wip

pred_test.py

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+# # %% [markdown]
+# # # Prediction on the test data
+
+# # %%
+import os
+ 
+import pandas as pd
+import numpy as np
+import tensorflow as tf
+
+
+model = tf.keras.models.load_model('model_pred/sign_car_detection_model')
+
+# Get the list of all files and directories
+path = "test_data"
+dir_list = os.listdir(path)
+ 
+print(dir_list)
+
+# %%
+from sklearn.preprocessing import OneHotEncoder, LabelEncoder
+tf.keras.utils.load_img
+
+class_names = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z', 'del', 'nothing', 'space']
+
+img_height=256
+img_width=256
+actual=[]
+pred=[]
+for i in dir_list:
+    for j in os.listdir(path+'/'+i):
+        file_path = path+'/'+i + '/' + j
+        actual.append(i)
+        test_image = tf.keras.utils.load_img(file_path, target_size = (256, 256))
+        test_image = tf.keras.utils.img_to_array(test_image)
+        test_image = np.expand_dims(test_image, axis = 0)
+        result = model.predict(test_image)
+        pred.append(class_names[np.argmax(result)])
+
+from sklearn.metrics import confusion_matrix, classification_report
+from sklearn.metrics import accuracy_score
+
+print("Test accuracy=",accuracy_score(pred,actual))
+print("Classification report:\n",classification_report(pred,actual))
+
+
+# %%
+
+
+