1702_deep_learning_face_detection.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "1702-deep_learning_face_detection.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": 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": "3.8.5"
    }
  },
  "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/1702_deep_learning_face_detection.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kEYvcfHRB8dw"
      },
      "source": [
        "![logo_jupyter.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j4RpK9pawQzP"
      },
      "source": [
        "# Face detection with OpenCV and deep learning\n",
        "### by [PyImageSearch.com](http://www.pyimagesearch.com)"
      ]
    },
    {
      "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 [Face detection with OpenCV and deep learning](https://www.pyimagesearch.com/2018/02/26/face-detection-with-opencv-and-deep-learning/) blog post published on 2018-02-26.\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",
        "As a reminder, these PyImageSearch Plus Jupyter Notebooks are not for sharing; please refer to the **Copyright** directly below and **Code License Agreement** in the last cell of this notebook. \n",
        "\n",
        "Happy hacking!\n",
        "\n",
        "*Adrian*\n",
        "\n",
        "<hr>\n",
        "\n",
        "***Copyright:*** *The contents of this Jupyter Notebook, unless otherwise indicated, are Copyright 2020 Adrian Rosebrock, PyimageSearch.com. All rights reserved. Content like this is made possible by the time invested by the authors. If you received this Jupyter Notebook and did not purchase it, please consider making future content possible by joining PyImageSearch Plus at http://pyimg.co/plus/ today.*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NFhAzQB3aNMa"
      },
      "source": [
        "### Download the code zip file"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7y0LG1EuaRlB"
      },
      "source": [
        "!wget https://s3-us-west-2.amazonaws.com/static.pyimagesearch.com/face-detection-opencv-deep-learning/deep-learning-face-detection.zip\n",
        "!unzip -qq deep-learning-face-detection.zip\n",
        "%cd deep-learning-face-detection"
      ],
      "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 imutils\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": "Jppw5-Bd56H-"
      },
      "source": [
        "### Face detection in images with OpenCV and deep learning\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "okM7Bpyeq8Kc"
      },
      "source": [
        "# construct the argument parse and parse the arguments\n",
        "#ap = argparse.ArgumentParser()\n",
        "#ap.add_argument(\"-i\", \"--image\", required=True,\n",
        "#\thelp=\"path to input image\")\n",
        "#ap.add_argument(\"-p\", \"--prototxt\", required=True,\n",
        "#\thelp=\"path to Caffe 'deploy' prototxt file\")\n",
        "#ap.add_argument(\"-m\", \"--model\", required=True,\n",
        "#\thelp=\"path to Caffe pre-trained model\")\n",
        "#ap.add_argument(\"-c\", \"--confidence\", type=float, default=0.5,\n",
        "#\thelp=\"minimum probability to filter weak detections\")\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\": \"rooster.jpg\",\n",
        "\t\"prototxt\": \"deploy.prototxt.txt\",\n",
        "\t\"model\": \"res10_300x300_ssd_iter_140000.caffemodel\",\n",
        "\t\"confidence\": 0.9\n",
        "}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wLxA7aFTlhvy"
      },
      "source": [
        "# load our serialized model from disk\n",
        "print(\"[INFO] loading model...\")\n",
        "net = cv2.dnn.readNetFromCaffe(args[\"prototxt\"], args[\"model\"])\n",
        "\n",
        "# load the input image and construct an input blob for the image\n",
        "# by resizing to a fixed 300x300 pixels and then normalizing it\n",
        "image = cv2.imread(args[\"image\"])\n",
        "(h, w) = image.shape[:2]\n",
        "blob = cv2.dnn.blobFromImage(cv2.resize(image, (300, 300)), 1.0,\n",
        "\t(300, 300), (104.0, 177.0, 123.0))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JnSZPFdalp9O"
      },
      "source": [
        "# pass the blob through the network and obtain the detections and\n",
        "# predictions\n",
        "print(\"[INFO] computing object detections...\")\n",
        "net.setInput(blob)\n",
        "detections = net.forward()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Ds_Vx_Dluez"
      },
      "source": [
        "# loop over the detections\n",
        "for i in range(0, detections.shape[2]):\n",
        "\t# extract the confidence (i.e., probability) associated with the\n",
        "\t# prediction\n",
        "\tconfidence = detections[0, 0, i, 2]\n",
        "\n",
        "\t# filter out weak detections by ensuring the `confidence` is\n",
        "\t# greater than the minimum confidence\n",
        "\tif confidence > args[\"confidence\"]:\n",
        "\t\t# compute the (x, y)-coordinates of the bounding box for the\n",
