wbia.web package¶
Subpackages¶
Submodules¶
wbia.web.apis module¶
Dependencies: flask, tornado
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wbia.web.apis.
annot_src_api
(rowid=None, fresh=False, **kwargs)[source]¶ Returns the image file of annot <aid>
Example
>>> # WEB_DOCTEST >>> from wbia.web.app import * # NOQA >>> import wbia >>> with wbia.opendb_bg_web('testdb1', start_job_queue=False, managed=True) as web_ibs: ... web_ibs.send_wbia_request('/api/annot/src/1/', type_='get') >>> print(resp)
- RESTful:
- Method: GET URL: /api/annot/src/<rowid>/
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wbia.web.apis.
background_src_api
(rowid=None, fresh=False, **kwargs)[source]¶ Returns the image file of annot <aid>
Example
>>> # WEB_DOCTEST >>> from wbia.web.app import * # NOQA >>> import wbia >>> with wbia.opendb_bg_web('testdb1', start_job_queue=False, managed=True) as web_ibs: ... web_ibs.send_wbia_request('/api/background/src/1/', type_='get') >>> print(resp)
- RESTful:
- Method: GET URL: /api/annot/src/<rowid>/
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wbia.web.apis.
hello_world
(*args, **kwargs)[source]¶ - CommandLine:
- python -m wbia.web.apis –exec-hello_world:0 python -m wbia.web.apis –exec-hello_world:1
Example
>>> # xdoctest: +REQUIRES(--web) >>> from wbia.web.app import * # NOQA >>> import wbia >>> web_ibs = wbia.opendb_bg_web(browser=True, start_job_queue=False, url_suffix='/api/test/helloworld/?test0=0') # start_job_queue=False) >>> print('web_ibs = %r' % (web_ibs,)) >>> print('Server will run until control c') >>> web_ibs.terminate2()
- Example1:
>>> # xdoctest: +REQUIRES(--web) >>> from wbia.web.app import * # NOQA >>> import wbia >>> import requests >>> import wbia >>> with wbia.opendb_bg_web('testdb1', start_job_queue=False, managed=True) as web_ibs: ... web_port = ibs.get_web_port_via_scan() ... if web_port is None: ... raise ValueError('IA web server is not running on any expected port') ... domain = 'http://<IP_ADDRESS>:%s' % (web_port, ) ... url = domain + '/api/test/helloworld/?test0=0' ... payload = { ... 'test1' : 'test1', ... 'test2' : None, # NOTICE test2 DOES NOT SHOW UP ... } ... resp = requests.post(url, data=payload) ... print(resp)
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wbia.web.apis.
image_conv_feature_api
(rowid=None, model='resnet50', **kwargs)[source]¶ - RESTful:
- Method: GET URL: /api/image/feature/json/<uuid>/
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wbia.web.apis.
image_conv_feature_api_json
(uuid=None, model='resnet50', **kwargs)[source]¶ - RESTful:
- Method: GET URL: /api/image/feature/json/<uuid>/
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wbia.web.apis.
image_src_api
(rowid=None, thumbnail=False, fresh=False, **kwargs)[source]¶ Returns the image file of image <gid>
Example
>>> # xdoctest: +REQUIRES(--web) >>> from wbia.web.app import * # NOQA >>> import wbia >>> with wbia.opendb_bg_web('testdb1', start_job_queue=False, managed=True) as web_ibs: ... web_ibs.send_wbia_request('/api/image/src/1/', type_='get') >>> print(resp)
- RESTful:
- Method: GET URL: /api/image/src/<rowid>/
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wbia.web.apis.
image_src_api_json
(uuid=None, **kwargs)[source]¶ Returns the image file of image <gid>
Example
>>> # xdoctest: +REQUIRES(--web) >>> from wbia.web.app import * # NOQA >>> import wbia >>> with wbia.opendb_bg_web('testdb1', start_job_queue=False, managed=True) as web_ibs: ... web_ibs.send_wbia_request('/api/image/src/json/1/', type_='get') >>> print(resp)
- RESTful:
- Method: GET URL: /api/image/src/<gid>/
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wbia.web.apis.
image_upload
(cleanup=True, **kwargs)[source]¶ Returns the gid for an uploaded image.
Parameters: - image (image binary) – the POST variable containing the binary (multi-form) image data
- **kwargs – Arbitrary keyword arguments; the kwargs are passed down to the add_images function
Returns: - gid corresponding to the image submitted.
lexigraphical order.
Return type: gid (rowids)
- RESTful:
- Method: POST URL: /api/upload/image/
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wbia.web.apis.
image_upload_zip
(**kwargs)[source]¶ Returns the gid_list for image files submitted in a ZIP archive. The image archive should be flat (no folders will be scanned for images) and must be smaller than 100 MB. The archive can submit multiple images, ideally in JPEG format to save space. Duplicate image uploads will result in the duplicate images receiving the same gid based on the hashed pixel values.
Parameters: - image_zip_archive (binary) – the POST variable containing the binary (multi-form) image archive data
- **kwargs – Arbitrary keyword arguments; the kwargs are passed down to the add_images function
Returns: - the list of gids corresponding to the images
submitted. The gids correspond to the image names sorted in lexigraphical order.
Return type: gid_list (list if rowids)
- RESTful:
- Method: POST URL: /api/image/zip
wbia.web.apis_detect module¶
Dependencies: flask, tornado.
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wbia.web.apis_detect.
aoi_cnn
(ibs, aid_list, testing=False, model_tag='candidacy', **kwargs)[source]¶
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wbia.web.apis_detect.
commit_detection_results
(ibs, gid_list, results_list, note=None, update_json_log=True)[source]¶
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wbia.web.apis_detect.
commit_detection_results_filtered
(ibs, gid_list, filter_species_list=None, filter_viewpoint_list=None, note=None, update_json_log=True)[source]¶
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wbia.web.apis_detect.
commit_localization_results
(ibs, gid_list, results_list, note=None, labeler_algo='pipeline', labeler_model_tag=None, use_labeler_species=False, orienter_algo=None, orienter_model_tag=None, update_json_log=True, **kwargs)[source]¶
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wbia.web.apis_detect.
detect_cnn_json
(ibs, gid_list, detect_func, config={}, **kwargs)[source]¶ Run animal detection in each image and returns json-ready formatted results, does not return annotations.
Parameters: gid_list (list) – list of image ids to run detection on Returns: dict of detection results (not annotations) Return type: results_dict (list) - CommandLine:
- python -m wbia.web.apis_detect –test-detect_cnn_yolo_json
Example
>>> # DISABLE_DOCTEST >>> from wbia.web.apis_detect import * # NOQA >>> import wbia >>> ibs = wbia.opendb('testdb1') >>> gid_list = ibs.get_valid_gids()[0:2] >>> results_dict = ibs.detect_cnn_yolo_json(gid_list) >>> print(results_dict)
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wbia.web.apis_detect.
detect_cnn_json_wrapper
(ibs, image_uuid_list, detect_func, **kwargs)[source]¶ Detect with CNN (general).
- REST:
- Method: GET URL: /api/detect/cnn/yolo/json/
Parameters: image_uuid_list (list) – list of image uuids to detect on.
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wbia.web.apis_detect.
detect_cnn_lightnet
(ibs, gid_list, model_tag=None, commit=True, testing=False, **kwargs)[source]¶ Run animal detection in each image. Adds annotations to the database as they are found.
Parameters: gid_list (list) – list of image ids to run detection on Returns: - list of lists of annotation ids detected in each
- image
Return type: aids_list (list) - CommandLine:
- python -m wbia.web.apis_detect –test-detect_cnn_lightnet –show
- RESTful:
- Method: PUT, GET URL: /api/detect/cnn/lightnet/
Example
>>> # DISABLE_DOCTEST >>> from wbia.web.apis_detect import * # NOQA >>> import wbia >>> ibs = wbia.opendb('PZ_MTEST') >>> gid_list = ibs.get_valid_gids()[:5] >>> aids_list = ibs.detect_cnn_lightnet(gid_list) >>> if ut.show_was_requested(): >>> import wbia.plottool as pt >>> from wbia.viz import viz_image >>> for fnum, gid in enumerate(gid_list): >>> viz_image.show_image(ibs, gid, fnum=fnum) >>> pt.show_if_requested() >>> # Remove newly detected annotations >>> ibs.delete_annots(ut.flatten(aids_list))
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wbia.web.apis_detect.
detect_cnn_yolo
(ibs, gid_list, model_tag=None, commit=True, testing=False, **kwargs)[source]¶ Run animal detection in each image. Adds annotations to the database as they are found.
Parameters: gid_list (list) – list of image ids to run detection on Returns: - list of lists of annotation ids detected in each
- image
Return type: aids_list (list) - CommandLine:
- python -m wbia.web.apis_detect –test-detect_cnn_yolo –show
- RESTful:
- Method: PUT, GET URL: /api/detect/cnn/yolo/
Example
>>> # DISABLE_DOCTEST >>> from wbia.web.apis_detect import * # NOQA >>> import wbia >>> ibs = wbia.opendb('PZ_MTEST') >>> gid_list = ibs.get_valid_gids()[:5] >>> aids_list = ibs.detect_cnn_yolo(gid_list) >>> if ut.show_was_requested(): >>> import wbia.plottool as pt >>> from wbia.viz import viz_image >>> for fnum, gid in enumerate(gid_list): >>> viz_image.show_image(ibs, gid, fnum=fnum) >>> pt.show_if_requested() >>> # Remove newly detected annotations >>> ibs.delete_annots(ut.flatten(aids_list))
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wbia.web.apis_detect.
detect_cnn_yolo_exists
(ibs, gid_list, testing=False)[source]¶ Check to see if a detection has been completed.
Parameters: gid_list (list) – list of image ids to run detection on Returns: - list of flags for if the detection has been run on
- the image
Return type: flag_list (list) - CommandLine:
- python -m wbia.web.apis_detect –test-detect_cnn_yolo_exists
- RESTful:
- Method: GET URL: /api/detect/cnn/yolo/exists/
Example
>>> # DISABLE_DOCTEST >>> from wbia.web.apis_detect import * # NOQA >>> import wbia >>> ibs = wbia.opendb('PZ_MTEST') >>> gid_list = ibs.get_valid_gids() >>> depc = ibs.depc_image >>> aids_list = ibs.detect_cnn_yolo(gid_list[:3], testing=True) >>> result = ibs.detect_cnn_yolo_exists(gid_list[:5]) >>> ibs.delete_annots(ut.flatten(aids_list)) >>> print(result) [True, True, True, False, False]
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wbia.web.apis_detect.
detect_random_forest
(ibs, gid_list, species, commit=True, **kwargs)[source]¶ Run animal detection in each image. Adds annotations to the database as they are found.
Parameters: - gid_list (list) – list of image ids to run detection on
- species (str) – string text of the species to identify
Returns: - list of lists of annotation ids detected in each
image
Return type: aids_list (list)
- CommandLine:
- python -m wbia.web.apis_detect –test-detect_random_forest –show
- RESTful:
- Method: PUT, GET URL: /api/detect/randomforest/
Example
>>> # DISABLE_DOCTEST >>> from wbia.web.apis_detect import * # NOQA >>> import wbia >>> ibs = wbia.opendb('testdb1') >>> gid_list = ibs.get_valid_gids()[0:2] >>> species = wbia.const.TEST_SPECIES.ZEB_PLAIN >>> aids_list = ibs.detect_random_forest(gid_list, species) >>> # Visualize results >>> if ut.show_was_requested(): >>> import wbia.plottool as pt >>> from wbia.viz import viz_image >>> for fnum, gid in enumerate(gid_list): >>> viz_image.show_image(ibs, gid, fnum=fnum) >>> pt.show_if_requested() >>> # Remove newly detected annotations >>> ibs.delete_annots(ut.flatten(aids_list))
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wbia.web.apis_detect.
detect_ws_injury
(ibs, gid_list)[source]¶ Classify if a whale shark is injured.
Parameters: gid_list (list) – list of image ids to run classification on Returns: - predictions is list of strings representing a possible tag.
- confidences is a list of floats of correspoinding cofidence to the prediction
Return type: result_list (dictionary)
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wbia.web.apis_detect.
get_species_with_detectors
(ibs)[source]¶ Get valid species for detection.
- RESTful:
- Method: GET URL: /api/detect/species/
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wbia.web.apis_detect.
get_working_species
(ibs)[source]¶ Get working species for detection.
- RESTful:
- Method: GET URL: /api/detect/species/working/
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wbia.web.apis_detect.
has_species_detector
(ibs, species_text)[source]¶ TODO: extend to use non-constant species.
