{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "Aw2UIYteKHYi" }, "source": [ "# matplotlib tutorial (7) nitta@tsuda.ac.jp\n", "\n", "# Chapter 7: Saving / loading / Displaying of Images\n", "\n", "\"Save to image file\".\n", "\n", "For the sake of simplicity, this tutorial will use the image data from the database of tensorflow.keras.\n", "\n", "Google Colab can use tensorflow by default, but if tensorflow is not installed in your jupyter notebook environment, install it whth the pip command.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "khL7hq7LKDwS" }, "outputs": [], "source": [ "## If you use jupyter notebook on your PC, please install 'tensorflow' package.\n", "# ! pip install tensorflow" ] }, { "cell_type": "markdown", "metadata": { "id": "Nu2X7nsxcFtD" }, "source": [ "

Rule [7-1]: When displaying image data of Numpy array format, convert grayscale images to uint8 type arrays of [0, 255] and color images to float32 type arrays of [0, 1], and then apply Axes.imshow().

\n", "\n", "\n", "\n", "For a gray scale image (number of channel = 1), convert it to a Numpy array with 'unit8' type elements of range [0, 255].\n", "And for a color image (number of channels = 3 or 4), convert it to a Numpy array with 'float32' type element of range [0.0, 1.0].\n", "\n", "Then apply the 'Axes.imshow()' function to the image.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "mLIYc8JnKRxG" }, "source": [ "## 7-1: Display a grayscale image, using Axes.imshow()\n", "\n", "The image displayed must be a 2-dimensional array.\n", "If the elements of the array are integers, the brightness (0: black, 255: white) is expressed in range [0, 255].\n", "When the element of the array are 'float', the brightness (0.0: black, 1.0: white) is expressed in the range [0.0, 1.0].\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 4890, "status": "ok", "timestamp": 1648474971166, "user": { "displayName": "Yoshihisa Nitta", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64", "userId": "15888006800030996813" }, "user_tz": -540 }, "id": "Px_7kXi-K6W4", "outputId": "b252feff-149b-41ec-e0ad-22f1c8f3e879" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2.8.0\n" ] } ], "source": [ "import tensorflow as tf\n", "\n", "print(tf.__version__)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 736, "status": "ok", "timestamp": 1648474971897, "user": { "displayName": "Yoshihisa Nitta", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64", "userId": "15888006800030996813" }, "user_tz": -540 }, "id": "7ddY9-UHLU0w", "outputId": "754239e3-59b3-4b70-e186-dcf98431637b" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n", "11493376/11490434 [==============================] - 0s 0us/step\n", "11501568/11490434 [==============================] - 0s 0us/step\n", "(60000, 28, 28) (60000,) (10000, 28, 28) (10000,)\n" ] } ], "source": [ "# Prepare the image data of MNIST (handwritten characters)\n", "\n", "(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()\n", "\n", "print(x_train.shape, y_train.shape, x_test.shape, y_test.shape)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 4, "status": "ok", "timestamp": 1648474971897, "user": { "displayName": "Yoshihisa Nitta", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64", "userId": "15888006800030996813" }, "user_tz": -540 }, "id": "lpS4Y7qxR7gB", "outputId": "53d808f4-fbbb-4aec-d516-e64d53005634" }, "outputs": [ { "data": { "text/plain": [ "numpy.uint8" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Examine the types of elements of the Numpy array of a gray scale image.\n", "type(x_train[0][0][0])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 173 }, "executionInfo": { "elapsed": 417, "status": "ok", "timestamp": 1648474972312, "user": { "displayName": "Yoshihisa Nitta", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64", "userId": "15888006800030996813" }, "user_tz": -540 }, "id": "U-7Kr-HxL2ij", "outputId": "edfac6a3-6ece-4608-91e0-546216bd091f" }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# sample code 7-1\n", "%matplotlib inline\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import tensorflow as tf\n", "\n", "fig, ax = plt.subplots(1, 2, figsize=(2.8 * 2, 2.8))\n", "\n", "img1 = x_train[0]\n", "img2 = 255 - img1 # invert image\n", "\n", "ax[0].imshow(img1, cmap='gray')\n", "ax[0].axis('off')\n", "\n", "ax[1].imshow(img2, cmap='gray')\n", "ax[1].axis('off')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "ybmCRQhFODos" }, "source": [ "## 7-2: Display color images, using Axes.imshow()\n", "\n", "Color image data is a tensor in (Rows, Cols, Channels) or (Channels, Rows, Cols) format.