Evaluate.py

#!/usr/bin/python3

import sys
import settings.EvaluationSettings as evalSettings
from src.Classifier import Classifier
from src.Evaluator import *
import time


def PrintHelp():
    print("Usage:  Evaluate.py  $(PATH_TO_DATA_SET_CATELOG)  $(Threshold)")
    print("    or, Evaluate.py  $(PATH_TO_DATA_SET_CATELOG)")
    print("        to find the best threshold.")


def PrintResults(loss_, frameAccuracy_, isThresholdOptimized_, threshold_, videoAccuracy_, duration_):
    floatPrecision = "{0:.4f}"
    if isThresholdOptimized_:
        print("\t     loss:", floatPrecision.format(loss_),
              "     frame accuracy:", floatPrecision.format(frameAccuracy_),
              "     best frame threshold:", threshold_,
              "     video accuracy:", floatPrecision.format(videoAccuracy_),
              "     duration:", "{0:.2f}".format(duration_) + "(s)\n")
    else:
        print("\t     loss:", floatPrecision.format(loss_),
              "     frame accuracy:", floatPrecision.format(frameAccuracy_),
              "     given frame threshold:", threshold_,
              "     video accuracy:", floatPrecision.format(videoAccuracy_),
              "     duration:", "{0:.2f}".format(duration_) + "(s)\n")


if __name__ == '__main__':
    numberOfArguments = len(sys.argv)
    if (numberOfArguments == 2) or (numberOfArguments == 3):
        PATH_TO_DATA_SET_CATELOG = sys.argv[1]
        classifier = Classifier()
        classifier.Build()
        evaluator = Evaluator("evaluate", PATH_TO_DATA_SET_CATELOG, classifier)

        with tf.Session() as session:
            init = tf.global_variables_initializer()
            session.run(init)

            print("Load Model from: ", evalSettings.PATH_TO_MODEL_CHECKPOINTS)
            modelLoader = tf.train.Saver()
            modelLoader.restore(session, evalSettings.PATH_TO_MODEL_CHECKPOINTS)

            startEvaluateTime = time.time()
            if numberOfArguments == 2:
                print("Start evaluate: ", PATH_TO_DATA_SET_CATELOG, ", and find the best threshold...")
                loss, frameAccuracy, threshold, videoAccuracy = evaluator.Evaluate(session,
                                                                                   currentEpoch_=0,
                                                                                   threshold_=None)
                endEvaluateTime = time.time()
                PrintResults(loss_=loss, frameAccuracy_=frameAccuracy, isThresholdOptimized_=True,
                             threshold_=threshold, videoAccuracy_=videoAccuracy,
                             duration_=(endEvaluateTime - startEvaluateTime))

            else:
                threshold = int(sys.argv[2])
                print("Start evaluate: ", PATH_TO_DATA_SET_CATELOG, ", with threshold : ", threshold)
                loss, frameAccuracy, threshold, videoAccuracy = evaluator.Evaluate(session,
                                                                                   currentEpoch_=0,
                                                                                   threshold_=threshold)
                endEvaluateTime = time.time()
                PrintResults(loss_=loss, frameAccuracy_=frameAccuracy, isThresholdOptimized_=False,
                             threshold_=threshold, videoAccuracy_=videoAccuracy,
                             duration_=(endEvaluateTime - startEvaluateTime))

        evaluator.Release()

    else:
        PrintHelp()