functime
print('Estimated stats' + ut.repr4(ut.dict_subset(locals(), [
'num_loops',
'functime', 'sleeptime', 'est_tsleep', 'est_tfunc', 'serial_cheat', 'buffer_size',
'est_needed_buffers',
])))
if show_serial:
with ut.Timer('serial') as t1:
# cheat for serial to make it go faster
for x in map(func, data[:len(data) // serial_cheat]):
if argmode:
sleepfunc(x)
else:
sleepfunc()
t_serial = serial_cheat * t1.ellapsed
print('...toc(\'adjusted_serial\') = %r' % (t_serial))
with ut.Timer('ut.buffered_generator') as t2:
gen_ = ut.buffered_generator(map(func, data), buffer_size=buffer_size)
for x in gen_:
if argmode:
sleepfunc(x)
else:
sleepfunc()
with ut.Timer('ut.generate') as t3:
gen_ = ut.generate(func, data, chunksize=buffer_size, quiet=1, verbose=0)
for x in gen_:
if argmode:
sleepfunc(x)
else:
sleepfunc( )
# Compare theoretical vs practical efficiency
print('\n Theoretical Results')
def parallel_efficiency(ellapsed, est_tsleep, est_tfunc):
return (1 - ((ellapsed - est_tsleep) / est_tfunc)) * 100
if show_serial:
print('Theoretical gain (serial) = %.3f%%' % (
parallel_efficiency(t_serial, est_tsleep, est_tfunc),))
print('Theoretical gain (ut.buffered_generator) = %.3f%%' % (
parallel_efficiency(t2.ellapsed, est_tsleep, est_tfunc),))
print('Theoretical gain (ut.generate) = %.2f%%' % (
parallel_efficiency(t3.ellapsed, est_tsleep, est_tfunc),))
if show_serial:
prac_tfunc = t_serial - est_tsleep
print('\n Practical Results')
print('Practical gain (serial) = %.3f%%' % (
parallel_efficiency(t1.ellapsed, est_tsleep, prac_tfunc),))
print('Practical gain (ut.buffered_generator) = %.3f%%' % (
parallel_efficiency(t2.ellapsed, est_tsleep, prac_tfunc),))
print('Practical gain (ut.generate) = %.2f%%' % (
parallel_efficiency(t3.ellapsed, est_tsleep, prac_tfunc),))
def add_images_json(ibs, image_uri_list, image_uuid_list, image_width_list,
image_height_list, image_orig_name_list=None, image_ext_list=None,
image_time_posix_list=None, image_gps_lat_list=None,
image_gps_lon_list=None, image_orientation_list=None,
image_notes_list=None, **kwargs):
"""
REST:
Method: POST
URL: /api/image/json/
Ignore:
sudo pip install boto
Args:
image_uri_list (list) : list of string image uris, most likely HTTP(S) or S3
encoded URLs. Alternatively, this can be a list of dictionaries (JSON
objects) that specify AWS S3 stored assets. An example below:
image_uri_list = [
'http://domain.com/example/asset1.png',
'/home/example/Desktop/example/asset2.jpg',
's3://s3.amazon.com/example-bucket-2/asset1-in-bucket-2.tif',
{
'bucket' : 'example-bucket-1',
'key' : 'example/asset1.png',
'auth_domain' : None, # Uses localhost
'auth_access_id' : None, # Uses system default
'auth_secret_key' : None, # Uses system default
},
{
'bucket' : 'example-bucket-1',
'key' : 'example/asset2.jpg',
# if unspecified, auth uses localhost and system defaults
},
{
'bucket' : 'example-bucket-2',
'key' : 'example/asset1-in-bucket-2.tif',
'auth_domain' : 's3.amazon.com',
'auth_access_id' : '____________________',
'auth_secret_key' : '________________________________________',
},
]
Note that you cannot specify AWS authentication access ids or secret keys
using string uri's. For specific authentication methods, please use the
latter list of dictionaries.
