From 4f305b00e80728819988d2aa87eac74d07bce0bf Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Brian=20=C3=93=20Donnell?= Date: Thu, 5 Sep 2019 09:36:30 -0400 Subject: [PATCH 1/6] Wrap db access functions with an auto-retry decorator --- db.py | 178 ++++++++++++++++++++++++++++++++++------------------------ 1 file changed, 106 insertions(+), 72 deletions(-) diff --git a/db.py b/db.py index 87432b9..0bf2173 100644 --- a/db.py +++ b/db.py @@ -4,6 +4,7 @@ import os import numpy as np import pymongo +import time from config import config from hopkins import hopkins from datetime import datetime @@ -12,15 +13,37 @@ client = pymongo.MongoClient(host=config.db_address, port=config.db_port) epidermal_db = client[config.db_name] +def auto_retry(call): + """ + Retry in the event of a mongo AutoReconnect error with an exponential backoff + """ + def _auto_retry(*args, **kwargs): + retries = 5 + for i in xrange(retries): + try: + return call(*args, **kwargs) + except pymongo.errors.AutoReconnect: + sleep = pow(2, i) + print( + 'MongoDB connection error. Retrying in {} seconds ({} of {})'.format( + sleep, i, retries + ) + ) + time.sleep(sleep) + return _auto_retry + +@auto_retry +def get_collection(coll): + return epidermal_db[coll] # Datasets # ############ -datasets = epidermal_db['datasets'] -samples = epidermal_db['samples'] +datasets = get_collection('datasets') +samples = get_collection('samples') # 'name' (str): Name to identify the dataset - +@auto_retry def get_dataset_info(s): # Add sample counts for dataset if s is not None: @@ -29,31 +52,31 @@ def get_dataset_info(s): s['user'] = None if user_id is None else get_user_by_id(user_id) return s - +@auto_retry def get_datasets_by_tag(tag_name): return datasets.find({'tags': {'$in': [tag_name]}, 'deleted': False}) - +@auto_retry def get_untagged_old_datasets(threshold_date): return datasets.find({'tags': [], 'date_accessed': {"$lt": threshold_date}}) - +@auto_retry def get_datasets(deleted=False): return [get_dataset_info(s) for s in datasets.find({'deleted': deleted})] - +@auto_retry def get_datasets_by_user(user_id): return [get_dataset_info(s) for s in datasets.find({'deleted': False, 'user_id': user_id})] - +@auto_retry def get_dataset_by_id(dataset_id): return get_dataset_info(datasets.find_one({'_id': dataset_id})) - +@auto_retry def get_dataset_by_name(dataset_name, deleted=False): return get_dataset_info(datasets.find_one({'name': dataset_name, 'deleted': deleted})) - +@auto_retry def get_example_dataset(): return get_dataset_info(datasets.find_one({ 'tags': { '$in': ["examples"] }, 'deleted': False })) @@ -65,18 +88,18 @@ def is_readonly_dataset(dataset): def is_readonly_dataset_id(dataset_id): return is_readonly_dataset(get_dataset_by_id(dataset_id)) - +@auto_retry def add_dataset(name, user_id=None, image_zoom=None, threshold_prob=None): dataset_record = {'name': name, 'deleted': False, 'date_added': datetime.now(), 'tags': [], 'user_id': user_id, 'image_zoom': image_zoom, 'threshold_prob': threshold_prob, 'date_accessed': datetime.now()} dataset_record['_id'] = datasets.insert_one(dataset_record).inserted_id return dataset_record - +@auto_retry def access_dataset(dataset_id): datasets.update_one({'_id': dataset_id}, {"$set": {'date_accessed': datetime.now()}}, upsert=False) - +@auto_retry def delete_dataset(dataset_id, recycle=True, delete_files=False): if recycle: access_dataset(dataset_id) @@ -86,41 +109,42 @@ def delete_dataset(dataset_id, recycle=True, delete_files=False): delete_sample(sample['_id'], delete_files=delete_files, do_access_dataset=False) datasets.delete_one({'_id': dataset_id}) - +@auto_retry def update_dataset_human_annotations(dataset_id): annotated_count = samples.count({'dataset_id': dataset_id, 'human_position_count': {'$gt': 0}}) datasets.update({'_id': dataset_id}, {"$set": {'human_annotation_count': annotated_count, 'date_accessed': datetime.now()}}, upsert=False) - +@auto_retry def add_dataset_tag(dataset_id, new_tag): datasets.update({'_id': dataset_id}, {"$addToSet": {'tags': new_tag}}, upsert=False) access_dataset(dataset_id) - +@auto_retry def