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pull #16

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d5aa12e
Create Aditya
AdityaK19 Aug 30, 2021
9dac78c
Create hello
AdityaK19 Aug 30, 2021
d77d2c1
Add files via upload
AdityaK19 Aug 30, 2021
502fa8f
Delete hello
AdityaK19 Aug 30, 2021
686d67a
Delete Aditya
AdityaK19 Aug 30, 2021
d0df072
old tasks committed
AdityaK19 Sep 2, 2021
8179ba9
Line Detect task committed
AdityaK19 Sep 2, 2021
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164 changes: 164 additions & 0 deletions Assignments/Aditya/ Sudoku_LINE_DETECTION.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "b71feeb8",
"metadata": {},
"outputs": [],
"source": [
"import cv2\n",
"import numpy as np\n",
"\n",
"img = cv2.imread('Sudoku.jpg')\n",
"\n",
"\n",
"#grayscale and canny edges\n",
"gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
"\n",
" #blur = cv2.GaussianBlur(img,(1,1),0)\n",
"\n",
"#kernel = np.ones((1,1),np.float32)/0.8\n",
"#blur = cv2.filter2D(img,-1,kernel)\n",
"blur = cv2.blur(img,(1,1))\n",
"edges = cv2.Canny(blur, 50, 400, apertureSize=3)\n",
"cv2.imshow('edges', edges)\n",
"\n",
"lines = cv2.HoughLines(edges, 1, np.pi/180, 222)\n",
"\n",
"\n",
"if lines is not None:\n",
" for i in range(0, len(lines)):\n",
" \n",
" rho = lines[i][0][0]\n",
" theta = lines[i][0][1]\n",
" a = np.cos(theta)\n",
" b = np.sin(theta)\n",
" x0 = a * rho\n",
" y0 = b * rho\n",
" pt1 = (int(x0 + 1000*(-b)), int(y0 + 1000*(a)))\n",
" pt2 = (int(x0 - 1000*(-b)), int(y0 - 1000*(a)))\n",
" cv2.line(img, pt1, pt2, (0,0,255), 3)\n",
" pass\n",
"\n",
"cv2.imshow('hough lines', img)\n",
"cv2.waitKey(0)\n",
"cv2.destroyAllWindows()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "7bf8045e",
"metadata": {},
"outputs": [],
"source": [
"for rho, theta in lines[0]:\n",
" a=np.cos(theta)\n",
" b=np.sin(theta)\n",
" x0=a*rho\n",
" y0=b*rho\n",
" x1=int(x0+1000*(-b))\n",
" y1=int(y0+1000*(a))\n",
" x2=int(x0-1000*(-b))\n",
" y2=int(x0-1000*(a))\n",
" cv2.line(image, (x1,y1), (x2,y2), (255,0,0),2) "
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "8cecb189",
"metadata": {},
"outputs": [],
"source": [
"# Python program to illustrate HoughLine\n",
"# method for line detection\n",
"import cv2\n",
"import numpy as np\n",
" \n",
"# Reading the required image in \n",
"# which operations are to be done. \n",
"# Make sure that the image is in the same \n",
"# directory in which this python program is\n",
"img = cv2.imread('Sudoku.jpg')\n",
"cv2.imshow('img', img)\n",
" \n",
"# Convert the img to grayscale\n",
"gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n",
" \n",
"# Apply edge detection method on the image\n",
"edges = cv2.Canny(gray,50,150,apertureSize = 3)\n",
" \n",
"# This returns an array of r and theta values\n",
"lines = cv2.HoughLines(edges,1,np.pi/180, 200)\n",
" \n",
"# The below for loop runs till r and theta values \n",
"# are in the range of the 2d array\n",
"for r,theta in lines[0]:\n",
" \n",
" # Stores the value of cos(theta) in a\n",
" a = np.cos(theta)\n",
" \n",
" # Stores the value of sin(theta) in b\n",
" b = np.sin(theta)\n",
" \n",
" # x0 stores the value rcos(theta)\n",
" x0 = a*r\n",
" \n",
" # y0 stores the value rsin(theta)\n",
" y0 = b*r\n",
" \n",
" # x1 stores the rounded off value of (rcos(theta)-1000sin(theta))\n",
" x1 = int(x0 + 1000*(-b))\n",
" \n",
" # y1 stores the rounded off value of (rsin(theta)+1000cos(theta))\n",
" y1 = int(y0 + 1000*(a))\n",
" \n",
" # x2 stores the rounded off value of (rcos(theta)+1000sin(theta))\n",
" x2 = int(x0 - 1000*(-b))\n",
" \n",
" # y2 stores the rounded off value of (rsin(theta)-1000cos(theta))\n",
" y2 = int(y0 - 1000*(a))\n",
" \n",
" # cv2.line draws a line in img from the point(x1,y1) to (x2,y2).\n",
" # (0,0,255) denotes the colour of the line to be \n",
" #drawn. In this case, it is red. \n",
" cv2.line(img,(x1,y1), (x2,y2), (0,0,255),2)\n",
" \n",
"# All the changes made in the input image are finally\n",
"# written on a new image houghlines.jpg\n",
"cv2.imshow('linesDetected.jpg', img)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f65d4371",
"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": "3.8.8"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
176 changes: 176 additions & 0 deletions Assignments/Aditya/Detect_shapes1.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "e440673a",
"metadata": {},
"outputs": [],
"source": [
"import cv2\n",
"import numpy as np\n",
