def api_root():
rules = current_app.url_map.iter_rules()
rule_dict = {}
for rule in rules:
methods = rule.methods
url = str(rule)
if '/api/' in url:
methods -= set(['HEAD', 'OPTIONS'])
if len(methods) == 0:
continue
if len(methods) > 1:
print('methods = %r' % (methods,))
method = list(methods)[0]
if method not in rule_dict.keys():
rule_dict[method] = []
rule_dict[method].append((method, url, ))
for method in rule_dict.keys():
rule_dict[method].sort()
url = '%s/api/core/dbname/' % (current_app.server_url, )
app_auth = controller_inject.get_url_authorization(url)
return appf.template(None, 'api',
app_url=url,
app_name=controller_inject.GLOBAL_APP_NAME,
app_secret=controller_inject.GLOBAL_APP_SECRET,
app_auth=app_auth,
rule_list=rule_dict)
def experiments_voting(**kwargs):
experiments_voting_initialize()
voting_data()
embed = dict(globals(), **locals())
return appf.template('experiments', 'voting', **embed)
def group_review():
prefill = request.args.get('prefill', '')
if len(prefill) > 0:
ibs = current_app.ibs
aid_list = ibs.get_valid_aids()
bad_species_list, bad_viewpoint_list = ibs.validate_annot_species_viewpoint_cnn(aid_list)
GROUP_BY_PREDICTION = True
if GROUP_BY_PREDICTION:
grouped_dict = ut.group_items(bad_viewpoint_list, ut.get_list_column(bad_viewpoint_list, 3))
grouped_list = grouped_dict.values()
regrouped_items = ut.flatten(ut.sortedby(grouped_list, map(len, grouped_list)))
candidate_aid_list = ut.get_list_column(regrouped_items, 0)
else:
candidate_aid_list = [ bad_viewpoint[0] for bad_viewpoint in bad_viewpoint_list]
elif request.args.get('aid_list', None) is not None:
aid_list = request.args.get('aid_list', '')
if len(aid_list) > 0:
aid_list = aid_list.replace('[', '')
aid_list = aid_list.replace(']', '')
aid_list = aid_list.strip().split(',')
candidate_aid_list = [ int(aid_.strip()) for aid_ in aid_list ]
else:
candidate_aid_list = ''
else:
candidate_aid_list = ''
return appf.template(None, 'group_review', candidate_aid_list=candidate_aid_list, mode_list=appf.VALID_TURK_MODES)
def error404(exception=None):
import traceback
exception_str = str(exception)
traceback_str = str(traceback.format_exc())
print('[web] %r' % (exception_str, ))
print('[web] %r' % (traceback_str, ))
return appf.template(None, '404', exception_str=exception_str,
traceback_str=traceback_str)
def image_view_api(gid=None, thumbnail=False, fresh=False, **kwargs):
r"""
Returns the base64 encoded image of image <gid>
RESTful:
Method: GET
URL: /image/view/<gid>/
"""
encoded = routes_ajax.image_src(gid, thumbnail=thumbnail, fresh=fresh, **kwargs)
return appf.template(None, 'single', encoded=encoded)
def view_imagesets():
ibs = current_app.ibs
filtered = True
imgsetid = request.args.get('imgsetid', '')
if len(imgsetid) > 0:
imgsetid_list = imgsetid.strip().split(',')
imgsetid_list = [ None if imgsetid_ == 'None' or imgsetid_ == '' else int(imgsetid_) for imgsetid_ in imgsetid_list ]
else:
imgsetid_list = ibs.get_valid_imgsetids()
filtered = False
start_time_posix_list = ibs.get_imageset_start_time_posix(imgsetid_list)
datetime_list = [
ut.unixtime_to_datetimestr(start_time_posix)
if start_time_posix is not None else
'Unknown'
for start_time_posix in start_time_posix_list
]
gids_list = [ ibs.get_valid_gids(imgsetid=imgsetid_) for imgsetid_ in imgsetid_list ]
aids_list = [ ut.flatten(ibs.get_image_aids(gid_list)) for gid_list in gids_list ]
images_reviewed_list = [ appf.imageset_image_processed(ibs, gid_list) for gid_list in gids_list ]
annots_reviewed_viewpoint_list = [ appf.imageset_annot_viewpoint_processed(ibs, aid_list) for aid_list in aids_list ]
annots_reviewed_quality_list = [ appf.imageset_annot_quality_processed(ibs, aid_list) for aid_list in aids_list ]
image_processed_list = [ images_reviewed.count(True) for images_reviewed in images_reviewed_list ]
annot_processed_viewpoint_list = [ annots_reviewed.count(True) for annots_reviewed in annots_reviewed_viewpoint_list ]
annot_processed_quality_list = [ annots_reviewed.count(True) for annots_reviewed in annots_reviewed_quality_list ]
reviewed_list = [ all(images_reviewed) and all(annots_reviewed_viewpoint) and all(annot_processed_quality) for images_reviewed, annots_reviewed_viewpoint, annot_processed_quality in zip(images_reviewed_list, annots_reviewed_viewpoint_list, annots_reviewed_quality_list) ]
imageset_list = zip(
imgsetid_list,
ibs.get_imageset_text(imgsetid_list),
ibs.get_imageset_num_gids(imgsetid_list),
image_processed_list,
ibs.get_imageset_num_aids(imgsetid_list),
annot_processed_viewpoint_list,
annot_processed_quality_list,
start_time_posix_list,
datetime_list,
reviewed_list,
)
imageset_list.sort(key=lambda t: t[7])
return appf.template('view', 'imagesets',
filtered=filtered,
imgsetid_list=imgsetid_list,
imgsetid_list_str=','.join(map(str, imgsetid_list)),
num_imgsetids=len(imgsetid_list),
imageset_list=imageset_list,
num_imagesets=len(imageset_list))
def turk_detection_dynamic():
ibs = current_app.ibs
gid = request.args.get('gid', None)
gpath = ibs.get_image_thumbpath(gid, ensure_paths=True, draw_annots=False)
image = ibs.get_images(gid)
image_src = appf.embed_image_html(image)
# Get annotations
width, height = ibs.get_image_sizes(gid)
aid_list = ibs.get_image_aids(gid)
annot_bbox_list = ibs.get_annot_bboxes(aid_list)
annot_thetas_list = ibs.get_annot_thetas(aid_list)
species_list = ibs.get_annot_species_texts(aid_list)
# Get annotation bounding boxes
annotation_list = []
for aid, annot_bbox, annot_theta, species in zip(aid_list, annot_bbox_list, annot_thetas_list, species_list):
temp = {}
temp['left'] = 100.0 * (annot_bbox[0] / width)
temp['top'] = 100.0 * (annot_bbox[1] / height)
temp['width'] = 100.0 * (annot_bbox[2] / width)
temp['height'] = 100.0 * (annot_bbox[3] / height)
temp['label'] = species
temp['id'] = aid
temp['theta'] = float(annot_theta)
annotation_list.append(temp)
if len(species_list) > 0:
species = max(set(species_list), key=species_list.count) # Get most common species
elif appf.default_species(ibs) is not None:
species = appf.default_species(ibs)
else:
species = KEY_DEFAULTS[SPECIES_KEY]
callback_url = '%s?imgsetid=%s' % (url_for('submit_detection'), gid, )
return appf.template('turk', 'detection_dynamic',
gid=gid,
refer_aid=None,
species=species,
image_path=gpath,
image_src=image_src,
annotation_list=annotation_list,
callback_url=callback_url,
callback_method='POST',
EMBEDDED_CSS=None,
EMBEDDED_JAVASCRIPT=None,
__wrapper__=False)
def review_graph_match_html(ibs, review_pair, cm_dict, query_config_dict,
_internal_state, callback_url,
callback_method='POST',
view_orientation='vertical', include_jquery=False):
r"""
Args:
ibs (ibeis.IBEISController): image analysis api
review_pair (dict): pair of annot uuids
cm_dict (dict):
query_config_dict (dict):
_internal_state (?):
callback_url (?):
callback_method (unicode): (default = u'POST')
view_orientation (unicode): (default = u'vertical')
include_jquery (bool): (default = False)
CommandLine:
python -m ibeis.web.apis_query review_graph_match_html --show
ibeis --web
python -m ibeis.web.apis_query review_graph_match_html --show --domain=localhost
Example:
>>> # WEB_DOCTEST
>>> from ibeis.web.apis_query import * # NOQA
>>> import ibeis
>>> web_ibs = ibeis.opendb_bg_web('testdb1') # , domain='http://52.33.105.88')
>>> aids = web_ibs.send_ibeis_request('/api/annot/', 'get')[0:2]
>>> uuid_list = web_ibs.send_ibeis_request('/api/annot/uuids/', type_='get', aid_list=aids)
>>> quuid_list = uuid_list[0:1]
>>> duuid_list = uuid_list
>>> query_config_dict = {
>>> # 'pipeline_root' : 'BC_DTW'
>>> }
>>> data = dict(
>>> query_annot_uuid_list=quuid_list, database_annot_uuid_list=duuid_list,
>>> query_config_dict=query_config_dict,
>>> )
>>> jobid = web_ibs.send_ibeis_request('/api/engine/query/graph/', **data)
>>> print('jobid = %r' % (jobid,))
>>> status_response = web_ibs.wait_for_results(jobid)
>>> result_response = web_ibs.read_engine_results(jobid)
>>> inference_result = result_response['json_result']
>>> print('inference_result = %r' % (inference_result,))
>>> auuid2_cm = inference_result['cm_dict']
>>> quuid = quuid_list[0]
>>> class_dict = auuid2_cm[str(quuid)]
>>> # Get information in frontend
>>> #ibs = ibeis.opendb('testdb1')
>>> #cm = match_obj = ibeis.ChipMatch.from_dict(class_dict, ibs=ibs)
>>> #match_obj.print_rawinfostr()
>>> # Make the dictionary a bit more managable
>>> #match_obj.compress_top_feature_matches(num=2)
>>> #class_dict = match_obj.to_dict(ibs=ibs)
>>> cm_dict = class_dict
>>> # Package for review
>>> review_pair = {'annot_uuid_1': quuid, 'annot_uuid_2': duuid_list[1]}
>>> callback_method = u'POST'
>>> view_orientation = u'vertical'
