1503_region_proposal_object_detection.ipynb

{
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  "metadata": {
    "colab": {
      "name": "1503-region_proposal_object_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
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      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.5"
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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/parekhakhil/pyImageSearch/blob/main/1503_region_proposal_object_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": [
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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j4RpK9pawQzP"
      },
      "source": [
        "# Region proposal object detection with OpenCV, Keras, and TensorFlow\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 [Region proposal object detection with OpenCV, Keras, and TensorFlow](https://www.pyimagesearch.com/2020/07/06/region-proposal-object-detection-with-opencv-keras-and-tensorflow/) blog post published on 2020-07-06.\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": "zAckSQgGaGHY"
      },
      "source": [
        "### Install the necessary packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n9FkaOfWwawu"
      },
      "source": [
        "!pip install tensorflow==2.2.0"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "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/region-proposal-object-detection/region-proposal-object-detection.zip\n",
        "!unzip -qq region-proposal-object-detection.zip\n",
        "%cd region-proposal-object-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",
        "from tensorflow.keras.applications import ResNet50\n",
        "from tensorflow.keras.applications.resnet50 import preprocess_input\n",
        "from tensorflow.keras.applications import imagenet_utils\n",
        "from tensorflow.keras.preprocessing.image import img_to_array\n",
        "from imutils.object_detection import non_max_suppression\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": "Jppw5-Bd56H-"
      },
      "source": [
        "### Implementing region proposal object detection with OpenCV, Keras, and TensorFlow"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X71RWICdLE3h"
      },
      "source": [
        "def selective_search(image, method=\"fast\"):\n",
        "\t# initialize OpenCV's selective search implementation and set the\n",
        "\t# input image\n",
        "\tss = cv2.ximgproc.segmentation.createSelectiveSearchSegmentation()\n",
        "\tss.setBaseImage(image)\n",
        "\n",
        "\t# check to see if we are using the *fast* but *less accurate* version\n",
        "\t# of selective search\n",
        "\tif method == \"fast\":\n",
        "\t\tss.switchToSelectiveSearchFast()\n",
        "\n",
        "\t# otherwise we are using the *slower* but *more accurate* version\n",
        "\telse:\n",
        "\t\tss.switchToSelectiveSearchQuality()\n",
        "\n",
        "\t# run selective search on the input image\n",
        "\trects = ss.process()\n",
        "\n",
        "\t# return the region proposal bounding boxes\n",
        "\treturn rects"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "okM7Bpyeq8Kc"
      },
      "source": [
        "# construct the argument parser and parse the arguments\n",
        "#ap = argparse.ArgumentParser()\n",
        "#ap.add_argument(\"-i\", \"--image\", required=True,\n",
        "#\thelp=\"path to the input image\")\n",
        "#ap.add_argument(\"-m\", \"--method\", type=str, default=\"fast\",\n",
        "#\tchoices=[\"fast\", \"quality\"],\n",
        "#\thelp=\"selective search method\")\n",
        "#ap.add_argument(\"-c\", \"--conf\", type=float, default=0.9,\n",
        "#\thelp=\"minimum probability to consider a classification/detection\")\n",
        "#ap.add_argument(\"-f\", \"--filter\", type=str, default=None,\n",
        "#\thelp=\"comma separated list of ImageNet labels to filter on\")\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\": \"beagle.png\",\n",
        "\t\"method\": \"fast\",\n",
        "\t\"conf\": 0.9,\n",
        "\t\"filter\": \"beagle\"\n",
        "}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QKxaph4NLQhi"
      },
      "source": [
        "# grab the label filters command line argument\n",
        "labelFilters = args[\"filter\"]\n",
        "\n",
        "# if the label filter is not empty, break it into a list\n",
        "if labelFilters is not None:\n",
        "\tlabelFilters = labelFilters.lower().split(\",\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dBFkiIGfLScd"
      },
      "source": [
        "# load ResNet from disk (with weights pre-trained on ImageNet)\n",
        "print(\"[INFO] loading ResNet...\")\n",
        "model = ResNet50(weights=\"imagenet\")\n",
        "\n",
        "# load the input image from disk and grab its dimensions\n",
        "image = cv2.imread(args[\"image\"])\n",
        "(H, W) = image.shape[:2]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S13Qg4pWLUSU"
      },
      "source": [
        "# run selective search on the input image\n",
        "print(\"[INFO] performing selective search with '{}' method...\".format(\n",
        "\targs[\"method\"]))\n",
        "rects = selective_search(image, method=args[\"method\"])\n",
        "print(\"[INFO] {} regions found by selective search\".format(len(rects)))\n",
        "\n",
        "# initialize the list of region proposals that we'll be classifying\n",
        "# along with their associated bounding boxes\n",
        "proposals = []\n",