\n", "The number of channels element is 3 which represents the brightness of RGB.\n", "\n", "In CIFAR10 using now, the image format is (Rows, Cols, Channels).\n", "\n", "When each element of RGB is an integer, it represents brightness (0: dark, 255: bright) in the range [0, 255].\n", "When each element of RGB is a floating fraction, the brightness (0.0: dark, 1.0: bright) is expressed in the range [0.0, 1.0].\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 7348, "status": "ok", "timestamp": 1648474979656, "user": { "displayName": "Yoshihisa Nitta", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64", "userId": "15888006800030996813" }, "user_tz": -540 }, "id": "J5P2Yrf_NI_E", "outputId": "f2b15839-240b-4362-bd80-b14b67fb491e" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n", "170500096/170498071 [==============================] - 4s 0us/step\n", "170508288/170498071 [==============================] - 4s 0us/step\n", "(50000, 32, 32, 3) (50000, 1) (10000, 32, 32, 3) (10000, 1)\n" ] } ], "source": [ "# Prepare image data of cifar10\n", "import tensorflow as tf\n", "\n", "(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()\n", "print(x_train.shape, y_train.shape, x_test.shape, y_test.shape)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 7, "status": "ok", "timestamp": 1648474979656, "user": { "displayName": "Yoshihisa Nitta", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64", "userId": "15888006800030996813" }, "user_tz": -540 }, "id": "QODyWdn8QZcc", "outputId": "ce37ad1e-f64e-4e06-ceff-6fd74234fc83" }, "outputs": [ { "data": { "text/plain": [ "numpy.uint8" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Examine the types of elements of the Numpy array of a RGB image.\n", "type(x_train[0][0][0][0])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 193 }, "executionInfo": { "elapsed": 6, "status": "ok", "timestamp": 1648474979656, "user": { "displayName": "Yoshihisa Nitta", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64", "userId": "15888006800030996813" }, "user_tz": -540 }, "id": "19r0JbaAQasg", "outputId": "2ea41613-2a06-41e4-ab2a-7610cdf9b5f4" }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# sample code 7-2\n", "%matplotlib inline\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import tensorflow as tf\n", "\n", "fig, ax = plt.subplots(1, 2, figsize=(3.2 * 2, 3.2))\n", "\n", "img1 = x_train[6] # [0, 255]\n", "img2 = img1.astype('float32') / 255. # [0.0, 1.0]\n", "\n", "ax[0].imshow(img1)\n", "ax[0].axis('off')\n", "\n", "ax[1].imshow(img2)\n", "ax[1].axis('off')\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 6, "status": "ok", "timestamp": 1648474979657, "user": { "displayName": "Yoshihisa Nitta", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64", "userId": "15888006800030996813" }, "user_tz": -540 }, "id": "bQ22G9DZRHYH", "outputId": "3de889f0-4df4-4eee-d6f8-78ce1094b50b" }, "outputs": [ { "data": { "text/plain": [ "numpy.float32" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# The types of elements of the Numpy array of a RGB image.\n", "# When using images in Deep Learning, it is easier to use 'float32' type in the range of [0.0, 1.0] or [-1.0, 1.0].\n", "type(img2[0,0,0])" ] }, { "cell_type": "markdown", "metadata": { "id": "yE2XopCsS2PO" }, "source": [ "## Preparation for 7-3: Download and extract image files from the network\n", "\n", "In preparation for 7-3, download (a part of) the face image file of VidTIMIT dataset from the network and extract it.\n", "\n", "Official WWW of VidTIMIT dataset:
\n", " \n", "http://conradsanderson.id.au/vidtimit/\n", "
\n", "\n", "zip files of 2 persons of VidTIMIT dataset:
\n", "\n", "https://zenodo.org/record/158963/files/fadg0.zip\n", "
\n", "\n", "https://zenodo.org/record/158963/files/faks0.zip\n", "
" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 46622, "status": "ok", "timestamp": 1648475026275, "user": { "displayName": "Yoshihisa Nitta", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64", "userId": "15888006800030996813" }, "user_tz": -540 }, "id": "OpTZVvM1SVFU", "outputId": "e0fc5851-21ac-4cee-89c4-1b348381c0da" }, "outputs": [ { "data": { "text/plain": [ "('data/fadg0.zip', )" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Download data from the specified URL to the specified path.\n", "import os\n", "import urllib.request\n", "\n", "url = 'https://zenodo.org/record/158963/files/fadg0.zip'\n", "filepath = 'data/fadg0.zip'\n", "\n", "dpath, fname = os.path.split(filepath)\n", "os.makedirs(dpath, exist_ok=True)\n", "urllib.request.urlretrieve(url, filepath)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 5, "status": "ok", "timestamp": 1648475026276, "user": { "displayName": "Yoshihisa Nitta", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64", "userId": "15888006800030996813" }, "user_tz": -540 }, "id": "So8GSwajXgsT", "outputId": "ba74f597-03a4-4f85-c09a-905ccf75d9e5" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "total 79684\n", "-rw-r--r-- 1 root root 81593138 Mar 28 13:43 fadg0.zip\n" ] } ], "source": [ "# Examine the downloaded file.\n", "if os.name == 'nt':\n", " LS = 'dir'\n", " LS_R = 'dir /s'\n", "else:\n", " LS = 'ls -l'\n", " LS_R = 'ls -lR'\n", "\n", "!{LS} data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ffhB7687X1UJ" }, "outputs": [], "source": [ "# Extract the zip file to the specified folder.\n", "import zipfile\n", "\n", "with zipfile.ZipFile(filepath, 'r') as f:\n", " f.extractall(dpath)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 246, "status": "ok", "timestamp": 1648475027656, "user": { "displayName": "Yoshihisa Nitta", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64", "userId": "15888006800030996813" }, "user_tz": -540 }, "id": "Md9ozu4AYOg0", "outputId": "cf4c6693-076b-4d89-90fc-9fb69869ff73" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "total 79688\n", "drwxr-xr-x 4 root root 4096 Mar 28 13:43 fadg0\n", "-rw-r--r-- 1 root root 81593138 Mar 28 13:43 fadg0.zip\n" ] } ], "source": [ "! {LS} data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 262, "status": "ok", "timestamp": 1648475027916, "user": { "displayName": "Yoshihisa Nitta", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64", "userId": "15888006800030996813" }, "user_tz": -540 }, "id": "ogbwATcjgyV9", "outputId": "d2da1afa-212c-428b-dc3c-61330ffdddd7" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "total 8\n", "drwxr-xr-x 2 root root 4096 Mar 28 13:43 audio\n", "drwxr-xr-x 15 root root 4096 Mar 28 13:43 video\n" ] } ], "source": [ "! {LS} data/fadg0" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 7, "status": "ok", "timestamp": 1648475027917, "user": { "displayName": "Yoshihisa Nitta", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64", "userId": "15888006800030996813" }, "user_tz": -540 }, "id": "5P1-CKZ_g2cl", "outputId": "17ac21f6-a360-48a4-f51e-2221720e99b6" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "total 84\n", "drwxr-xr-x 2 root root 12288 Mar 28 13:43 head\n", "drwxr-xr-x 2 root root 12288 Mar 28 13:43 head2\n", "drwxr-xr-x 2 root root 20480 Mar 28 13:43 head3\n", "drwxr-xr-x 2 root root 4096 Mar 28 13:43 sa1\n", "drwxr-xr-x 2 root root 4096 Mar 28 13:43 sa2\n", "drwxr-xr-x 2 root root 4096 Mar 28 13:43 si1279\n", "drwxr-xr-x 2 root root 4096 Mar 28 13:43 si1909\n", "drwxr-xr-x 2 root root 4096 Mar 28 13:43 si649\n", "drwxr-xr-x 2 root root 4096 Mar 28 13:43 sx109\n", "drwxr-xr-x 2 root root 4096 Mar 28 13:43 sx19\n", "drwxr-xr-x 2 root root 4096 Mar 28 13:43 sx199\n", "drwxr-xr-x 2 root root 4096 Mar 28 13:43 sx289\n", "drwxr-xr-x 2 root root 4096 Mar 28 13:43 sx379\n" ] } ], "source": [ "! {LS} data/fadg0/video" ] }, { "cell_type": "markdown", "metadata": { "id": "drARplrLe7e2" }, "source": [ "## 7-3: Load images from files\n", "\n", "It is asumed that the image file is in the following path.\n", "\n", "./data/fadg0/video/head/[0-9]*" ] }, { "cell_type": "markdown", "metadata": { "id": "XgDH3MvuV1jY" }, "source": [ "