>>> include_jquery = False
>>> kw = dict(
>>> review_pair=review_pair,
>>> cm_dict=cm_dict,
>>> query_config_dict=query_config_dict,
>>> _internal_state=None,
>>> callback_url = None,
>>> )
>>> html_str = web_ibs.send_ibeis_request('/api/review/query/graph/', type_='get', **kw)
>>> web_ibs.terminate2()
>>> ut.quit_if_noshow()
>>> import plottool as pt
>>> ut.render_html(html_str)
>>> ut.show_if_requested()
Example2:
>>> # DISABLE_DOCTEST
>>> # This starts off using web to get information, but finishes the rest in python
>>> from ibeis.web.apis_query import * # NOQA
>>> import ibeis
>>> ut.exec_funckw(review_graph_match_html, globals())
>>> web_ibs = ibeis.opendb_bg_web('testdb1') # , domain='http://52.33.105.88')
>>> aids = web_ibs.send_ibeis_request('/api/annot/', 'get')[0:2]
>>> uuid_list = web_ibs.send_ibeis_request('/api/annot/uuids/', type_='get', aid_list=aids)
>>> quuid_list = uuid_list[0:1]
>>> duuid_list = uuid_list
>>> query_config_dict = {
>>> # 'pipeline_root' : 'BC_DTW'
>>> }
>>> data = dict(
>>> query_annot_uuid_list=quuid_list, database_annot_uuid_list=duuid_list,
>>> query_config_dict=query_config_dict,
>>> )
>>> jobid = web_ibs.send_ibeis_request('/api/engine/query/graph/', **data)
>>> status_response = web_ibs.wait_for_results(jobid)
>>> result_response = web_ibs.read_engine_results(jobid)
>>> web_ibs.terminate2()
>>> # NOW WORK IN THE FRONTEND
>>> inference_result = result_response['json_result']
>>> auuid2_cm = inference_result['cm_dict']
>>> quuid = quuid_list[0]
>>> class_dict = auuid2_cm[str(quuid)]
>>> # Get information in frontend
>>> ibs = ibeis.opendb('testdb1')
>>> cm = ibeis.ChipMatch.from_dict(class_dict, ibs=ibs)
>>> cm.print_rawinfostr()
>>> # Make the dictionary a bit more managable
>>> cm.compress_top_feature_matches(num=1)
>>> cm.print_rawinfostr()
>>> class_dict = cm.to_dict(ibs=ibs)
>>> cm_dict = class_dict
>>> # Package for review ( CANT CALL DIRECTLY BECAUSE OF OUT OF CONTEXT )
>>> review_pair = {'annot_uuid_1': quuid, 'annot_uuid_2': duuid_list[1]}
>>> x = review_graph_match_html(ibs, review_pair, cm_dict,
>>> query_config_dict, _internal_state=None,
>>> callback_url=None)
>>> ut.quit_if_noshow()
>>> import plottool as pt
>>> ut.render_html(html_str)
>>> ut.show_if_requested()
"""
from ibeis.algo.hots import chip_match
# from ibeis.algo.hots.query_request import QueryRequest
proot = query_config_dict.get('pipeline_root', 'vsmany')
proot = query_config_dict.get('proot', proot)
if proot.upper() == 'BC_DTW':
cls = chip_match.AnnotMatch # ibs.depc_annot.requestclass_dict['BC_DTW']
else:
cls = chip_match.ChipMatch
view_orientation = view_orientation.lower()
if view_orientation not in ['vertical', 'horizontal']:
view_orientation = 'horizontal'
# unpack info
try:
annot_uuid_1 = review_pair['annot_uuid_1']
annot_uuid_2 = review_pair['annot_uuid_2']
except Exception:
#??? HACK
# FIXME:
print('[!!!!] review_pair = %r' % (review_pair,))
review_pair = review_pair[0]
annot_uuid_1 = review_pair['annot_uuid_1']
annot_uuid_2 = review_pair['annot_uuid_2']
aid_1 = ibs.get_annot_aids_from_uuid(annot_uuid_1)
aid_2 = ibs.get_annot_aids_from_uuid(annot_uuid_2)
cm = cls.from_dict(cm_dict, ibs=ibs)
qreq_ = ibs.new_query_request([aid_1], [aid_2],
cfgdict=query_config_dict)
# Get score
idx = cm.daid2_idx[aid_2]
match_score = cm.name_score_list[idx]
#match_score = cm.aid2_score[aid_2]
image_matches = make_review_image(aid_2, cm, qreq_,
view_orientation=view_orientation)
image_matches_src = appf.embed_image_html(image_matches)
image_clean = make_review_image(aid_2, cm, qreq_,
view_orientation=view_orientation,
draw_matches=False)
image_clean_src = appf.embed_image_html(image_clean)
if False:
from ibeis.web import apis_query
root_path = dirname(abspath(apis_query.__file__))
else:
root_path = dirname(abspath(__file__))
css_file_list = [
['css', 'style.css'],
['include', 'bootstrap', 'css', 'bootstrap.css'],
]
json_file_list = [
['javascript', 'script.js'],
['include', 'bootstrap', 'js', 'bootstrap.js'],
]
if include_jquery:
json_file_list = [
['javascript', 'jquery.min.js'],
] + json_file_list
EMBEDDED_CSS = ''
EMBEDDED_JAVASCRIPT = ''
css_template_fmtstr = '<style type="text/css" ia-dependency="css">%s</style>\n'
json_template_fmtstr = '<script type="text/javascript" ia-dependency="javascript">%s</script>\n'
for css_file in css_file_list:
css_filepath_list = [root_path, 'static'] + css_file
with open(join(*css_filepath_list)) as css_file:
EMBEDDED_CSS += css_template_fmtstr % (css_file.read(), )
for json_file in json_file_list:
json_filepath_list = [root_path, 'static'] + json_file
with open(join(*json_filepath_list)) as json_file:
EMBEDDED_JAVASCRIPT += json_template_fmtstr % (json_file.read(), )
return appf.template('turk', 'query_match_insert',
match_score=match_score,
image_clean_src=image_clean_src,
image_matches_src=image_matches_src,
annot_uuid_1=str(annot_uuid_1),
annot_uuid_2=str(annot_uuid_2),
view_orientation=view_orientation,
callback_url=callback_url,
callback_method=callback_method,
EMBEDDED_CSS=EMBEDDED_CSS,
EMBEDDED_JAVASCRIPT=EMBEDDED_JAVASCRIPT)
def experiments_interest(dbtag1='demo-jasonp', dbtag2='demo-chuck', **kwargs):
from uuid import UUID
from ibeis.other.detectfuncs import general_overlap, general_parse_gt
dbtag1 = str(dbtag1)
dbtag2 = str(dbtag2)
ibs1 = experiment_init_db(dbtag1)
ibs2 = experiment_init_db(dbtag2)
dbdir1 = ibs1.dbdir
dbdir2 = ibs2.dbdir
gid_list1 = ibs1.get_valid_gids(reviewed=1)
gid_list2 = ibs2.get_valid_gids(reviewed=1)
gt_dict1 = general_parse_gt(ibs1, gid_list1)
gt_dict2 = general_parse_gt(ibs2, gid_list2)
uuid_list1 = sorted(map(str, ibs1.get_image_uuids(gid_list1)))
uuid_list2 = sorted(map(str, ibs2.get_image_uuids(gid_list2)))
gid_pair_list = []
index1, index2 = 0, 0
stats_global = {
'disagree_interest1': 0,
'disagree_interest2': 0,
'annot1': 0,
'annot2': 0,
}
while index1 < len(uuid_list1) or index2 < len(uuid_list2):
uuid1 = UUID(uuid_list1[index1]) if index1 < len(uuid_list1) else None
uuid2 = UUID(uuid_list2[index2]) if index2 < len(uuid_list2) else None
if uuid1 is None and uuid2 is None:
break
gid1 = ibs1.get_image_gids_from_uuid(uuid1)
gid2 = ibs2.get_image_gids_from_uuid(uuid2)
print('%s %s' % (
index1,
index2,
))
stats = None
if uuid1 is not None and uuid2 is not None:
if uuid1 == uuid2:
gt_list1 = gt_dict1[uuid1]
gt_list2 = gt_dict2[uuid2]
stats_global['annot1'] += len(gt_list1)
stats_global['annot2'] += len(gt_list2)
overlap = general_overlap(gt_list1, gt_list2)
if 0 in overlap.shape:
index_list1 = []
index_list2 = []
else:
index_list1 = np.argmax(overlap, axis=1)
index_list2 = np.argmax(overlap, axis=0)
pair_list1 = set(enumerate(index_list1))
pair_list2 = set(enumerate(index_list2))
pair_list2 = set([_[::-1] for _ in pair_list2])
pair_union = pair_list1 | pair_list2
pair_intersect = pair_list1 & pair_list2
pair_diff_sym = pair_list1 ^ pair_list2
pair_diff1 = pair_list1 - pair_list2
pair_diff2 = pair_list2 - pair_list1
message_list = []
if len(gt_list1) > 0 and len(gt_list2) == 0:
message_list.append('Jason has annotations, Chuck none')
if len(gt_list1) == 0 and len(gt_list2) > 0:
message_list.append('Chuck has annotations, Jason none')
if len(pair_diff1) > 0 and len(pair_diff2) == 0:
message_list.append('Jason has additional annotations')
if len(pair_diff1) == 0 and len(pair_diff2) > 0:
message_list.append('Chuck has additional annotations')
if len(pair_diff1) > 0 and len(pair_diff2) > 0:
message_list.append('Assignment mismatch')
disagree = 0
for index1_, index2_ in pair_intersect:
gt1 = gt_list1[index1_]
gt2 = gt_list2[index2_]
interest1 = gt1['interest']
interest2 = gt2['interest']
if interest1 != interest2:
disagree += 1
if interest1 > interest2:
stats_global['disagree_interest1'] += 1
if interest2 > interest1:
stats_global['disagree_interest2'] += 1
if disagree > 0:
message_list.append('Interest mismatch')
stats = {
'num_annot1': len(gt_list1),
'num_annot2': len(gt_list2),
'num_interest1':
len([_ for _ in gt_list1 if _['interest']]),
'num_interest2':
len([_ for _ in gt_list2 if _['interest']]),
'conflict': len(message_list) > 0,
'message': '<br/>'.join(message_list),
}
else:
if uuid1 < uuid2:
gid2 = None
else:
gid1 = None
gid_pair_list.append((gid1, gid2, stats))
if gid1 is not None:
index1 += 1
if gid2 is not None:
index2 += 1
embed = dict(globals(), **locals())
return appf.template('experiments', 'interest', **embed)
def review_detection_html(ibs, image_uuid, result_list, callback_url, callback_method='POST', include_jquery=False, config=None):
"""
Return the detection review interface for a particular image UUID and a list of results for that image.