- RESTful:
- Method: GET URL: /api/detect/species/enabled/
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wbia.web.apis_detect.
labeler_cnn
(ibs, aid_list, testing=False, algo='pipeline', model_tag='candidacy', **kwargs)[source]¶
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wbia.web.apis_detect.
models_cnn
(ibs, config_dict, parse_classes_func, parse_line_func, check_hash=False, hidden_models=[], **kwargs)[source]¶
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wbia.web.apis_detect.
models_cnn_lightnet
(ibs, **kwargs)[source]¶ Return the models (and their labels) for the YOLO CNN detector
- RESTful:
- Method: PUT, GET URL: /api/labels/cnn/lightnet/
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wbia.web.apis_detect.
models_cnn_yolo
(ibs, **kwargs)[source]¶ Return the models (and their labels) for the YOLO CNN detector
- RESTful:
- Method: PUT, GET URL: /api/labels/cnn/yolo/
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wbia.web.apis_detect.
process_detection_html
(ibs, **kwargs)[source]¶ Process the return from the detection review interface. Pass the POST result from the detection review form directly to this function unmodified.
Returns: Same format as func:start_detect_image Return type: detection results (dict) - RESTful:
- Method: POST URL: /api/review/detect/cnn/yolo/
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wbia.web.apis_detect.
review_detection_html
(ibs, image_uuid, result_list, callback_url, callback_method='POST', include_jquery=False, config=None)[source]¶ Return the detection review interface for a particular image UUID and a list of results for that image.
Parameters: - 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: json response with the detection web interface in html
Return type: template (html)
- RESTful:
- Method: GET URL: /api/review/detect/cnn/yolo/
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wbia.web.apis_detect.
review_detection_test
(image_uuid=None, result_list=None, callback_url=None, callback_method='POST', **kwargs)[source]¶
wbia.web.apis_engine module¶
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wbia.web.apis_engine.
ensure_simple_server
(port=5832)[source]¶ - CommandLine:
- python -m wbia.web.apis_engine –exec-ensure_simple_server python -m utool.util_web –exec-start_simple_webserver
Example
>>> # DISABLE_DOCTEST >>> from wbia.web.apis_engine import * # NOQA >>> result = ensure_simple_server() >>> print(result)
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wbia.web.apis_engine.
start_detect_image_lightnet
(ibs, image_uuid_list, callback_url=None, callback_method=None, **kwargs)[source]¶ - REST:
- Method: GET/api/engine/detect/cnn/lightnet/ URL:
Parameters: - image_uuid_list (list) – list of image uuids to detect on.
- callback_url (url) – url that will be called when detection succeeds or fails
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wbia.web.apis_engine.
start_detect_image_yolo
(ibs, image_uuid_list, callback_url=None, callback_method=None, **kwargs)[source]¶ - REST:
- Method: GET URL: /api/engine/detect/cnn/yolo/
Parameters: - image_uuid_list (list) – list of image uuids to detect on.
- callback_url (url) – url that will be called when detection succeeds or fails
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wbia.web.apis_engine.
start_flukebook_sync
(ibs, **kwargs)[source]¶ - REST:
- Method: GET URL: /api/engine/flukebook/sync/
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wbia.web.apis_engine.
start_identify_annots
(ibs, qannot_uuid_list, dannot_uuid_list=None, pipecfg={}, callback_url=None, callback_method=None)[source]¶ - REST:
- Method: GET URL: /api/engine/query/annot/rowid/
Parameters: - qannot_uuid_list (list) – specifies the query annotations to identify.
- dannot_uuid_list (list) – specifies the annotations that the algorithm is allowed to use for identification. If not specified all annotations are used. (default=None)
- pipecfg (dict) – dictionary of pipeline configuration arguments (default=None)
- CommandLine:
# Run as main process python -m wbia.web.apis_engine –exec-start_identify_annots:0 # Run using server process python -m wbia.web.apis_engine –exec-start_identify_annots:1
# Split into multiple processes python -m wbia.web.apis_engine –main –bg python -m wbia.web.apis_engine –exec-start_identify_annots:1 –fg
python -m wbia.web.apis_engine –exec-start_identify_annots:1 –domain http://<IP_ADDRESS>
python -m wbia.web.apis_engine –exec-start_identify_annots:1 –duuids=[] python -m wbia.web.apis_engine –exec-start_identify_annots:1 –domain http://<IP_ADDRESS> –duuids=03a17411-c226-c960-d180-9fafef88c880
Example
>>> # DISABLE_DOCTEST >>> from wbia.web.apis_engine import * # NOQA >>> from wbia.web import apis_engine >>> import wbia >>> ibs, qaids, daids = wbia.testdata_expanded_aids( >>> defaultdb='PZ_MTEST', a=['default:qsize=2,dsize=10']) >>> qannot_uuid_list = ibs.get_annot_uuids(qaids) >>> dannot_uuid_list = ibs.get_annot_uuids(daids) >>> pipecfg = {} >>> ibs.initialize_job_manager() >>> jobid = ibs.start_identify_annots(qannot_uuid_list, dannot_uuid_list, pipecfg) >>> result = ibs.wait_for_job_result(jobid, timeout=None, freq=2) >>> print(result) >>> import utool as ut >>> #print(ut.to_json(result)) >>> ibs.close_job_manager()
Example
>>> # DISABLE_DOCTEST >>> from wbia.web.apis_engine import * # NOQA >>> import wbia >>> ibs = wbia.opendb('testdb1') # , domain='http://<IP_ADDRESS>') >>> aids = ibs.get_valid_aids()[0:2] >>> qaids = aids[0:1] >>> daids = aids >>> query_config_dict = { >>> #'pipeline_root' : 'BC_DTW' >>> } >>> qreq_ = ibs.new_query_request(qaids, daids, cfgdict=query_config_dict) >>> cm_list = qreq_.execute()
Example
>>> # xdoctest: +REQUIRES(--web) >>> from wbia.web.apis_engine import * # NOQA >>> import wbia >>> with wbia.opendb_bg_web('testdb1', managed=True) as web_ibs: # , domain='http://<IP_ADDRESS>') ... aids = web_ibs.send_wbia_request('/api/annot/', 'get')[0:2] ... uuid_list = web_ibs.send_wbia_request('/api/annot/uuid/', type_='get', aid_list=aids) ... quuid_list = ut.get_argval('--quuids', type_=list, default=uuid_list) ... duuid_list = ut.get_argval('--duuids', type_=list, default=uuid_list) ... data = dict( ... qannot_uuid_list=quuid_list, dannot_uuid_list=duuid_list, ... pipecfg={}, ... callback_url='http://<IP_ADDRESS>:5832' ... ) ... # Start callback server ... bgserver = ensure_simple_server() ... # -- ... jobid = web_ibs.send_wbia_request('/api/engine/query/annot/rowid/', **data) ... status_response = web_ibs.wait_for_results(jobid, delays=[1, 5, 30]) ... print('status_response = %s' % (status_response,)) ... result_response = web_ibs.read_engine_results(jobid) ... print('result_response = %s' % (result_response,)) ... cm_dict = result_response['json_result'][0] ... print('Finished test') ... bgserver.terminate2()
- Ignore:
- qaids = daids = ibs.get_valid_aids() jobid = ibs.start_identify_annots(**payload)
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wbia.web.apis_engine.
start_identify_annots_query
(ibs, query_annot_uuid_list=None, query_annot_name_list=None, database_annot_uuid_list=None, database_annot_name_list=None, matching_state_list=[], query_config_dict={}, echo_query_params=True, include_qaid_in_daids=True, callback_url=None, callback_method=None)[source]¶ - REST:
- Method: GET URL: /api/engine/query/graph/
Parameters: - query_annot_uuid_list (list) – specifies the query annotations to identify.
- query_annot_name_list (list) – specifies the query annotation names
- database_annot_uuid_list (list) – specifies the annotations that the algorithm is allowed to use for identification. If not specified all annotations are used. (default=None)
- database_annot_name_list (list) – specifies the database annotation names (default=None)
- matching_state_list (list of tuple) – the list of matching state 3-tuples corresponding to the query_annot_uuid_list (default=None)
- query_config_dict (dict) – dictionary of algorithmic configuration arguments. (default=None)
- echo_query_params (bool) – flag for if to return the original query parameters with the result
- CommandLine:
- # Normal mode python -m wbia.web.apis_engine start_identify_annots_query # Split mode wbia –web python -m wbia.web.apis_engine start_identify_annots_query –show –domain=localhost
Example
>>> # DISABLE_DOCTEST >>> from wbia.web.apis_engine import * # NOQA >>> import wbia >>> #domain = 'localhost' >>> domain = None >>> with wbia.opendb_bg_web('testdb1', domain=domain, managed=True) as web_ibs: # , domain='http://<IP_ADDRESS>') ... aids = web_ibs.send_wbia_request('/api/annot/', 'get')[0:3] ... uuid_list = web_ibs.send_wbia_request('/api/annot/uuid/', type_='get', aid_list=aids) ... quuid_list = ut.get_argval('--quuids', type_=list, default=uuid_list)[0:1] ... duuid_list = ut.get_argval('--duuids', type_=list, default=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_wbia_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) ... print('result_response = %s' % (ut.repr3(result_response),)) ... inference_result = result_response['json_result'] ... if isinstance(inference_result, six.string_types): ... print(inference_result) ... cm_dict = inference_result['cm_dict'] ... quuid = quuid_list[0] ... cm = cm_dict[str(quuid)]
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wbia.web.apis_engine.
start_identify_annots_query_complete
(ibs, annot_uuid_list=None, annot_name_list=None, matching_state_list=[], query_config_dict={}, k=5, echo_query_params=True, callback_url=None, callback_method=None)[source]¶ - REST:
- Method: GET URL: /api/engine/query/complete/
Parameters: - annot_uuid_list (list) – specifies the query annotations to identify.
- annot_name_list (list) – specifies the query annotation names
- matching_state_list (list of tuple) – the list of matching state 3-tuples corresponding to the query_annot_uuid_list (default=None)
- query_config_dict (dict) – dictionary of algorithmic configuration arguments. (default=None)
- echo_query_params (bool) – flag for if to return the original query parameters with the result
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wbia.web.apis_engine.
start_labeler_cnn
(ibs, annot_uuid_list, callback_url=None, callback_method=None, **kwargs)[source]¶
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wbia.web.apis_engine.
start_predict_ws_injury_interim_svm
(ibs, annot_uuid_list, callback_url=None, callback_method=None, **kwargs)[source]¶ - REST:
- Method: POST URL: /api/engine/classify/whaleshark/injury/
Parameters: - annot_uuid_list (list) – list of annot uuids to detect on.
- callback_url (url) – url that will be called when detection succeeds or fails
- CommandLine:
- python -m wbia.web.apis_engine start_predict_ws_injury_interim_svm
Example
>>> # DISABLE_DOCTEST >>> from wbia.web.apis_engine import * # NOQA >>> from wbia.web import apis_engine >>> import wbia >>> ibs, qaids, daids = wbia.testdata_expanded_aids( >>> defaultdb='WS_ALL', a=['default:qsize=2,dsize=10']) >>> annot_uuid_list = ibs.get_annot_uuids(qaids) >>> ibs.initialize_job_manager() >>> jobid = ibs.start_predict_ws_injury_interim_svm(annot_uuid_list) >>> result = ibs.wait_for_job_result(jobid, timeout=None, freq=2) >>> print(result) >>> import utool as ut >>> #print(ut.to_json(result)) >>> ibs.close_job_manager()
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wbia.web.apis_engine.
start_review_query_chips_best
(ibs, annot_uuid, callback_url=None, callback_method=None, **kwargs)[source]¶
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wbia.web.apis_engine.
start_web_query_all
(ibs)[source]¶ - REST:
- Method: GET URL: /api/engine/query/web/
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wbia.web.apis_engine.
start_wic_image
(ibs, image_uuid_list, callback_url=None, callback_method=None, **kwargs)[source]¶ - REST:
- Method: GET URL: /api/engine/wic/cnn/
Parameters: - image_uuid_list (list) – list of image uuids to detect on.
