Updating the input image sizes to have a higher resolution. Currently unclean data is reaching 70% accuracy.

ModelTraining/graphResults.py

@@ -0,0 +1,62 @@
+import tensorflow as tf
+import pandas as pd
+import numpy as np
+import os
+import seaborn as sn
+import matplotlib.pyplot as plt
+
+from sklearn.metrics import accuracy_score, confusion_matrix
+
+
+def print_preds(reals, preds):
+    acc = accuracy_score(reals, predicts)
+    conf_mat = confusion_matrix(reals, predicts)
+    print("Testing accuracy score is ", acc)
+    print("Confusion Matrix", conf_mat)
+
+    df_cm = pd.DataFrame(conf_mat, index=[i for i in ["Block", "Meter", "Sign"]],
+                         columns=[i for i in ["Block", "Meter", "Sign"]])
+    plt.figure(figsize=(10, 7))
+    sn.heatmap(df_cm, annot=True)
+    plt.show()
+
+
+data = pd.read_csv("sub1_non_transfer.csv")
+files_list = list(data["fname"])
+reals = list(data["true_val"])
+predicts = list(data["prediction"])
+
+reals2 = []
+wrong_files = []
+for root, dirs, files in os.walk(".\\photos"):
+    for file in files:
+        if file in files_list:
+            x = data.loc[data["fname"] == file].values[0]
+            if (x[1] != x[2]):
+                print(x)
+                wrong_files.append((os.path.join(root, file), x[1]))
+            reals2.append(root.split("\\")[-1])
+
+print_preds(reals, predicts)
+print_preds(reals2, predicts)
+
+import matplotlib.image as mpimg
+from shutil import copyfile, rmtree
+
+for file, pred in wrong_files:
+    print(file)
+    # img = mpimg.imread(file)
+    # # end
+    # # from now on you can use img as an image, but make sure you know what you are doing!
+    # imgplot = plt.imshow(img)
+    dest = file.split("\\")
+    dest[1] = "failed"
+    dest[-1] = pred + dest[-1]
+    dest = "\\".join(dest)
+    if not os.path.exists(os.path.dirname(dest)):
+        try:
+            os.makedirs(os.path.dirname(dest))
+        except Exception as e:
+            print(e)
+    copyfile(file, dest)
+    plt.show()