remove_dataset_tag(dataset_id, tag_name): datasets.update({'_id': dataset_id}, {"$pull": {'tags': tag_name}}, upsert=False) access_dataset(dataset_id) - +@auto_retry def set_dataset_user(dataset_id, user_id): datasets.update({'_id': dataset_id}, {"$set": {'user_id': user_id}}, upsert=False) access_dataset(dataset_id) - +@auto_retry def set_dataset_threshold_prob(dataset_id, new_threshold_prob): datasets.update({'_id': dataset_id}, {"$set": {'threshold_prob': new_threshold_prob}}, upsert=False) access_dataset(dataset_id) # Fix dataset date added where it's missing +@auto_retry def fix_dataset_date_added(): for d in datasets.find({'date_added': None}): print 'Updating ', d datasets.update({'_id': d['_id']}, {"$set": {'date_added': datetime.now(), 'date_accessed': datetime.now()}}, upsert=False) - +@auto_retry def fix_dataset_date_accessed(): for d in datasets.find({'date_accessed': None}): print 'Updating ', d @@ -128,6 +152,7 @@ def fix_dataset_date_accessed(): # Fix dataset human annotation count where it's missing +@auto_retry def fix_dataset_human_annotation_count(): for d in datasets.find({'human_annotation_count': None}): print 'Updating ', d @@ -135,6 +160,7 @@ def fix_dataset_human_annotation_count(): # Fix datasets to have an empty tag array +@auto_retry def fix_dataset_tags(): for d in datasets.find({'tags': None}): datasets.update({'_id': d['_id']}, {"$set": {'tags': [], 'date_accessed': datetime.now()}}, upsert=False) @@ -151,21 +177,21 @@ def fix_dataset_tags(): # 'size': array[2]: Image size [px] # 'date_added': datetime when the sample was uploaded - +@auto_retry def get_unprocessed_samples(dataset_id=None): query = {'processed': False, 'error': False} if dataset_id is not None: query['dataset_id'] = dataset_id return list(samples.find(query)) - +@auto_retry def get_processed_samples(dataset_id=None): query = {'processed': True, 'error': False} if dataset_id is not None: query['dataset_id'] = dataset_id return sorted(list(samples.find(query)), key=lambda x: x['name']) - +@auto_retry def get_human_annotated_samples(dataset_id=None, train_label=None): if train_label is not None: result = [] @@ -178,14 +204,14 @@ def get_human_annotated_samples(dataset_id=None, train_label=None): result = list(samples.find(query)) return result - +@auto_retry def get_human_unannotated_samples(dataset_id=None): query = {'annotated': False} if dataset_id is not None: query['dataset_id'] = dataset_id return list(samples.find(query)) - +@auto_retry def get_samples(dataset_id=None): if dataset_id is not None: query = {'dataset_id': dataset_id} @@ -197,6 +223,7 @@ def get_samples(dataset_id=None): # Get next sample in dataset, starting from prev_sample_id # If annotated is not None, it is used as a filter for possible next samples # If there is no next sample (or it would be identical to prev_sample_id), return None +@auto_retry def get_next_sample_id(dataset_id, prev_sample_id, annotated=None, reverse_direction=False): sorted_samples = sorted(get_samples(dataset_id), key=lambda x: x['name'], reverse=reverse_direction) scnt = len(sorted_samples) @@ -214,6 +241,7 @@ def get_next_sample_id(dataset_id, prev_sample_id, annotated=None, reverse_direc # Get index of given sample in dataset +@auto_retry def get_sample_index(dataset_id, sample_id): sorted_samples = sorted(get_samples(dataset_id), key=lambda x: x['name']) index = [s['_id'] for s in sorted_samples].index(sample_id) @@ -222,14 +250,14 @@ def get_sample_index(dataset_id, sample_id): next_index = (index + 1) % sample_count return index, sample_count, sorted_samples[prev_index]['_id'], sorted_samples[next_index]['_id'] - +@auto_retry def get_error_samples(dataset_id=None): query = {'error': True} if dataset_id is not None: query['dataset_id'] = dataset_id return list(samples.find(query)) - +@auto_retry def add_sample(name, filename, size, dataset_id=None): sample_record = {'name': name, 'filename': filename, 'dataset_id': dataset_id, 'size': size, 'processed': False, 'annotated': False, 'error': False, 'error_string': None, 'date_added': datetime.now()} @@ -237,20 +265,20 @@ def add_sample(name, filename, size, dataset_id=None): access_dataset(dataset_id) return sample_record - +@auto_retry def