Args:
image_uuid (UUID): the UUID of the image you want to review detections for
result_list (list of dict): list of detection results returned by the detector
callback_url (str): URL that the review form will submit to (action) when
the user is complete with their review
callback_method (str): HTTP method the review form will submit to (method).
Defaults to 'POST'
Returns:
template (html): json response with the detection web interface in html
RESTful:
Method: GET
URL: /api/review/detect/cnn/yolo/
"""
ibs.web_check_uuids(image_uuid_list=[image_uuid])
gid = ibs.get_image_gids_from_uuid(image_uuid)
if gid is None:
return 'INVALID IMAGE UUID'
default_config = {
'autointerest' : False,
'interest_bypass' : False,
'metadata' : True,
'metadata_viewpoint' : False,
'metadata_quality' : False,
'metadata_flags' : True,
'metadata_flags_aoi' : True,
'metadata_flags_multiple' : False,
'metadata_species' : True,
'metadata_label' : True,
'metadata_quickhelp' : True,
'parts' : False,
'modes_rectangle' : True,
'modes_diagonal' : True,
'modes_diagonal2' : True,
'staged' : False,
}
if config is not None:
default_config.update(config)
gpath = ibs.get_image_thumbpath(gid, ensure_paths=True, draw_annots=False)
image = ibs.get_images(gid)
image_src = appf.embed_image_html(image)
width, height = ibs.get_image_sizes(gid)
if width <= 0 or width is None or height <= 0 or height is None:
vals = (image_uuid, width, height, )
raise IOError('Image %r for review has either no width or no height (w = %s, h = %s)' % vals)
annotation_list = []
for result in result_list:
quality = result.get('quality', None)
if quality in [-1, None]:
quality = 0
elif quality <= 2:
quality = 1
elif quality > 2:
quality = 2
viewpoint1 = result.get('viewpoint1', None)
viewpoint2 = result.get('viewpoint2', None)
viewpoint3 = result.get('viewpoint3', None)
if viewpoint1 is None and viewpoint2 is None and viewpoint3 is None:
viewpoint = result.get('viewpoint', None)
viewpoint1, viewpoint2, viewpoint3 = appf.convert_viewpoint_to_tuple(viewpoint)
annotation_list.append({
'id' : result.get('id', None),
'left' : 100.0 * (result.get('left', result['xtl']) / width),
'top' : 100.0 * (result.get('top', result['ytl']) / height),
'width' : 100.0 * (result['width'] / width),
'height' : 100.0 * (result['height'] / height),
'species' : result.get('species', result['class']),
'theta' : result.get('theta', 0.0),
'viewpoint1' : viewpoint1,
'viewpoint2' : viewpoint2,
'viewpoint3' : viewpoint3,
'quality' : quality,
'multiple' : 'true' if result.get('multiple', None) == 1 else 'false',
'interest' : 'true' if result.get('interest', None) == 1 else 'false',
})
species = KEY_DEFAULTS[SPECIES_KEY]
root_path = dirname(abspath(__file__))
css_file_list = [
['include', 'jquery-ui', 'jquery-ui.min.css'],
['include', 'jquery.ui.rotatable', 'jquery.ui.rotatable.css'],
['css', 'style.css'],
]
json_file_list = [
['include', 'jquery-ui', 'jquery-ui.min.js'],
['include', 'jquery.ui.rotatable', 'jquery.ui.rotatable.min.js'],
['include', 'bbox_annotator_percent.js'],
['javascript', 'script.js'],
['javascript', 'turk-detection.js'],
]
if include_jquery:
json_file_list = [
['javascript', 'jquery.min.js'],
] + json_file_list
EMBEDDED_CSS = ''
EMBEDDED_JAVASCRIPT = ''
css_template_fmtstr = '<style type="text/css" ia-dependency="css">%s</style>\n'
json_template_fmtstr = '<script type="text/javascript" ia-dependency="javascript">%s</script>\n'
for css_file in css_file_list:
css_filepath_list = [root_path, 'static'] + css_file
with open(join(*css_filepath_list)) as css_file:
EMBEDDED_CSS += css_template_fmtstr % (css_file.read(), )
for json_file in json_file_list:
json_filepath_list = [root_path, 'static'] + json_file
with open(join(*json_filepath_list)) as json_file:
EMBEDDED_JAVASCRIPT += json_template_fmtstr % (json_file.read(), )
species_rowids = ibs._get_all_species_rowids()
species_nice_list = ibs.get_species_nice(species_rowids)
combined_list = sorted(zip(species_nice_list, species_rowids))
species_nice_list = [ combined[0] for combined in combined_list ]
species_rowids = [ combined[1] for combined in combined_list ]
species_text_list = ibs.get_species_texts(species_rowids)
species_list = list(zip(species_nice_list, species_text_list))
species_list = [ ('Unspecified', const.UNKNOWN) ] + species_list
# Collect mapping of species to parts
aid_list = ibs.get_valid_aids()
part_species_rowid_list = ibs.get_annot_species_rowids(aid_list)
part_species_text_list = ibs.get_species_texts(part_species_rowid_list)
part_rowids_list = ibs.get_annot_part_rowids(aid_list)
part_types_list = map(ibs.get_part_types, part_rowids_list)
zipped = list(zip(part_species_text_list, part_types_list))
species_part_dict = {
const.UNKNOWN: set([])
}
for part_species_text, part_type_list in zipped:
if part_species_text not in species_part_dict:
species_part_dict[part_species_text] = set([const.UNKNOWN])
for part_type in part_type_list:
species_part_dict[part_species_text].add(part_type)
species_part_dict[const.UNKNOWN].add(part_type)
# Add any images that did not get added because they aren't assigned any annotations
for species_text in species_text_list:
if species_text not in species_part_dict:
species_part_dict[species_text] = set([const.UNKNOWN])
for key in species_part_dict:
species_part_dict[key] = sorted(list(species_part_dict[key]))
species_part_dict_json = json.dumps(species_part_dict)
orientation_flag = '0'
if species is not None and 'zebra' in species:
orientation_flag = '1'
settings_key_list = [
('ia-detection-setting-orientation', orientation_flag),
('ia-detection-setting-parts-assignments', '1'),
('ia-detection-setting-toggle-annotations', '1'),
('ia-detection-setting-toggle-parts', '0'),
('ia-detection-setting-parts-show', '0'),
('ia-detection-setting-parts-hide', '0'),
]
settings = {
settings_key: request.cookies.get(settings_key, settings_default) == '1'
for (settings_key, settings_default) in settings_key_list
}
return appf.template('turk', 'detection_insert',
gid=gid,
refer_aid=None,
species=species,
image_path=gpath,
image_src=image_src,
config=default_config,
settings=settings,
annotation_list=annotation_list,
species_list=species_list,
species_part_dict_json=species_part_dict_json,
callback_url=callback_url,
callback_method=callback_method,
EMBEDDED_CSS=EMBEDDED_CSS,
EMBEDDED_JAVASCRIPT=EMBEDDED_JAVASCRIPT)
def view_experiments(**kwargs):
return appf.template('experiments')
def upload():
return appf.template(None, 'upload')
def view_images():
ibs = current_app.ibs
filtered = True
imgsetid_list = []
gid = request.args.get('gid', '')
imgsetid = request.args.get('imgsetid', '')
page = max(0, int(request.args.get('page', 1)))
if len(gid) > 0:
gid_list = gid.strip().split(',')
gid_list = [ None if gid_ == 'None' or gid_ == '' else int(gid_) for gid_ in gid_list ]
elif len(imgsetid) > 0:
imgsetid_list = imgsetid.strip().split(',')
imgsetid_list = [ None if imgsetid_ == 'None' or imgsetid_ == '' else int(imgsetid_) for imgsetid_ in imgsetid_list ]
gid_list = ut.flatten([ ibs.get_valid_gids(imgsetid=imgsetid) for imgsetid_ in imgsetid_list ])
else:
gid_list = ibs.get_valid_gids()
filtered = False
# Page
page_start = min(len(gid_list), (page - 1) * appf.PAGE_SIZE)
page_end = min(len(gid_list), page * appf.PAGE_SIZE)
page_total = int(math.ceil(len(gid_list) / appf.PAGE_SIZE))
page_previous = None if page_start == 0 else page - 1
