105_opencv_cropping.ipynb

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  "metadata": {
    "colab": {
      "name": "105-opencv_cropping.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/parekhakhil/pyImageSearch/blob/main/105_opencv_cropping.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j4RpK9pawQzP"
      },
      "source": [
        "# Crop Image with OpenCV\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8ntZ1AkXZIxY"
      },
      "source": [
        "## Welcome to **[PyImageSearch Plus](http://pyimg.co/plus)** Jupyter Notebooks!\n",
        "\n",
        "This notebook is associated with the [Crop Image with OpenCV](https://www.pyimagesearch.com/2021/01/19/crop-image-with-opencv/) blog post published on 01-19-2021.\n",
        "\n",
        "Only the code for the blog post is here. Most codeblocks have a 1:1 relationship with what you find in the blog post with two exceptions: (1) Python classes are not separate files as they are typically organized with PyImageSearch projects, and (2) Command Line Argument parsing is replaced with an `args` dictionary that you can manipulate as needed.\n",
        "\n",
        "We recommend that you execute (press ▶️) the code block-by-block, as-is, before adjusting parameters and `args` inputs. Once you've verified that the code is working, you are welcome to hack with it and learn from manipulating inputs, settings, and parameters. For more information on using Jupyter and Colab, please refer to these resources:\n",
        "\n",
        "*   [Jupyter Notebook User Interface](https://jupyter-notebook.readthedocs.io/en/stable/notebook.html#notebook-user-interface)\n",
        "*   [Overview of Google Colaboratory Features](https://colab.research.google.com/notebooks/basic_features_overview.ipynb)\n",
        "\n",
        "\n",
        "Happy hacking!\n",
        "\n",
        "\n",
        "\n",
        "<hr>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NFhAzQB3aNMa"
      },
      "source": [
        "### Download the code zip file"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7y0LG1EuaRlB"
      },
      "source": [
        "!wget https://pyimagesearch-code-downloads.s3-us-west-2.amazonaws.com/opencv-cropping/opencv-cropping.zip\n",
        "!unzip -qq opencv-cropping.zip\n",
        "%cd opencv-cropping"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_SgTVT3HagGZ"
      },
      "source": [
        "## Blog Post Code"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wcrOk6pURp50"
      },
      "source": [
        "### Import Packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VJaCNlDDRz6d"
      },
      "source": [
        "# import the necessary packages\n",
        "from matplotlib import pyplot as plt\n",
        "import numpy as np\n",
        "import argparse\n",
        "import cv2"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lBrLwCtN5kqy"
      },
      "source": [
        "### Function to display images in Jupyter Notebooks and Google Colab"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fRw969Dp5Kdm"
      },
      "source": [
        "def plt_imshow(title, image):\n",
        "\t# convert the image frame BGR to RGB color space and display it\n",
        "\timage = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
        "\tplt.imshow(image)\n",
        "\tplt.title(title)\n",
        "\tplt.grid(False)\n",
        "\tplt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HQEOQ4VTzaf-"
      },
      "source": [
        "### Understanding image cropping with OpenCV and NumPy array slicing\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BCHrjTxIzO8J"
      },
      "source": [
        "I = np.arange(0, 25)\n",
        "I"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q0KpQFF3zpl4"
      },
      "source": [
        "I = I.reshape((5, 5))\n",
        "I"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e0uMnBqxzti3"
      },
      "source": [
        "I[0:3, 0:2]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SerCBcbmz1km"
      },
      "source": [
        "I[3:5, 1:5]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qmRDPisyz8wv"
      },
      "source": [
        "### Implementing image cropping with OpenCV\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-2ANlDU0z4C3"
      },
      "source": [
        "# # construct the argument parser and parse the arguments\n",
        "# ap = argparse.ArgumentParser()\n",
        "# ap.add_argument(\"-i\", \"--image\", type=str, default=\"adrian.png\",\n",
        "# \thelp=\"path to the input image\")\n",
        "# args = vars(ap.parse_args())\n",
        "\n",
        "# since we are using Jupyter Notebooks we can replace our argument\n",
        "# parsing code with *hard coded* arguments and values\n",
        "args = {\n",
        "\t\"image\": \"adrian.png\"\n",
        "}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qba6OkzG0Hop"
      },
      "source": [
        "# load the input image and display it to our screen\n",
        "image = cv2.imread(args[\"image\"])\n",
        "plt_imshow(\"Original\", image)\n",
        "\n",
        "# cropping an image with OpenCV is accomplished via simple NumPy\n",
        "# array slices in startY:endY, startX:endX order -- here we are\n",
        "# cropping the face from the image (these coordinates were\n",
        "# determined using photo editing software such as Photoshop,\n",
        "# GIMP, Paint, etc.)\n",
        "face = image[85:250, 85:220]\n",
        "plt_imshow(\"Face\", face)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZUQI3AzP0RLq"
      },
      "source": [
        "# apply another image crop, this time extracting the body\n",
        "body = image[90:450, 0:290]\n",
        "plt_imshow(\"Body\", body)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4ogkNauArL6u"
      },
      "source": [
        "For a detailed walkthrough of the concepts and code, be sure to refer to the full tutorial, [*Crop Image with OpenCV*](https://www.pyimagesearch.com/2021/01/19/crop-image-with-opencv/) published on 01-19-2021."
      ]
    }
  ]
}