"\n",
"image = cv2.imread('Untitled.jpg')\n",
"cv2.imshow('shapes', image)\n",
"\n",
"gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
" \n",
"\n",
"ret, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)\n",
"\n",
"contours, heirarchy = cv2.findContours(thresh,cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n",
"len(contours)\n",
"\n",
"cnt_list = []\n",
"len(cnt_list)\n",
"i = 0\n",
"\n",
"for cnt in contours:\n",
" if i == 0:\n",
" i = 1\n",
" continue\n",
" approx = cv2.approxPolyDP(cnt, 0.01*cv2.arcLength(cnt, True), True)\n",
" i = len(approx)\n",
" cnt_list.append(i)\n",
" if len(approx) == 3:\n",
" shape_name = \"Triangle 3\"\n",
" cv2.drawContours(image, [cnt], 0, (0, 255, 0), 5)\n",
"\n",
" #placing text at centre\n",
" M = cv2.moments(cnt)\n",
" cx = int(M['m10'] / M['m00'])\n",
" cy = int(M['m01'] / M['m00'])\n",
" cv2.putText(image, shape_name, (cx-50, cy), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,0,0), 1)\n",
" \n",
" elif len(approx) == 5:\n",
" shape_name = \"Pentagon 5\"\n",
" cv2.drawContours(image, [cnt], -1, (0,255,0), 5)\n",
" #placing text at centre\n",
" M = cv2.moments(cnt)\n",
" cx = int(M['m10'] / M['m00'])\n",
" cy = int(M['m01'] / M['m00'])\n",
" cv2.putText(image, shape_name, (cx-50, cy), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,0,0), 1)\n",
" \n",
" elif len(approx) == 6:\n",
" shape_name = \"Hexagon 6\"\n",
" cv2.drawContours(image, [cnt], -1, (0,255,0), 5)\n",
" #placing text at centre\n",
" M = cv2.moments(cnt)\n",
" cx = int(M['m10'] / M['m00'])\n",
" cy = int(M['m01'] / M['m00'])\n",
" cv2.putText(image, shape_name, (cx-50, cy), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,0,0), 1)\n",
" \n",
" elif len(approx) == 8:\n",
" shape_name = \"Octagon 8\"\n",
" cv2.drawContours(image, [cnt], -1, (0,255,0), 5)\n",
" #placing text at centre\n",
" M = cv2.moments(cnt)\n",
" cx = int(M['m10'] / M['m00'])\n",
" cy = int(M['m01'] / M['m00'])\n",
" cv2.putText(image, shape_name, (cx-50, cy), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,0,0), 1) \n",
" \n",
" elif len(approx) == 12:\n",
" shape_name = \"Star 12\"\n",
" cv2.drawContours(image, [cnt], -1, (0,255,0), 5)\n",
" #placing text at centre\n",
" M = cv2.moments(cnt)\n",
" cx = int(M['m10'] / M['m00'])\n",
" cy = int(M['m01'] / M['m00'])\n",
" cv2.putText(image, shape_name, (cx-50, cy), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,0,0), 1)\n",
" \n",
" elif len(approx) == 4:\n",
" x, y, w, h = cv2.boundingRect(approx)\n",
" aspectRatio = float(w)/h\n",
" if aspectRatio >= 0.5 and aspectRatio <= 1.5:\n",
" shape_name = \"Square 4\"\n",
" cv2.drawContours(image, [cnt], -1, (0,255,0), 5)\n",
" #placing text at centre\n",
" M = cv2.moments(cnt)\n",
" cx = int(M['m10'] / M['m00'])\n",
" cy = int(M['m01'] / M['m00'])\n",
" cv2.putText(image, shape_name, (cx-50, cy), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,0,0), 1) \n",
" else:\n",
" shape_name = \"Rectangle 4\"\n",
" cv2.drawContours(image, [cnt], -1, (0,255,0), 5)\n",
" #placing text at centre\n",
" M = cv2.moments(cnt)\n",
" cx = int(M['m10'] / M['m00'])\n",
" cy = int(M['m01'] / M['m00'])\n",
" cv2.putText(image, shape_name, (cx-50, cy), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,0,0), 1)\n",
" \n",
" elif len(approx) > 15:\n",
" shape_name = \"Circle\"\n",
" cv2.drawContours(image, [cnt], -1, (0,255,0), 5)\n",
" #placing text at centre\n",
" M = cv2.moments(cnt)\n",
" cx = int(M['m10'] / M['m00'])\n",
" cy = int(M['m01'] / M['m00'])\n",
" cv2.putText(image, shape_name, (cx-50, cy), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,0,0), 1)\n",
" \n",
" \n",
"print(len(cnt_list))\n",
"\n",
"cnt_list.sort()\n",
"print(len(cnt_list))\n",
"\n",
"for i in cnt_list:\n",
" if i == 3:\n",
" print(\"triangle\")\n",
" elif i == 4:\n",
" print(\"quadrilateral\")\n",
" elif i == 5:\n",
" print(\"Pentagon\")\n",
" elif i == 6:\n",
" print(\"Hexagon\")\n",
" elif i == 8:\n",
" print(\"Octagon\")\n",
" elif i == 12:\n",
" print(\"Star\")\n",
" elif i > 15:\n",
" print(\"Circle\")\n",
" \n",
"cv2.imshow(\"image1\", image)\n",
"cv2.waitKey(0)\n",
"cv2.clearAllWindows() \n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5448b713",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "3276c7b9",
"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": "3.8.8"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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