page_next = None if page_end == len(gid_list) else page + 1
gid_list = gid_list[page_start:page_end]
print('[web] Loading Page [ %d -> %d ] (%d), Prev: %s, Next: %s' % (page_start, page_end, len(gid_list), page_previous, page_next, ))
image_unixtime_list = ibs.get_image_unixtime(gid_list)
datetime_list = [
ut.unixtime_to_datetimestr(image_unixtime)
if image_unixtime is not None
else
'Unknown'
for image_unixtime in image_unixtime_list
]
image_list = zip(
gid_list,
[ ','.join(map(str, imgsetid_list_)) for imgsetid_list_ in ibs.get_image_imgsetids(gid_list) ],
ibs.get_image_gnames(gid_list),
image_unixtime_list,
datetime_list,
ibs.get_image_gps(gid_list),
ibs.get_image_party_tag(gid_list),
ibs.get_image_contributor_tag(gid_list),
ibs.get_image_notes(gid_list),
appf.imageset_image_processed(ibs, gid_list),
)
image_list.sort(key=lambda t: t[3])
return appf.template('view', 'images',
filtered=filtered,
imgsetid_list=imgsetid_list,
imgsetid_list_str=','.join(map(str, imgsetid_list)),
num_imgsetids=len(imgsetid_list),
gid_list=gid_list,
gid_list_str=','.join(map(str, gid_list)),
num_gids=len(gid_list),
image_list=image_list,
num_images=len(image_list),
page=page,
page_start=page_start,
page_end=page_end,
page_total=page_total,
page_previous=page_previous,
page_next=page_next)
def review_detection_html(ibs,
image_uuid,
result_list,
callback_url,
callback_method='POST',
include_jquery=False):
"""
Returns the detection review interface for a particular image UUID and a list of
results for that image.
Args:
image_uuid (UUID): the UUID of the image you want to review detections for
result_list (list of dict): list of detection results returned by the detector
callback_url (str): URL that the review form will submit to (action) when
the user is complete with their review
callback_method (str): HTTP method the review form will submit to (method).
Defaults to 'POST'
Returns:
template (html): json response with the detection web interface in html
RESTful:
Method: GET
URL: /api/review/detect/cnn/yolo/
"""
ibs.web_check_uuids(image_uuid_list=[image_uuid])
gid = ibs.get_image_gids_from_uuid(image_uuid)
if gid is None:
return 'INVALID IMAGE UUID'
gpath = ibs.get_image_thumbpath(gid, ensure_paths=True, draw_annots=False)
image = ibs.get_images(gid)
image_src = appf.embed_image_html(image)
width, height = ibs.get_image_sizes(gid)
if width <= 0 or width is None or height <= 0 or height is None:
vals = (
image_uuid,
width,
height,
)
raise IOError(
'Image %r for review has either no width or no height (w = %s, h = %s)'
% vals)
annotation_list = []
for result in result_list:
annotation_list.append({
'left': 100.0 * (result['xtl'] / width),
'top': 100.0 * (result['ytl'] / height),
'width': 100.0 * (result['width'] / width),
'height': 100.0 * (result['height'] / height),
'label': result['class'],
'id': None,
'theta': result.get('theta', 0.0),
})
species = KEY_DEFAULTS[SPECIES_KEY]
root_path = dirname(abspath(__file__))
css_file_list = [
['include', 'jquery-ui', 'jquery-ui.min.css'],
['include', 'jquery.ui.rotatable', 'jquery.ui.rotatable.css'],
['css', 'style.css'],
]
json_file_list = [
['include', 'jquery-ui', 'jquery-ui.min.js'],
['include', 'jquery.ui.rotatable', 'jquery.ui.rotatable.min.js'],
['include', 'bbox_annotator_percent.js'],
['javascript', 'script.js'],
['javascript', 'turk-detection.js'],
]
if include_jquery:
json_file_list = [
['javascript', 'jquery.min.js'],
] + json_file_list
EMBEDDED_CSS = ''
EMBEDDED_JAVASCRIPT = ''
css_template_fmtstr = '<style type="text/css" ia-dependency="css">%s</style>\n'
json_template_fmtstr = '<script type="text/javascript" ia-dependency="javascript">%s</script>\n'
for css_file in css_file_list:
css_filepath_list = [root_path, 'static'] + css_file
with open(join(*css_filepath_list)) as css_file:
EMBEDDED_CSS += css_template_fmtstr % (css_file.read(), )
for json_file in json_file_list:
json_filepath_list = [root_path, 'static'] + json_file
with open(join(*json_filepath_list)) as json_file:
EMBEDDED_JAVASCRIPT += json_template_fmtstr % (json_file.read(), )
return appf.template('turk',
'detection_insert',
gid=gid,
refer_aid=None,
species=species,
image_path=gpath,
image_src=image_src,
annotation_list=annotation_list,
callback_url=callback_url,
callback_method=callback_method,
EMBEDDED_CSS=EMBEDDED_CSS,
EMBEDDED_JAVASCRIPT=EMBEDDED_JAVASCRIPT)
def turk_quality():
"""
PZ Needs Tags:
17242
14468
14427
15946
14771
14084
4102
6074
3409
GZ Needs Tags;
1302
CommandLine:
python -m ibeis.web.app --exec-turk_quality --db PZ_Master1
python -m ibeis.web.app --exec-turk_quality --db GZ_Master1
python -m ibeis.web.app --exec-turk_quality --db GIRM_Master1
Example:
>>> # SCRIPT
>>> from ibeis.other.ibsfuncs import * # NOQA
>>> import ibeis
>>> ibs = ibeis.opendb(defaultdb='testdb1')
>>> aid_list_ = ibs.find_unlabeled_name_members(qual=True)
>>> valid_views = ['primary', 'primary1', 'primary-1']
>>> aid_list = ibs.filter_aids_to_viewpoint(aid_list_, valid_views, unknown_ok=False)
>>> ibs.start_web_annot_groupreview(aid_list)
"""
ibs = current_app.ibs
tup = appf.get_turk_annot_args(appf.imageset_annot_quality_processed)
(aid_list, reviewed_list, imgsetid, src_ag, dst_ag, progress, aid, previous) = tup
value = ibs.get_annot_qualities(aid)
if value == -1:
value = None
if value == 0:
value = 1
review = 'review' in request.args.keys()
finished = aid is None
display_instructions = request.cookies.get('quality_instructions_seen', 1) == 0
if not finished:
gid = ibs.get_annot_gids(aid)
gpath = ibs.get_annot_chip_fpath(aid)
image = vt.imread(gpath)
image_src = appf.embed_image_html(image)
else:
gid = None
gpath = None
image_src = None
imagesettext = ibs.get_imageset_text(imgsetid)
return appf.template('turk', 'quality',
imgsetid=imgsetid,
src_ag=src_ag,
dst_ag=dst_ag,
gid=gid,
aid=aid,
value=value,
image_path=gpath,
image_src=image_src,
previous=previous,
imagesettext=imagesettext,
progress=progress,
finished=finished,
display_instructions=display_instructions,
review=review)
def turk_additional():
ibs = current_app.ibs
imgsetid = request.args.get('imgsetid', '')
imgsetid = None if imgsetid == 'None' or imgsetid == '' else int(imgsetid)
gid_list = ibs.get_valid_gids(imgsetid=imgsetid)
aid_list = ut.flatten(ibs.get_image_aids(gid_list))
nid_list = ibs.get_annot_nids(aid_list)
reviewed_list = appf.imageset_annot_additional_processed(ibs, aid_list, nid_list)
try:
progress = '%0.2f' % (100.0 * reviewed_list.count(True) / len(aid_list), )
except ZeroDivisionError:
progress = '0.00'
imagesettext = None if imgsetid is None else ibs.get_imageset_text(imgsetid)
aid = request.args.get('aid', '')
if len(aid) > 0:
aid = int(aid)
else:
aid_list_ = ut.filterfalse_items(aid_list, reviewed_list)
if len(aid_list_) == 0:
aid = None
else:
# aid = aid_list_[0]
aid = random.choice(aid_list_)
previous = request.args.get('previous', None)
value_sex = ibs.get_annot_sex([aid])[0]
if value_sex >= 0:
value_sex += 2
else:
value_sex = None
value_age_min, value_age_max = ibs.get_annot_age_months_est([aid])[0]
value_age = None
if (value_age_min is -1 or value_age_min is None) and (value_age_max is -1 or value_age_max is None):
value_age = 1
if (value_age_min is 0 or value_age_min is None) and value_age_max == 2:
value_age = 2
elif value_age_min is 3 and value_age_max == 5:
value_age = 3
elif value_age_min is 6 and value_age_max == 11:
value_age = 4