set_sample_data(sample_id, image_size): samples.update({'_id': sample_id}, {"$set": {'size': image_size}}, upsert=False) - +@auto_retry def set_sample_error(sample_id, error_string): print 'Sample %s error: %s' % (str(sample_id), error_string) samples.update({'_id': sample_id}, {"$set": {'error': True, 'error_string': error_string}}, upsert=False) - +@auto_retry def get_sample_by_id(sample_id): return samples.find_one({'_id': sample_id}) - +@auto_retry def delete_sample(sample_id, delete_files=False, do_access_dataset=True): sample = samples.find_one_and_delete({'_id': sample_id}) if sample is None: @@ -275,11 +303,11 @@ def delete_sample(sample_id, delete_files=False, do_access_dataset=True): access_dataset(sample['dataset_id']) return True - +@auto_retry def get_sample_count(): return samples.count() - +@auto_retry def fix_default_sample_names(): # Set name=filename for all samples without name for s in samples.find({}): @@ -287,7 +315,7 @@ def fix_default_sample_names(): print 'Naming %s' % s['filename'] samples.update({'_id': s['_id']}, {"$set": {'name': s['filename']}}, upsert=False) - +@auto_retry def fix_sample_date_added(): # Update date_added field for samples for s in samples.find({'date_added': None}): @@ -296,35 +324,36 @@ def fix_sample_date_added(): # Set image quality measures +@auto_retry def set_image_measures(sample_id, image_measures): samples.update({'_id': sample_id}, {"$set": image_measures}, upsert=False) # Sample queue for non-primary models and image validation runs # ################################################################# -sample_queue = epidermal_db['sample_queue'] +sample_queue = get_collection('sample_queue') # 'sample_id' (id): Link into samples collection # 'model_id' (id): Link into model collection # 'validation_model_id' (id): Link into model collection for validation set queue items - +@auto_retry def get_queued_samples(model_id=None): query = {} if model_id is not None: query['model_id'] = model_id return sample_queue.find(query) - +@auto_retry def queue_sample(sample_id, model_id): rec = {'sample_id': sample_id, 'model_id': model_id} sample_queue.update(rec, rec, upsert=True) - +@auto_retry def queue_validation(train_model_id, validation_model_id): rec = {'validation_model_id': validation_model_id, 'model_id': train_model_id} sample_queue.update(rec, rec, upsert=True) - +@auto_retry def unqueue_sample(queue_item_id): sample_queue.delete_one({'_id': queue_item_id}) @@ -332,7 +361,7 @@ def unqueue_sample(queue_item_id): # Image validation runs # ######################### -validation_results = epidermal_db['validation_results'] +validation_results = get_collection('validation_results') # 'train_model_id' (id): Evaluated model # 'validation_model_id' (id): Model to which the evaluation dataset belongs # 'image_subset' (str): Which dataset (train, val, test) @@ -340,6 +369,7 @@ def unqueue_sample(queue_item_id): # 'worst_predictions' (dict('Distractor', 'Target') of list of [sample_name, prediction_value]): Top 25 worst # (mis-)classifications +@auto_retry def save_validation_results(train_model_id, validation_model_id, image_subset, confusion_matrix, worst_predictions): query = { 'train_model_id': train_model_id, @@ -351,7 +381,7 @@ def save_validation_results(train_model_id, validation_model_id, image_subset, c results['worst_predictions'] = worst_predictions validation_results.update(query, results, upsert=True) - +@auto_retry def get_all_validation_results(train_model_id=None, validation_model_id=None): q = {} if train_model_id is not None: @@ -360,12 +390,12 @@ def get_all_validation_results(train_model_id=None, validation_model_id=None): q['validation_model_id'] = validation_model_id return list(validation_results.find(q)) - +@auto_retry def get_validation_results(train_model_id, validation_model_id, image_subset): return validation_results.find_one({'train_model_id': train_model_id, 'validation_model_id': validation_model_id, 'image_subset': image_subset}) - +@auto_retry def get_validation_results_by_id(val_id): return validation_results.find_one({'_id': val_id}) @@ -373,13 +403,13 @@ def get_validation_results_by_id(val_id): # Human annotations # ##################### -human_annotations = epidermal_db['human_annotations'] +human_annotations = get_collection('human_annotations') # 'sample_id' (id): Link into samples collection # 'user_id' (id): Annotating user # 'positions' (array[n] of array[2]): [x,y] positions of detected stomata [px] # 'margin': Stomata annotation margin [px] - +@auto_retry def get_human_annotations(sample_id): def resolve(annotation): user = get_user_by_id(annotation['user_id']) @@ -388,7 +418,7 @@ def resolve(annotation): return annotation return [resolve(s) for s in human_annotations.find({'sample_id': sample_id})] - +@auto_retry def set_human_annotation(sample_id, user_id, positions, margin, base_annotations=None): annotation_lookup = {'sample_id': sample_id} annotation_record = {'sample_id': sample_id, 'user_id': user_id, 'positions': positions, 'margin': margin, @@ -403,12 +433,13 @@ def set_human_annotation(sample_id, user_id, positions, margin, base_annotations update_dataset_human_annotations(dataset_id) add_dataset_tag(dataset_id, 'has_annotations') - +@auto_retry def get_human_annotation_count(): return human_annotations.count() # Fix count where it's missing +@auto_retry def count_human_annotations(): for s in samples.find({'annotated': True}): if s.get('human_position_count') is None: @@ -422,7 +453,7 @@ def count_human_annotations(): # Machine annotations # ####################### -machine_annotations = epidermal_db['machine_annotations'] +machine_annotations = get_collection('machine_annotations') # 'sample_id' (id): Link into samples collection # 'model_id' (id): Annotating model # 'heatmap_filename' (str): Filename of heatmap (numpy) @@ -430,25 +461,25 @@ def count_human_annotations(): # 'positions' (array[n] of array[2]): [x,y] positions of detected stomata [px] # 'margin': Stomata detection margin [px] - +@auto_retry def get_machine_annotations(sample_id, model_id=None): if model_id is None: model_id = get_primary_model()['_id'] return list(machine_annotations.find({'sample_id': sample_id, 'model_id': model_id})) - +@auto_retry def get_all_model_machine_annotations(sample_id): return list(machine_annotations.find({'sample_id': sample_id})) - +@auto_retry def get_machine_annotations_for_model(model_id): return machine_annotations.find({'model_id': model_id}) - +@auto_retry def remove_machine_annotations_for_model(model_id): return machine_annotations.delete_many({'model_id': model_id}) - +@auto_retry def remove_machine_annotations_for_dataset(dataset_id): asamples = get_samples(dataset_id=dataset_id) c = 0 @@ -464,7 +495,7 @@ def remove_machine_annotations_for_dataset(dataset_id): access_dataset(dataset_id) return c - +@auto_retry def add_machine_annotation(sample_id, model_id, heatmap_filename, heatmap_image_filename, positions, margin, is_primary_model, scale=1.0): annotation_query = {'sample_id': sample_id, 'model_id': model_id} @@ -477,7 +508,7 @@ def add_machine_annotation(sample_id, model_id, heatmap_filename, heatmap_image_ set_primary_machine_annotation(sample_id, positions) return annotation_record - +@auto_retry def set_primary_machine_annotation(sample_id, positions): if positions is None: sample_update = {'processed': False, @@ -493,7 +524,7 @@ def set_primary_machine_annotation(sample_id, positions): 'error_string': None} samples.update({'_id': sample_id}, {"$set": sample_update}, upsert=False) - +@auto_retry def update_machine_annotation_positions(sample_id, machine_annotation_id, positions, is_primary_model): print 'update_machine_annotation_positions' print 'sample_id', sample_id @@ -504,7 +535,7 @@ def update_machine_annotation_positions(sample_id, machine_annotation_id, positi if is_primary_model: samples.update({'_id': sample_id}, {"$set": {'machine_position_count': len(positions)}}, upsert=False) - +@auto_retry def delete_all_machine_annotations(): r = machine_annotations.delete_many({}) sample_update = {'processed': False, @@ -518,6 +549,7 @@ def delete_all_machine_annotations(): # Update the machine position count and hopkins field based on primary model results +@auto_retry def fix_primary_machine_annotations(): model_id = get_primary_model()['_id'] for s in samples.find(): @@ -530,16 +562,17 @@ def fix_primary_machine_annotations(): # Users # ######### -user = epidermal_db['user'] +user = get_collection('user') # 'email' (str): User email +@auto_retry def get_user_by_id(user_id): return user.find_one({'_id': user_id}) # Models # ########## -models = epidermal_db['models'] +models = get_collection('models') # 'name' (str): Model name # 'margin' (int): Margin at each side that the model does not predict [px] # 'date_added' (datetime): When the model training was issued @@ -550,14 +583,14 @@ def get_user_by_id(user_id): model_status_failed = 'failed' model_status_dataset = 'dataset' - +@auto_retry def get_or_add_model(model_name, margin): model = models.find_one({'name': model_name}) if model is None: model = add_model(model_name, margin) return model - +@auto_retry def get_models(details=False, status=None): filter = {} if status is not None: @@ -574,7 +607,7 @@ def get_models(details=False, status=None): model['machine_annotation_count'] = machine_annotations.count({'model_id': model['_id']}) return rval - +@auto_retry def add_model(model_name, margin, sample_limit=None, train_tag='train', scheduled_primary=False, status=model_status_scheduled, dataset_only=False): model_record = {'name': model_name, @@ -588,28 +621,28 @@ def add_model(model_name, margin, sample_limit=None, train_tag='train', schedule model_record['_id'] = models.insert_one(model_record).inserted_id return model_record - +@auto_retry def delete_model(model_id): if model_id is None: raise RuntimeError('Invalid model.') models.delete_one({'_id': model_id}) - +@auto_retry def set_model_parameters(model_id, new_settings): result = models.update_one({'_id': model_id}, {"$set": new_settings}, upsert=False) print 'result', result if not result.modified_count: raise RuntimeError('set_model_parameters: Model ID %s not found.' % str(model_id)) - +@auto_retry def get_model_by_id(model_id): return models.find_one({'_id': model_id}) - +@auto_retry def get_model_by_name(model_name): return models.find_one({'name': model_name}) - +@auto_retry def rename_model(old_model_name, new_model_name): if models.find_one({'name': new_model_name}): raise RuntimeError('Target name exists.') @@ -617,11 +650,11 @@ def rename_model(old_model_name, new_model_name): if not result.modified_count: raise RuntimeError('rename_model: Old model %s not found.' % old_model_name) - +@auto_retry def get_primary_model(): return models.find_one({'primary': True}) - +@auto_retry def set_primary_model(model_id): previous_primary = get_primary_model() result = models.update_one({'_id': model_id}, {"$set": {'primary': True}}, upsert=False) @@ -631,7 +664,7 @@ def set_primary_model(model_id): models.update_one({'_id': previous_primary['_id']}, {"$set": {'primary': False}}, upsert=False) fix_primary_machine_annotations() - +@auto_retry def set_model_status(model_id, new_status): assert new_status in {model_status_scheduled, model_status_training, model_status_trained, model_status_failed, model_status_dataset} @@ -641,23 +674,24 @@ def set_model_status(model_id, new_status): # Status # ########### -status = epidermal_db['status'] +status = get_collection('status') # 'component' (str): Component name for status string # 'staus' (str): Status string - +@auto_retry def get_status(component): rec = status.find_one({'component': component}) if rec is None: return 'Unknown' return rec['status'] - +@auto_retry def set_status(component, status_string): status.update({'component': component}, {"$set": {'component': component, 'status': status_string}}, upsert=True) # Helpers +@auto_retry def print_annotation_table(): for s in samples.find({}): if (s.get('human_position_count') is not None) or (s.get('machine_position_count') is not None): From 18647e36b3ab84cb3ab2ef0a3af1eb2d5a2fbe51 Mon Sep 17 00:00:00 2001 From: Karl Date: Thu, 25 Feb 2021 12:26:25 -0500 Subject: [PATCH 2/6] Update about.html Updated text, citation, email address --- templates/about.html | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/templates/about.html b/templates/about.html index d7aa3ca..bb33ba1 100644 --- a/templates/about.html +++ b/templates/about.html @@ -13,10 +13,11 @@

