Mynuddin-dev · June 30, 2022 08:35
diff --git a/traffic.py b/traffic.py
 import cv2
 import glob
 import numpy as np
 import os
 import sys
 import tensorflow as tf

 from sklearn.model_selection import train_test_split

 EPOCHS = 10
 IMG_WIDTH = 30
 IMG_HEIGHT = 30
 NUM_CATEGORIES = 43
 TEST_SIZE = 0.4


 def main():

    # Check command-line arguments
    if len(sys.argv) not in [2, 3]:
        sys.exit("Usage: python traffic.py data_directory [model.h5]")

    # Get image arrays and labels for all image files
    images, labels = load_data(sys.argv[1])
    print(len(labels))
    print(images[0].shape)

    # Split data into training and testing sets
    labels = tf.keras.utils.to_categorical(labels)
    x_train, x_test, y_train, y_test = train_test_split(
        np.array(images), np.array(labels), test_size=TEST_SIZE
    )

    # Get a compiled neural network
    model = get_model()

    # Fit model on training data
    model.fit(x_train, y_train, epochs=EPOCHS)

    # Evaluate neural network performance
    model.evaluate(x_test,  y_test, verbose=2)

    # Save model to file
    if len(sys.argv) == 3:
        filename = sys.argv[2]
        model.save(filename)
        print(f"Model saved to {filename}.")


 def load_data(data_dir):
    """
    Load image data from directory `data_dir`.
    Assume `data_dir` has one directory named after each category, numbered
    0 through NUM_CATEGORIES - 1. Inside each category directory will be some
    number of image files.
    Return tuple `(images, labels)`. `images` should be a list of all
    of the images in the data directory, where each image is formatted as a
    numpy ndarray with dimensions IMG_WIDTH x IMG_HEIGHT x 3. `labels` should
    be a list of integer labels, representing the categories for each of the
    corresponding `images`.
    """

    images=[]
    labels = []

    path=data_dir
    train_dataset = ()

    for folder_name in os.listdir(path):

        # print(folder_name)

        Picturss = glob.glob(path+'/'+folder_name+'/*.ppm')

        for i in Picturss:
            img = cv2.imread(str(i))

            # cv2.imshow("jc",img)
            # cv2.waitKey(0)
            # cv2.destroyAllWindows()

            img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
            img = cv2.resize(img, (IMG_HEIGHT, IMG_WIDTH))

            images.append(np.array(img))
            labels.append(folder_name)


    labels=np.array(labels,dtype=np.int64)
    return (images, labels)


    # raise NotImplementedError


 def get_model():
    """
    Returns a compiled convolutional neural network model. Assume that the
    `input_shape` of the first layer is `(IMG_WIDTH, IMG_HEIGHT, 3)`.
    The output layer should have `NUM_CATEGORIES` units, one for each category.
    """
    # raise NotImplementedError
 # Create a convolutional neural network
    model = tf.keras.models.Sequential([

        # Convolutional layer. Learn 32 filters using a 3x3 kernel
        tf.keras.layers.Conv2D(
            32, (3, 3), activation="relu", input_shape=(IMG_HEIGHT, IMG_WIDTH, 3)
        ),

        # Max-pooling layer, using 2x2 pool size
        tf.keras.layers.MaxPooling2D(pool_size=(2, 2)),

        # Flatten units
        tf.keras.layers.Flatten(),

        # Add a hidden layer with dropout
        tf.keras.layers.Dense(900, activation="relu"),
        tf.keras.layers.Dropout(0.3),

        # Add an output layer with output units for all 10 digits
        tf.keras.layers.Dense(NUM_CATEGORIES, activation="softmax")
    ])

 # Train neural network
    model.compile(
        optimizer="adam",
        loss="categorical_crossentropy",
        metrics=["accuracy"]
    )

    return model

 if __name__ == "__main__":
    main()
	import cv2
	import glob
	import numpy as np
	import os
	import sys
	import tensorflow as tf

	from sklearn.model_selection import train_test_split

	EPOCHS = 10
	IMG_WIDTH = 30
	IMG_HEIGHT = 30
	NUM_CATEGORIES = 43
	TEST_SIZE = 0.4


	def main():

	# Check command-line arguments
	if len(sys.argv) not in [2, 3]:
	sys.exit("Usage: python traffic.py data_directory [model.h5]")

	# Get image arrays and labels for all image files
	images, labels = load_data(sys.argv[1])
	print(len(labels))
	print(images[0].shape)

	# Split data into training and testing sets
	labels = tf.keras.utils.to_categorical(labels)
	x_train, x_test, y_train, y_test = train_test_split(
	np.array(images), np.array(labels), test_size=TEST_SIZE
	)

	# Get a compiled neural network
	model = get_model()

	# Fit model on training data
	model.fit(x_train, y_train, epochs=EPOCHS)

	# Evaluate neural network performance
	model.evaluate(x_test, y_test, verbose=2)

	# Save model to file
	if len(sys.argv) == 3:
	filename = sys.argv[2]
	model.save(filename)
	print(f"Model saved to {filename}.")


	def load_data(data_dir):
	"""
	Load image data from directory `data_dir`.
	Assume `data_dir` has one directory named after each category, numbered
	0 through NUM_CATEGORIES - 1. Inside each category directory will be some
	number of image files.
	Return tuple `(images, labels)`. `images` should be a list of all
	of the images in the data directory, where each image is formatted as a
	numpy ndarray with dimensions IMG_WIDTH x IMG_HEIGHT x 3. `labels` should
	be a list of integer labels, representing the categories for each of the
	corresponding `images`.
	"""

	images=[]
	labels = []

	path=data_dir
	train_dataset = ()

	for folder_name in os.listdir(path):

	# print(folder_name)

	Picturss = glob.glob(path+'/'+folder_name+'/*.ppm')

	for i in Picturss:
	img = cv2.imread(str(i))

	# cv2.imshow("jc",img)
	# cv2.waitKey(0)
	# cv2.destroyAllWindows()

	img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
	img = cv2.resize(img, (IMG_HEIGHT, IMG_WIDTH))

	images.append(np.array(img))
	labels.append(folder_name)


	labels=np.array(labels,dtype=np.int64)
	return (images, labels)


	# raise NotImplementedError


	def get_model():
	"""
	Returns a compiled convolutional neural network model. Assume that the
	`input_shape` of the first layer is `(IMG_WIDTH, IMG_HEIGHT, 3)`.
	The output layer should have `NUM_CATEGORIES` units, one for each category.
	"""
	# raise NotImplementedError
	# Create a convolutional neural network
	model = tf.keras.models.Sequential([

	# Convolutional layer. Learn 32 filters using a 3x3 kernel
	tf.keras.layers.Conv2D(
	32, (3, 3), activation="relu", input_shape=(IMG_HEIGHT, IMG_WIDTH, 3)
	),

	# Max-pooling layer, using 2x2 pool size
	tf.keras.layers.MaxPooling2D(pool_size=(2, 2)),

	# Flatten units
	tf.keras.layers.Flatten(),

	# Add a hidden layer with dropout
	tf.keras.layers.Dense(900, activation="relu"),
	tf.keras.layers.Dropout(0.3),

	# Add an output layer with output units for all 10 digits
	tf.keras.layers.Dense(NUM_CATEGORIES, activation="softmax")
	])

	# Train neural network
	model.compile(
	optimizer="adam",
	loss="categorical_crossentropy",
	metrics=["accuracy"]
	)

	return model

	if __name__ == "__main__":
	main()