Delete CNN.txt

CNN.txt

-# Import necessary libraries
-import matplotlib.pyplot as plt
-import numpy as np
-import cv2
-import os
-import PIL
-import tensorflow as tf
-from PIL import Image
-from tensorflow import keras
-from tensorflow.keras import layers
-from tensorflow.keras.models import Sequential
-
-# Define the path to the dataset directory
-data_dir='.\\datasets\\balls_images'
-
-# Print the path
-print(data_dir)
-
-# Convert path to a pathlib object
-import pathlib
-data_dir = pathlib.Path(data_dir)
-
-# Print the pathlib object
-print(data_dir)
-
-# List the image files in the dataset directory
-list(data_dir.glob('*/*.jpg'))[:5]
-
-# Count the total number of images in the dataset
-image_count = len(list(data_dir.glob('*/*.jpg')))
-print(image_count)
-
-# Define the list of legal ball images
-legal_ball = list(data_dir.glob('legal_ball/*'))
-
-# Display an example image from the legal ball images
-PIL.Image.open(str(legal_ball[1]))
-
-# Define a dictionary containing the paths of all ball images grouped by ball type
-balls_images_dict = {
-    'no_ball': list(data_dir.glob('no_ball/*')),
-    'legal_ball': list(data_dir.glob('legal_ball/*')),
-}
-
-# Define a dictionary containing the class labels for each ball type
-balls_labels_dict = {
-    'no_ball': 0,
-    'legal_ball': 1,
-}
-
-# Display the paths to the first five 'no_ball' images
-balls_images_dict['no_ball'][:5]
-
-# Load an image using OpenCV and display its shape
-img = cv2.imread(str(balls_images_dict['no_ball'][0]))
-img.shape
-
-# Resize the image and display its new shape
-cv2.resize(img,(180,180)).shape
-
-# Initialize empty lists for the features and labels
-X, y = [], []
-
-# Iterate through each ball type and its corresponding images
-for ball_type, images in balls_images_dict.items():
-    for image in images:
-        # Load the image using OpenCV and resize it to (224,224)
-        img = cv2.imread(str(image))
-        resized_img = cv2.resize(img,(224,224))
-        # Append the resized image to the feature list and its label to the label list
-        X.append(resized_img)
-        y.append(balls_labels_dict[ball_type])
-
-# Convert the feature and label lists to numpy arrays
-X = np.array(X)
-y = np.array(y)
-
-# Split the dataset into training and testing sets
-from sklearn.model_selection import train_test_split
-X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
-
-# Normalize the pixel values of the training and testing sets
-X_train_scaled = X_train / 255
-X_test_scaled = X_test / 255
-
-# Define the number of classes
-num_classes = 2
-
-# Define the convolutional neural network model
-model = Sequential([
-  layers.Conv2D(16, 3, padding='same', activation='relu'),
-  layers.MaxPooling2D(),
-  layers.Conv2D(32, 3, padding='same', activation='relu'),
-  layers.MaxPooling2D(),
-  layers.Conv2D(64, 3, padding='same', activation='relu'),
-  layers.MaxPooling2D(),
-  layers.Flatten(),
-  layers.Dense(128, activation='relu'),
-  layers.Dense(num_classes)
-])
-
-# Compile the model
-model.compile(optimizer='adam',
-              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
-              metrics=['accuracy'])
-
-# Train the model
-model.fit(X_train_scaled, y_train, epochs=5)
-
-model.evaluate(X_test_scaled,y_test)
-
-loss, accuracy = model.evaluate(X_test_scaled,y_test)
-
-print('Accuracy:', accuracy)
-
-plt.imshow(X[0])
-
-# save the model
-model.save("balls_model.h5")
-
-
-from tensorflow.keras.models import load_model
-
-loaded_model = load_model("balls_model.h5")