wehub-resource-sync
282 lines
8.3 KiB
Plaintext
282 lines
8.3 KiB
Plaintext
{
|
|
"cells": [
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
|
|
},
|
|
"source": [
|
|
"This is a companion notebook for the book [Deep Learning with Python, Second Edition](https://www.manning.com/books/deep-learning-with-python-second-edition?a_aid=keras&a_bid=76564dff). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, figures, and pseudocode.\n\n**If you want to be able to follow what's going on, I recommend reading the notebook side by side with your copy of the book.**\n\nThis notebook was generated for TensorFlow 2.6."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
|
|
},
|
|
"source": [
|
|
"# Advanced deep learning for computer vision"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
|
|
},
|
|
"source": [
|
|
"## Three essential computer vision tasks"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
|
|
},
|
|
"source": [
|
|
"## An image segmentation example"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"!wget http://www.robots.ox.ac.uk/~vgg/data/pets/data/images.tar.gz\n",
|
|
"!wget http://www.robots.ox.ac.uk/~vgg/data/pets/data/annotations.tar.gz\n",
|
|
"!tar -xf images.tar.gz\n",
|
|
"!tar -xf annotations.tar.gz"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"import os\n",
|
|
"\n",
|
|
"input_dir = \"images/\"\n",
|
|
"target_dir = \"annotations/trimaps/\"\n",
|
|
"\n",
|
|
"input_img_paths = sorted(\n",
|
|
" [os.path.join(input_dir, fname)\n",
|
|
" for fname in os.listdir(input_dir)\n",
|
|
" if fname.endswith(\".jpg\")])\n",
|
|
"target_paths = sorted(\n",
|
|
" [os.path.join(target_dir, fname)\n",
|
|
" for fname in os.listdir(target_dir)\n",
|
|
" if fname.endswith(\".png\") and not fname.startswith(\".\")])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"import matplotlib.pyplot as plt\n",
|
|
"from tensorflow.keras.utils import load_img, img_to_array\n",
|
|
"\n",
|
|
"plt.axis(\"off\")\n",
|
|
"plt.imshow(load_img(input_img_paths[9]))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"def display_target(target_array):\n",
|
|
" normalized_array = (target_array.astype(\"uint8\") - 1) * 127\n",
|
|
" plt.axis(\"off\")\n",
|
|
" plt.imshow(normalized_array[:, :, 0])\n",
|
|
"\n",
|
|
"img = img_to_array(load_img(target_paths[9], color_mode=\"grayscale\"))\n",
|
|
"display_target(img)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"import numpy as np\n",
|
|
"import random\n",
|
|
"\n",
|
|
"img_size = (200, 200)\n",
|
|
"num_imgs = len(input_img_paths)\n",
|
|
"\n",
|
|
"random.Random(1337).shuffle(input_img_paths)\n",
|
|
"random.Random(1337).shuffle(target_paths)\n",
|
|
"\n",
|
|
"def path_to_input_image(path):\n",
|
|
" return img_to_array(load_img(path, target_size=img_size))\n",
|
|
"\n",
|
|
"def path_to_target(path):\n",
|
|
" img = img_to_array(\n",
|
|
" load_img(path, target_size=img_size, color_mode=\"grayscale\"))\n",
|
|
" img = img.astype(\"uint8\") - 1\n",
|
|
" return img\n",
|
|
"\n",
|
|
"input_imgs = np.zeros((num_imgs,) + img_size + (3,), dtype=\"float32\")\n",
|
|
"targets = np.zeros((num_imgs,) + img_size + (1,), dtype=\"uint8\")\n",
|
|
"for i in range(num_imgs):\n",
|
|
" input_imgs[i] = path_to_input_image(input_img_paths[i])\n",
|
|
" targets[i] = path_to_target(target_paths[i])\n",
|
|
"\n",
|
|
"num_val_samples = 1000\n",
|
|
"train_input_imgs = input_imgs[:-num_val_samples]\n",
|
|
"train_targets = targets[:-num_val_samples]\n",
|
|
"val_input_imgs = input_imgs[-num_val_samples:]\n",
|
|
