Create Predictive.py

Predictive.py

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+import numpy as np
+import cv2
+import glob
+from tensorflow.keras.models import Sequential, load_model
+from sklearn.preprocessing import LabelBinarizer
+from sklearn.model_selection import train_test_split
+from tensorflow.keras.utils import to_categorical  # Correct import for TensorFlow 2.4.0
+
+global size
+size = 100
+model = Sequential()
+model = load_model('D:/Downloads/potholes/sample.h5')
+
+# Load Testing data: non-pothole
+nonPotholeTestImages = glob.glob("D:/Downloads/potholes/plain/*.jpg")
+test2 = [cv2.imread(img, 0) for img in nonPotholeTestImages]
+for i in range(0, len(test2)):
+    test2[i] = cv2.resize(test2[i], (size, size))
+temp4 = np.asarray(test2)
+
+# Load Testing data: potholes
+potholeTestImages = glob.glob("D:/Downloads/potholes/pot/*.jpg")
+test1 = [cv2.imread(img, 0) for img in potholeTestImages]
+for i in range(0, len(test1)):
+    test1[i] = cv2.resize(test1[i], (size, size))
+temp3 = np.asarray(test1)
+
+X_test = []
+X_test.extend(temp3)
+X_test.extend(temp4)
+X_test = np.asarray(X_test)
+
+X_test = X_test.reshape(X_test.shape[0], size, size, 1)
+
+# Prepare labels for testing data
+y_test1 = np.ones([temp3.shape[0]], dtype=int)
+y_test2 = np.zeros([temp4.shape[0]], dtype=int)
+
+y_test = []
+y_test.extend(y_test1)
+y_test.extend(y_test2)
+y_test = np.asarray(y_test)
+
+y_test = to_categorical(y_test)  # Use to_categorical for label encoding
+
+# Predict the classes
+tests = model.predict(X_test)
+tests = np.argmax(tests, axis=1)  # Convert probabilities to class labels (0 or 1)
+
+for i in range(len(X_test)):
+    print(">>> Predicted=%s" % (tests[i]))
+
+# Optionally, evaluate the model
+# metrics = model.evaluate(X_test, y_test)
+# for metric_i in range(len(model.metrics_names)):
+#     metric_name = model.metrics_names[metric_i]
+#     metric_value = metrics[metric_i]
+#     print('{}: {}'.format(metric_name, metric_value))