elif value_age_min is 12 and value_age_max == 23:
value_age = 5
elif value_age_min is 24 and value_age_max == 35:
value_age = 6
elif value_age_min is 36 and (value_age_max > 36 or value_age_max is None):
value_age = 7
review = 'review' in request.args.keys()
finished = aid is None
display_instructions = request.cookies.get('additional_instructions_seen', 1) == 0
if not finished:
gid = ibs.get_annot_gids(aid)
gpath = ibs.get_annot_chip_fpath(aid)
image = vt.imread(gpath)
image_src = appf.embed_image_html(image)
else:
gid = None
gpath = None
image_src = None
name_aid_list = None
nid = ibs.get_annot_name_rowids(aid)
if nid is not None:
name_aid_list = ibs.get_name_aids(nid)
quality_list = ibs.get_annot_qualities(name_aid_list)
quality_text_list = ibs.get_annot_quality_texts(name_aid_list)
yaw_text_list = ibs.get_annot_yaw_texts(name_aid_list)
name_aid_combined_list = list(zip(
name_aid_list,
quality_list,
quality_text_list,
yaw_text_list,
))
name_aid_combined_list.sort(key=lambda t: t[1], reverse=True)
else:
name_aid_combined_list = []
return appf.template('turk', 'additional',
imgsetid=imgsetid,
gid=gid,
aid=aid,
value_sex=value_sex,
value_age=value_age,
image_path=gpath,
name_aid_combined_list=name_aid_combined_list,
image_src=image_src,
previous=previous,
imagesettext=imagesettext,
progress=progress,
finished=finished,
display_instructions=display_instructions,
review=review)
def root():
return appf.template(None)
def turk_viewpoint():
"""
CommandLine:
python -m ibeis.web.app --exec-turk_viewpoint --db PZ_Master1
Example:
>>> # SCRIPT
>>> from ibeis.other.ibsfuncs import * # NOQA
>>> import ibeis
>>> ibs = ibeis.opendb(defaultdb='PZ_Master1')
>>> aid_list_ = ibs.find_unlabeled_name_members(suspect_yaws=True)
>>> aid_list = ibs.filter_aids_to_quality(aid_list_, 'good', unknown_ok=False)
>>> ibs.start_web_annot_groupreview(aid_list)
"""
ibs = current_app.ibs
tup = appf.get_turk_annot_args(appf.imageset_annot_viewpoint_processed)
(aid_list, reviewed_list, imgsetid, src_ag, dst_ag, progress, aid, previous) = tup
value = appf.convert_yaw_to_old_viewpoint(ibs.get_annot_yaws(aid))
review = 'review' in request.args.keys()
finished = aid is None
display_instructions = request.cookies.get('viewpoint_instructions_seen', 1) == 0
if not finished:
gid = ibs.get_annot_gids(aid)
gpath = ibs.get_annot_chip_fpath(aid)
image = vt.imread(gpath)
image_src = appf.embed_image_html(image)
species = ibs.get_annot_species_texts(aid)
else:
gid = None
gpath = None
image_src = None
species = None
imagesettext = ibs.get_imageset_text(imgsetid)
species_rowids = ibs._get_all_species_rowids()
species_nice_list = ibs.get_species_nice(species_rowids)
combined_list = sorted(zip(species_nice_list, species_rowids))
species_nice_list = [ combined[0] for combined in combined_list ]
species_rowids = [ combined[1] for combined in combined_list ]
species_text_list = ibs.get_species_texts(species_rowids)
species_selected_list = [ species == species_ for species_ in species_text_list ]
species_list = zip(species_nice_list, species_text_list, species_selected_list)
species_list = [ ('Unspecified', const.UNKNOWN, True) ] + species_list
return appf.template('turk', 'viewpoint',
imgsetid=imgsetid,
src_ag=src_ag,
dst_ag=dst_ag,
gid=gid,
aid=aid,
value=value,
image_path=gpath,
image_src=image_src,
previous=previous,
species_list=species_list,
imagesettext=imagesettext,
progress=progress,
finished=finished,
display_instructions=display_instructions,
review=review)
def turk_detection():
ibs = current_app.ibs
refer_aid = request.args.get('refer_aid', None)
imgsetid = request.args.get('imgsetid', '')
imgsetid = None if imgsetid == 'None' or imgsetid == '' else int(imgsetid)
gid_list = ibs.get_valid_gids(imgsetid=imgsetid)
reviewed_list = appf.imageset_image_processed(ibs, gid_list)
progress = '%0.2f' % (100.0 * reviewed_list.count(True) / len(gid_list), )
imagesettext = None if imgsetid is None else ibs.get_imageset_text(imgsetid)
gid = request.args.get('gid', '')
if len(gid) > 0:
gid = int(gid)
else:
gid_list_ = ut.filterfalse_items(gid_list, reviewed_list)
if len(gid_list_) == 0:
gid = None
else:
# gid = gid_list_[0]
gid = random.choice(gid_list_)
previous = request.args.get('previous', None)
finished = gid is None
review = 'review' in request.args.keys()
display_instructions = request.cookies.get('detection_instructions_seen', 1) == 0
display_species_examples = False # request.cookies.get('detection_example_species_seen', 0) == 0
if not finished:
gpath = ibs.get_image_thumbpath(gid, ensure_paths=True, draw_annots=False)
image = ibs.get_images(gid)
image_src = appf.embed_image_html(image)
# Get annotations
width, height = ibs.get_image_sizes(gid)
aid_list = ibs.get_image_aids(gid)
annot_bbox_list = ibs.get_annot_bboxes(aid_list)
annot_thetas_list = ibs.get_annot_thetas(aid_list)
species_list = ibs.get_annot_species_texts(aid_list)
# Get annotation bounding boxes
annotation_list = []
for aid, annot_bbox, annot_theta, species in zip(aid_list, annot_bbox_list, annot_thetas_list, species_list):
temp = {}
temp['left'] = 100.0 * (annot_bbox[0] / width)
temp['top'] = 100.0 * (annot_bbox[1] / height)
temp['width'] = 100.0 * (annot_bbox[2] / width)
temp['height'] = 100.0 * (annot_bbox[3] / height)
temp['label'] = species
temp['id'] = aid
temp['theta'] = float(annot_theta)
annotation_list.append(temp)
if len(species_list) > 0:
species = max(set(species_list), key=species_list.count) # Get most common species
elif appf.default_species(ibs) is not None:
species = appf.default_species(ibs)
else:
species = KEY_DEFAULTS[SPECIES_KEY]
else:
gpath = None
species = None
image_src = None
annotation_list = []
callback_url = '%s?imgsetid=%s' % (url_for('submit_detection'), imgsetid, )
return appf.template('turk', 'detection',
imgsetid=imgsetid,
gid=gid,
refer_aid=refer_aid,
species=species,
image_path=gpath,
image_src=image_src,
previous=previous,
imagesettext=imagesettext,
progress=progress,
finished=finished,
annotation_list=annotation_list,
display_instructions=display_instructions,
display_species_examples=display_species_examples,
callback_url=callback_url,
callback_method='POST',
EMBEDDED_CSS=None,
EMBEDDED_JAVASCRIPT=None,
review=review)
def turk():
imgsetid = request.args.get('imgsetid', '')
imgsetid = None if imgsetid == 'None' or imgsetid == '' else int(imgsetid)
return appf.template('turk', None, imgsetid=imgsetid)
def view_names():
ibs = current_app.ibs
filtered = True
aid_list = []
imgsetid_list = []
gid_list = []
nid = request.args.get('nid', '')
aid = request.args.get('aid', '')
gid = request.args.get('gid', '')
imgsetid = request.args.get('imgsetid', '')
page = max(0, int(request.args.get('page', 1)))
if len(nid) > 0:
nid_list = nid.strip().split(',')
nid_list = [ None if nid_ == 'None' or nid_ == '' else int(nid_) for nid_ in nid_list ]
if len(aid) > 0:
aid_list = aid.strip().split(',')
aid_list = [ None if aid_ == 'None' or aid_ == '' else int(aid_) for aid_ in aid_list ]
nid_list = ibs.get_annot_name_rowids(aid_list)
elif len(gid) > 0:
gid_list = gid.strip().split(',')
gid_list = [ None if gid_ == 'None' or gid_ == '' else int(gid_) for gid_ in gid_list ]
aid_list = ut.flatten(ibs.get_image_aids(gid_list))
nid_list = ibs.get_annot_name_rowids(aid_list)
elif len(imgsetid) > 0:
imgsetid_list = imgsetid.strip().split(',')
imgsetid_list = [ None if imgsetid_ == 'None' or imgsetid_ == '' else int(imgsetid_) for imgsetid_ in imgsetid_list ]