"val_targets = targets[-num_val_samples:]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"from tensorflow import keras\n",
|
|
"from tensorflow.keras import layers\n",
|
|
"\n",
|
|
"def get_model(img_size, num_classes):\n",
|
|
" inputs = keras.Input(shape=img_size + (3,))\n",
|
|
" x = layers.Rescaling(1./255)(inputs)\n",
|
|
"\n",
|
|
" x = layers.Conv2D(64, 3, strides=2, activation=\"relu\", padding=\"same\")(x)\n",
|
|
" x = layers.Conv2D(64, 3, activation=\"relu\", padding=\"same\")(x)\n",
|
|
" x = layers.Conv2D(128, 3, strides=2, activation=\"relu\", padding=\"same\")(x)\n",
|
|
" x = layers.Conv2D(128, 3, activation=\"relu\", padding=\"same\")(x)\n",
|
|
" x = layers.Conv2D(256, 3, strides=2, padding=\"same\", activation=\"relu\")(x)\n",
|
|
" x = layers.Conv2D(256, 3, activation=\"relu\", padding=\"same\")(x)\n",
|
|
"\n",
|
|
" x = layers.Conv2DTranspose(256, 3, activation=\"relu\", padding=\"same\")(x)\n",
|
|
" x = layers.Conv2DTranspose(256, 3, activation=\"relu\", padding=\"same\", strides=2)(x)\n",
|
|
" x = layers.Conv2DTranspose(128, 3, activation=\"relu\", padding=\"same\")(x)\n",
|
|
" x = layers.Conv2DTranspose(128, 3, activation=\"relu\", padding=\"same\", strides=2)(x)\n",
|
|
" x = layers.Conv2DTranspose(64, 3, activation=\"relu\", padding=\"same\")(x)\n",
|
|
" x = layers.Conv2DTranspose(64, 3, activation=\"relu\", padding=\"same\", strides=2)(x)\n",
|
|
"\n",
|
|
" outputs = layers.Conv2D(num_classes, 3, activation=\"softmax\", padding=\"same\")(x)\n",
|
|
"\n",
|
|
" model = keras.Model(inputs, outputs)\n",
|
|
" return model\n",
|
|
"\n",
|
|
"model = get_model(img_size=img_size, num_classes=3)\n",
|
|
"model.summary()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"model.compile(optimizer=\"rmsprop\", loss=\"sparse_categorical_crossentropy\")\n",
|
|
"\n",
|
|
"callbacks = [\n",
|
|
" keras.callbacks.ModelCheckpoint(\"oxford_segmentation.keras\",\n",
|
|
" save_best_only=True)\n",
|
|
"]\n",
|
|
"\n",
|
|
"history = model.fit(train_input_imgs, train_targets,\n",
|
|
" epochs=50,\n",
|
|
" callbacks=callbacks,\n",
|
|
" batch_size=64,\n",
|
|
" validation_data=(val_input_imgs, val_targets))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"epochs = range(1, len(history.history[\"loss\"]) + 1)\n",
|
|
"loss = history.history[\"loss\"]\n",
|
|
"val_loss = history.history[\"val_loss\"]\n",
|
|
"plt.figure()\n",
|
|
"plt.plot(epochs, loss, \"bo\", label=\"Training loss\")\n",
|
|
"plt.plot(epochs, val_loss, \"b\", label=\"Validation loss\")\n",
|
|
"plt.title(\"Training and validation loss\")\n",
|
|
"plt.legend()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"from tensorflow.keras.utils import array_to_img\n",
|
|
"\n",
|
|
"model = keras.models.load_model(\"oxford_segmentation.keras\")\n",
|
|
"\n",
|
|
"i = 4\n",
|
|
"test_image = val_input_imgs[i]\n",
|
|
"plt.axis(\"off\")\n",
|
|
"plt.imshow(array_to_img(test_image))\n",
|
|
"\n",
|
|
"mask = model.predict(np.expand_dims(test_image, 0))[0]\n",
|
|
"\n",
|
|
"def display_mask(pred):\n",
|
|
" mask = np.argmax(pred, axis=-1)\n",
|
|
" mask *= 127\n",
|
|
" plt.axis(\"off\")\n",
|
|
" plt.imshow(mask)\n",
|
|
"\n",
|
|
"display_mask(mask)"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"colab": {
|
|
"collapsed_sections": [],
|
|
"name": "chapter09_part01_image-segmentation.i",
|
|
"private_outputs": false,
|
|
"provenance": [],
|
|
"toc_visible": true
|
|
},
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.7.0"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 0
|
|
} |