- callback_url (url) – url that will be called when detection succeeds or fails
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wbia.web.apis_engine.
web_check_uuids
(ibs, image_uuid_list=[], qannot_uuid_list=[], dannot_uuid_list=[])[source]¶ Parameters: - ibs (wbia.IBEISController) – image analysis api
- image_uuid_list (list) – (default = [])
- qannot_uuid_list (list) – (default = [])
- dannot_uuid_list (list) – (default = [])
- CommandLine:
- python -m wbia.web.apis_engine –exec-web_check_uuids –show
Example
>>> # DISABLE_DOCTEST >>> from wbia.web.apis_engine import * # NOQA >>> import wbia >>> ibs = wbia.opendb(defaultdb='testdb1') >>> image_uuid_list = [] >>> qannot_uuid_list = ibs.get_annot_uuids([1, 1, 2, 3, 2, 4]) >>> dannot_uuid_list = ibs.get_annot_uuids([1, 2, 3]) >>> try: >>> web_check_uuids(ibs, image_uuid_list, qannot_uuid_list, >>> dannot_uuid_list) >>> except controller_inject.WebDuplicateUUIDException: >>> pass >>> else: >>> raise AssertionError('Should have gotten WebDuplicateUUIDException') >>> try: >>> web_check_uuids(ibs, [1, 2, 3], qannot_uuid_list, >>> dannot_uuid_list) >>> except controller_inject.WebMissingUUIDException as ex: >>> pass >>> else: >>> raise AssertionError('Should have gotten WebMissingUUIDException') >>> print('Successfully reported errors')
wbia.web.apis_json module¶
Dependencies: flask, tornado
-
wbia.web.apis_json.
add_annotmatch_json
(ibs, match_annot_uuid1_list, match_annot_uuid2_list, match_evidence_decision_list=None, match_meta_decision_list=None, match_confidence_list=None, match_user_list=None, match_tag_list=None, match_modified_list=None, match_count_list=None)[source]¶
-
wbia.web.apis_json.
add_annots_json
(ibs, image_uuid_list, annot_bbox_list, annot_theta_list, annot_viewpoint_list=None, annot_quality_list=None, annot_species_list=None, annot_multiple_list=None, annot_interest_list=None, annot_name_list=None, **kwargs)[source]¶ - REST:
- Method: POST URL: /api/annot/json/
- Ignore:
- sudo pip install boto
Parameters: - image_uuid_list (list of str) – list of image UUIDs to be used in IBEIS IA
- annot_bbox_list (list of 4-tuple) – list of bounding box coordinates encoded as a 4-tuple of the values (xtl, ytl, width, height) where xtl is the ‘top left corner, x value’ and ytl is the ‘top left corner, y value’.
- annot_theta_list (list of float) – list of radian rotation around center. Defaults to 0.0 (no rotation).
- annot_species_list (list of str) – list of species for the annotation, if known. If the list is partially known, use None (null in JSON) for unknown entries.
- annot_name_list (list of str) – list of names for the annotation, if known. If the list is partially known, use None (null in JSON) for unknown entries.
- **kwargs – key-value pairs passed to the ibs.add_annots() function.
- CommandLine:
- python -m wbia.web.app –test-add_annots_json
Example
>>> # DISABLE_DOCTEST >>> import wbia >>> from wbia.control.IBEISControl import * # NOQA >>> web_instance = wbia.opendb(db='testdb1') >>> _payload = { >>> 'image_uuid_list': [ >>> uuid.UUID('7fea8101-7dec-44e3-bf5d-b8287fd231e2'), >>> uuid.UUID('c081119a-e08e-4863-a710-3210171d27d6'), >>> ], >>> 'annot_uuid_list': [ >>> uuid.UUID('fe1547c5-1425-4757-9b8f-b2b4a47f552d'), >>> uuid.UUID('86d3959f-7167-4822-b99f-42d453a50745'), >>> ], >>> 'annot_bbox_list': [ >>> [0, 0, 1992, 1328], >>> [0, 0, 1194, 401], >>> ], >>> } >>> aid_list = wbia.web.app.add_annots_json(web_instance, **_payload) >>> print(aid_list) >>> print(web_instance.get_annot_image_uuids(aid_list)) >>> print(web_instance.get_annot_uuids(aid_list)) >>> print(web_instance.get_annot_bboxes(aid_list))
-
wbia.web.apis_json.
add_images_json
(ibs, image_uri_list, image_unixtime_list=None, image_gps_lat_list=None, image_gps_lon_list=None, **kwargs)[source]¶ - REST:
- Method: POST URL: /api/image/json/
- Ignore:
- sudo pip install boto
Parameters: - image_uri_list (list) –
list of string image uris, most likely HTTP(S) or S3 encoded URLs. Alternatively, this can be a list of dictionaries (JSON objects) that specify AWS S3 stored assets. An example below:
- image_uri_list = [
- ‘http://domain.com/example/asset1.png’,
‘/home/example/Desktop/example/asset2.jpg’,
‘s3://s3.amazon.com/example-bucket-2/asset1-in-bucket-2.tif’,
{’bucket’ : ‘example-bucket-1’, ‘key’ : ‘example/asset1.png’, ‘auth_domain’ : None, # Uses <IP_ADDRESS> ‘auth_access_id’ : None, # Uses system default ‘auth_secret_key’ : None, # Uses system default
}, {
’bucket’ : ‘example-bucket-1’, ‘key’ : ‘example/asset2.jpg’, # if unspecified, auth uses <IP_ADDRESS> and system defaults}, {
’bucket’ : ‘example-bucket-2’, ‘key’ : ‘example/asset1-in-bucket-2.tif’, ‘auth_domain’ : ‘s3.amazon.com’, ‘auth_access_id’ : ‘____________________’, ‘auth_secret_key’ : ‘________________________________________’,},
]
Note that you cannot specify AWS authentication access ids or secret keys using string uri’s. For specific authentication methods, please use the latter list of dictionaries.
- image_time_posix_list (list of int) – list of image’s POSIX timestamps
- image_gps_lat_list (list of float) – list of image’s GPS latitude values
- image_gps_lon_list (list of float) – list of image’s GPS longitude values
- **kwargs – key-value pairs passed to the ibs.add_images() function.
- CommandLine:
python -m wbia.web.apis_json –test-add_images_json
,”bucket”:”flukebook-prod-asset-store”,”key”:”“
Example
>>> # xdoctest: +REQUIRES(--web) >>> from wbia.control.IBEISControl import * # NOQA >>> import wbia >>> import uuid >>> web_instance = wbia.opendb(db='testdb1') >>> _payload = { >>> 'image_uri_list': [ >>> 'https://upload.wikimedia.org/wikipedia/commons/4/49/Zebra_running_Ngorongoro.jpg', >>> { >>> 'bucket' : 'test-asset-store', >>> 'key' : 'caribwhale/20130903-JAC-0002.JPG', >>> }, >>> { >>> 'bucket' : 'flukebook-prod-asset-store', >>> 'key' : '3/a/3a76b0e8-1c64-403d-ace1-679cf2f081c0/f2.jpg', >>> }, >>> ], >>> 'image_uuid_list': [ >>> uuid.uuid4(), >>> uuid.uuid4(), >>> uuid.uuid4(), >>> ], >>> 'image_width_list': [ >>> 1992, >>> 1194, >>> 500, >>> ], >>> 'image_height_list': [ >>> 1328, >>> 401, >>> 500, >>> ], >>> } >>> gid_list = wbia.web.apis_json.add_images_json(web_instance, **_payload) >>> print(gid_list) >>> print(web_instance.get_image_uuids(gid_list)) >>> print(web_instance.get_image_uris(gid_list)) >>> print(web_instance.get_image_paths(gid_list)) >>> print(web_instance.get_image_uris_original(gid_list))
-
wbia.web.apis_json.
add_imagesets_json
(ibs, imageset_text_list, imageset_uuid_list=None, config_rowid_list=None, imageset_notes_list=None, imageset_occurence_flag_list=None)[source]¶ Adds a list of imagesets.
Parameters: - imagesettext_list (list) –
- imageset_uuid_list (list) –
- config_rowid_list (list) –
- notes_list (list) –
Returns: added imageset uuids
Return type: imageset_uuid_list (list)
- RESTful:
- Method: POST URL: /api/imageset/json/
-
wbia.web.apis_json.
add_names_json
(ibs, name_text_list, name_uuid_list=None, name_note_list=None)[source]¶
-
wbia.web.apis_json.
add_parts_json
(ibs, annot_uuid_list, part_bbox_list, part_theta_list, **kwargs)[source]¶ - REST:
- Method: POST URL: /api/part/json/
- Ignore:
- sudo pip install boto
Parameters: - annot_uuid_list (list of str) – list of annot UUIDs to be used in IBEIS IA
- part_uuid_list (list of str) – list of annotations UUIDs to be used in IBEIS IA
- part_bbox_list (list of 4-tuple) – list of bounding box coordinates encoded as a 4-tuple of the values (xtl, ytl, width, height) where xtl is the ‘top left corner, x value’ and ytl is the ‘top left corner, y value’.
- part_theta_list (list of float) – list of radian rotation around center. Defaults to 0.0 (no rotation).
- **kwargs – key-value pairs passed to the ibs.add_annots() function.
-
wbia.web.apis_json.
add_review_json
(ibs, review_annot_uuid1_list, review_annot_uuid2_list, review_evidence_decision_list, review_meta_decision_list=None, review_uuid_list=None, review_user_list=None, review_user_confidence_list=None, review_tags_list=None, review_client_start_time_posix=None, review_client_end_time_posix=None, review_server_start_time_posix=None, review_server_end_time_posix=None)[source]¶
-
wbia.web.apis_json.
add_species_json
(ibs, species_nice_list, species_text_list=None, species_code_list=None, species_uuid_list=None, species_note_list=None, skip_cleaning=False)[source]¶
-
wbia.web.apis_json.
chaos_imageset
(ibs)[source]¶ - REST:
- Method: POST URL: /api/image/json/
Parameters: image_uuid_list (list of str) – list of image UUIDs to be delete from IBEIS
-
wbia.web.apis_json.
delete_annots_json
(ibs, annot_uuid_list)[source]¶ - REST:
- Method: DELETE URL: /api/annot/json/
Parameters: annot_uuid_list (list of str) – list of annot UUIDs to be delete from IBEIS
-
wbia.web.apis_json.
delete_images_json
(ibs, image_uuid_list)[source]¶ - REST:
- Method: DELETE URL: /api/image/json/
Parameters: image_uuid_list (list of str) – list of image UUIDs to be delete from IBEIS
-
wbia.web.apis_json.
delete_imageset_json
(ibs, imageset_uuid_list)[source]¶ - REST:
- Method: DELETE URL: /api/imageset/json/
Parameters: imageset_uuid_list (list of str) – list of imageset UUIDs to be delete from IBEIS
-
wbia.web.apis_json.
delete_name_json
(ibs, name_uuid_list)[source]¶ - REST:
- Method: DELETE URL: /api/name/json/
Parameters: name_uuid_list (list of str) – list of name UUIDs to be delete from IBEIS
-
wbia.web.apis_json.
delete_species_json
(ibs, species_uuid_list)[source]¶ - REST:
- Method: DELETE URL: /api/species/json/
Parameters: species_uuid_list (list of str) – list of species UUIDs to be delete from IBEIS
-
wbia.web.apis_json.
get_imageset_imgsetids_from_text_json
(ibs, imageset_text_list, **kwargs)[source]¶
-
wbia.web.apis_json.
image_base64_api_json
(ibs, uuid=None, thumbnail=False, fresh=False, **kwargs)[source]¶
-
wbia.web.apis_json.
set_annot_name_texts_json
(ibs, annot_uuid_list, name_text_list, **kwargs)[source]¶
-
wbia.web.apis_json.
set_annot_quality_texts_json
(ibs, annot_uuid_list, quality_text_list, **kwargs)[source]¶
-
wbia.web.apis_json.
set_annot_species_json
(ibs, annot_uuid_list, species_text_list, **kwargs)[source]¶
-
wbia.web.apis_json.
set_annot_tag_text_json
(ibs, annot_uuid_list, annot_tags_list, **kwargs)[source]¶
-
wbia.web.apis_json.
set_annot_viewpoints_json
(ibs, annot_uuid_list, viewpoint_list, **kwargs)[source]¶
-
wbia.web.apis_json.
set_annotmatch_confidence_json
(ibs, match_annot_uuid1_list, match_annot_uuid2_list, match_confidence_list, **kwargs)[source]¶
-
wbia.web.apis_json.
set_annotmatch_count_json
(ibs, match_annot_uuid1_list, match_annot_uuid2_list, match_count_list, **kwargs)[source]¶
-
wbia.web.apis_json.
set_annotmatch_evidence_decision_json
(ibs, match_annot_uuid1_list, match_annot_uuid2_list, match_decision_list, **kwargs)[source]¶
-
wbia.web.apis_json.
set_annotmatch_meat_decision_json
(ibs, match_annot_uuid1_list, match_annot_uuid2_list, match_decision_list, **kwargs)[source]¶
-
wbia.web.apis_json.
set_annotmatch_posixtime_modified_json
(ibs, match_annot_uuid1_list, match_annot_uuid2_list, match_modified_list, **kwargs)[source]¶
-
wbia.web.apis_json.
set_annotmatch_reviewer_json
(ibs, match_annot_uuid1_list, match_annot_uuid2_list, match_user_list, **kwargs)[source]¶
-
wbia.web.apis_json.
set_annotmatch_tag_text_json
(ibs, match_annot_uuid1_list, match_annot_uuid2_list, match_tags_list, **kwargs)[source]¶
-
wbia.web.apis_json.
set_exemplars_from_quality_and_viewpoint_json
(ibs, annot_uuid_list, annot_name_list, **kwargs)[source]¶
wbia.web.apis_microsoft module¶
Dependencies: flask, tornado.
