add trainer

trainer.py

@@ -0,0 +1,76 @@
+import numpy as np
+import pandas as pd
+import os
+from sklearn.metrics import classification_report
+import seaborn as sn
+from sklearn.utils import shuffle
+import matplotlib.pyplot as plt
+import cv2
+import tensorflow as tf
+from tqdm import tqdm
+
+sn.set(font_scale=1.4)
+
+class_names = ['buildings', 'forest', 'glacier', 'mountain', 'sea', 'street']
+class_names_label = {class_name: i for i, class_name in enumerate(class_names)}
+nb_classes = len(class_names)
+print(class_names_label)
+IMAGE_SIZE = (150, 150)
+
+
+def load_data():
+    DIRECTORY = "C:/Users/Professional/PycharmProjects/intelimageclassifier/imgdataset"
+    CATEGORY = ["seg_train", "seg_test"]
+    output = []
+    for category in CATEGORY:
+        path = os.path.join(DIRECTORY, category)
+        images = []
+        labels = []
+        print("Loading {}".format(category))
+        for folder in os.listdir(path):
+            label = class_names_label[folder]
+            # Iterate through each image in our folder
+            for file in os.listdir(os.path.join(path, folder)):
+                # Get the path name of the image
+                img_path = os.path.join(os.path.join(path, folder), file)
+                # Open and resize the ing
+                image = cv2.imread(img_path)
+                image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+                image = cv2.resize(image, IMAGE_SIZE)
+                # Append the image and its corresponding Label to the output
+                images.append(image)
+                labels.append(label)
+        # Convert both the images and labels to a numpy array
+        images = np.array(images, dtype='float32')
+        labels = np.array(labels, dtype='int32')
+        output.append((images, labels))
+    return output
+
+
+(train_images, train_labels), (test_images, test_labels) = load_data()
+train_images, train_labels = shuffle(train_images, train_labels, random_state=25)
+print("Train: ", train_images.shape, train_labels.shape)
+print("Test: ", test_images.shape, test_labels.shape)
+
+model = tf.keras.models.Sequential([
+    tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)),
+    tf.keras.layers.MaxPooling2D(2, 2),
+    tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),
+    tf.keras.layers.MaxPooling2D(2, 2),
+    tf.keras.layers.Conv2D(128, (3, 3), activation='relu'),
+    tf.keras.layers.MaxPooling2D(2, 2),
+    tf.keras.layers.Conv2D(128, (3, 3), activation='relu'),
+    tf.keras.layers.MaxPooling2D(2, 2),
+    tf.keras.layers.Flatten(),
+    tf.keras.layers.Dense(512, activation='relu'),
+    tf.keras.layers.Dense(6, activation='softmax')
+])
+
+model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
+model.fit(train_images, train_labels, epochs=10, validation_split=0.1)
+
+# Evaluate the model
+model.evaluate(test_images, test_labels)
+
+# save the model
+model.save("model.h5")