def _resize_src(image, resize=False, **kwargs):
# Load image
if resize is None:
image_src = appf.embed_image_html(image, target_width=None, target_height=None)
elif resize:
image = appf.resize_via_web_parameters(image)
image_src = appf.embed_image_html(image, target_width=None, target_height=None)
else:
image_src = appf.embed_image_html(image)
return image_src
def experiments_voting_center_src(ibs, aoi=False, **kwargs):
aoi_dict = voting_data(**kwargs)
ibs, team_list = experiments_voting_initialize()
image = np.zeros((100, 100, 1), dtype=np.float32)
for annot_uuid in aoi_dict:
if aoi and not aoi_dict[annot_uuid]:
continue
aid = ibs.get_annot_aids_from_uuid(annot_uuid)
gid = ibs.get_annot_gids(aid)
width, height = ibs.get_image_sizes(gid)
(x, y, w, h) = ibs.get_annot_bboxes(aid)
cx = x + (w / 2.0)
cy = y + (h / 2.0)
cx /= width
cy /= height
cx = int(np.round(cx * 100.0))
cy = int(np.round(cy * 100.0))
if 0 <= cx and cx < 100 and 0 <= cy and cy < 100:
image[cx, cy] += 1.0
maximum = np.max(image)
image /= maximum
image = _normalize_image(image)
# Load image
return maximum, appf.embed_image_html(image, target_width=None)
def part_src(part_rowid, **kwargs):
ibs = current_app.ibs
if 'dim_size' not in kwargs:
kwargs['dim_size'] = max(
int(appf.TARGET_WIDTH),
int(appf.TARGET_HEIGHT),
)
image = ibs.get_part_chips(part_rowid, config2_=kwargs)
image_src = appf.embed_image_html(image, target_height=300)
return image_src
def annotation_src(aid=None, ibs=None, **kwargs):
if ibs is None:
ibs = current_app.ibs
if 'dim_size' not in kwargs:
kwargs['dim_size'] = max(
int(appf.TARGET_WIDTH),
int(appf.TARGET_HEIGHT),
)
image = ibs.get_annot_chips(aid, config2_=kwargs)
# image_src = _resize_src(image, **kwargs)
image_src = appf.embed_image_html(image, target_height=300)
return image_src
def experiments_image_src(tag=None, **kwargs):
tag = tag.strip().split('-')
db = tag[0]
gid = int(tag[1])
ibs = experiment_init_db(db)
config = {
'thumbsize': 800,
}
gpath = ibs.get_image_thumbpath(gid, ensure_paths=True, **config)
# Load image
image = vt.imread(gpath, orient='auto')
image = appf.resize_via_web_parameters(image)
return appf.embed_image_html(image, target_width=None)
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 image_src(gid=None, thumbnail=False, fresh=False, **kwargs):
thumbnail = thumbnail or 'thumbnail' in request.args or 'thumbnail' in request.form
ibs = current_app.ibs
gid = int(gid)
if thumbnail:
gpath = ibs.get_image_thumbpath(gid, ensure_paths=True)
fresh = fresh or 'fresh' in request.args or 'fresh' in request.form
if fresh:
import os
os.remove(gpath)
gpath = ibs.get_image_thumbpath(gid, ensure_paths=True)
else:
gpath = ibs.get_image_paths(gid)
# Load image
image = vt.imread(gpath, orient='auto')
image = appf.resize_via_web_parameters(image)
return appf.embed_image_html(image, target_width=None)
def image_src(gid=None, thumbnail=False, fresh=False, **kwargs):
thumbnail = thumbnail or 'thumbnail' in request.args or 'thumbnail' in request.form
ibs = current_app.ibs
gid = int(gid)
if thumbnail:
gpath = ibs.get_image_thumbpath(gid, ensure_paths=True)
fresh = fresh or 'fresh' in request.args or 'fresh' in request.form
if fresh:
import os
os.remove(gpath)
gpath = ibs.get_image_thumbpath(gid, ensure_paths=True)
else:
gpath = ibs.get_image_paths(gid)
# Load image
image = vt.imread(gpath, orient='auto')
image = appf.resize_via_web_parameters(image)
return appf.embed_image_html(image, target_width=None)
def probchip_src(aid=None, ibs=None, **kwargs):
import cv2
if ibs is None:
ibs = current_app.ibs
if 'dim_size' not in kwargs:
kwargs['dim_size'] = max(
int(appf.TARGET_WIDTH),
int(appf.TARGET_HEIGHT),
)
image_filepath = ibs.get_annot_probchip_fpath(aid, config2_=kwargs)
image = cv2.imread(image_filepath)
x, y, w, h = ibs.get_annot_bboxes(aid)
image = cv2.resize(image, (w, h))
# image_src = _resize_src(image, **kwargs)
image_src = appf.embed_image_html(image, target_height=300)
return image_src
def experiments_voting_area_src(ibs, aoi=False, **kwargs):
aoi_dict = voting_data(**kwargs)
ibs, team_list = experiments_voting_initialize()
image = np.zeros((100, 100, 1), dtype=np.float32)
for annot_uuid in aoi_dict:
if aoi and not aoi_dict[annot_uuid]:
continue
aid = ibs.get_annot_aids_from_uuid(annot_uuid)
gid = ibs.get_annot_gids(aid)
width, height = ibs.get_image_sizes(gid)
(x, y, w, h) = ibs.get_annot_bboxes(aid)
x0 = x
y0 = y
x1 = x + w
y1 = y + h
x0 /= width
y0 /= height
x1 /= width
y1 /= height
x0 = min(max(x0, 0.0), 1.0)
y0 = min(max(y0, 0.0), 1.0)
x1 = min(max(x1, 0.0), 1.0)
y1 = min(max(y1, 0.0), 1.0)
x0 = int(np.around(x0 * 100.0))
y0 = int(np.around(y0 * 100.0))
x1 = int(np.around(x1 * 100.0))
y1 = int(np.around(y1 * 100.0))
image[x0:x1, y0:y1] += 1.0
maximum = np.max(image)
image /= maximum
image = _normalize_image(image)
# Load image
return maximum, appf.embed_image_html(image, target_width=None)
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 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_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 annotation_src(aid=None):
ibs = current_app.ibs
gpath = ibs.get_annot_chip_fpath(aid)
image = vt.imread(gpath)
image_src = appf.embed_image_html(image, target_width=200)
return image_src
def annotation_src(aid=None):
ibs = current_app.ibs
gpath = ibs.get_annot_chip_fpath(aid)
image = vt.imread(gpath)
image_src = appf.embed_image_html(image, target_width=200)
return image_src
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 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 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_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 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)