image_uuid_list (list of str) : list of image UUIDs to be used in IBEIS IA
image_width_list (list of int) : list of image widths
image_height_list (list of int) : list of image heights
image_orig_name_list (list of str): list of original image names
image_ext_list (list of str): list of original image names
image_time_posix_list (list of int): list of image's POSIX timestamps
image_gps_lat_list (list of float): list of image's GPS latitude values
image_gps_lon_list (list of float): list of image's GPS longitude values
image_orientation_list (list of int): list of image's orientation flags
image_notes_list (list of str) : optional list of any related notes with
the images
**kwargs : key-value pairs passed to the ibs.add_images() function.
CommandLine:
python -m ibeis.web.app --test-add_images_json
Example:
>>> # WEB_DOCTEST
>>> from ibeis.control.IBEISControl import * # NOQA
>>> import ibeis
>>> web_instance = ibeis.opendb(db='testdb1')
>>> _payload = {
>>> 'image_uri_list': [
>>> 'https://upload.wikimedia.org/wikipedia/commons/4/49/Zebra_running_Ngorongoro.jpg',
>>> {
>>> 'bucket' : 'test-asset-store',
>>> 'key' : 'caribwhale/20130903-JAC-0002.JPG',
>>> },
>>> ],
>>> 'image_uuid_list': [
>>> uuid.UUID('7fea8101-7dec-44e3-bf5d-b8287fd231e2'),
>>> uuid.UUID('c081119a-e08e-4863-a710-3210171d27d6'),
>>> ],
>>> 'image_width_list': [
>>> 1992,
>>> 1194,
>>> ],
>>> 'image_height_list': [
>>> 1328,
>>> 401,
>>> ],
>>> }
>>> gid_list = ibeis.web.app.add_images_json(web_instance, **_payload)
>>> print(gid_list)
>>> print(web_instance.get_image_uuids(gid_list))
>>> print(web_instance.get_image_uris(gid_list))
>>> print(web_instance.get_image_paths(gid_list))
>>> print(web_instance.get_image_uris_original(gid_list))
"""
def _get_standard_ext(gpath):
ext = splitext(gpath)[1].lower()
return '.jpg' if ext == '.jpeg' else ext
def _parse_imageinfo(index):
def _resolve_uri():
list_ = image_uri_list
if list_ is None or index >= len(list_) or list_[index] is None:
raise ValueError('Must specify all required fields')
value = list_[index]
if isinstance(value, dict):
value = ut.s3_dict_encode_to_str(value)
return value
def _resolve(list_, default='', assert_=False):
if list_ is None or index >= len(list_) or list_[index] is None:
if assert_:
raise ValueError('Must specify all required fields')
return default
return list_[index]
uri = _resolve_uri()
orig_gname = basename(uri)
ext = _get_standard_ext(uri)
uuid_ = _resolve(image_uuid_list, assert_=True)
if isinstance(uuid_, six.string_types):
uuid_ = uuid.UUID(uuid_)
param_tup = (
uuid_,
uri,
uri,
_resolve(image_orig_name_list, default=orig_gname),
_resolve(image_ext_list, default=ext),
int(_resolve(image_width_list, assert_=True)),
int(_resolve(image_height_list, assert_=True)),
int(_resolve(image_time_posix_list, default=-1)),
float(_resolve(image_gps_lat_list, default=-1.0)),
float(_resolve(image_gps_lon_list, default=-1.0)),
int(_resolve(image_orientation_list, default=0)),
_resolve(image_notes_list),
)
return param_tup
# TODO: FIX ME SO THAT WE DON'T HAVE TO LOCALIZE EVERYTHING
kwargs['auto_localize'] = kwargs.get('auto_localize', True)
kwargs['sanitize'] = kwargs.get('sanitize', False)
index_list = range(len(image_uri_list))
params_gen = ut.generate(_parse_imageinfo, index_list, adjust=True,
force_serial=True, **kwargs)
params_gen = list(params_gen)
gpath_list = [ _[0] for _ in params_gen ]
gid_list = ibs.add_images(gpath_list, params_list=params_gen, **kwargs) # NOQA
# return gid_list
image_uuid_list = ibs.get_image_uuids(gid_list)
return image_uuid_list
def chip_tester():
import plottool as pt
pt.ensure_pylab_qt4()
from ibeis.core_annots import * # NOQA
import ibeis
defaultdb = 'GZ_ALL'
ibs = ibeis.opendb(defaultdb=defaultdb)
aid_list = ibs.get_valid_aids()
depc = ibs.depc_annot
chips_orig = depc.get_property('chips', aid_list, 'img', config={})
chips_aeq = depc.get_property('chips', aid_list, 'img', config={'adapteq': True})
