Object detector codes added-hacktober

Advance/ObjectDetector/00_Introduction-Main-Goals-of-the-Project.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Tasks"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Face Detection\n",
+    "- Eyes Detection\n",
+    "- Face & Eyes Detection\n",
+    "- Pedestrians Detection\n",
+    "- Cars Detection\n",
+    "- Car Plate Detection"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can Run the cell by:\n",
+    "- clicking on the Run button\n",
+    "- Shift + Enter (<b>Windows</b>).\n",
+    "- Control + Enter (<b>Mac</b>)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When we have finished with our Jupyter Notebook we go to:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<b>File</b> -> <b>Save and Checkpoint</b> and then"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<b>File</b> -> <b>Close and Halt</b>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### How to Tune Cascade Classifier"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "(https://docs.opencv.org/2.4/modules/objdetect/doc/cascade_classification.html#cascadeclassifier-detectmultiscale)\n",
+    "\n",
+    "<b>detectMultiscale</b>(input image, <b>Scale Factor</b>, <b>Min Neighbors</b>)\n",
+    "- <b>Scale Factor</b> Parameter specifying how much yhe image size is reduced at each image scale.\n",
+    "\n",
+    "\n",
+    "- <b>Min Neighbors</b> Parameter specifying how many neighbors each candidate rectangle should have to retain it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "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": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

Advance/ObjectDetector/04_Pedestrians_Detection.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Import Libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import cv2\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create Classifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "body_classifier = cv2.CascadeClassifier('Haarcascades/haarcascade_fullbody.xml')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Capture Video"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cap = cv2.VideoCapture('Video/People_Walking.mp4')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### It's a Kind of Magic"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# While Loop\n",
+    "while cap.isOpened():\n",
+    "\n",
+    "\n",
+    "    # Read the capture\n",
+    "    ret, frame = cap.read()\n",
+    "    \n",
+    "    # Pass the Frame to the Classifier\n",
+    "    bodies = body_classifier.detectMultiScale(frame, 1.3 ,5)\n",
+    "    \n",
+    "    # if Statement\n",
+    "    if ret == True:\n",
+    "    \n",
+    "        # Bound Boxes to Identified Bodies\n",
+    "        for (x, y, w, h) in bodies:\n",
+    "            cv2.rectangle(frame,\n",
+    "                         (x,y),\n",
+    "                         (x+w,y+h),\n",
+    "                         (255, 125,0),\n",
+    "                         5)\n",
+    "            cv2.imshow('Pedestraints', frame)\n",
+    "        \n",
+    "        # Exit with Esc button\n",
+    "        if cv2.waitKey(1)==27:\n",
+    "            break\n",
+    "        \n",
+    "    # else Statement\n",
+    "    else:\n",
+    "        break\n",
+    "    \n",
+    "# Release the Capture & Destroy All Windows\n",
+    "\n",
+    "cap.release()\n",
+    "cv2.destroyAllWindows()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "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": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

Advance/ObjectDetector/05_Car_Detection.ipynb

@@ -0,0 +1,128 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Import Libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import cv2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create Classifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "car_detector = cv2.CascadeClassifier('Haarcascades/haarcascade_car.xml')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Capture Video"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cap = cv2.VideoCapture('Video/Vehicles.mp4')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### It's a Kind of Magic"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# while Loop\n",
+    "while cap.isOpened():\n",
+    "\n",
+    "\n",
+    "    # Read the capture\n",
+    "    ret, frame = cap.read()\n",
+    "    \n",
+    "    # Pass the Frame to the Classifier\n",
+    "    cars = car_detector.detectMultiScale(frame, 1.3 , 5)\n",
+    "    \n",
+    "    # for Loop\n",
+    "    for (x,y,w,h) in cars:\n",
+    "    \n",
+    "        # Bound Boxes to Identified Bodies\n",
+    "        cv2.rectangle(frame,\n",
+    "                     (x,y),\n",
+    "                     (x+w,y+h),\n",
+    "                     (0,255,255),\n",
+    "                     2)\n",
+    "        cv2.imshow('Cars', frame)\n",
+    "        \n",
+    "        \n",
+    "    # Exit with Esc button\n",
+    "    if cv2.waitKey(1) & 0xFF == 27:\n",
+    "        break\n",
+    "    \n",
+    "    \n",
+    "# Release the Capture & Destroy All Windows\n",
+    "cap.release()\n",
+    "cv2.destroyAllWindows()\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "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": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}