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