gid_list = ut.flatten([ ibs.get_valid_gids(imgsetid=imgsetid_) for imgsetid_ in imgsetid_list ])
aid_list = ut.flatten(ibs.get_image_aids(gid_list))
nid_list = ibs.get_annot_name_rowids(aid_list)
else:
nid_list = ibs.get_valid_nids()
filtered = False
# Page
appf.PAGE_SIZE_ = int(appf.PAGE_SIZE / 5)
page_start = min(len(nid_list), (page - 1) * appf.PAGE_SIZE_)
page_end = min(len(nid_list), page * appf.PAGE_SIZE_)
page_total = int(math.ceil(len(nid_list) / appf.PAGE_SIZE_))
page_previous = None if page_start == 0 else page - 1
page_next = None if page_end == len(nid_list) else page + 1
nid_list = nid_list[page_start:page_end]
print('[web] Loading Page [ %d -> %d ] (%d), Prev: %s, Next: %s' % (page_start, page_end, len(nid_list), page_previous, page_next, ))
aids_list = ibs.get_name_aids(nid_list)
annotations_list = [ zip(
aid_list_,
ibs.get_annot_gids(aid_list_),
[ ','.join(map(str, imgsetid_list_)) for imgsetid_list_ in ibs.get_annot_imgsetids(aid_list_) ],
ibs.get_annot_image_names(aid_list_),
ibs.get_annot_names(aid_list_),
ibs.get_annot_exemplar_flags(aid_list_),
ibs.get_annot_species_texts(aid_list_),
ibs.get_annot_yaw_texts(aid_list_),
ibs.get_annot_quality_texts(aid_list_),
ibs.get_annot_sex_texts(aid_list_),
ibs.get_annot_age_months_est(aid_list_),
[ reviewed_viewpoint and reviewed_quality for reviewed_viewpoint, reviewed_quality in zip(appf.imageset_annot_viewpoint_processed(ibs, aid_list_), appf.imageset_annot_quality_processed(ibs, aid_list_)) ],
) for aid_list_ in aids_list ]
name_list = zip(
nid_list,
annotations_list
)
name_list.sort(key=lambda t: t[0])
return appf.template('view', 'names',
filtered=filtered,
imgsetid_list=imgsetid_list,
imgsetid_list_str=','.join(map(str, imgsetid_list)),
num_imgsetids=len(imgsetid_list),
gid_list=gid_list,
gid_list_str=','.join(map(str, gid_list)),
num_gids=len(gid_list),
aid_list=aid_list,
aid_list_str=','.join(map(str, aid_list)),
num_aids=len(aid_list),
nid_list=nid_list,
nid_list_str=','.join(map(str, nid_list)),
num_nids=len(nid_list),
name_list=name_list,
num_names=len(name_list),
page=page,
page_start=page_start,
page_end=page_end,
page_total=page_total,
page_previous=page_previous,
page_next=page_next)
def demo(*args, **kwargs):
# Return HTML
embedded = dict(globals(), **locals())
return appf.template(None, 'demo', **embedded)
def review_detection_html(ibs, image_uuid, result_list, callback_url, callback_method='POST', include_jquery=False):
"""
Returns the detection review interface for a particular image UUID and a list of
results for that image.
Args:
image_uuid (UUID): the UUID of the image you want to review detections for
result_list (list of dict): list of detection results returned by the detector
callback_url (str): URL that the review form will submit to (action) when
the user is complete with their review
callback_method (str): HTTP method the review form will submit to (method).
Defaults to 'POST'
Returns:
template (html): json response with the detection web interface in html
RESTful:
Method: GET
URL: /api/review/detect/cnn/yolo/
"""
ibs.web_check_uuids(image_uuid_list=[image_uuid])
gid = ibs.get_image_gids_from_uuid(image_uuid)
if gid is None:
return 'INVALID IMAGE UUID'
gpath = ibs.get_image_thumbpath(gid, ensure_paths=True, draw_annots=False)
image = ibs.get_images(gid)
image_src = appf.embed_image_html(image)
width, height = ibs.get_image_sizes(gid)
if width <= 0 or width is None or height <= 0 or height is None:
vals = (image_uuid, width, height, )
raise IOError('Image %r for review has either no width or no height (w = %s, h = %s)' % vals)
annotation_list = []
for result in result_list:
annotation_list.append({
'left' : 100.0 * (result['xtl'] / width),
'top' : 100.0 * (result['ytl'] / height),
'width' : 100.0 * (result['width'] / width),
'height' : 100.0 * (result['height'] / height),
'label' : result['class'],
'id' : None,
'theta' : result.get('theta', 0.0),
})
species = KEY_DEFAULTS[SPECIES_KEY]
root_path = dirname(abspath(__file__))
css_file_list = [
['include', 'jquery-ui', 'jquery-ui.min.css'],
['include', 'jquery.ui.rotatable', 'jquery.ui.rotatable.css'],
['css', 'style.css'],
]
json_file_list = [
['include', 'jquery-ui', 'jquery-ui.min.js'],
['include', 'jquery.ui.rotatable', 'jquery.ui.rotatable.min.js'],
['include', 'bbox_annotator_percent.js'],
['javascript', 'script.js'],
['javascript', 'turk-detection.js'],
]
if include_jquery:
json_file_list = [
['javascript', 'jquery.min.js'],
] + json_file_list
EMBEDDED_CSS = ''
EMBEDDED_JAVASCRIPT = ''
css_template_fmtstr = '<style type="text/css" ia-dependency="css">%s</style>\n'
json_template_fmtstr = '<script type="text/javascript" ia-dependency="javascript">%s</script>\n'
for css_file in css_file_list:
css_filepath_list = [root_path, 'static'] + css_file
with open(join(*css_filepath_list)) as css_file:
EMBEDDED_CSS += css_template_fmtstr % (css_file.read(), )
for json_file in json_file_list:
json_filepath_list = [root_path, 'static'] + json_file
with open(join(*json_filepath_list)) as json_file:
EMBEDDED_JAVASCRIPT += json_template_fmtstr % (json_file.read(), )
return appf.template('turk', 'detection_insert',
gid=gid,
refer_aid=None,
species=species,
image_path=gpath,
image_src=image_src,
annotation_list=annotation_list,
callback_url=callback_url,
callback_method=callback_method,
EMBEDDED_CSS=EMBEDDED_CSS,
EMBEDDED_JAVASCRIPT=EMBEDDED_JAVASCRIPT)
def review_graph_match_html(ibs,
review_pair,
cm_dict,
query_config_dict,
_internal_state,
callback_url,
callback_method='POST',
view_orientation='vertical',
include_jquery=False):
r"""
Args:
ibs (ibeis.IBEISController): image analysis api
review_pair (dict): pair of annot uuids
cm_dict (dict):
query_config_dict (dict):
_internal_state (?):
callback_url (?):
callback_method (unicode): (default = u'POST')
view_orientation (unicode): (default = u'vertical')
include_jquery (bool): (default = False)
CommandLine:
python -m ibeis.web.apis_query review_graph_match_html --show
ibeis --web
python -m ibeis.web.apis_query review_graph_match_html --show --domain=localhost
Example:
>>> # WEB_DOCTEST
>>> from ibeis.web.apis_query import * # NOQA
>>> import ibeis
>>> web_ibs = ibeis.opendb_bg_web('testdb1') # , domain='http://52.33.105.88')
>>> aids = web_ibs.send_ibeis_request('/api/annot/', 'get')[0:2]
>>> uuid_list = web_ibs.send_ibeis_request('/api/annot/uuids/', type_='get', aid_list=aids)
>>> quuid_list = uuid_list[0:1]
>>> duuid_list = uuid_list
>>> query_config_dict = {
>>> # 'pipeline_root' : 'BC_DTW'
>>> }
>>> data = dict(
>>> query_annot_uuid_list=quuid_list, database_annot_uuid_list=duuid_list,
>>> query_config_dict=query_config_dict,
>>> )
>>> jobid = web_ibs.send_ibeis_request('/api/engine/query/graph/', **data)
>>> print('jobid = %r' % (jobid,))
>>> status_response = web_ibs.wait_for_results(jobid)
>>> result_response = web_ibs.read_engine_results(jobid)
>>> inference_result = result_response['json_result']
>>> print('inference_result = %r' % (inference_result,))
>>> auuid2_cm = inference_result['cm_dict']
>>> quuid = quuid_list[0]
>>> class_dict = auuid2_cm[str(quuid)]
>>> # Get information in frontend
>>> #ibs = ibeis.opendb('testdb1')
>>> #cm = match_obj = ibeis.ChipMatch.from_dict(class_dict, ibs=ibs)
>>> #match_obj.print_rawinfostr()
>>> # Make the dictionary a bit more managable
