Delete predict.py

predict.py

-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-
-@author: Gunasinghe M.D.
-"""
-
-import pandas as pd
-import numpy as np
-import cv2
-import argparse
-
-from keras.models import load_model
-from keras.preprocessing.image import ImageDataGenerator
-from sklearn.metrics import confusion_matrix, accuracy_score
-
-from myutil import probas_to_classes
-
-
-# Loading and compiling presaved trained CNN
-model = load_model('drawing_classification.h5')
-
-label = {0: "Circle", 1: "Square", 2: "Triangle"}
-
-
-def predict_one(file_name):
-
-    img = cv2.imread(file_name)
-    img = cv2.resize(img, (28, 28))
-    img = np.reshape(img, [1, 28, 28, 3])
-    classes = model.predict_classes(img)[0]
-    category = label[classes]
-    print("\nAnd {1} is the {0}".format(category, file_name))
-    # return category
-
-
-def predict_dataset(input_dir):
-    test_datagen = ImageDataGenerator(rescale=1. / 255)
-    test_generator = test_datagen.flow_from_directory("shapes/test",
-                                                      target_size=(28, 28),
-                                                      color_mode="rgb",
-                                                      shuffle=False,
-                                                      class_mode='categorical',
-                                                      batch_size=1)
-    filenames = test_generator.filenames
-    nb_samples = len(filenames)
-    predict = model.predict_generator(test_generator, steps=nb_samples)
-    return predict, test_generator
-
-
-def main():
-    # Instantiate the parser
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        '--testdata', help='Classify images on test data', action='store_true')
-    parser.add_argument(
-        '--validationdata', help='Classify images on test data', action='store_true')
-
-    parser.add_argument('--image', help='Input your image file name')
-    args = parser.parse_args()
-
-    on_dataset = False
-    if args.testdata:
-        print("Classify images on test dataset")
-        on_dataset = True
-        input_dir = "shapes/test"
-    if args.validationdata:
-        print("Classify images on validation dataset")
-        on_dataset = True
-        input_dir = "shapes/validation"
-
-    if on_dataset:
-        predict, test_generator = predict_dataset(input_dir)
-        y_pred = probas_to_classes(predict)
-        y_true = test_generator.classes
-        X_images = test_generator.filenames
-        cm = confusion_matrix(y_true, y_pred)
-        ac = accuracy_score(y_true, y_pred)
-        for ele in list(zip(X_images, y_true, y_pred)):
-            print(ele)
-    else:
-        file_name = args.image
-        predict_one(file_name)
-
-if __name__ == '__main__':
-    main()