8.contours_shape_detection.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to stack images\n",
    "def stackImages(scale,imgArray):\n",
    "    rows = len(imgArray)\n",
    "    cols = len(imgArray[0])\n",
    "    rowsAvailable = isinstance(imgArray[0], list)\n",
    "    width = imgArray[0][0].shape[1]\n",
    "    height = imgArray[0][0].shape[0]\n",
    "    if rowsAvailable:\n",
    "        for x in range ( 0, rows):\n",
    "            for y in range(0, cols):\n",
    "                if imgArray[x][y].shape[:2] == imgArray[0][0].shape [:2]:\n",
    "                    imgArray[x][y] = cv2.resize(imgArray[x][y], (0, 0), None, scale, scale)\n",
    "                else:\n",
    "                    imgArray[x][y] = cv2.resize(imgArray[x][y], (imgArray[0][0].shape[1], imgArray[0][0].shape[0]), None, scale, scale)\n",
    "                if len(imgArray[x][y].shape) == 2: imgArray[x][y]= cv2.cvtColor( imgArray[x][y], cv2.COLOR_GRAY2BGR)\n",
    "        imageBlank = np.zeros((height, width, 3), np.uint8)\n",
    "        hor = [imageBlank]*rows\n",
    "        hor_con = [imageBlank]*rows\n",
    "        for x in range(0, rows):\n",
    "            hor[x] = np.hstack(imgArray[x])\n",
    "        ver = np.vstack(hor)\n",
    "    else:\n",
    "        for x in range(0, rows):\n",
    "            if imgArray[x].shape[:2] == imgArray[0].shape[:2]:\n",
    "                imgArray[x] = cv2.resize(imgArray[x], (0, 0), None, scale, scale)\n",
    "            else:\n",
    "                imgArray[x] = cv2.resize(imgArray[x], (imgArray[0].shape[1], imgArray[0].shape[0]), None,scale, scale)\n",
    "            if len(imgArray[x].shape) == 2: imgArray[x] = cv2.cvtColor(imgArray[x], cv2.COLOR_GRAY2BGR)\n",
    "        hor= np.hstack(imgArray)\n",
    "        ver = hor\n",
    "    return ver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define shape edges and type (only including triangles, squares, rectangles, and circles)\n",
    "def getContours(img):\n",
    "    contours,hierarchy = cv2.findContours(img,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)\n",
    "    for cnt in contours:\n",
    "        area = cv2.contourArea(cnt)\n",
    "        print(area)\n",
    "        if area>500:\n",
    "            cv2.drawContours(imgContour, cnt, -1, (255, 0, 0), 3)\n",
    "            peri = cv2.arcLength(cnt,True)\n",
    "            #print(peri)\n",
    "            approx = cv2.approxPolyDP(cnt,0.02*peri,True)\n",
    "            print(len(approx))\n",
    "            objCor = len(approx)\n",
    "            x, y, w, h = cv2.boundingRect(approx)\n",
    "\n",
    "            # Define shape names by number of corners \n",
    "            if objCor ==3: objectType =\"Triangle\"\n",
    "            elif objCor == 4:\n",
    "                aspRatio = w/float(h)\n",
    "                # Decreased aspect ratio to identify rectangles correctly\n",
    "                if aspRatio >0.95 and aspRatio <1.05: objectType= \"Square\"\n",
    "                else:objectType=\"Rectangle\"\n",
    "            elif objCor>4: objectType= \"Circle\"\n",
    "            else:objectType=\"None\"\n",
    "\n",
    "            cv2.rectangle(imgContour,(x,y),(x+w,y+h),(0,255,0),2)\n",
    "            cv2.putText(imgContour,objectType,\n",
    "                        ((x+5),y+(h//2)),cv2.FONT_HERSHEY_COMPLEX,0.7,\n",
    "                        (0,0,0),2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = 'shapes.png'\n",
    "img = cv2.imread(path)\n",
    "imgContour = img.copy()\n",
    "imgGray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n",
    "imgBlur = cv2.GaussianBlur(imgGray,(7,7),1)\n",
    "imgCanny = cv2.Canny(imgBlur,50,50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "4681.5\n",
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   ],
   "source": [
    "# Print areas and number of corners\n",
    "getContours(imgCanny)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "imgBlank = np.zeros_like(img)\n",
    "imgStack = stackImages(0.8,([img,imgGray,imgBlur],\n",
    "                            [imgCanny,imgContour,imgBlank]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv2.imshow(\"Stack\", imgStack)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv2.waitKey(0)"
   ]
  }
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