>>> #match_obj.compress_top_feature_matches(num=2)
>>> #class_dict = match_obj.to_dict(ibs=ibs)
>>> cm_dict = class_dict
>>> # Package for review
>>> review_pair = {'annot_uuid_1': quuid, 'annot_uuid_2': duuid_list[1]}
>>> callback_method = u'POST'
>>> view_orientation = u'vertical'
>>> include_jquery = False
>>> kw = dict(
>>> review_pair=review_pair,
>>> cm_dict=cm_dict,
>>> query_config_dict=query_config_dict,
>>> _internal_state=None,
>>> callback_url = None,
>>> )
>>> html_str = web_ibs.send_ibeis_request('/api/review/query/graph/', type_='get', **kw)
>>> web_ibs.terminate2()
>>> ut.quit_if_noshow()
>>> import plottool as pt
>>> ut.render_html(html_str)
>>> ut.show_if_requested()
Example2:
>>> # DISABLE_DOCTEST
>>> # This starts off using web to get information, but finishes the rest in python
>>> from ibeis.web.apis_query import * # NOQA
>>> import ibeis
>>> ut.exec_funckw(review_graph_match_html, globals())
>>> web_ibs = ibeis.opendb_bg_web('testdb1') # , domain='http://52.33.105.88')
>>> aids = web_ibs.send_ibeis_request('/api/annot/', 'get')[0:2]
>>> uuid_list = web_ibs.send_ibeis_request('/api/annot/uuids/', type_='get', aid_list=aids)
>>> quuid_list = uuid_list[0:1]
>>> duuid_list = uuid_list
>>> query_config_dict = {
>>> # 'pipeline_root' : 'BC_DTW'
>>> }
>>> data = dict(
>>> query_annot_uuid_list=quuid_list, database_annot_uuid_list=duuid_list,
>>> query_config_dict=query_config_dict,
>>> )
>>> jobid = web_ibs.send_ibeis_request('/api/engine/query/graph/', **data)
>>> status_response = web_ibs.wait_for_results(jobid)
>>> result_response = web_ibs.read_engine_results(jobid)
>>> web_ibs.terminate2()
>>> # NOW WORK IN THE FRONTEND
>>> inference_result = result_response['json_result']
>>> auuid2_cm = inference_result['cm_dict']
>>> quuid = quuid_list[0]
>>> class_dict = auuid2_cm[str(quuid)]
>>> # Get information in frontend
>>> ibs = ibeis.opendb('testdb1')
>>> cm = ibeis.ChipMatch.from_dict(class_dict, ibs=ibs)
>>> cm.print_rawinfostr()
>>> # Make the dictionary a bit more managable
>>> cm.compress_top_feature_matches(num=1)
>>> cm.print_rawinfostr()
>>> class_dict = cm.to_dict(ibs=ibs)
>>> cm_dict = class_dict
>>> # Package for review ( CANT CALL DIRECTLY BECAUSE OF OUT OF CONTEXT )
>>> review_pair = {'annot_uuid_1': quuid, 'annot_uuid_2': duuid_list[1]}
>>> x = review_graph_match_html(ibs, review_pair, cm_dict,
>>> query_config_dict, _internal_state=None,
>>> callback_url=None)
>>> ut.quit_if_noshow()
>>> import plottool as pt
>>> ut.render_html(html_str)
>>> ut.show_if_requested()
"""
from ibeis.algo.hots import chip_match
# from ibeis.algo.hots.query_request import QueryRequest
proot = query_config_dict.get('pipeline_root', 'vsmany')
proot = query_config_dict.get('proot', proot)
if proot.upper() == 'BC_DTW':
cls = chip_match.AnnotMatch # ibs.depc_annot.requestclass_dict['BC_DTW']
else:
cls = chip_match.ChipMatch
view_orientation = view_orientation.lower()
if view_orientation not in ['vertical', 'horizontal']:
view_orientation = 'horizontal'
# unpack info
try:
annot_uuid_1 = review_pair['annot_uuid_1']
annot_uuid_2 = review_pair['annot_uuid_2']
except Exception:
#??? HACK
# FIXME:
print('[!!!!] review_pair = %r' % (review_pair, ))
review_pair = review_pair[0]
annot_uuid_1 = review_pair['annot_uuid_1']
annot_uuid_2 = review_pair['annot_uuid_2']
aid_1 = ibs.get_annot_aids_from_uuid(annot_uuid_1)
aid_2 = ibs.get_annot_aids_from_uuid(annot_uuid_2)
cm = cls.from_dict(cm_dict, ibs=ibs)
qreq_ = ibs.new_query_request([aid_1], [aid_2], cfgdict=query_config_dict)
# Get score
idx = cm.daid2_idx[aid_2]
match_score = cm.name_score_list[idx]
#match_score = cm.aid2_score[aid_2]
image_matches = make_review_image(aid_2,
cm,
qreq_,
view_orientation=view_orientation)
image_matches_src = appf.embed_image_html(image_matches)
image_clean = make_review_image(aid_2,
cm,
qreq_,
view_orientation=view_orientation,
draw_matches=False)
image_clean_src = appf.embed_image_html(image_clean)
if False:
from ibeis.web import apis_query
root_path = dirname(abspath(apis_query.__file__))
else:
root_path = dirname(abspath(__file__))
css_file_list = [
['css', 'style.css'],
['include', 'bootstrap', 'css', 'bootstrap.css'],
]
json_file_list = [
['javascript', 'script.js'],
['include', 'bootstrap', 'js', 'bootstrap.js'],
]
if include_jquery:
json_file_list = [
['javascript', 'jquery.min.js'],
] + json_file_list
EMBEDDED_CSS = ''
EMBEDDED_JAVASCRIPT = ''
css_template_fmtstr = '<style type="text/css" ia-dependency="css">%s</style>\n'
json_template_fmtstr = '<script type="text/javascript" ia-dependency="javascript">%s</script>\n'
for css_file in css_file_list:
css_filepath_list = [root_path, 'static'] + css_file
with open(join(*css_filepath_list)) as css_file:
EMBEDDED_CSS += css_template_fmtstr % (css_file.read(), )
for json_file in json_file_list:
json_filepath_list = [root_path, 'static'] + json_file
with open(join(*json_filepath_list)) as json_file:
EMBEDDED_JAVASCRIPT += json_template_fmtstr % (json_file.read(), )
return appf.template('turk',
'query_match_insert',
match_score=match_score,
image_clean_src=image_clean_src,
image_matches_src=image_matches_src,
annot_uuid_1=str(annot_uuid_1),
annot_uuid_2=str(annot_uuid_2),
view_orientation=view_orientation,
callback_url=callback_url,
callback_method=callback_method,
EMBEDDED_CSS=EMBEDDED_CSS,
EMBEDDED_JAVASCRIPT=EMBEDDED_JAVASCRIPT)
def view():
def _date_list(gid_list):
unixtime_list = ibs.get_image_unixtime(gid_list)
datetime_list = [
ut.unixtime_to_datetimestr(unixtime)
if unixtime is not None else
'UNKNOWN'
for unixtime in unixtime_list
]
datetime_split_list = [ datetime.split(' ') for datetime in datetime_list ]
date_list = [ datetime_split[0] if len(datetime_split) == 2 else 'UNKNOWN' for datetime_split in datetime_split_list ]
return date_list
ibs = current_app.ibs
aid_list = ibs.filter_aids_count()
gid_list = ibs.get_annot_gids(aid_list)
nid_list = ibs.get_annot_name_rowids(aid_list)
date_list = _date_list(gid_list)
gid_list_unique = list(set(gid_list))
date_list_unique = _date_list(gid_list_unique)
date_taken_dict = {}
for gid, date in zip(gid_list_unique, date_list_unique):
if date not in date_taken_dict:
date_taken_dict[date] = [0, 0]
date_taken_dict[date][1] += 1
gid_list_all = ibs.get_valid_gids()
date_list_all = _date_list(gid_list_all)
for gid, date in zip(gid_list_all, date_list_all):
if date in date_taken_dict:
date_taken_dict[date][0] += 1
value = 0
label_list = []
value_list = []
index_list = []
seen_set = set()
current_seen_set = set()
previous_seen_set = set()
last_date = None
date_seen_dict = {}
for index, (aid, nid, date) in enumerate(zip(aid_list, nid_list, date_list)):
index_list.append(index + 1)
# Add to counters
if date not in date_seen_dict:
date_seen_dict[date] = [0, 0, 0, 0]
date_seen_dict[date][0] += 1
if nid not in current_seen_set:
current_seen_set.add(nid)
date_seen_dict[date][1] += 1
if nid in previous_seen_set:
date_seen_dict[date][3] += 1
if nid not in seen_set:
seen_set.add(nid)
value += 1
date_seen_dict[date][2] += 1
# Add to register
value_list.append(value)
# Reset step (per day)
if date != last_date and date != 'UNKNOWN':
last_date = date
previous_seen_set = set(current_seen_set)
current_seen_set = set()
label_list.append(date)
else:
label_list.append('')
