multiple_images_subplots.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Plotting multiple images as subplots with titles and 1 colorbar\n",
        "\n",
        "Yedidya Harris"
      ],
      "metadata": {
        "id": "O-LqVenFg0tw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Read your images here as img0, img1, etc.\n",
        "\n",
        "# For this example, i'll create 4 black images\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "# size of first image\n",
        "x = 100\n",
        "y = 100\n",
        "\n",
        "#creating the images\n",
        "for i in range(4):\n",
        "\n",
        "  globals()[\"img\" + str(i)] = np.zeros([x,y,3],dtype=np.uint8) # the globals lets us call a variable with an index from the loop\n",
        "  globals()[\"img\" + str(i)].fill(0) # fill the img\n",
        "  print(f'img{i} shape:', (globals()[\"img\" + str(i)]).shape) # print the shape of each image\n",
        "  x-=30 # just do make the images have different shapes\n",
        "  y+=50"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wtMv6cFueska",
        "outputId": "61a9c675-7a35-421e-a4f6-8ca8186464a2"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "img0 shape: (100, 100, 3)\n",
            "img1 shape: (70, 150, 3)\n",
            "img2 shape: (40, 200, 3)\n",
            "img3 shape: (10, 250, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        },
        "id": "xe8Cx8RheNc7",
        "outputId": "0a422a12-f02d-4ad2-a094-08122953f961"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 5 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# The plotting\n",
        "\n",
        "# import relevant libs\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import cv2\n",
        "\n",
        "# plt.rcParams['axes.facecolor']='black' # make bg color of the plot to black if needed (segmented images)\n",
        "\n",
        "# resize images to shape of img0\n",
        "for i in range(4):\n",
        "  globals()[\"img\" + str(i)] = cv2.resize(globals()[\"img\" + str(i)], dsize=(100,100), interpolation=cv2.INTER_CUBIC)\n",
        "\n",
        "\n",
        "# subplots\n",
        "fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(15,10)) # set number of rows, cols, and size\n",
        "for i,ax in enumerate(axes.flat): #iterating after a list of axes\n",
        "    im = ax.imshow(globals()[\"img\" + str(i)], cmap='jet', vmin=25, vmax=29) #image source,set cmap, min,max of the colorbar, \n",
        "    ax.set_title(f'Image {i}', fontsize=20, y=1)\n",
        "\n",
        "    ax.set_xticks([]) # remove the xticks\n",
        "    ax.set_yticks([]) # remove the yticks\n",
        "\n",
        "    #remove frame(black border)\n",
        "    ax.spines['top'].set_visible(False)\n",
        "    ax.spines['right'].set_visible(False)\n",
        "    ax.spines['bottom'].set_visible(False)\n",
        "    ax.spines['left'].set_visible(False)\n",
        "\n",
        "\n",
        "# more settings\n",
        "fig.subplots_adjust(right=0.6) # distance between cols of images\n",
        "cbar_ax = fig.add_axes([0.65, 0.25, 0.03, 0.5]) # location of the colorbar, and size [right/left, top/bottom, width of colorbar, size]\n",
        "fig.colorbar(im, cax=cbar_ax) # adding the colorbar\n",
        "\n",
        "plt.show()"
      ]
    }
  ]
}