        "boxes = []"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YaH10q9RLWdE"
      },
      "source": [
        "# loop over the region proposal bounding box coordinates generated by\n",
        "# running selective search\n",
        "for (x, y, w, h) in rects:\n",
        "\t# if the width or height of the region is less than 10% of the\n",
        "\t# image width or height, ignore it (i.e., filter out small\n",
        "\t# objects that are likely false-positives)\n",
        "\tif w / float(W) < 0.1 or h / float(H) < 0.1:\n",
        "\t\tcontinue\n",
        "\n",
        "\t# extract the region from the input image, convert it from BGR to\n",
        "\t# RGB channel ordering, and then resize it to 224x224 (the input\n",
        "\t# dimensions required by our pre-trained CNN)\n",
        "\troi = image[y:y + h, x:x + w]\n",
        "\troi = cv2.cvtColor(roi, cv2.COLOR_BGR2RGB)\n",
        "\troi = cv2.resize(roi, (224, 224))\n",
        "\n",
        "\t# further preprocess by the ROI\n",
        "\troi = img_to_array(roi)\n",
        "\troi = preprocess_input(roi)\n",
        "\n",
        "\t# update our proposals and bounding boxes lists\n",
        "\tproposals.append(roi)\n",
        "\tboxes.append((x, y, w, h))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nKA1P6nXLZ02"
      },
      "source": [
        "# convert the proposals list into NumPy array and show its dimensions\n",
        "proposals = np.array(proposals)\n",
        "print(\"[INFO] proposal shape: {}\".format(proposals.shape))\n",
        "\n",
        "# classify each of the proposal ROIs using ResNet and then decode the\n",
        "# predictions\n",
        "print(\"[INFO] classifying proposals...\")\n",
        "preds = model.predict(proposals)\n",
        "preds = imagenet_utils.decode_predictions(preds, top=1)\n",
        "\n",
        "# initialize a dictionary which maps class labels (keys) to any\n",
        "# bounding box associated with that label (values)\n",
        "labels = {}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GwdHQcxjLdBQ"
      },
      "source": [
        "# loop over the predictions\n",
        "for (i, p) in enumerate(preds):\n",
        "\t# grab the prediction information for the current region proposal\n",
        "\t(imagenetID, label, prob) = p[0]\n",
        "\n",
        "\t# only if the label filters are not empty *and* the label does not\n",
        "\t# exist in the list, then ignore it\n",
        "\tif labelFilters is not None and label not in labelFilters:\n",
        "\t\tcontinue\n",
        "\n",
        "\t# filter out weak detections by ensuring the predicted probability\n",
        "\t# is greater than the minimum probability\n",
        "\tif prob >= args[\"conf\"]:\n",
        "\t\t# grab the bounding box associated with the prediction and\n",
        "\t\t# convert the coordinates\n",
        "\t\t(x, y, w, h) = boxes[i]\n",
        "\t\tbox = (x, y, x + w, y + h)\n",
        "\n",
        "\t\t# grab the list of predictions for the label and add the\n",
        "\t\t# bounding box + probability to the list\n",
        "\t\tL = labels.get(label, [])\n",
        "\t\tL.append((box, prob))\n",
        "\t\tlabels[label] = L"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c27SMs8gLg6m"
      },
      "source": [
        "# loop over the labels for each of detected objects in the image\n",
        "for label in labels.keys():\n",
        "\t# clone the original image so that we can draw on it\n",
        "\tprint(\"[INFO] showing results for '{}'\".format(label))\n",
        "\tclone = image.copy()\n",
        "\n",
        "\t# loop over all bounding boxes for the current label\n",
        "\tfor (box, prob) in labels[label]:\n",
        "\t\t# draw the bounding box on the image\n",
        "\t\t(startX, startY, endX, endY) = box\n",
        "\t\tcv2.rectangle(clone, (startX, startY), (endX, endY),\n",
        "\t\t\t(0, 255, 0), 2)\n",
        "\n",
        "\t# show the results *before* applying non-maxima suppression, then\n",
        "\t# clone the image again so we can display the results *after*\n",
        "\t# applying non-maxima suppression\n",
        "\tplt_imshow(\"Before\", clone)\n",
        "\tclone = image.copy()\n",
        "\n",
        "    # extract the bounding boxes and associated prediction\n",
        "\t# probabilities, then apply non-maxima suppression\n",
        "\tboxes = np.array([p[0] for p in labels[label]])\n",
        "\tproba = np.array([p[1] for p in labels[label]])\n",
        "\tboxes = non_max_suppression(boxes, proba)\n",
        "\n",
        "\t# loop over all bounding boxes that were kept after applying\n",
        "\t# non-maxima suppression\n",
        "\tfor (startX, startY, endX, endY) in boxes:\n",
        "\t\t# draw the bounding box and label on the image\n",
        "\t\tcv2.rectangle(clone, (startX, startY), (endX, endY),\n",
        "\t\t\t(0, 255, 0), 2)\n",
        "\t\ty = startY - 10 if startY - 10 > 10 else startY + 10\n",
        "\t\tcv2.putText(clone, label, (startX, y),\n",
        "\t\t\tcv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 255, 0), 2)\n",
        "\n",
        "\t# show the output after apply non-maxima suppression\n",
        "\tplt_imshow(\"After\", clone)"
      ],
      "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, [*Region proposal object detection with OpenCV, Keras, and TensorFlow*](https://www.pyimagesearch.com/2020/07/06/region-proposal-object-detection-with-opencv-keras-and-tensorflow/) published on 2020-07-06."
      ]
    },
    {
      "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",
        "```"
      ]
    }
  ]
}