# def optimization1(x, a, b, c):
# return a * np.log(b * x) + c
# def optimization2(x, a, b, c):
# return a * np.sqrt(x) ** b + c
# def optimization3(x, a, b, c):
# return 1.0 / (a * np.exp(-b * x) + c)
# def process(func, opts, domain, zero_index, zero_value):
# values = func(domain, *opts)
# diff = values[zero_index] - zero_value
# values -= diff
# values[ values < 0.0 ] = 0.0
# values[:zero_index] = 0.0
# values = values.astype(int)
# return list(values)
# optimization_funcs = [
# optimization1,
# optimization2,
# optimization3,
# ]
# # Get data
# x = np.array(index_list)
# y = np.array(value_list)
# # Fit curves
# end = int(len(index_list) * 1.25)
# domain = np.array(range(1, end))
# zero_index = len(value_list) - 1
# zero_value = value_list[zero_index]
# regressed_opts = [ curve_fit(func, x, y)[0] for func in optimization_funcs ]
# prediction_list = [
# process(func, opts, domain, zero_index, zero_value)
# for func, opts in zip(optimization_funcs, regressed_opts)
# ]
# index_list = list(domain)
prediction_list = []
date_seen_dict.pop('UNKNOWN', None)
bar_label_list = sorted(date_seen_dict.keys())
bar_value_list1 = [ date_taken_dict[date][0] for date in bar_label_list ]
bar_value_list2 = [ date_taken_dict[date][1] for date in bar_label_list ]
bar_value_list3 = [ date_seen_dict[date][0] for date in bar_label_list ]
bar_value_list4 = [ date_seen_dict[date][1] for date in bar_label_list ]
bar_value_list5 = [ date_seen_dict[date][2] for date in bar_label_list ]
bar_value_list6 = [ date_seen_dict[date][3] for date in bar_label_list ]
# label_list += ['Models'] + [''] * (len(index_list) - len(label_list) - 1)
# value_list += [0] * (len(index_list) - len(value_list))
# Counts
imgsetid_list = ibs.get_valid_imgsetids()
gid_list = ibs.get_valid_gids()
aid_list = ibs.get_valid_aids()
nid_list = ibs.get_valid_nids()
contrib_list = ibs.get_valid_contrib_rowids()
# nid_list = ibs.get_valid_nids()
aid_list_count = ibs.filter_aids_count()
# gid_list_count = list(set(ibs.get_annot_gids(aid_list_count)))
nid_list_count_dup = ibs.get_annot_name_rowids(aid_list_count)
nid_list_count = list(set(nid_list_count_dup))
# Calculate the Petersen-Lincoln index form the last two days
try:
c1 = bar_value_list4[-2]
c2 = bar_value_list4[-1]
c3 = bar_value_list6[-1]
pl_index = int(math.ceil( (c1 * c2) / c3 ))
pl_error_num = float(c1 * c1 * c2 * (c2 - c3))
pl_error_dom = float(c3 ** 3)
pl_error = int(math.ceil( 1.96 * math.sqrt(pl_error_num / pl_error_dom) ))
except IndexError:
# pl_index = 'Undefined - Zero recaptured (k = 0)'
pl_index = 0
pl_error = 0
except ZeroDivisionError:
# pl_index = 'Undefined - Zero recaptured (k = 0)'
pl_index = 0
pl_error = 0
# Get the markers
gid_list_markers = ibs.get_annot_gids(aid_list_count)
gps_list_markers = map(list, ibs.get_image_gps(gid_list_markers))
gps_list_markers_all = map(list, ibs.get_image_gps(gid_list))
REMOVE_DUP_CODE = True
if not REMOVE_DUP_CODE:
# Get the tracks
nid_track_dict = ut.ddict(list)
for nid, gps in zip(nid_list_count_dup, gps_list_markers):
if gps[0] == -1.0 and gps[1] == -1.0:
continue
nid_track_dict[nid].append(gps)
gps_list_tracks = [ nid_track_dict[nid] for nid in sorted(nid_track_dict.keys()) ]
else:
__nid_list, gps_track_list, aid_track_list = ibs.get_name_gps_tracks(aid_list=aid_list_count)
gps_list_tracks = list(map(lambda x: list(map(list, x)), gps_track_list))
gps_list_markers = [ gps for gps in gps_list_markers if tuple(gps) != (-1, -1, ) ]
gps_list_markers_all = [ gps for gps in gps_list_markers_all if tuple(gps) != (-1, -1, ) ]
gps_list_tracks = [
[ gps for gps in gps_list_track if tuple(gps) != (-1, -1, ) ]
for gps_list_track in gps_list_tracks
]
valid_aids = ibs.get_valid_aids()
valid_gids = ibs.get_valid_gids()
valid_aids_ = ibs.filter_aids_custom(valid_aids)
valid_gids_ = ibs.filter_gids_custom(valid_gids)
used_gids = list(set( ibs.get_annot_gids(valid_aids) ))
used_contrib_tags = list(set( ibs.get_image_contributor_tag(used_gids) ))
# Get Age and sex (By Annot)
# annot_sex_list = ibs.get_annot_sex(valid_aids_)
# annot_age_months_est_min = ibs.get_annot_age_months_est_min(valid_aids_)
# annot_age_months_est_max = ibs.get_annot_age_months_est_max(valid_aids_)
# age_list = [[0, 0, 0], [0, 0, 0], [0, 0, 0]]
# for sex, min_age, max_age in zip(annot_sex_list, annot_age_months_est_min, annot_age_months_est_max):
# if sex not in [0, 1]:
# sex = 2
# # continue
# if (min_age is None or min_age < 12) and max_age < 12:
# age_list[sex][0] += 1
# elif 12 <= min_age and min_age < 36 and 12 <= max_age and max_age < 36:
# age_list[sex][1] += 1
# elif 36 <= min_age and (36 <= max_age or max_age is None):
# age_list[sex][2] += 1
# Get Age and sex (By Name)
name_sex_list = ibs.get_name_sex(nid_list_count)
name_age_months_est_mins_list = ibs.get_name_age_months_est_min(nid_list_count)
name_age_months_est_maxs_list = ibs.get_name_age_months_est_max(nid_list_count)
age_list = [[0, 0, 0], [0, 0, 0], [0, 0, 0]]
age_unreviewed = 0
age_ambiguous = 0
for nid, sex, min_ages, max_ages in zip(nid_list_count, name_sex_list, name_age_months_est_mins_list, name_age_months_est_maxs_list):
if len(set(min_ages)) > 1 or len(set(max_ages)) > 1:
# print('[web] Invalid name %r: Cannot have more than one age' % (nid, ))
age_ambiguous += 1
continue
min_age = None
max_age = None
if len(min_ages) > 0:
min_age = min_ages[0]
if len(max_ages) > 0:
max_age = max_ages[0]
# Histogram
if (min_age is None and max_age is None) or (min_age is -1 and max_age is -1):
# print('[web] Unreviewded name %r: Specify the age for the name' % (nid, ))
age_unreviewed += 1
continue
if sex not in [0, 1]:
sex = 2
# continue
if (min_age is None or min_age < 12) and max_age < 12:
age_list[sex][0] += 1
elif 12 <= min_age and min_age < 36 and 12 <= max_age and max_age < 36:
age_list[sex][1] += 1
elif 36 <= min_age and (36 <= max_age or max_age is None):
age_list[sex][2] += 1
dbinfo_str = dbinfo()
return appf.template('view',
line_index_list=index_list,
line_label_list=label_list,
line_value_list=value_list,
prediction_list=prediction_list,
pl_index=pl_index,
pl_error=pl_error,
gps_list_markers=gps_list_markers,
gps_list_markers_all=gps_list_markers_all,
gps_list_tracks=gps_list_tracks,
bar_label_list=bar_label_list,
bar_value_list1=bar_value_list1,
bar_value_list2=bar_value_list2,
bar_value_list3=bar_value_list3,
bar_value_list4=bar_value_list4,
bar_value_list5=bar_value_list5,
bar_value_list6=bar_value_list6,
age_list=age_list,
age_ambiguous=age_ambiguous,
age_unreviewed=age_unreviewed,
dbinfo_str=dbinfo_str,
imgsetid_list=imgsetid_list,
imgsetid_list_str=','.join(map(str, imgsetid_list)),
num_imgsetids=len(imgsetid_list),
gid_list=gid_list,
gid_list_str=','.join(map(str, gid_list)),
num_gids=len(gid_list),
contrib_list=contrib_list,
contrib_list_str=','.join(map(str, contrib_list)),
num_contribs=len(contrib_list),
gid_list_count=valid_gids_,
gid_list_count_str=','.join(map(str, valid_gids_)),
num_gids_count=len(valid_gids_),
aid_list=aid_list,
aid_list_str=','.join(map(str, aid_list)),
num_aids=len(aid_list),
aid_list_count=valid_aids_,
aid_list_count_str=','.join(map(str, valid_aids_)),
num_aids_count=len(valid_aids_),
nid_list=nid_list,
nid_list_str=','.join(map(str, nid_list)),
num_nids=len(nid_list),
nid_list_count=nid_list_count,
nid_list_count_str=','.join(map(str, nid_list_count)),
num_nids_count=len(nid_list_count),
used_gids=used_gids,
num_used_gids=len(used_gids),
used_contribs=used_contrib_tags,
num_used_contribs=len(used_contrib_tags))