chips_heq = depc.get_property('chips', aid_list, 'img', config={'histeq': True})
import pyhesaff
nkpts_list = np.array(list(ut.generate(pyhesaff.detect_num_kpts_in_image, chips_orig, force_serial=ibs.force_serial)))
nkpts_list = np.array(nkpts_list)
nfeats_orig = np.array(ibs.depc_annot.get('feat', aid_list, 'num_feats'))
nfeats_hteq = np.array(ibs.depc_annot.get('feat', aid_list, 'num_feats', config={'histeq': True}))
nfeats_ateq = np.array(ibs.depc_annot.get('feat', aid_list, 'num_feats', config={'adapteq': True}))
sortx = np.array(nfeats_orig).argsort()
sortx = np.array(nfeats_hteq).argsort()
sortx = np.array(nfeats_ateq).argsort()
aids = ut.take(aid_list, sortx)
chips = chips_orig
chips_bad = ut.take(chips, sortx)
chips_good = ut.take(chips, sortx[::-1])
import ibeis.viz.interact.interact_chip
ibeis.viz.interact.interact_chip.interact_multichips(ibs, aids)
iteract_obj = pt.interact_multi_image.MultiImageInteraction(chips_bad, nPerPage=15)
iteract_obj.start()
iteract_obj = pt.interact_multi_image.MultiImageInteraction(chips_good, nPerPage=15)
iteract_obj.start()
x = sklearn.cluster.KMeans(2)
x.fit(np.nan_to_num(measures))
import vtool.quality_classifier
from vtool.quality_classifier import contrast_measures
chips128 = depc.get_property('chips', aid_list, 'img', config={'dim_size': 256})
gray_chips = [vt.convert_colorspace(x, 'GRAY') for x in ut.ProgIter(chips128)]
measures = list(ut.generate(contrast_measures, gray_chips, force_serial=ibs.force_serial))
measures = np.array(measures)
measures = np.nan_to_num(measures)
y = measures.T[3]
sortx = y.argsort()
ys = y.take(sortx)
pca = sklearn.decomposition.PCA(1)
pca.fit(measures)
pca_measure = pca.transform(measures)
nfeats_white = (nfeats_orig - nfeats_orig.mean()) / nfeats_orig.std()
pca_white = (pca_measure - pca_measure.mean()) / pca_measure.std()
sortx = nfeats_white.argsort()
pt.plt.plot(pca_white[sortx], 'x')
pt.plt.plot(nfeats_white[sortx], '.')
pyhesaff.detect_feats_in_image
svc = sklearn.svm.LinearSVC()
svc.fit(measures, nfeats_orig > 500)
svc.predict(measures) == (nfeats_orig > 500)
svr = sklearn.svm.LinearSVR()
svr.fit(measures, nfeats_orig)
svr.predict(measures)
depc['feat'].get_config_history(z1)
depc['feat'].get_config_history(z2)
pt.plt.plot(nfeats_hteq[sortx], 'x')
pt.plt.plot(nfeats_orig[sortx], '.')
pt.plt.plot(nfeats_ateq[sortx], 'o')
z1 = ibs.depc_annot.get_rowids('feat', aid_list, config={'histeq': True})
z2 = ibs.depc_annot.get_rowids('feat', aid_list)
assert len(set(z1).intersection(z2)) == 0
def add_images_json(ibs,
image_uri_list,
image_uuid_list,
image_width_list,
image_height_list,
image_orig_name_list=None,
image_ext_list=None,
image_time_posix_list=None,
image_gps_lat_list=None,
image_gps_lon_list=None,
image_orientation_list=None,
image_notes_list=None,
**kwargs):
"""
REST:
Method: POST
URL: /api/image/json/
Ignore:
sudo pip install boto
Args:
image_uri_list (list) : list of string image uris, most likely HTTP(S) or S3
encoded URLs. Alternatively, this can be a list of dictionaries (JSON
objects) that specify AWS S3 stored assets. An example below:
image_uri_list = [
'http://domain.com/example/asset1.png',
'/home/example/Desktop/example/asset2.jpg',
's3://s3.amazon.com/example-bucket-2/asset1-in-bucket-2.tif',
{
'bucket' : 'example-bucket-1',
'key' : 'example/asset1.png',
'auth_domain' : None, # Uses localhost
'auth_access_id' : None, # Uses system default
'auth_secret_key' : None, # Uses system default
},
{
'bucket' : 'example-bucket-1',
'key' : 'example/asset2.jpg',
# if unspecified, auth uses localhost and system defaults
},
{
'bucket' : 'example-bucket-2',
'key' : 'example/asset1-in-bucket-2.tif',
'auth_domain' : 's3.amazon.com',
'auth_access_id' : '____________________',
'auth_secret_key' : '________________________________________',
},
]
Note that you cannot specify AWS authentication access ids or secret keys
using string uri's. For specific authentication methods, please use the
latter list of dictionaries.