Rule [7-2]: Use the load_img() function of 'tensorflow.keras' to load image data from an image file.

\n", "\n", "load_img() / img_to_array() / array_to_img() / save_img() function is in either \n", "
    \n", "
  • tensorflow.keras.utils
  • \n", "
  • tensorflow.keras.preprocessing.image
  • \n", "
\n", "depending on the version of tensorflow.\n", "\n", "
    \n", "
  • load_img(path, grayscale=false, color_mode='rgb', target_size=None, interpolation='nearest') ... Load an image file from the specified path and returns it as PIL format data.
  • \n", "
\n", "\n", "Since the return value of the load_img() function is PIL format image data, it is easier to use later if it is converted to a Numpy array.\n", "\n", "
\n",
    "    image_pil = load_img(path)\n",
    "
" ] }, { "cell_type": "markdown", "metadata": { "id": "A_5LmlyZovqM" }, "source": [ "

Rule [7-3]: The image data returned from load_img() function might be immediately converted from PIL format to Numpy array.

\n", "\n", "load_img() / img_to_array() / array_to_img() / save_img() function is in either\n", "
    \n", "
  • tensorflow.keras.utils
  • \n", "
  • tensorflow.keras.preprocessing.image
  • \n", "
\n", "depending on the version of tensorflow.\n", "\n", "Since the return value of the load_img() function is PIL format image data, it is easier to use later if it is converted to a Numpy array.\n", "You can use the img_to_array() function for this conversion, but the numpy.array() function seems to be more popular.\n", "\n", "
    \n", "
  • img_to_array(img, data_format=None, dtype=None) ... Convert PIL format image data to Numpy array.
  • \n", "
  • numpy.array(object, dtype=None, ...)
  • \n", "
\n", "\n", "The img_to_array() function returns a Numpy array whose element is 'uint8' of range [0, 255].\n", "Applying np.array() to PIL format data without specifying dtype parameter also returns a Numpy array whose element is 'uint8' of range [0, 255].\n", "\n", "\n", "
\n",
    "    image_uint8 = np.array(imgage_pil)\n",
    "
\n" ] }, { "cell_type": "markdown", "metadata": { "id": "22tUIHBqVXNH" }, "source": [ "

Rule [7-4]:Convert image data to the Numpy array with 'float32' element type of the range [0.0, 1.0] or [-1.0, 1.0].

\n", "\n", "When passing image data through a neural network, convert it to the Numpy array with 'float32' element type of the range [0.0, 1.0] or [-1.0, 1.0].\n", "\n", "Use the following code to convert from a Numpy array of 'uint8' element type of range [0, 255]. \n", "\n", "
\n",
    "  image = image_uint8.astype('float32') / 255.   # [0, 255] --> [0, 1]\n",
    "or\n",
    "  image = image_uint8.astype('float32') / 127.5 - 127.5  # [0, 255] --> [-1, 1]\n",
    "
\n", "\n", "The code of the reverse conversion is as follows.\n", "\n", "
\n",
    "    image_uint8 = (image * 255).astype('uint8')   # [0, 1] ---> [0, 255]\n",
    "or\n",
    "    image_uint8 = ((image + 1) * 127.5).astype('uint8') # [-1, 1] --> [0, 255]\n",
    "
" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "WZnzGyybc2Nd" }, "outputs": [], "source": [ "# Get the file paths at once\n", "import os\n", "import glob\n", "\n", "DATA_DIR = './data/fadg0/video/head'" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "jkxeWasJhuHT" }, "outputs": [], "source": [ "import re\n", "\n", "def atoi(text):\n", " return int(text) if text.isdigit() else text\n", "\n", "def natural_keys(text):\n", " return [ atoi(c) for c in re.split(r'(\\d+)', text)]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 4, "status": "ok", "timestamp": 1648475027917, "user": { "displayName": "Yoshihisa Nitta", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64", "userId": "15888006800030996813" }, "user_tz": -540 }, "id": "rMgzKUrxhuk7", "outputId": "6345d60c-999a-4ced-9874-45f2194b1208" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "346\n", "./data/fadg0/video/head/001\n" ] } ], "source": [ "# Use glob.glob to load the files in name order. The key argument was specified to support numbers in filenames.\n", "DATA_PATHS = sorted(glob.glob(os.path.join(DATA_DIR, '*')), key=natural_keys)\n", "\n", "print(len(DATA_PATHS))\n", "print(DATA_PATHS[0])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "RNHomMGMh_7S" }, "outputs": [], "source": [ "# sample code 7-3\n", "# Loading image files and converting them to Numpy arrays.\n", "\n", "import numpy as np\n", "import tensorflow as tf\n", "\n", "image_uint8 = np.array(tf.keras.preprocessing.image.load_img(DATA_PATHS[0]))\n", "image = image_uint8.astype('float32') / 255.0" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 282 }, "executionInfo": { "elapsed": 1085, "status": "ok", "timestamp": 1648475028999, "user": { "displayName": "Yoshihisa Nitta", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64", "userId": "15888006800030996813" }, "user_tz": -540 }, "id": "iwDaZjzoiw9_", "outputId": "e79c5048-ac9a-4c90-f529-c4531f9cfd21" }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "fig, ax = plt.subplots(1,1,figsize=(6,6))\n", "ax.imshow(image)\n", "ax.axis('off')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "0L2nvGb_zHOK" }, "source": [ "

Rule [7-5]:When displaying or saving a color image data, convert it to the Numpy array of 'float32' element with range[0.0, 1.0].