-
wbia.web.apis_microsoft.
microsoft_annotation_add
(ibs, image, bbox, theta=None, species=None, viewpoint=None, name=None, *args, **kwargs)[source]¶ Add an Annotation to the system and return the UUID — parameters: - name: image
in: body description: A Image model for which the annotation will be added as a child required: true schema:
$ref: “#/definitions/Image”name: bbox in: formData description: a 4-tuple of coordinates that defines a rectangle in the format (x-axis top left corner, y-axis top left corner, width, height) in pixels. These values are expected to be bounded by the size of the parent image. required: true type: array items:
type: number format: float
name: theta in: formData description: a rotation around the center of the annotation, in radians. Default is 0.0 required: false type: number
name: species in: formData description: a user-defined string to specify the species of the annotation (e.g. ‘zebra’ or ‘massai_giraffe’). This value is used to filter matches and run-time models for ID. Default is None. required: false type: string
name: viewpoint in: formData description: a user-defined string to specify the viewpoint of the annotation (e.g. ‘right’ or ‘front_left’). This value is used to filter matches and run-time models for ID. Default is None. required: false type: string
name: name in: body description: a Name model to assign to the created Annotation required: false schema:
$ref: “#/definitions/Name”
consumes: - multipart/form-data produces: - application/json responses:
- 200:
description: Returns an Annotations model with a UUID schema:
$ref: “#/definitions/Annotation”- 400:
- description: Invalid input parameter
-
wbia.web.apis_microsoft.
microsoft_annotation_delete
(ibs, annotation)[source]¶ Delete an Annotation model from the database. — parameters: - name: annotation
in: body description: A Annotation model to delete required: true schema:
$ref: “#/definitions/Annotation”produces: - application/json responses:
- 200:
description: Returns a success status flag (True if deleted) schema:
type: boolean- 400:
- description: Invalid input parameter
-
wbia.web.apis_microsoft.
microsoft_annotation_metadata
(ibs, annotation, *args, **kwargs)[source]¶ Get the Annotation’s metadata (bbox, theta, species, viewpoint).
We provide no setters for Annotations. If you wish to modify the metadata on an Annotation model, please delete it and add it again. Modifying the metadata on an annotation may alter its derived UUID — parameters: - name: annotation
in: body description: A Annotation model required: true schema:
$ref: “#/definitions/Image”produces: - application/json responses:
- 200:
description: Returns a JSON object with the metadata for the annotation schema:
type: object- 400:
- description: Invalid input parameter
-
wbia.web.apis_microsoft.
microsoft_core_specification_swagger
(*args, **kwargs)[source]¶ Returns the API specification in the Swagger 2.0 (OpenAPI) JSON format.
The Swagger API specification (https://github.com/OAI/OpenAPI-Specification/blob/master/versions/2.0.md) provides a standardized method to export REST API documentation and examples. Our documentation is built on-demand with the help of the Python package flask-swagger (https://github.com/gangverk/flask-swagger).
The API specification includes GET, POST, PUT, and DELETE methods and Model definitions. — definitions: - schema:
id: Image description: An Image is a semantic construct that represents an uploaded image. Images can be uploaded for later processing or be used immediately for detection. Object detection will create Annotation models, which have a required Image parent. An Image can have multiple detections (i.e. Annotation models). required:
- uuid
- properties:
- uuid:
- description: a deterministically-derived UUID based on the image pixels, which can be used to identify duplicate Images. type: string format: uuid
- schema:
id: Annotation description: An Annotation is a semantic construct that represents a committed detection, with a bounding box and species classification assigned as stored attributes. An Annotation is required to have a parent Image. All bounding box coordinates are relative to the size of the parent Image that was uploaded. required:
- uuid
- bbox
- properties:
- uuid:
description: a deterministically-derived UUID based on the parent image’s UUID and the bounding box coordinate (xtl, ytl, width, height) and orientation (theta), which can be used to identify duplicate Annotations. type: string format: uuid
- bbox:
description: a 4-tuple of coordinates that defines a rectangle in the format (x-axis top left corner, y-axis top left corner, width, height) in pixels. These values are expected to be bounded by the size of the parent image. type: array
- theta:
description: a rotation around the center of the annotation, in radians type: number
- species:
description: a user-defined string to specify the species of the annotation (e.g. ‘zebra’ or ‘massai_giraffe’). This value is used to filter matches and run-time models for ID. type: string
- viewpoint:
description: a user-defined string to specify the viewpoint of the annotation (e.g. ‘right’ or ‘front_left’). This value is used to filter matches and run-time models for ID. type: string
- name:
description: the name of the individual format: uuid type: string
- schema:
id: Detection description: A Detection is a semantic constrict that represents an un-committed detection. A Detection can be committed to an Annotation to be stored permanently on the parent Image. required:
- _image
- score
- bbox
- xtl
- ytl
- xbr
- ybr
- width
- height
- theta
- label
- properties:
- image:
description: The Image that this Detection was found in $ref: “#/definitions/Image”
- score:
description: The detection’s classification score type: integer format: int32
- bbox:
description: The bounding box for this annotation, represented in the format (xtl, ytl, width, height) type: array items:
type: number format: float
- xtl:
description: The pixel coordinate for the top-left corner along the x-axis (xtl = x-axis top left) for the bounding box type: integer format: int32
- ytl:
description: The pixel coordinate for the top-left corner along the y-axis (ytl = y-axis top left) for the bounding box type: integer format: int32
- xbr:
description: The pixel coordinate for the bottom-right corner along the x-axis (ytl = x-axis bottom right) for the bounding box type: integer format: int32
- ybr:
description: The pixel coordinate for the bottom-right corner along the y-axis (ytl = y-axis bottom right) for the bounding box type: integer format: int32
- width:
description: The pixel width for the bounding box type: integer format: int32
- height:
description: The pixel height for the bounding box type: integer format: int32
- theta:
description: The rotation of the bounding box around its center, represented in radians type: number format: float
- species:
description: The semantic species classification (class label) of the bounding box type: string
- viewpoint:
description: The semantic viewpoint classification (class label) of the bounding box type: string
- schema:
id: Name description: A Name is the identification label for a group of Annotations required:
- uuid
- alias
- properties:
- uuid:
description: a deterministically-derived UUID based on the image pixels, which can be used to identify duplicate Images. type: string format: uuid
- alias:
description: a string alias for this individual, helpful for user-facing interfaces type: string
- schema:
id: Task description: A Task is a semantic construct that represents a background task (i.e. detection) in an asynchronous call. A Task has an optional callback on completion or the status (and result) can be checked via the API required:
- uuid
- properties:
- uuid:
description: a random UUID to identify a given asynchronous call, used to check status and results of a background task type: string format: uuid
produces: - application/json responses:
- 200:
- description: Returns the Swagger 2.0 JSON format
-
wbia.web.apis_microsoft.
microsoft_core_status
(ibs, *args, **kwargs)[source]¶ Returns the health status of the API back-end; optionally can be used as a service heatbeat. — produces: - application/json responses:
- 200:
description: Returns the status of the server schema:
type: object properties:
- status:
- type: string enum: - healthy - warning - critical
- examples:
- application/json:
- status: healthy
-
wbia.web.apis_microsoft.
microsoft_detect
(ibs, images, model, score_threshold=0.0, use_nms=True, nms_threshold=0.4, __jobid__=None, *args, **kwargs)[source]¶
-
wbia.web.apis_microsoft.
microsoft_detect_batch
(ibs, images, model, score_threshold=0.0, use_nms=True, nms_threshold=0.4, asynchronous=True, callback_url=None, callback_method=None, *args, **kwargs)[source]¶ The asynchronous variant of POST ‘detect’ that takes in a list of Image models and returns a task ID.
It may be more ideal for a particular application to upload many images at one time and perform processing later in a large batch. This type of batch detection is certainly much more efficient because the detection on GPU can process more images in parallel. However, if you intend to run the detector on an upload as quickly as possible, please use the on-demand, non-batched API. — parameters: - name: images
in: body description: A JSON list of Image models for which detection will be processed. required: true type: array- name: model in: formData description: The model name to use with detection. Available models can be queried using the API $PREFIX/detect/model/ required: true type: string
- name: score_threshold in: formData description: A score threshold to filter the detection results. Default 0.0. Must be in range [0, 1], inclusive required: false type: number format: float
- name: use_nms in: formData description: Flag to turn on/off Non-Maximum Suppression (NMS). Default True. required: false type: boolean
- name: nms_threshold in: formData description: A Intersection-over-Union (IoU) threshold to suppress detection results with NMS. Default 0.0. Must be in range [0, 1], inclusive required: false type: number format: float
- name: callback_url in: formData description: The URL of where to callback when the task is completed, must be a fully resolvable address and accessible. The callback will include a ‘body’ parameter called task which will provide a Task model required: false type: string format: url
- name: callback_method in: formData description: The HTTP method for which to make the callback. Default POST. required: false type: string enum: - get - post - put - delete
consumes: - multipart/form-data responses:
- 200:
description: Returns a Task model schema:
$ref: “#/definitions/Task”- 400:
- description: Invalid input parameter
- x-task-response:
description: The task returns an array of arrays of Detection models, in parallel lists with the provided Image models schema:
type: array items:
- schema:
type: array items:
$ref: “#/definitions/Detection”
-
wbia.web.apis_microsoft.
microsoft_detect_input_validation
(model, score_threshold, use_nms, nms_threshold)[source]¶
-
wbia.web.apis_microsoft.
microsoft_detect_model
(ibs, *args, **kwargs)[source]¶ Returns the available models and their supported species for detection. These models are pre-trained and are downloaded as needed on first start-up.
The current model names that are supported for demoing and their species: - hammerhead -> shark_hammerhead - jaguar -> jaguar - lynx -> lynx - manta -> manta_ray_giant - seaturtle -> fish, ignore, person, turtle_green, turtle_green+head, turtle_hawksbill, turtle_hawksbill+head - ggr2 -> giraffe, zebra — produces: - application/json responses:
- 200:
description: Returns an object of models (keys) and their supported species (values) listed as an array of strings. schema:
type: object
-
wbia.web.apis_microsoft.
microsoft_detect_upload
(ibs, model, score_threshold=0.0, use_nms=True, nms_threshold=0.4, *args, **kwargs)[source]¶ Returns the detection results for an uploaded image and a provided model configuration.
The uploaded image will be used to perform object detection for a given model. A detection will return at most 845 Detections (13 x 13 grid of 5 bounding boxes based on the YOLO v2 by Redmon et al.). — parameters: - name: image
in: formData description: The image for which detection will be processed. required: true type: file enum: - image/png - image/jpg - image/tiff- name: model in: formData description: The model name to use with detection. Available models can be queried using the API $PREFIX/detect/model/ required: true type: string
- name: score_threshold in: formData description: A score threshold to filter the detection results. Default 0.0. Must be in range [0, 1], inclusive required: false type: number format: float
- name: use_nms in: formData description: Flag to turn on/off Non-Maximum Suppression (NMS). Default True. Must be in range [0, 1], inclusive required: false type: boolean
- name: nms_threshold in: formData description: A Intersection-over-Union (IoU) threshold to suppress detection results with NMS. Default 0.0. Must be in range [0, 1], inclusive required: false type: number format: float
consumes: - multipart/form-data responses:
- 200:
description: Returns an array of Detection models on the uploaded image schema:
type: array items:
$ref: “#/definitions/Detection”- 400:
- description: Invalid input parameter
- 415:
- description: Unsupported media type in the request body. Currently only image/png, image/jpeg, image/tiff are supported.