About

-

Stomata Counter was born in the minds of Karl and Sven after a chance encounter at GSA 2016 in Denver, Colorado. Karl is an evolutionary biologist who studies plants at multiple time, geographic, and genomic scales. When he's not day dreaming about fossil plants, he spends most of his time studying the population genomics and evolutionary effects of hybridization in Poplars at the University of Vermont. Sven is a post-doc at Brown University and a whiz at the command line. Feel free to contact karl at kf [at] uvm.edu.

+

Stomata Counter started as a collaboration between the University of Vermont, Brown Univeristy, and the Smithsonian Institution. During Karl's dissertation, he was face with counting stomata from several thousand images and became determined not to do it manually. At GSA in 2016, Karl and Sven met and came up with a draft version of a CNN to estiamte counts of stomata. The extensive biological collections at the National Museum of Natural History and the United States Botanic garden were instrumental in extending the training data set to species outside if Populus.

+

Feel free to contact Karl at karl.fetter [at] gmail.com with questions.

How to Cite

-

Please cite us if you use this tool for your research. Citations grease the wheels and keep us employed! You can cite the bioRxiv manuscript with this citation. -

Fetter, K., Eberhardt, S., Barclay, R.S., Wing, S. and Keller, S.R., 2018. StomataCounter: a deep learning method applied to automatic stomatal identification and counting. BioRxiv, p.327494. +

Please cite us if you use this tool for your research. +

Fetter, K.C., Eberhardt, S., Barclay, R.S., Wing, S. and Keller, S.R., 2019. StomataCounter: a neural network for automatic stomata identification and counting. New Phytologist, 223(3), pp.1671-1681.

Funding

Funding to create Stomata Counter was provided by an NSF grant to Dr. Stephen Keller (Award # 1461868) and a Smithsonian Institution Fellowship to Karl Fetter. From e4334d5881a9e39cf9817314168682c7999ac263 Mon Sep 17 00:00:00 2001 From: Karl Date: Fri, 9 Apr 2021 11:17:56 -0400 Subject: [PATCH 3/6] Update typo --- templates/about.html | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/templates/about.html b/templates/about.html index bb33ba1..ca4630d 100644 --- a/templates/about.html +++ b/templates/about.html @@ -13,8 +13,8 @@

About

-

Stomata Counter started as a collaboration between the University of Vermont, Brown Univeristy, and the Smithsonian Institution. During Karl's dissertation, he was face with counting stomata from several thousand images and became determined not to do it manually. At GSA in 2016, Karl and Sven met and came up with a draft version of a CNN to estiamte counts of stomata. The extensive biological collections at the National Museum of Natural History and the United States Botanic garden were instrumental in extending the training data set to species outside if Populus.

-

Feel free to contact Karl at karl.fetter [at] gmail.com with questions.

+

Stomata Counter started as a collaboration between the University of Vermont, Brown Univeristy, and the Smithsonian Institution. During Karl's dissertation, he was face with counting stomata from several thousand images and became determined not to do it manually. At GSA in 2016, Karl and Sven met and came up with a draft version of a CNN to estiamte counts of stomata. The extensive biological collections at the National Museum of Natural History and the United States Botanic garden were instrumental in extending the training data set to species outside of Populus.

+

Feel free to contact Karl (karl.fetter [at] gmail.com) with questions.

How to Cite

Please cite us if you use this tool for your research.

