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
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)
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'
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__))
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)
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! :)")
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)
def build_database(dataset): """ Populate database with sample dataset. """ from build_database import build_database build_database(dataset)