-
wbia.web.apis_microsoft.
microsoft_get_test_data
(ibs, dataset)[source]¶ Return test data annotations. — parameters: - name: dataset
in: body description: A name for the dataset. Must be one of ‘zebra’, ‘dolphin’, ‘humpback’ required: true type: stringproduces: - application/json responses:
- 200:
- description: A JSON object with the query and database Annotation model lists
- 400:
- description: Invalid input parameter
-
wbia.web.apis_microsoft.
microsoft_identify
(ibs, query_annotation, database_annotations, algorithm, callback_url=None, callback_method=None, *args, **kwargs)[source]¶ The asynchronous call to identify a list of pre-uploaded query annotations against a database of annotations. Returns a task ID.
This call expects all of the data has been already uploaded and the appropriate metadata set. For example, this call expects any known ground-truth nameshave already been made and assigned to the appropriate Annotation models. Conversely, we also provide a method for marking individual reviews between two Annotation models. The decision between a given pair should always be one of [‘match’, ‘nomatch’, or ‘notcomp’]. The full docuemntation for this call can be seen in the POST ‘decision’ API. — parameters: - name: query_annotation
in: formData description: An Annotation models to query for an identification required: true schema:
$ref: “#/definitions/Annotation”name: database_annotations in: formData description: A JSON list of Annotation models to compare the query Annotation against required: true type: array items:
$ref: “#/definitions/Annotation”
name: algorithm in: formData description: The algorithm you with to run ID with. Must be one of “HotSpotter”, “CurvRank”, “Finfindr”, or “Deepsense” required: true type: string
name: callback_url in: formData description: The URL of where to callback when the task is completed, must be a fully resolvable address and accessible. The callback will include a ‘body’ parameter called task which will provide a Task model required: false type: string format: url
name: callback_method in: formData description: The HTTP method for which to make the callback. Default POST. required: false type: string enum: - get - post - put - delete
consumes: - multipart/form-data responses:
- 200:
description: Returns a Task model schema:
$ref: “#/definitions/Task”- 400:
- description: Invalid input parameter
-
wbia.web.apis_microsoft.
microsoft_identify_visualize
(reference, query_annotation, database_annotation, version='heatmask')[source]¶ Visualize the results of matching, precomputed and rendered to disk for easy look-up.
— parameters: - name: reference
in: formData description: A reference string to refer to the rendered match results on disk for a given identification Task required: true type: stringname: query_annotation in: formData description: The query Annotation model required: true schema:
$ref: “#/definitions/Annotation”
name: database_annotation in: formData description: The database Annotation model required: true schema:
$ref: “#/definitions/Annotation”
name: version in: formData description: The version of the visualization. Must be one of “heatmask” or “original”. Defaults to “heatmask” required: false type: string
consumes: - multipart/form-data responses:
- 200:
description: Returns a Task model schema:
$ref: “#/definitions/Task”- 400:
- description: Invalid input parameter
-
wbia.web.apis_microsoft.
microsoft_image_annotations
(ibs, image, *args, **kwargs)[source]¶ Get the Annotation models for a given Image model — parameters: - name: image
in: body description: A Image model to use for querying associated Annotation UUIDs required: true schema:
$ref: “#/definitions/Image”produces: - application/json responses:
- 200:
description: Returns a (possibly empty) list of Annotations models schema:
type: array items:
$ref: “#/definitions/Annotation”- 400:
- description: Invalid input parameter
-
wbia.web.apis_microsoft.
microsoft_image_delete
(ibs, image)[source]¶ Delete an Image model from the database.
Also deletes any derived Detection models or any added Annotations for the deleted Image model. — parameters: - name: image
in: body description: A Image model to delete required: true schema:
$ref: “#/definitions/Image”produces: - application/json responses:
- 200:
description: Returns a success status flag (True if deleted) schema:
type: boolean- 400:
- description: Invalid input parameter
-
wbia.web.apis_microsoft.
microsoft_image_upload
(ibs, *args, **kwargs)[source]¶ Upload an Image to the system and return the UUID — parameters: - name: image
in: formData description: The image to upload. required: true type: file enum: - image/png - image/jpg - image/tiffconsumes: - multipart/form-data produces: - application/json responses:
- 200:
description: Returns an Image model with a UUID schema:
$ref: “#/definitions/Image”- 400:
- description: Invalid input parameter
- 415:
- description: Unsupported media type in the request body. Currently only image/png, image/jpeg, image/tiff are supported.
-
wbia.web.apis_microsoft.
microsoft_name_add
(ibs, alias, *args, **kwargs)[source]¶ Add a Name to the system and return the UUID — parameters: - name: alias
in: formData description: a user-defined string to specify an alias for this name, useful for forward facing interfaces required: false type: stringconsumes: - multipart/form-data produces: - application/json responses:
- 200:
description: Returns an Name model with a UUID schema:
$ref: “#/definitions/Name”- 400:
- description: Invalid input parameter
-
wbia.web.apis_microsoft.
microsoft_name_annotations
(ibs, name, *args, **kwargs)[source]¶ Get the Annotation models for a given Name model — parameters: - name: name
in: body description: A Name model to use for querying associated Annotation UUIDs required: true schema:
$ref: “#/definitions/Name”produces: - application/json responses:
- 200:
description: Returns a (possibly empty) list of Annotations models schema:
type: array items:
$ref: “#/definitions/Annotation”- 400:
- description: Invalid input parameter
-
wbia.web.apis_microsoft.
microsoft_name_delete
(ibs, name)[source]¶ Delete a Name model from the database.
This operation DOES NOT delete any associated Annotation models with this Name, it simply disassociates them. — parameters: - name: name
in: body description: A Name model to delete required: true schema:
$ref: “#/definitions/Name”produces: - application/json responses:
- 200:
description: Returns a success status flag (True if deleted) schema:
type: boolean- 400:
- description: Invalid input parameter
-
wbia.web.apis_microsoft.
microsoft_task_result
(ibs, task)[source]¶ Retrieve the result of a completed asynchronous Task — parameters: - name: task
in: body description: A Task model required: true schema:
$ref: “#/definitions/Task”- responses:
- 200:
- description: Returns the result of the provided Task
- 400:
- description: Invalid input parameter
-
wbia.web.apis_microsoft.
microsoft_task_status
(ibs, task)[source]¶ Check the status of an asynchronous Task.
A Task is an asynchronous task that was launched as a background process with an optional callback. The status of a given Task with a UUID can be checked with this call. The status of the call depends on where in the execution queue the Task is currently, which will be processed in a first-come-first-serve list and only one Task at a time to present atomicity of the API.
The status can be one of the following: - received -> The Task request was received but has not passed any input validation. - accepted -> The Task request has passed basic input validation and will be queued soon for execution. - queued -> The Task is queued in the execution list and will be processed in order and one at a time. - working -> The Task is being processed, awaiting completed results or an error exception - publishing -> The Task is publishing the results of the background API call. - completed -> One of two end states: the Task is complete, completed results available for downloading with the REST API. - exception -> One of two end states: the Task has encountered an error, an error message can be received using the results REST API. - unknown -> The Task you asked for is not known, indicating that the either UUID is not recognized (i.e. a Task with that ID was never currently created) or the server has been restarted.
Important: when the API server is restarted, all queued and running background Tasks are killed and all Task requests and cached results are deleted. — parameters: - name: task
in: body description: A Task model required: true schema:
$ref: “#/definitions/Task”- responses:
- 200:
description: Returns the status of the provided Task schema:
type: object properties:
- status:
- type: string enum: - received - accepted - queued - working - publishing - completed - exception - unknown
- 400:
- description: Invalid input parameter
wbia.web.apis_query module¶
Dependencies: flask, tornado
- SeeAlso:
- routes.turk_identification
-
wbia.web.apis_query.
ensure_review_image
(ibs, aid, cm, qreq_, view_orientation='vertical', draw_matches=True, draw_heatmask=False, verbose=False)[source]¶ ” Create the review image for a pair of annotations
- CommandLine:
- python -m wbia.web.apis_query ensure_review_image –show
Example
>>> # SCRIPT >>> from wbia.web.apis_query import * # NOQA >>> import wbia >>> cm, qreq_ = wbia.testdata_cm('PZ_MTEST', a='default:dindex=0:10,qindex=0:1') >>> ibs = qreq_.ibs >>> aid = cm.get_top_aids()[0] >>> tt = ut.tic('make image') >>> image, _ = ensure_review_image(ibs, aid, cm, qreq_) >>> ut.toc(tt) >>> ut.quit_if_noshow() >>> print('image.shape = %r' % (image.shape,)) >>> print('image.dtype = %r' % (image.dtype,)) >>> ut.print_object_size(image) >>> import wbia.plottool as pt >>> pt.imshow(image) >>> ut.show_if_requested()
-
wbia.web.apis_query.
ensure_review_image_v2
(ibs, match, draw_matches=False, draw_heatmask=False, view_orientation='vertical', overlay=True)[source]¶
-
wbia.web.apis_query.
get_recognition_query_aids
(ibs, is_known, species=None)[source]¶ DEPCIRATE
- RESTful:
- Method: GET URL: /api/query/annot/rowid/
-
wbia.web.apis_query.
process_graph_match_html
(ibs, **kwargs)[source]¶ - RESTful:
- Method: POST URL: /api/review/query/graph/
-
wbia.web.apis_query.
query_chips
(ibs, qaid_list=None, daid_list=None, cfgdict=None, use_cache=None, use_bigcache=None, qreq_=None, return_request=False, verbose=False, save_qcache=None, prog_hook=None, return_cm_dict=False, return_cm_simple_dict=False)[source]¶ Submits a query request to the hotspotter recognition pipeline. Returns a list of QueryResult objects.
Parameters: - qaid_list (list) – a list of annotation ids to be submitted as queries
- daid_list (list) – a list of annotation ids used as the database that will be searched
- cfgdict (dict) – dictionary of configuration options used to create a new QueryRequest if not already specified
- use_cache (bool) – turns on/off chip match cache (default: True)
- use_bigcache (bool) – turns one/off chunked chip match cache (default: True)
- qreq (QueryRequest) – optional, a QueryRequest object that overrides all previous settings
- return_request (bool) – returns the request which will be created if one is not already specified
- verbose (bool) – default=False, turns on verbose printing
Returns: - a list of ChipMatch objects containing the matching
annotations, scores, and feature matches
Return type: list
- Returns(2):
- tuple: (cm_list, qreq_) - a list of query results and optionally the
- QueryRequest object used
- RESTful:
- Method: PUT URL: /api/query/chip/
- CommandLine:
python -m wbia.web.apis_query –test-query_chips
# Test speed of single query python -m wbia –tf IBEISController.query_chips –db PZ_Master1
-a default:qindex=0:1,dindex=0:500 –nocache-hs- python -m wbia –tf IBEISController.query_chips –db PZ_Master1
- -a default:qindex=0:1,dindex=0:3000 –nocache-hs
python -m wbia.web.apis_query –test-query_chips:1 –show python -m wbia.web.apis_query –test-query_chips:2 –show
Example
>>> # SLOW_DOCTEST >>> # xdoctest: +SKIP >>> from wbia.control.IBEISControl import * # NOQA >>> import wbia >>> qreq_ = wbia.testdata_qreq_() >>> ibs = qreq_.ibs >>> cm_list = qreq_.execute() >>> cm = cm_list[0] >>> ut.quit_if_noshow() >>> cm.ishow_analysis(qreq_) >>> ut.show_if_requested()
Example
>>> # SLOW_DOCTEST >>> # xdoctest: +SKIP >>> import wbia >>> from wbia.control.IBEISControl import * # NOQA >>> qaid_list = [1] >>> daid_list = [1, 2, 3, 4, 5] >>> ibs = wbia.opendb_test(db='testdb1') >>> qreq_ = ibs.new_query_request(qaid_list, daid_list) >>> cm = ibs.query_chips(qaid_list, daid_list, use_cache=False, qreq_=qreq_)[0] >>> ut.quit_if_noshow() >>> cm.ishow_analysis(qreq_) >>> ut.show_if_requested()
- Example1:
>>> # SLOW_DOCTEST >>> # xdoctest: +SKIP >>> import wbia >>> from wbia.control.IBEISControl import * # NOQA >>> qaid_list = [1] >>> daid_list = [1, 2, 3, 4, 5] >>> ibs = wbia.opendb_test(db='testdb1') >>> cfgdict = {'pipeline_root':'BC_DTW'} >>> qreq_ = ibs.new_query_request(qaid_list, daid_list, cfgdict=cfgdict, verbose=True) >>> cm = ibs.query_chips(qreq_=qreq_)[0] >>> ut.quit_if_noshow() >>> cm.ishow_analysis(qreq_) >>> ut.show_if_requested()
-
wbia.web.apis_query.