\n", "\n", "When displaying or saving a color image data, convert it to the Numpy array of 'float32' element with range[0.0, 1.0].\n", "Use the following code to convert a Numpy array of 'float32' element type of the range [-1.0, 1.0] to the range [0.0, 1.0].\n", "\n", "To convert between a Numpy array with 'float32' element type of range [0.0, 1.0] and that of range [-1.0, 1.0].\n", "\n", "
\n",
    "    image = image * 2 - 1      # [0, 1] ---> [-1, 1]\n",
    "    image = (image + 1) / 2    # [-1, 1] --> [0, 1]\n",
    "
\n", "\n", "Use the numpy.clip() function to guarantee the range of element values.\n", "\n", "
    \n", "
  • numpy.clip(a, a_min, a_max, ...)
  • \n", "
    \n",
        "    Parameters:\n",
        "        a: array\n",
        "        a_min: Change value less than a_min to a_min\n",
        "        a_max: Change value greater than a_max to a_max\n",
        "    Returns:\n",
        "        clipped_array: array whose element value range is [a_min, a_max]\n",
        "
    \n", "
\n", "\n", "
\n",
    "    image = np.clip(image, 0, 1)    # clipping element values between 0 and 1.\n",
    "
" ] }, { "cell_type": "markdown", "metadata": { "id": "KnyEHSBa0Pjz" }, "source": [ "## 7-4: Conversion of the range of element values of image data\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "J8ZP1hNs1UPr" }, "outputs": [], "source": [ "# sample code 7-4\n", "# [0, 1] --> [-1, 1]\n", "imageMP = image * 2 - 1\n", "\n", "# [-1, 1] --> [0, 1]\n", "image2 = np.clip((imageMP + 1) * 0.5, 0.0, 1.0)" ] }, { "cell_type": "markdown", "metadata": { "id": "vLgRnpXyscI6" }, "source": [ "

Rule [7-6]: Usae the save_img() function to save image data in Numpy array to a file.

\n", "\n", "The function of load_img() / img_to_array() / array_to_img() / save_img() is in either\n", "
    \n", "
  • tensorflow.keras.utils
  • \n", "
  • tensorflow.keras.preprocessing.image
  • \n", "
\n", "depending the version of tensorflow.\n", "\n", "
    \n", "
  • save_img(path, x, ...) ... Saves the Numpy array to the file of the specified path.
  • \n", "
\n", "\n", "The format of the image file to be saved can be specified by the file_format parameter, but if omitted, it is determined from the extension of the file name.\n", "\n", "
\n",
    "    image_pil = load_img(path)\n",
    "
" ] }, { "cell_type": "markdown", "metadata": { "id": "cmGmb7TXq_4S" }, "source": [ "## 7-5 Save image\n", "\n", "Image data might be treaded as a Numpy array of the element type 'float32' and value range [0.0, 1.0] or [-1.0, 1.0].\n", "If the range of element is [-1.0, 1.0], it is necessary to be converted to the range of [0.0, 1.0] when displaying or saving it.\n", "\n", "To save it, use the save_img() function of 'tensorflow.keras'.\n", "\n", "
    \n", "
  • save_img(path, image, data_format=None, file_format=None, scale=True) ... Save the Numpy array of image data to the file path.
  • \n", "
" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 6, "status": "ok", "timestamp": 1648475028999, "user": { "displayName": "Yoshihisa Nitta", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgJLeg9AmjfexROvC3P0wzJdd5AOGY_VOu-nxnh=s64", "userId": "15888006800030996813" }, "user_tz": -540 }, "id": "Pcy7UhbCjE-l", "outputId": "2f874b0c-aaff-486f-a3a7-8f623a347574" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-rw-r--r-- 1 root root 14875 Mar 28 13:43 data/new_image.jpg\n" ] } ], "source": [ "# sample code 7-5\n", "import tensorflow as tf\n", "\n", "save_path = 'data/new_image.jpg'\n", "tf.keras.preprocessing.image.save_img(save_path, image)\n", "\n", "! {LS} {save_path}\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "gV4myDFjygFK" }, "outputs": [], "source": [] } ], "metadata": { "colab": { "authorship_tag": "ABX9TyO+6AqD5ntsyDETdoE/3unO", "collapsed_sections": [], "name": "matplotlib_tutorial_07_en.ipynb", "provenance": [] }, "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": "3.6.13" } }, "nbformat": 4, "nbformat_minor": 1 }