コード例 #1
0
def test_client():
    connex_app = create_app()
    flask_app = connex_app.app
    tc = flask_app.test_client()

    with flask_app.app_context():
        db.create_all()
        db.session.commit()

        from build_database import build_database
        build_database('test-small')

        yield tc
コード例 #2
0
import numpy as np
from get_params import get_params
from build_database import build_database
from get_features import get_features
from train_classifier import train_classifier
from classify import classify
from eval_classification import eval_classification
from eval_classification import plot_confusion_matrix
import warnings
warnings.filterwarnings("ignore")

#Extraccio dels parametres
params = get_params()
#Creacio de la base de dades
params['split'] = 'train'
build_database(params)
params['split'] = 'val'
build_database(params)
#Extraccio de les caracteri­stiques
get_features(params)
#Entrenem un model de classificacio
train_classifier(params)
#Classificacio
classify(params)
#Avaluacio de la classificacio
f1, precision, recall, accuracy, cm, labels = eval_classification(params)
print "Mesures:\n"
print f1
print "-F1:", np.mean(f1)
print "-Precision:", np.mean(precision)
print "-Recall:", np.mean(recall)
コード例 #3
0
import os
from build_database import build_database
from get_features import get_features
from rank import rank
from classify import classify
from evaluate_ranking import evaluate_ranking
from evaluate_classification import evaluate_classification

ruta1=os.path.dirname(os.path.abspath(__file__))+'\\TerrassaBuildings900\\val\\images'
ruta2=os.path.dirname(os.path.abspath(__file__))+'\\TerrassaBuildings900\\train\\images'
savepath1=os.path.dirname(os.path.abspath(__file__))+'\\TerrassaBuildings900\\val'
savepath2=os.path.dirname(os.path.abspath(__file__))+'\\TerrassaBuildings900\\train'

build_database(ruta1,savepath1);
build_database(ruta2,savepath2);

get_features(ruta1,savepath1,savepath1);
get_features(ruta2,savepath2,savepath2);

savepath_principal=os.path.dirname(os.path.abspath(__file__))
features_val=os.path.dirname(os.path.abspath(__file__))+'\\TerrassaBuildings900\\val'
features_train=os.path.dirname(os.path.abspath(__file__))+'\\TerrassaBuildings900\\train'
rank(features_val,features_train,savepath_principal);

feat=os.path.dirname(os.path.abspath(__file__))+'\\TerrassaBuildings900\\val\\Features.txt'
path_out=os.path.dirname(os.path.abspath(__file__))
labels=os.path.dirname(os.path.abspath(__file__))+'\\labels.txt'
classify(feat,path_out,labels)

path=os.path.dirname(os.path.abspath(__file__))
Gt_val_test=os.path.dirname(os.path.abspath(__file__))+'\\TerrassaBuildings900\\val\\annotation.txt'
コード例 #4
0
from get_features import get_features
from rank import rank
from classify import classify
from evaluate_ranking import evaluate_ranking
from evaluate_classification import evaluate_classification

ruta1 = os.path.dirname(
    os.path.abspath(__file__)) + '\\TerrassaBuildings900\\val\\images'
ruta2 = os.path.dirname(
    os.path.abspath(__file__)) + '\\TerrassaBuildings900\\train\\images'
savepath1 = os.path.dirname(
    os.path.abspath(__file__)) + '\\TerrassaBuildings900\\val'
savepath2 = os.path.dirname(
    os.path.abspath(__file__)) + '\\TerrassaBuildings900\\train'

build_database(ruta1, savepath1)
build_database(ruta2, savepath2)

get_features(ruta1, savepath1, savepath1)
get_features(ruta2, savepath2, savepath2)

savepath_principal = os.path.dirname(os.path.abspath(__file__))
features_val = os.path.dirname(
    os.path.abspath(__file__)) + '\\TerrassaBuildings900\\val'
features_train = os.path.dirname(
    os.path.abspath(__file__)) + '\\TerrassaBuildings900\\train'
rank(features_val, features_train, savepath_principal)

feat = os.path.dirname(
    os.path.abspath(__file__)) + '\\TerrassaBuildings900\\val\\Features.txt'
path_out = os.path.dirname(os.path.abspath(__file__))
コード例 #5
0
ファイル: Main_Sessio6.py プロジェクト: gdsa-upc/Egara-View
import numpy as np
from get_params import get_params
from build_database import build_database
from get_features import get_features
from train_classifier import train_classifier
from classify import classify
from eval_classification import eval_classification
from eval_classification import plot_confusion_matrix
import warnings
warnings.filterwarnings("ignore")

#Extraccio dels parametres
params=get_params()
#Creacio de la base de dades
params['split']='train'
build_database(params)
params['split']='val'
build_database(params)
#Extraccio de les caracteri­stiques
get_features(params)
#Entrenem un model de classificacio
train_classifier(params)
#Classificacio
classify(params)
#Avaluacio de la classificacio
f1, precision, recall, accuracy,cm, labels = eval_classification(params)
print "Mesures:\n"    
print f1
print "-F1:", np.mean(f1)
print "-Precision:", np.mean(precision)
print "-Recall:", np.mean(recall)
コード例 #6
0
for indicator_id in all_indicator_ids:
    if any([glob_match(glob, indicator_id) for glob in indicator_globs]):
        indicators.append(indicator_id)

# Run the pipeline for each indicator
for indicator in indicators:
    pipeline = indicator.split("_")[0]

    print("Extracting indicator", indicator, "via pipeline", pipeline, "...")

    # Load the pipeline
    module = importlib.import_module("." + pipeline, "pipelines")
    run_pipeline = getattr(module, "run_pipeline")

    # Run the pipeline
    run_pipeline(indicator)
    print(
        "Extracting indicator", indicator, "via pipeline", pipeline, "...", "Done! :)"
    )

# Rebuild the database
print("Rebuilding database", "...")
build_database()
print("Rebuilding database", "...", "Done! :)")

# Rebuild badges
print("Remaking badges", "...")
is_full_extraction = len(indicators) == len(all_indicator_ids)
make_badges(full_extraction=is_full_extraction)
print("Remaking badges", "...", "Done! :)")
コード例 #7
0
import os
from build_database import build_database

ruta = os.path.dirname(os.path.abspath(__file__))
path1 = ruta + "\\TerrassaBuildings900\\train\\images"
path2 = ruta + "\\TerrassaBuildings900\\valid\\images"
savepath = ruta + "\\Build_database"
build_database(path1, savepath)
build_database(path2, savepath)
コード例 #8
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def build_database(dataset):
    """
    Populate database with sample dataset.
    """
    from build_database import build_database
    build_database(dataset)