Fetter, K.C., Eberhardt, S., Barclay, R.S., Wing, S. and Keller, S.R., 2019. StomataCounter: a neural network for automatic stomata identification and counting. New Phytologist, 223(3), pp.1671-1681. From d325b3fa9a8c17850611403e6946a7e04b8592fe Mon Sep 17 00:00:00 2001 From: Karl Date: Fri, 9 Apr 2021 11:21:49 -0400 Subject: [PATCH 4/6] Update base.html updated karl's email --- templates/base.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/templates/base.html b/templates/base.html index a151b38..e971a2c 100644 --- a/templates/base.html +++ b/templates/base.html @@ -89,7 +89,7 @@

Maintenance on Sat 03/23/2019: We're running some maintenan {% block content %}
{% endblock %} From b3dd3a9e0ab1394f1e6453a3bb789116ec3964cb Mon Sep 17 00:00:00 2001 From: Karl Date: Fri, 9 Apr 2021 11:26:25 -0400 Subject: [PATCH 5/6] Update index.html --- templates/index.html | 10 +--------- 1 file changed, 1 insertion(+), 9 deletions(-) diff --git a/templates/index.html b/templates/index.html index 82173d8..88dc2c6 100644 --- a/templates/index.html +++ b/templates/index.html @@ -24,21 +24,13 @@

Stomata Counter

Welcome to StomataCounter!

-

StomataCounter is a tool to work with plant epidermal micrographs to phenotype stomatal density. Supporting this method is a convolutional neural network trained about about 4,700 micrographs from 700 species of plants. You can read about how StomataCounter was developed and the tests we performed to validate StomataCounter's effectiveness in the preprint. We recommend you register an account with us so that you can return to your jobs after you leave the website.

+

StomataCounter is a tool to work with plant epidermal micrographs to phenotype stomatal density. Supporting this method is a convolutional neural network trained about about 4,700 micrographs from 700 species of plants. You can read about how StomataCounter was developed and the tests we performed to validate StomataCounter's effectiveness in the New Phytologist manuscript. We recommend you register an account with us so that you can return to your jobs after you leave the website.

Instructions for using this site

The simplest way to use StomataCounter is to upload a jpeg using the upload button below, refresh your browser on the Dataset page after a few moments, then export the results using the Dataset operations pulldown menu.

Most users will have several hundred images to measure and uploading zip files of jpegs is more convencient. You can add more zipfiles or individual images to a dataset by navigating to that dataset's page and following the instructions to add more images. Once StomataCounter has finished detecting and counting stomata, you should view the results of all or a set of images to determine how well the method performed. Click on an image to view the result. You should annotate 50 or 100 images (or whatever number you're comfortable with) and view the correlation of human to automatic stomata counts. This is done by clicking on the Dataset operations drop down menu and selecting "Export correlation graph".

Stomata annotations are added to an image by clicking on image in a dataset and clicking the annotate button. There are two annotation modes. Enter the basic annotation mode by clicking "Annotate" in the list of actions. In this mode, you'll have to add an annotation for each stomata by clicking once to add, and twice to remove. We provided a faster method of annotating using the "Annotate from automatic" annotation mode. In this mode, StomataCounter's best quess of where a stoma is located is provided as an annotation. You then choose to accept, reject, or modify annotations. The automatic annotation mode is a great time saver and you'll only spend a few seconds annotating each image.

When you are ready to download and analyze your output data, click on the "Export results as csv" in the Dataset operations drop down menu. Along with the results visible in the dataset table, each image is scored for nine image quality scores. These quality scores are very informative and can be helpful for you to filter results from low quality images. You should read more about these quality scores from PyImq in their original publication.

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Stomata Counter

A tool to automatically estimate stomatal density from epidermal micrographs. Experimental web frontend.

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StomataCounter is experiencing an error

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Hi Users, as many of you have noticed, the image processor has not been working for some time. We apologize for the inconvenience and are working on a soltuion. Until the site is working, you have a few options. +

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  • Downlod and install the python pacakge from GitHub and work locally. Your output will be a csv of annotations and heatmaps and you won't have the ability to manually annotate images.
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  • Use the python scripts that are installed on PlantIT. You still won't get the annotation UI, but you don't have to install anything.
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  • If you want the annotation UI and don't care for the neural net annotations, use the hack users have discovered and upload images, then click on them to annotate. Your annotations will still be saved and you can export the results file nomally.
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+ We are working to resolve the issues on the site and ask for your patience. Thanks for using the tool. +

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