import os

from basic.constant import ROOT_PATH

from basic.util import readImageSet

from basic.annotationtable import readConcepts,readAnnotationsFrom

from basic.metric import getScorer

from simpleknn.bigfile import BigFile

if __name__ == '__main__':

rootpath = ROOT_PATH

trainCollection = 'voc2008train'

trainAnnotationName = 'conceptsvoc2008train.txt'

testCollection = 'voc2008val'

testAnnotationName = 'conceptsvoc2008val.txt'

feature = 'dsift'

modelName = 'fastlinear'

modelName = 'fik50'

metric = 'AP'

scorer = getScorer(metric)

if modelName.startswith('fik'):

from fiksvm.fiksvm import fiksvm_load_model as load_model

else:

from fastlinear.fastlinear import fastlinear_load_model as load_model

test_imset = readImageSet(testCollection, testCollection, rootpath=rootpath)

test_feat_file = BigFile(os.path.join(rootpath,testCollection,'FeatureData',feature))

test_renamed, test_vectors = test_feat_file.read(test_imset)

concepts = readConcepts(testCollection, testAnnotationName, rootpath=rootpath)

print ('### %s' % os.path.join(trainCollection, 'Models', trainAnnotationName, feature, modelName))

results = []

for concept in concepts:

model_file_name = os.path.join(rootpath, trainCollection, 'Models', trainAnnotationName, feature, modelName, '%s.model' % concept)

model = load_model(model_file_name)

ranklist = [(test_renamed[i], model.predict(test_vectors[i])) for i in range(len(test_renamed))]

ranklist.sort(key=lambda v:v[1], reverse=True)

names,labels = readAnnotationsFrom(testCollection, testAnnotationName, concept, skip_0=True, rootpath=rootpath)

test_name2label = dict(zip(names,labels))

sorted_labels = [test_name2label[x[0]] for x in ranklist if x[0] in test_name2label]

perf = scorer.score(sorted_labels)

print ('%s %g' % (concept, perf))

results.append((concept, perf))

mean_perf = sum([x[1] for x in results]) / len(concepts)

print ('mean%s %g' % (metric, mean_perf))