SOL4Py Samples

We have used RoadSigns on the web-sites to create a roadsigns_base_dataset to TorchRoadSignsDataSetAugmentor:

JAF

JAPAN DRIVER'S LICENCE

Road signs in Japan

See aslo the web site: Traffic Signs Classification with a Convolutional Neural Network

We have created the following python scripts to create TorchRoadSignsClassifer sample program.

TorchRoadSignsDataset

TorchRoadSignsDataSetAugmentor

TorchRoadSignsModel

The training process for TorchRoadSignsModel can be monitored by TrainingProcessMonitor program as show below






The following screen captures are some classification results by TorchRoadSignsClassifer.py programs.
From these pictures, you can see some incorrect classifications.

>

#******************************************************************************
#
#  Copyright (c) 2018-2019 Antillia.com TOSHIYUKI ARAI. ALL RIGHTS RESERVED.
#
#    This program is free software: you can redistribute it and/or modify
#    it under the terms of the GNU General Public License as published by
#    the Free Software Foundation, either version 3 of the License, or
#    (at your option) any later version.
#
#    This program is distributed in the hope that it will be useful,
#    but WITHOUT ANY WARRANTY; without even the implied warranty of
#    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
#    GNU General Public License for more details.
#
#    You should have received a copy of the GNU General Public License
#    along with this program.  If not, see <http://www.gnu.org/licenses/>.
#
#******************************************************************************

 
# 2019/07/13
# 2019/09/16

#  TorchRoadSignsClassifier.py

# encodig: utf-8

import sys
import os
import time
import traceback
import pandas as pd
import seaborn as sns

import matplotlib.pyplot as plt
import numpy as np


sys.path.append('../../')


from SOL4Py.torch.ZTorchImagePreprocessor import ZTorchImagePreprocessor
from SOL4Py.ZTorchImageClassifierView import *

from TorchRoadSignsModel import *

ROADSIGNS = 0


############################################################
# Classifier View

class MainView(ZTorchImageClassifierView):  
  # Class variables

  # ClassifierView Constructor
  def __init__(self, title, x, y, width, height):
    super(MainView, self).__init__(title, x, y, width, height,
                     datasets = {"RoadSigns": ROADSIGNS})
    
    self.model_loaded = False
    
    self.image       = None

    self.resize = 64 #128
    self.crop   = 64 #128

    #  Load trained model
    self.classes = self.get_class_names()
    
    self.model = TorchRoadSignsModel(self.dataset_id, mainv=self)
    
    if self.model.is_trained():
      self.model.load_dataset()
      self.model.create()
      self.model.load()    # Load a trained weight
      #self.model.evaluate()
      self.model_loaded = True
    else:
      print("You have to create a model file")
      print("Please run: python RoadSignsModel.py " + str(self.dataset_id))
      QMessageBox.warning(self, "MNIST", 
           "Mode file is missing.\nPlease run: python RoadSignsModel.py " + str(self.dataset_id))

    self.show()


  def classify(self):
    self.write("--------------------------------------------")
    self.write("classify start.")
    self.write(self.filename)
    input = Variable(self.image_tensor)
    index = self.model.predict(input)
    label   = self.classes[index]
    self.write("Prediction: {}".format(label) )
    self.write("classify end.")


############################################################
#    
if main(__name__):

  try:
    app_name  = os.path.basename(sys.argv[0])
    applet    = QApplication(sys.argv)
  
    main_view = MainView(app_name, 40, 40, 900, 500)
    main_view.show ()

    applet.exec_()

  except:
    traceback.print_exc()