        "\t\t# object\n",
        "\t\tbox = detections[0, 0, i, 3:7] * np.array([w, h, w, h])\n",
        "\t\t(startX, startY, endX, endY) = box.astype(\"int\")\n",
        " \n",
        "\t\t# draw the bounding box of the face along with the associated\n",
        "\t\t# probability\n",
        "\t\ttext = \"{:.2f}%\".format(confidence * 100)\n",
        "\t\ty = startY - 10 if startY - 10 > 10 else startY + 10\n",
        "\t\tcv2.rectangle(image, (startX, startY), (endX, endY),\n",
        "\t\t\t(0, 0, 255), 2)\n",
        "\t\tcv2.putText(image, text, (startX, y),\n",
        "\t\t\tcv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2)\n",
        "\n",
        "# show the output image\n",
        "plt_imshow(\"Output\", image)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mpu5BV0HmvKH"
      },
      "source": [
        "### Face detection in video with OpenCV and deep learning\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MuQDkLEtm_mK"
      },
      "source": [
        "# first, let's get a video on which we can run our face detector \n",
        "!wget https://colab-notebook-videos.s3-us-west-2.amazonaws.com/opencv_face_detection.mp4"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jUW0xqCfxEol"
      },
      "source": [
        "# construct the argument parse and parse the arguments\n",
        "#ap = argparse.ArgumentParser()\n",
        "#ap.add_argument(\"-p\", \"--prototxt\", required=True,\n",
        "#\thelp=\"path to Caffe 'deploy' prototxt file\")\n",
        "#ap.add_argument(\"-m\", \"--model\", required=True,\n",
        "#\thelp=\"path to Caffe pre-trained model\")\n",
        "#ap.add_argument(\"-c\", \"--confidence\", type=float, default=0.5,\n",
        "#\thelp=\"minimum probability to filter weak detections\")\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\"input\" : \"opencv_face_detection.mp4\",\n",
        "\t\"output\" : \"output.avi\",\n",
        "\t\"prototxt\" : \"deploy.prototxt.txt\",\n",
        "\t\"model\" : \"res10_300x300_ssd_iter_140000.caffemodel\",\n",
        "\t\"confidence\" : 0.5\n",
        "}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kRsLdP4Rmx4y"
      },
      "source": [
        "# load our serialized model from disk\n",
        "print(\"[INFO] loading model...\")\n",
        "net = cv2.dnn.readNetFromCaffe(args[\"prototxt\"], args[\"model\"])\n",
        "\n",
        "# grab a reference to the video file and initialize pointer to output\n",
        "# video file\n",
        "print(\"[INFO] opening video file...\")\n",
        "vs = cv2.VideoCapture(args[\"input\"])\n",
        "writer = None"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "58nd07_bxMV2"
      },
      "source": [
        "# loop over the frames from the video stream\n",
        "while True:\n",
        "\t# grab the next frame\n",
        "\tframe = vs.read()[1]\n",
        "\n",
        "\t# if we did not grab a frame then we have reached the end of the\n",
        "\t# video\n",
        "\tif frame is None:\n",
        "\t\tbreak\n",
        "\n",
        "\t# resize the frame to have a maximum width of 400 pixels\n",
        "\tframe = imutils.resize(frame, width=400)\n",
        "\n",
        "\t# grab the frame dimensions and convert it to a blob\n",
        "\t(h, w) = frame.shape[:2]\n",
        "\tblob = cv2.dnn.blobFromImage(cv2.resize(frame, (300, 300)), 1.0,\n",
        "\t\t(300, 300), (104.0, 177.0, 123.0))\n",
        " \n",
        "\t# pass the blob through the network and obtain the detections and\n",
        "\t# predictions\n",
        "\tnet.setInput(blob)\n",
        "\tdetections = net.forward()\n",
        "\n",
        "\t# loop over the detections\n",
        "\tfor i in range(0, detections.shape[2]):\n",
        "\t\t# extract the confidence (i.e., probability) associated with the\n",
        "\t\t# prediction\n",
        "\t\tconfidence = detections[0, 0, i, 2]\n",
        "\n",
        "\t\t# filter out weak detections by ensuring the `confidence` is\n",
        "\t\t# greater than the minimum confidence\n",
        "\t\tif confidence < args[\"confidence\"]:\n",
        "\t\t\tcontinue\n",
        "\n",
        "\t\t# compute the (x, y)-coordinates of the bounding box for the\n",
        "\t\t# object\n",
        "\t\tbox = detections[0, 0, i, 3:7] * np.array([w, h, w, h])\n",
        "\t\t(startX, startY, endX, endY) = box.astype(\"int\")\n",
        " \n",
        "\t\t# draw the bounding box of the face along with the associated\n",
        "\t\t# probability\n",
        "\t\ttext = \"{:.2f}%\".format(confidence * 100)\n",
        "\t\ty = startY - 10 if startY - 10 > 10 else startY + 10\n",
        "\t\tcv2.rectangle(frame, (startX, startY), (endX, endY),\n",
        "\t\t\t(0, 0, 255), 2)\n",
        "\t\tcv2.putText(frame, text, (startX, y),\n",
        "\t\t\tcv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2)\n",
        " \n",
        "\t# if the video writer is None *AND* we are supposed to write\n",