query_chips_dict
(ibs, *args, **kwargs)[source]¶ Runs query_chips, but returns a json compatible dictionary
- RESTful:
- Method: GET URL: /api/query/chip/dict/
-
wbia.web.apis_query.
query_chips_graph
(ibs, qaid_list, daid_list, user_feedback=None, query_config_dict={}, echo_query_params=True, cache_images=True, n=16, view_orientation='horizontal', return_summary=True, **kwargs)[source]¶
-
wbia.web.apis_query.
query_chips_graph_complete
(ibs, aid_list, query_config_dict={}, k=5, **kwargs)[source]¶
-
wbia.web.apis_query.
query_chips_graph_match_thumb
(extern_reference, query_annot_uuid, database_annot_uuid, version)[source]¶
-
wbia.web.apis_query.
query_chips_graph_v2
(ibs, annot_uuid_list=None, query_config_dict={}, review_callback_url=None, review_callback_method='POST', finished_callback_url=None, finished_callback_method='POST', creation_imageset_rowid_list=None, **kwargs)[source]¶ - CommandLine:
python -m wbia.web.apis_query –test-query_chips_graph_v2:0
python -m wbia reset_mtest_graph
python -m wbia –db PZ_MTEST –web –browser –url=/turk/identification/hardcase/ python -m wbia –db PZ_MTEST –web –browser –url=/turk/identification/graph/
Example
>>> # xdoctest: +REQUIRES(--web) >>> from wbia.web.apis_query import * >>> import wbia >>> # Open local instance >>> ibs = wbia.opendb('PZ_MTEST') >>> uuid_list = ibs.annots().uuids[0:10] >>> # Start up the web instance >>> web_ibs = wbia.opendb_bg_web(db='PZ_MTEST', web=True, browser=False) >>> data = dict(annot_uuid_list=uuid_list) >>> resp = web_ibs.send_wbia_request('/api/query/graph/v2/', **data) >>> print('resp = %r' % (resp,)) >>> #cmdict_list = json_dict['response'] >>> #assert 'score_list' in cmdict_list[0]
Example
>>> # DEBUG_SCRIPT >>> from wbia.web.apis_query import * >>> # Hack a flask context >>> current_app = ut.DynStruct() >>> current_app.GRAPH_CLIENT_DICT = {} >>> old = query_chips_graph_v2.__globals__.get('current_app', None) >>> query_chips_graph_v2.__globals__['current_app'] = current_app >>> import wbia >>> ibs = wbia.opendb('PZ_MTEST') >>> #ut.exec_funckw(query_chips_graph_v2, globals()) >>> # Run function in main process >>> query_chips_graph_v2(ibs) >>> # Reset context >>> query_chips_graph_v2.__globals__['current_app'] = old
-
wbia.web.apis_query.
query_chips_simple_dict
(ibs, *args, **kwargs)[source]¶ Runs query_chips, but returns a json compatible dictionary
Parameters: as query_chips (same) – - RESTful:
- Method: GET URL: /api/query/chip/dict/simple/
- SeeAlso:
- query_chips
- CommandLine:
python -m wbia.web.apis_query –test-query_chips_simple_dict:0 python -m wbia.web.apis_query –test-query_chips_simple_dict:1
python -m wbia.web.apis_query –test-query_chips_simple_dict:0 –humpbacks
Example
>>> # xdoctest: +REQUIRES(--web) >>> from wbia.control.IBEISControl import * # NOQA >>> import wbia >>> ibs = wbia.opendb(defaultdb='testdb1') >>> #qaid = ibs.get_valid_aids()[0:3] >>> qaids = ibs.get_valid_aids() >>> daids = ibs.get_valid_aids() >>> dict_list = ibs.query_chips_simple_dict(qaids, daids) >>> qgids = ibs.get_annot_image_rowids(qaids) >>> qnids = ibs.get_annot_name_rowids(qaids) >>> for dict_, qgid, qnid in list(zip(dict_list, qgids, qnids)): >>> dict_['qgid'] = qgid >>> dict_['qnid'] = qnid >>> dict_['dgid_list'] = ibs.get_annot_image_rowids(dict_['daid_list']) >>> dict_['dnid_list'] = ibs.get_annot_name_rowids(dict_['daid_list']) >>> dict_['dgname_list'] = ibs.get_image_gnames(dict_['dgid_list']) >>> dict_['qgname'] = ibs.get_image_gnames(dict_['qgid']) >>> result = ut.repr2(dict_list, nl=2, precision=2, hack_liststr=True) >>> result = result.replace('u\'', '"').replace('\'', '"') >>> print(result)
Example
>>> # xdoctest: +REQUIRES(--web) >>> from wbia.control.IBEISControl import * # NOQA >>> import time >>> import wbia >>> import requests >>> # Start up the web instance >>> web_instance = wbia.opendb_in_background(db='testdb1', web=True, browser=False) >>> time.sleep(10) >>> web_port = ibs.get_web_port_via_scan() >>> if web_port is None: >>> raise ValueError('IA web server is not running on any expected port') >>> baseurl = 'http://<IP_ADDRESS>:%s' % (web_port, ) >>> data = dict(qaid_list=[1]) >>> resp = requests.get(baseurl + '/api/query/chip/simple/dict/', data=data) >>> print(resp) >>> web_instance.terminate() >>> json_dict = resp.json() >>> cmdict_list = json_dict['response'] >>> assert 'score_list' in cmdict_list[0]
-
wbia.web.apis_query.
query_chips_test
(ibs, aid=None, limited=False, census_annotations=True, **kwargs)[source]¶ - CommandLine:
- python -m wbia.web.apis_query query_chips_test
Example
>>> # SLOW_DOCTEST >>> # xdoctest: +SKIP >>> from wbia.control.IBEISControl import * # NOQA >>> import wbia >>> qreq_ = wbia.testdata_qreq_(defaultdb='testdb1') >>> ibs = qreq_.ibs >>> result_dict = ibs.query_chips_test() >>> print(result_dict)
-
wbia.web.apis_query.
review_graph_match_config_v2
(ibs, graph_uuid, aid1=None, aid2=None, view_orientation='vertical', view_version=1)[source]¶
-
wbia.web.apis_query.
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)[source]¶ Parameters: - ibs (wbia.IBEISController) – image analysis api
- review_pair (dict) – pair of annot uuids
- cm_dict (dict) –
- query_config_dict (dict) –
- _internal_state –
- callback_url (str) –
- callback_method (unicode) – (default = u’POST’)
- view_orientation (unicode) – (default = u’vertical’)
- include_jquery (bool) – (default = False)
- CommandLine:
python -m wbia.web.apis_query review_graph_match_html –show
wbia –web python -m wbia.web.apis_query review_graph_match_html –show –domain=localhost
Example
>>> # xdoctest: +REQUIRES(--web) >>> from wbia.web.apis_query import * # NOQA >>> import wbia >>> web_ibs = wbia.opendb_bg_web('testdb1') # , domain='http://<IP_ADDRESS>') >>> aids = web_ibs.send_wbia_request('/api/annot/', 'get')[0:2] >>> uuid_list = web_ibs.send_wbia_request('/api/annot/uuid/', 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_wbia_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 = wbia.opendb('testdb1') >>> #cm = match_obj = wbia.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_wbia_request('/api/review/query/graph/', type_='get', **kw) >>> web_ibs.terminate2() >>> ut.quit_if_noshow() >>> import wbia.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 wbia.web.apis_query import * # NOQA >>> import wbia >>> ut.exec_funckw(review_graph_match_html, globals()) >>> web_ibs = wbia.opendb_bg_web('testdb1') # , domain='http://<IP_ADDRESS>') >>> aids = web_ibs.send_wbia_request('/api/annot/', 'get')[0:2] >>> uuid_list = web_ibs.send_wbia_request('/api/annot/uuid/', 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_wbia_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 = wbia.opendb('testdb1') >>> cm = wbia.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 wbia.plottool as pt >>> ut.render_html(html_str) >>> ut.show_if_requested()
-
wbia.web.apis_query.
review_graph_match_html_v2
(ibs, graph_uuid, callback_url=None, callback_method='POST', view_orientation='vertical', view_version=1, include_jquery=False)[source]¶
wbia.web.apis_sync module¶
Dependencies: flask, tornado
- SeeAlso:
- routes.turk_identification
wbia.web.app module¶
Dependencies: flask, tornado
-
wbia.web.app.
start_from_wbia
(ibs, port=None, browser=None, precache=None, url_suffix=None, start_job_queue=None, start_web_loop=True)[source]¶ Parse command line options and start the server.
- CommandLine:
- python -m wbia –db PZ_MTEST –web python -m wbia –db PZ_MTEST –web –browser
-
wbia.web.app.
start_tornado
(ibs, port=None, browser=None, url_suffix=None, start_web_loop=True, fallback=True)[source]¶ Initialize the web server
-
wbia.web.app.
start_web_annot_groupreview
(ibs, aid_list)[source]¶ Parameters: - ibs (IBEISController) – wbia controller object
- aid_list (list) – list of annotation rowids
- CommandLine:
- python -m wbia.tag_funcs –exec-start_web_annot_groupreview –db PZ_Master1 python -m wbia.tag_funcs –exec-start_web_annot_groupreview –db GZ_Master1 python -m wbia.tag_funcs –exec-start_web_annot_groupreview –db GIRM_Master1
Example
>>> # SCRIPT >>> from wbia.tag_funcs import * # NOQA >>> import wbia >>> #ibs = wbia.opendb(defaultdb='PZ_Master1') >>> ibs = wbia.opendb(defaultdb='GZ_Master1') >>> #aid_list = ibs.get_valid_aids() >>> # ----- >>> any_tags = ut.get_argval('--tags', type_=list, default=['Viewpoint']) >>> min_num = ut.get_argval('--min_num', type_=int, default=1) >>> prop = any_tags[0] >>> filtered_annotmatch_rowids = filter_annotmatch_by_tags(ibs, None, any_tags=any_tags, min_num=min_num) >>> aid1_list = (ibs.get_annotmatch_aid1(filtered_annotmatch_rowids)) >>> aid2_list = (ibs.get_annotmatch_aid2(filtered_annotmatch_rowids)) >>> aid_list = list(set(ut.flatten([aid2_list, aid1_list]))) >>> result = start_web_annot_groupreview(ibs, aid_list) >>> print(result)
-
wbia.web.app.
tst_html_error
()[source]¶ This test will show what our current errors look like
- CommandLine:
- python -m wbia.web.app –exec-tst_html_error
Example
>>> # DISABLE_DOCTEST >>> from wbia.web.app import * # NOQA >>> import wbia >>> web_ibs = wbia.opendb_bg_web(browser=True, start_job_queue=False, url_suffix='/api/image/imagesettext/?__format__=True')
wbia.web.appfuncs module¶
Bases:
object
-
wbia.web.appfuncs.
embed_image_html
(imgBGR, target_width=1200.0, target_height=800.0)[source]¶ Creates an image embedded in HTML base64 format.
-
wbia.web.appfuncs.
get_turk_annot_args
(is_reviewed_func, speed_hack=False)[source]¶ Helper to return aids in an imageset or a group review
-
wbia.web.appfuncs.
get_turk_image_args
(is_reviewed_func)[source]¶ Helper to return gids in an imageset or a group review
-
wbia.web.appfuncs.
imageset_annot_canonical
(ibs, aid_list, canonical_part_type='__CANONICAL__')[source]¶
-
wbia.web.appfuncs.
imageset_image_processed
(ibs, gid_list, is_staged=False, reviews_required=3)[source]¶
-
wbia.web.appfuncs.
imageset_part_contour_processed
(ibs, part_rowid_list, reviewed_flag_progress=True)[source]¶
wbia.web.graph_server module¶
-
class
wbia.web.graph_server.