image_uuid_list (list of str) : list of image UUIDs to be used in IBEIS IA
image_width_list (list of int) : list of image widths
image_height_list (list of int) : list of image heights
image_orig_name_list (list of str): list of original image names
image_ext_list (list of str): list of original image names
image_time_posix_list (list of int): list of image's POSIX timestamps
image_gps_lat_list (list of float): list of image's GPS latitude values
image_gps_lon_list (list of float): list of image's GPS longitude values
image_orientation_list (list of int): list of image's orientation flags
image_notes_list (list of str) : optional list of any related notes with
the images
**kwargs : key-value pairs passed to the ibs.add_images() function.
CommandLine:
python -m ibeis.web.app --test-add_images_json
Example:
>>> # WEB_DOCTEST
>>> from ibeis.control.IBEISControl import * # NOQA
>>> import ibeis
>>> web_instance = ibeis.opendb(db='testdb1')
>>> _payload = {
>>> 'image_uri_list': [
>>> 'https://upload.wikimedia.org/wikipedia/commons/4/49/Zebra_running_Ngorongoro.jpg',
>>> {
>>> 'bucket' : 'test-asset-store',
>>> 'key' : 'caribwhale/20130903-JAC-0002.JPG',
>>> },
>>> ],
>>> 'image_uuid_list': [
>>> uuid.UUID('7fea8101-7dec-44e3-bf5d-b8287fd231e2'),
>>> uuid.UUID('c081119a-e08e-4863-a710-3210171d27d6'),
>>> ],
>>> 'image_width_list': [
>>> 1992,
>>> 1194,
>>> ],
>>> 'image_height_list': [
>>> 1328,
>>> 401,
>>> ],
>>> }
>>> gid_list = ibeis.web.app.add_images_json(web_instance, **_payload)
>>> print(gid_list)
>>> print(web_instance.get_image_uuids(gid_list))
>>> print(web_instance.get_image_uris(gid_list))
>>> print(web_instance.get_image_paths(gid_list))
>>> print(web_instance.get_image_uris_original(gid_list))
"""
def _get_standard_ext(gpath):
ext = splitext(gpath)[1].lower()
return '.jpg' if ext == '.jpeg' else ext
def _parse_imageinfo(index):
def _resolve_uri():
list_ = image_uri_list
if list_ is None or index >= len(list_) or list_[index] is None:
raise ValueError('Must specify all required fields')
value = list_[index]
if isinstance(value, dict):
value = ut.s3_dict_encode_to_str(value)
return value
def _resolve(list_, default='', assert_=False):
if list_ is None or index >= len(list_) or list_[index] is None:
if assert_:
raise ValueError('Must specify all required fields')
return default
return list_[index]
uri = _resolve_uri()
orig_gname = basename(uri)
ext = _get_standard_ext(uri)
uuid_ = _resolve(image_uuid_list, assert_=True)
if isinstance(uuid_, six.string_types):
uuid_ = uuid.UUID(uuid_)
param_tup = (
uuid_,
uri,
uri,
_resolve(image_orig_name_list, default=orig_gname),
_resolve(image_ext_list, default=ext),
int(_resolve(image_width_list, assert_=True)),
int(_resolve(image_height_list, assert_=True)),
int(_resolve(image_time_posix_list, default=-1)),
float(_resolve(image_gps_lat_list, default=-1.0)),
float(_resolve(image_gps_lon_list, default=-1.0)),
int(_resolve(image_orientation_list, default=0)),
_resolve(image_notes_list),
)
return param_tup
# TODO: FIX ME SO THAT WE DON'T HAVE TO LOCALIZE EVERYTHING
kwargs['auto_localize'] = kwargs.get('auto_localize', True)
kwargs['sanitize'] = kwargs.get('sanitize', False)
index_list = range(len(image_uri_list))
params_gen = ut.generate(_parse_imageinfo,
index_list,
adjust=True,
force_serial=True,
**kwargs)
params_gen = list(params_gen)
gpath_list = [_[0] for _ in params_gen]
gid_list = ibs.add_images(gpath_list, params_list=params_gen,
**kwargs) # NOQA
# return gid_list
image_uuid_list = ibs.get_image_uuids(gid_list)
return image_uuid_list