        "\t# the output video to disk initialize the writer\n",
        "\tif writer is None and args[\"output\"] is not None:\n",
        "\t\tfourcc = cv2.VideoWriter_fourcc(*\"MJPG\")\n",
        "\t\twriter = cv2.VideoWriter(args[\"output\"], fourcc, 20,\n",
        "\t\t\t(frame.shape[1], frame.shape[0]), True)\n",
        "  \n",
        "\t# if the writer is not None, write the frame with recognized\n",
        "\t# faces to disk\n",
        "\tif writer is not None:\n",
        "\t\twriter.write(frame)\n",
        "\n",
        "# do a bit of cleanup\n",
        "vs.release()\n",
        "\n",
        "# check to see if the video writer point needs to be released\n",
        "if writer is not None:\n",
        "\twriter.release()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VpSXMhHhHOJw"
      },
      "source": [
        "Note that the above code block may take time to execute. If you are interested to view the video within Colab just execute the following code blocks. Note that it may be time-consuming.\n",
        "\n",
        "Our output video is produced in `.avi` format. First, we need to convert it to `.mp4` format."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lhYbtMwnHTp-"
      },
      "source": [
        "!ffmpeg -i output.avi output.mp4"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r0nmX4tA3A3E",
        "cellView": "form"
      },
      "source": [
        "#@title Display video inline\n",
        "from IPython.display import HTML\n",
        "from base64 import b64encode\n",
        "\n",
        "mp4 = open(\"output.mp4\", \"rb\").read()\n",
        "dataURL = \"data:video/mp4;base64,\" + b64encode(mp4).decode()\n",
        "HTML(\"\"\"\n",
        "<video width=400 controls>\n",
        "      <source src=\"%s\" type=\"video/mp4\">\n",
        "</video>\n",
        "\"\"\" % dataURL)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-qXbjl4iYWvK"
      },
      "source": [
        "This code is referred from [this StackOverflow thread](https://stackoverflow.com/a/57378660/7636462). "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4ogkNauArL6u"
      },
      "source": [
        "For a detailed walkthrough of the concepts and code, be sure to refer to the full tutorial, [*Face detection with OpenCV and deep learning*](https://www.pyimagesearch.com/2018/02/26/face-detection-with-opencv-and-deep-learning/) published on 2018-02-26."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sef8alx4cGYc"
      },
      "source": [
        "# Code License Agreement\n",
        "```\n",
        "Copyright (c) 2020 PyImageSearch.com\n",
        "\n",
        "SIMPLE VERSION\n",
        "Feel free to use this code for your own projects, whether they are\n",
        "purely educational, for fun, or for profit. THE EXCEPTION BEING if\n",
        "you are developing a course, book, or other educational product.\n",
        "Under *NO CIRCUMSTANCE* may you use this code for your own paid\n",
        "educational or self-promotional ventures without written consent\n",
        "from Adrian Rosebrock and PyImageSearch.com.\n",
        "\n",
        "LONGER, FORMAL VERSION\n",
        "Permission is hereby granted, free of charge, to any person obtaining\n",
        "a copy of this software and associated documentation files\n",
        "(the \"Software\"), to deal in the Software without restriction,\n",
        "including without limitation the rights to use, copy, modify, merge,\n",
        "publish, distribute, sublicense, and/or sell copies of the Software,\n",
        "and to permit persons to whom the Software is furnished to do so,\n",
        "subject to the following conditions:\n",
        "The above copyright notice and this permission notice shall be\n",
        "included in all copies or substantial portions of the Software.\n",
        "Notwithstanding the foregoing, you may not use, copy, modify, merge,\n",
        "publish, distribute, sublicense, create a derivative work, and/or\n",
        "sell copies of the Software in any work that is designed, intended,\n",
        "or marketed for pedagogical or instructional purposes related to\n",
        "programming, coding, application development, or information\n",
        "technology. Permission for such use, copying, modification, and\n",
        "merger, publication, distribution, sub-licensing, creation of\n",
        "derivative works, or sale is expressly withheld.\n",
        "THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\n",
        "EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES\n",
        "OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND\n",
        "NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS\n",
        "BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN\n",
        "ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN\n",
        "CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n",
        "SOFTWARE.\n",
        "```"
      ]
    }
  ]
}