GraphActor
[source]¶ Bases:
futures_actors.process_actor.ProcessActor
- CommandLine:
- python -m wbia.web.graph_server GraphActor
- Doctest:
>>> from wbia.web.graph_server import * >>> actor = GraphActor() >>> payload = testdata_start_payload() >>> # Start the process >>> start_resp = actor.handle(payload) >>> print('start_resp = {!r}'.format(start_resp)) >>> # Respond with a user decision >>> user_request = actor.handle({'action': 'continue_review'}) >>> # Wait for a response and the GraphActor in another proc >>> edge, priority, edge_data = user_request[0] >>> user_resp_payload = testdata_feedback_payload(edge, 'match') >>> content = actor.handle(user_resp_payload) >>> actor.infr.dump_logs()
- Doctest:
>>> from wbia.web.graph_server import * >>> import wbia >>> actor = GraphActor() >>> config = { >>> 'manual.n_peek' : 1, >>> 'manual.autosave' : False, >>> 'ranking.enabled' : False, >>> 'autoreview.enabled' : False, >>> 'redun.enabled' : False, >>> 'redun.enabled' : False, >>> 'queue.conf.thresh' : 'absolutely_sure', >>> 'algo.hardcase' : True, >>> } >>> # Start the process >>> dbdir = wbia.sysres.db_to_dbdir('PZ_MTEST') >>> payload = {'action': 'start', 'dbdir': dbdir, 'aids': 'all', >>> 'config': config, 'init': 'annotmatch'} >>> start_resp = actor.handle(payload) >>> print('start_resp = {!r}'.format(start_resp)) >>> # Respond with a user decision >>> user_request = actor.handle({'action': 'continue_review'}) >>> print('user_request = {!r}'.format(user_request)) >>> # Wait for a response and the GraphActor in another proc >>> edge, priority, edge_data = user_request[0] >>> user_resp_payload = testdata_feedback_payload(edge, 'match') >>> content = actor.handle(user_resp_payload) >>> actor.infr.dump_logs() >>> actor.infr.status()
-
class
wbia.web.graph_server.
GraphClient
(graph_uuid=None, callbacks={}, autoinit=False)[source]¶ Bases:
object
- CommandLine:
- python -m wbia.web.graph_server GraphClient
Example
>>> # ENABLE_DOCTEST >>> from wbia.web.graph_server import * >>> import wbia >>> client = GraphClient(autoinit=True) >>> # Start the GraphActor in another proc >>> payload = testdata_start_payload() >>> client.post(payload).result() >>> f1 = client.post({'action': 'continue_review'}) >>> f1.add_done_callback(test_foo) >>> user_request = f1.result() >>> # Wait for a response and the GraphActor in another proc >>> edge, priority, edge_data = user_request[0] >>> user_resp_payload = testdata_feedback_payload(edge, 'match') >>> f2 = client.post(user_resp_payload) >>> f2.result() >>> # Debug by getting the actor over a mp.Pipe >>> f3 = client.post({'action': 'debug'}) >>> actor = f3.result() >>> actor.infr.dump_logs() >>> #print(client.post({'action': 'logs'}).result())
# Ignore: # >>> from wbia.web.graph_server import * # >>> import wbia # >>> client = GraphClient(autoinit=True) # >>> # Start the GraphActor in another proc # >>> client.post(testdata_start_payload(list(range(1, 10)))).result() # >>> # # >>> f1 = client.post({‘action’: ‘continue_review’}) # >>> user_request = f1.result() # >>> # The infr algorithm needs a review # >>> edge, priority, edge_data = user_request[0] # >>> # # >>> client.post(testdata_feedback_payload(edge, ‘match’)) # >>> client.post({‘action’: ‘continue_review’}) # >>> client.post(testdata_feedback_payload(edge, ‘match’)) # >>> client.post(testdata_feedback_payload(edge, ‘match’)) # >>> client.post({‘action’: ‘continue_review’}) # >>> client.post(testdata_feedback_payload(edge, ‘match’)) # >>> client.post({‘action’: ‘wait’, ‘num’: float(30)}) # >>> client.post({‘action’: ‘continue_review’}) # >>> client.post(testdata_feedback_payload(edge, ‘match’)) # >>> client.post(testdata_feedback_payload(edge, ‘match’)) # >>> client.post({‘action’: ‘continue_review’}) # >>> client.post(testdata_feedback_payload(edge, ‘match’)) # >>> client.post({‘action’: ‘continue_review’}) # >>> client.post(testdata_feedback_payload(edge, ‘match’)) # >>> client.post({‘action’: ‘continue_review’}) # >>> client.post(testdata_feedback_payload(edge, ‘match’)) # >>> client.post(testdata_feedback_payload(edge, ‘match’)) # >>> client.post({‘action’: ‘continue_review’}) # >>> client.post(testdata_feedback_payload(edge, ‘match’)) # >>> client.post({‘action’: ‘continue_review’}) # >>> client.post(testdata_feedback_payload(edge, ‘match’)) # >>> client.post({‘action’: ‘continue_review’}) # >>> client.post(testdata_feedback_payload(edge, ‘match’)) # >>> client.post(testdata_feedback_payload(edge, ‘match’)) # >>> client.post({‘action’: ‘continue_review’}) # >>> client.post(testdata_feedback_payload(edge, ‘match’)) # >>> client.post({‘action’: ‘continue_review’}) # >>> client.post(testdata_feedback_payload(edge, ‘match’)) # >>> client.post({‘action’: ‘continue_review’})
-
rrr
(verbose=True, reload_module=True)¶ special class reloading function This function is often injected as rrr of classes
wbia.web.job_engine module¶
Accepts and handles requests for tasks.
Each of the following runs in its own Thread/Process.
BASICALLY DO A CLIENT/SERVER TO SPAWN PROCESSES AND THEN A PUBLISH SUBSCRIBE TO RETURN DATA
- Accepter:
- Receives tasks and requests Delegates tasks and responds to requests Tasks are delgated to an engine
- Engine:
- the engine accepts requests. the engine immediately responds WHERE it will be ready. the engine sends a message to the collector saying that something will be ready. the engine then executes a task. The engine is given direct access to the data.
- Collector:
The collector accepts requests The collector can respond:
- <ResultContent>
- Results are ready.
- Results are not ready.
- Unknown jobid.
- Error computing results.
- Progress percent.
References
Simple task farm, with routed replies in pyzmq http://stackoverflow.com/questions/7809200/implementing-task-farm-messaging-pattern-with-zeromq https://gist.github.com/minrk/1358832
Notes
We are essentially goint to be spawning two processes. We can test these simultaniously using
python -m wbia.web.job_engine job_engine_tester
- We can test these separately by first starting the background server
python -m wbia.web.job_engine job_engine_tester –bg
- Alternative:
- python -m wbia.web.job_engine job_engine_tester –bg –no-engine python -m wbia.web.job_engine job_engine_tester –bg –only-engine –fg-engine
- And then running the forground process
- python -m wbia.web.job_engine job_engine_tester –fg
-
class
wbia.web.job_engine.
JobInterface
(id_, port_dict, ibs=None)[source]¶ Bases:
object
-
initialize_client_thread
()[source]¶ Creates a ZMQ object in this thread. This talks to background processes.
-
queue_job
(action, callback_url=None, callback_method=None, *args, **kwargs)[source]¶ - IBEIS:
- This is just a function that lives in the main thread and ships off a job.
- FIXME: I do not like having callback_url and callback_method specified
- like this with args and kwargs. If these must be there then they should be specified first, or THE PREFERED OPTION IS args and kwargs should not be specified without the * syntax
The client - sends messages, and receives replies after they have been processed by the
-
-
wbia.web.job_engine.
collect_queue_loop
(port_dict)¶
-
wbia.web.job_engine.
collector_loop
(port_dict, dbdir, containerized)[source]¶ Service that stores completed algorithm results
-
wbia.web.job_engine.
engine_loop
(id_, port_dict, dbdir, containerized)[source]¶ - IBEIS:
This will be part of a worker process with its own IBEISController instance.
Needs to send where the results will go and then publish the results there.
The engine_loop - receives messages, performs some action, and sends a reply, preserving the leading two message parts as routing identities
-
wbia.web.job_engine.
get_job_id_list
(ibs)[source]¶ Web call that returns the list of job ids
- CommandLine:
# Run Everything together python -m wbia.web.job_engine –exec-get_job_status
# Start job queue in its own process python -m wbia.web.job_engine job_engine_tester –bg # Start web server in its own process ./main.py –web –fg pass # Run foreground process python -m wbia.web.job_engine –exec-get_job_status:0 –fg
Example
>>> # xdoctest: +REQUIRES(--web) >>> from wbia.web.job_engine import * # NOQA >>> import wbia >>> with wbia.opendb_bg_web('testdb1', managed=True) as web_ibs: # , domain='http://<IP_ADDRESS>') ... # Test get status of a job id that does not exist ... response = web_ibs.send_wbia_request('/api/engine/job/', jobid='badjob')
-
wbia.web.job_engine.
get_job_metadata
(ibs, jobid)[source]¶ Web call that returns the metadata of a job
- CommandLine:
# Run Everything together python -m wbia.web.job_engine –exec-get_job_metadata
# Start job queue in its own process python -m wbia.web.job_engine job_engine_tester –bg # Start web server in its own process ./main.py –web –fg pass # Run foreground process python -m wbia.web.job_engine –exec-get_job_metadata:0 –fg
Example
>>> # WEB_DOCTEST >>> from wbia.web.job_engine import * # NOQA >>> import wbia >>> with wbia.opendb_bg_web('testdb1', managed=True) as web_ibs: # , domain='http://<IP_ADDRESS>') ... # Test get metadata of a job id that does not exist ... response = web_ibs.send_wbia_request('/api/engine/job/metadata/', jobid='badjob')
-
wbia.web.job_engine.
get_job_status
(ibs, jobid=None)[source]¶ Web call that returns the status of a job
- Returns one of:
- received - job has been received, but not ingested yet accepted - job has been accepted (validated) queued - job has been transferred to the engine queue working - job is being worked on by the engine publishing - job is done on the engine, pushing results to collector completed | exception - job is complete or has an error
- CommandLine:
# Run Everything together python -m wbia.web.job_engine –exec-get_job_status
# Start job queue in its own process python -m wbia.web.job_engine job_engine_tester –bg # Start web server in its own process ./main.py –web –fg pass # Run foreground process python -m wbia.web.job_engine –exec-get_job_status:0 –fg
Example
>>> # xdoctest: +REQUIRES(--web) >>> from wbia.web.job_engine import * # NOQA >>> import wbia >>> with wbia.opendb_bg_web('testdb1', managed=True) as web_ibs: # , domain='http://<IP_ADDRESS>') ... # Test get status of a job id that does not exist ... response = web_ibs.send_wbia_request('/api/engine/job/status/', jobid='badjob')
-
wbia.web.job_engine.
initialize_job_manager
(ibs)[source]¶ Starts a background zmq job engine. Initializes a zmq object in this thread that can talk to the background processes.
Run from the webserver
- CommandLine:
- python -m wbia.web.job_engine –exec-initialize_job_manager:0
Example
>>> # DISABLE_DOCTEST >>> from wbia.web.job_engine import * # NOQA >>> import wbia >>> ibs = wbia.opendb(defaultdb='testdb1') >>> from wbia.web import apis_engine >>> from wbia.web import job_engine >>> ibs.load_plugin_module(job_engine) >>> ibs.load_plugin_module(apis_engine) >>> ibs.initialize_job_manager() >>> print('Initializqation success. Now closing') >>> ibs.close_job_manager() >>> print('Closing success.')
Example
>>> # xdoctest: +REQUIRES(--web) >>> from wbia.web.job_engine import * # NOQA >>> import wbia >>> import requests >>> with wbia.opendb_bg_web(db='testdb1', managed=True) as web_instance: ... web_port = ibs.get_web_port_via_scan() ... if web_port is None: ... raise ValueError('IA web server is not running on any expected port') ... baseurl = 'http://<IP_ADDRESS>:%s' % (web_port, ) ... _payload = {'image_attrs_list': [], 'annot_attrs_list': []} ... payload = ut.map_dict_vals(ut.to_json, _payload) ... resp1 = requests.post(baseurl + '/api/test/helloworld/?f=b', data=payload) ... #resp2 = requests.post(baseurl + '/api/image/json/', data=payload) ... #print(resp2) ... #json_dict = resp2.json() ... #text = json_dict['response'] ... #print(text)
-
wbia.web.job_engine.
job_engine_tester
()[source]¶ - CommandLine:
python -m wbia.web.job_engine –exec-job_engine_tester python -b -m wbia.web.job_engine –exec-job_engine_tester
python -m wbia.web.job_engine job_engine_tester python -m wbia.web.job_engine job_engine_tester –bg python -m wbia.web.job_engine job_engine_tester –fg
Example
>>> # SCRIPT >>> from wbia.web.job_engine import * # NOQA >>> job_engine_tester()
-
wbia.web.job_engine.
make_queue_loop
(name='collect')[source]¶ Standard queue loop
Parameters: name (None) – (default = None)
-
wbia.web.job_engine.
on_collect_request
(ibs, collect_request, collector_data, shelve_path, containerized=False)[source]¶ Run whenever the collector recieves a message
-
wbia.web.job_engine.
on_engine_request
(ibs, jobid, action, args, kwargs)[source]¶ Run whenever the engine recieves a message
wbia.web.prometheus module¶
wbia.web.routes module¶
Dependencies: flask, tornado
-
wbia.web.routes.
commit_current_query_object_names
(query_object, ibs)[source]¶ Parameters: - query_object (wbia.AnnotInference) –
- ibs (wbia.IBEISController) – image analysis api
-
wbia.web.routes.
image_view_api
(gid=None, thumbnail=False, fresh=False, **kwargs)[source]¶ Returns the base64 encoded image of image <gid>
- RESTful:
- Method: GET URL: /image/view/<gid>/
-
wbia.web.routes.
load_identification_query_object
(autoinit=False, global_feedback_limit=50, **kwargs)[source]¶
-
wbia.web.routes.
precompute_current_review_match_images
(ibs, query_object, global_feedback_limit=50, view_orientation='vertical')[source]¶
-
wbia.web.routes.
sightings
(html_encode=True, complete=True, include_images=False, kaia=False, **kwargs)[source]¶
-
wbia.web.routes.
turk_annotation
(**kwargs)[source]¶ - CommandLine:
- python -m wbia.web.app –exec-turk_annotation –db PZ_Master1
Example
>>> # SCRIPT >>> from wbia.other.ibsfuncs import * # NOQA >>> import wbia >>> ibs = wbia.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)
-
wbia.web.routes.
turk_annotation_canonical
(imgsetid=None, samples=200, species=None, version=1, **kwargs)[source]¶
-
wbia.web.routes.
turk_detection
(gid=None, only_aid=None, refer_aid=None, imgsetid=None, previous=None, previous_only_aid=None, staged_super=False, progress=None, **kwargs)[source]¶
-
wbia.web.routes.
turk_detection_canonical
(aid=None, imgsetid=None, previous=None, previous_only_aid=None, **kwargs)[source]¶
-
wbia.web.routes.
turk_identification
(aid1=None, aid2=None, use_engine=False, global_feedback_limit=50, **kwargs)[source]¶ - CommandLine:
- python -m wbia.web.routes turk_identification –db PZ_Master1 python -m wbia.web.routes turk_identification –db PZ_MTEST python -m wbia.web.routes turk_identification –db testdb1 –show
Example
>>> # SCRIPT >>> from wbia.other.ibsfuncs import * # NOQA >>> import wbia >>> with wbia.opendb_bg_web('testdb1', managed=True) as web_ibs: ... resp = web_ibs.get('/turk/identification/lnbnn/') >>> ut.quit_if_noshow() >>> import wbia.plottool as pt >>> ut.render_html(resp.content) >>> ut.show_if_requested()
-
wbia.web.routes.
turk_identification_graph
(graph_uuid=None, aid1=None, aid2=None, annot_uuid_list=None, hardcase=None, view_orientation='vertical', view_version=1, hogwild=False, hogwild_species=None, creation_imageset_rowid_list=None, kaia=False, **kwargs)[source]¶ - CommandLine:
python -m wbia.web.routes turk_identification_graph –db PZ_Master1 python -m wbia.web.routes turk_identification_graph –db PZ_MTEST python -m wbia.web.routes turk_identification_graph –db testdb1 –show
python -m wbia –db PZ_MTEST –web –browser –url=/turk/identification/graph/ –noengine
Example
>>> # SCRIPT >>> from wbia.other.ibsfuncs import * # NOQA >>> import wbia >>> with wbia.opendb_bg_web('testdb1', managed=True) as web_ibs: ... resp = web_ibs.get('/turk/identification/graph/') >>> ut.quit_if_noshow() >>> import wbia.plottool as pt >>> ut.render_html(resp.content) >>> ut.show_if_requested()
-
wbia.web.routes.
turk_identification_graph_refer
(imgsetid, species=None, tier=1, year=2019, option=None, **kwargs)[source]¶
-
wbia.web.routes.
turk_identification_hardcase
(*args, **kwargs)[source]¶ - CommandLine:
python -m wbia –db PZ_MTEST –web –browser –url=/turk/identification/hardcase/ python -m wbia –db PZ_MTEST –web –browser –url=/turk/identification/graph/
>>> # SCRIPT >>> from wbia.other.ibsfuncs import * # NOQA >>> import wbia >>> with wbia.opendb_bg_web('PZ_Master1', managed=True) as web_ibs: ... resp = web_ibs.get('/turk/identification/hardcase/')
- Ignore:
import wbia ibs, aids = wbia.testdata_aids(‘PZ_Master1’, a=’:species=zebra_plains’) print(len(aids)) infr = wbia.AnnotInference(ibs, aids=aids, autoinit=’staging’) infr.load_published() print(ut.repr4(infr.status())) infr.qt_review_loop()
verifiers = infr.learn_evaluation_verifiers()
verif = verifiers[‘match_state’] edges = list(infr.edges()) real = list(infr.edge_decision_from(edges)) hardness = 1 - verif.easiness(edges, real)
-
wbia.web.routes.
turk_part_types
(part_rowid=None, imgsetid=None, previous=None, hotkeys=8, refresh=False, previous_part_types=None, **kwargs)[source]¶
-
wbia.web.routes.
turk_quality
(**kwargs)[source]¶ - PZ Needs Tags:
- 17242 14468 14427 15946 14771 14084 4102 6074 3409
GZ Needs Tags; 1302
- CommandLine:
- python -m wbia.web.app –exec-turk_quality –db PZ_Master1 python -m wbia.web.app –exec-turk_quality –db GZ_Master1 python -m wbia.web.app –exec-turk_quality –db GIRM_Master1
Example
>>> # SCRIPT >>> from wbia.other.ibsfuncs import * # NOQA >>> import wbia >>> ibs = wbia.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)
-
wbia.web.routes.
turk_species
(hotkeys=8, refresh=False, previous_species_rowids=None, **kwargs)[source]¶
-
wbia.web.routes.
turk_viewpoint
(**kwargs)[source]¶ - CommandLine:
- python -m wbia.web.app –exec-turk_viewpoint –db PZ_Master1
Example
>>> # SCRIPT >>> from wbia.other.ibsfuncs import * # NOQA >>> import wbia >>> ibs = wbia.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)
-
wbia.web.routes.
turk_viewpoint2
(**kwargs)[source]¶ - CommandLine:
- python -m wbia.web.app –exec-turk_viewpoint –db PZ_Master1
Example
>>> # SCRIPT >>> from wbia.other.ibsfuncs import * # NOQA >>> import wbia >>> ibs = wbia.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)
wbia.web.routes_csv module¶
Dependencies: flask, tornado
wbia.web.routes_demo module¶
Dependencies: flask, tornado
wbia.web.routes_experiments module¶
Dependencies: flask, tornado
-
wbia.web.routes_experiments.
experiments_interest
(dbtag1='demo-jasonp', dbtag2='demo-chuck', **kwargs)[source]¶
wbia.web.routes_submit module¶
Dependencies: flask, tornado
-
wbia.web.routes_submit.
group_review_submit
(**kwargs)[source]¶ - CommandLine:
- python -m wbia.web.app –exec-group_review_submit
Example
>>> # UNSTABLE_DOCTEST >>> from wbia.web.app import * # NOQA >>> import wbia >>> import wbia.web >>> ibs = wbia.opendb('testdb1') >>> aid_list = ibs.get_valid_aids()[::2] >>> ibs.start_web_annot_groupreview(aid_list)
wbia.web.test_api module¶
This is a proof of concept for connecting to an authenticated Qubica Server
-
wbia.web.test_api.
get_api_result
(uri, user_email=None, user_enc_pass=None, **kwargs)[source]¶ Make a GET API request to the server
-
wbia.web.test_api.
post_api_result
(uri, user_email=None, user_enc_pass=None, **kwargs)[source]¶ Make a GET API request to the server
-
wbia.web.test_api.
run_test_api
()[source]¶ - CommandLine:
- python -m wbia.web.test_api –test-run_test_api
Example
>>> # xdoctest: +REQUIRES(--web) >>> from wbia.web.test_api import * # NOQA >>> response = run_test_api() >>> print('Server response: %r' % (response, )) >>> result = response (200, u'{"status": {"cache": -1, "message": "", "code": 200, "success": true}, "response": "testdb1"}', <bound method Response.json of <Response [200]>>)


This pane limited to a range of 100 to 300 pixels wide with the minAsize / maxAsize properties of the plugin..
testing
some content


Gecode license and authors
GECODE LICENSE AGREEMENT
This software and its documentation are copyrighted by the individual authors as listed in each file. All files, unless explicitly disclaimed in individual files, may be used according to the terms of the MIT license, which reads as follows:
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
 IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Gecode authors
The following authors have contributed to the Gecode library and its documentation (in alphabetical order of firstname):
- Christian Schulte<EMAIL_ADDRESS>- Christopher Mears<EMAIL_ADDRESS>- David Rijsman<EMAIL_ADDRESS>- Denys Duchier<EMAIL_ADDRESS>- Filip Konvicka<EMAIL_ADDRESS>- Gabor Szokoli<EMAIL_ADDRESS>- Gabriel Hjort Blindell<EMAIL_ADDRESS>- Gregory Crosswhite<EMAIL_ADDRESS>- Guido Tack<EMAIL_ADDRESS>- Håkan Kjellerstrand<EMAIL_ADDRESS>- Joseph Scott<EMAIL_ADDRESS>- Kevin Leo<EMAIL_ADDRESS>- Linnea Ingmar<EMAIL_ADDRESS>- Lubomir Moric<EMAIL_ADDRESS>- Matthias Balzer<EMAIL_ADDRESS>- Maxim Shishmarev<EMAIL_ADDRESS>- Mikael Lagerkvist<EMAIL_ADDRESS>- Patrick Pekczynski<EMAIL_ADDRESS>- Raphael Reischuk<EMAIL_ADDRESS>- Roberto Castañeda Lozano<EMAIL_ADDRESS>- Samuel Gagnon<EMAIL_ADDRESS>- Stefano Gualandi<EMAIL_ADDRESS>- Tias Guns<EMAIL_ADDRESS>- Vincent Barichard<EMAIL_ADDRESS>The above authors are grateful to the following people who have provided bugfixes:
- Alexander Samoilov
- David Rijsman
- Geoffrey Chu
- Grégoire Dooms
- Gustavo Gutierrez
- Olof Sivertsson
- Stefano Gualandi
- Zandra Norman
We are also grateful for our users reporting bugs and suggesting improvements.


Do you know how long your battery will last in emergency situations? Try this interactive to plot discharge curves and even generate custom equations for each curve!
This interactive shows you how crucial communication and rapid decisions are during times of crisis. Make choices and see if you can make the best decisions!


@* * Copyright 2019 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @import play.api.data.Form @import uk.gov.hmrc.play.views.html.helpers.form @import core.views.html.helpers._ @import core.config.AppConfig @import incometax.business.forms.AccountingMethodPropertyForm @import core.forms.submapping.AccountingMethodMapping @import incometax.business.models.{AccountingMethodModel, AccountingMethodPropertyModel} @import core.views.html.templates.main_template @(accountingMethodForm: Form[AccountingMethodPropertyModel], postAction: Call, isEditMode: Boolean, backUrl: String)(implicit request: Request[_], messages: Messages, appConfig: AppConfig) @question = @{ messages("property.accounting_method.heading") } @main_template(title = messages("property.accounting_method.title"), form = accountingMethodForm) { @back_link(backUrl) @summaryErrorHelper(accountingMethodForm)
@messages("property.accounting_method.accordion.content.para")


« CSS « CSS Reference
Summary
overflow-x
specifies whether to clip content, render a scroll bar or display overflow content of a block-level element, when it overflows at the left and right edges.
Syntax
overflow-x: visible | hidden | scroll | auto
Values
- visible
- Content is not clipped, it may be rendered outside the content box.
- hidden
- The content is clipped and no scrollbars are provided.
- scroll
- The content is clipped and desktop browsers use scrollbars, whether or not any content is clipped. This avoids any problem with scrollbars appearing and disappearing in a dynamic environment. Printers may print overflowing content.
- auto
- Depends on the user agent. Desktop browsers like Firefox provide scrollbars if content overflows.
Browser compatibility
| Browser | Lowest Version |
| Internet Explorer |
5.0 (*)
|
| Firefox | 1.5 |
| Opera | 9.5 |
| Safari | 3.0 |
(*) IE8 introduced -ms-overflow-x
as a synonym for overflow-x
. Don't use the -ms-
prefix.