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Repository: milaan9/Python_Computer_Vision_from_Scratch
Branch: main
Commit: 271e4211ea48
Files: 9
Total size: 7.2 MB
Directory structure:
gitextract_3swu7okc/
├── 01_Dynamic_Time_Warping/
│ └── Dynamic_Time_Warping.ipynb
├── 02_Canny_Edge_Detection/
│ └── Canny_Edge_Detection.ipynb
├── 03_Eigen_Faces/
│ └── Eigen_Faces.ipynb
├── 04_Sobel_Filter/
│ └── Sobel_Operator.ipynb
├── 05_Hough_Line_Detection/
│ └── Hough_Lines.ipynb
├── 06_Feature_Detection/
│ └── Feature_Detection.ipynb
├── 07_Image_Manipulation/
│ └── Image_Manipulation_and_Filtering.ipynb
├── LICENSE
└── README.md
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FILE CONTENTS
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FILE: 01_Dynamic_Time_Warping/Dynamic_Time_Warping.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Dynamic Time Warping (DTW)\n",
"This notebook describes the DTW concepts.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Time Series\n",
"* A time series is a collection of observations made sequentially.\n",
"* Time series occur in different fields such as medical, scientific and businesses domains.\n",
"* Finding the similarity between two time series is useful for clustering and classification.\n",
"\n",
"\n",
"\n",
"## Dynamic Time Warping (DTW)\n",
"Dynamic Time Warping (DTW) is an algorithm for measuring similarity between two sequences which may not have the same length.\n",
"\n",
"<img src=\"BWQ6YDNCD5RH21TRCV6K7URPA05UCM0T.png\"/>\n",
"\n",
"\n",
"In general, the DTW maps each element in the first sequence to an element in the second series. Assuming that a distance function is defined for each pair of points, the goal is to find a mapping that minimizes the total distance between all the points.\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Automatic pdb calling has been turned OFF\n"
]
},
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"source": [
"try:\n",
" if __IPYTHON__:\n",
" from IPython import get_ipython\n",
"\n",
" get_ipython().magic('matplotlib inline')\n",
" from ipython_utilities import *\n",
" from ipywidgets import interact, fixed, FloatSlider, IntSlider, Label, Checkbox, FloatRangeSlider\n",
" from IPython.display import display\n",
"\n",
" in_ipython_flag = True\n",
"except:\n",
" in_ipython_flag = False\n",
"import cv2 as cv\n",
"from matplotlib import pyplot as plt\n",
"import numpy as np\n",
"from PIL import Image\n",
"from ipywidgets import interact, fixed, FloatSlider, IntSlider, Label, Checkbox, FloatRangeSlider\n",
"\n",
"%matplotlib inline\n",
"%pdb\n",
"\n",
"def display_two_sequences(n1,n2):\n",
" index1 = np.linspace(0,15,n1)\n",
" index2 = np.linspace(0,15,n2)\n",
" A = 5*np.sin(index1)\n",
" B = 3*np.sin(index2 + 1)\n",
" # s1 = [1, 2, 3, 4]\n",
" # s2 = [2, 3, 5, 6, 8]\n",
" # ob_dtw = cl_dtw()\n",
" # distance,_ = ob_dtw.calculate_dtw_distance(s1, s2)\n",
"\n",
" fig = plt.figure(figsize=(12,4))\n",
" plt.plot(index1, A, '-ro', label='A')\n",
" plt.plot(index2, B, '-bo' ,label='B')\n",
" plt.ylabel('value')\n",
" plt.xlabel('index')\n",
" plt.legend()\n",
"\n",
" plt.show()\n",
" plt.pause(0.001)\n",
" \n",
"interact(display_two_sequences,\n",
" n1=IntSlider(min=0, \n",
" max=100, step=1,value=5,\n",
" description='# of points in sequence 1',\n",
" continuous_update=True),\n",
" n2=IntSlider(min=0, \n",
" max=100, step=1,value=7,\n",
" description='# of points in sequence 2',\n",
" continuous_update=True));\n",
"\n",
"# arrange_widgets_in_grid(controls)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Consider the two sequences $A=\\{a_1, a_2, ... , a_M\\}$ and $B=\\{b_1, b_2, ... , b_N\\}$ as shown above. Where $a_k$ is the $k_{th}$ sample point in the sequence $A$ and $b_k$ is the $k_{th}$ sample point in the sequence $B$. To find the similarity of these two sequences we need to calculate the total distance between the two sequences. To calculate the total distance we can connects each point in sequence $A$ to a point in sequence $B$ and accumulate the distances between the corresponding points. Since there are so many different possibilities for connecting different points, then the question is what is the best possible arrangement which results in the minimum total distance? \n",
"In order for the similarity measure to be meaningful we need to impose some constraints on how the points on the two sequence should be connected:\n",
"* **Boundary conditions:** the first points should be connected to each other and the last points should be connected to each point.\n",
"* **Monotonicity:** The alignment can not go backward. \n",
"* **Continuity:** The alignment can not skip an element.\n",
"* **Warping window:** The same point (feature) should not be repeated too many times.\n",
"\n",
"### Path\n",
"A path $P$ is defined as an ordered set of 2-tuples:\n",
"\n",
"$$\\large P=p_1,p_2, .....p_q$$\n",
"$$\\large p_k=(i_k,j_k)$$\n",
"\n",
"where $q$ is the number of connections (correspondences) and $i_k$ and $j_k$ are the indexes of the connecting elements. $P_s$ is called a \"Warping\" function.\n",
"\n",
"For example $P=(1,1), (1,2), (2,3), ... $ means:\n",
"* Point 1 in sequence A is connected to point 1 in sequence B\n",
"* Point 1 in sequence A is connected to point 2 in sequence B\n",
"* Point 2 in sequence A is connected to point 3 in sequence B\n",
"* ...\n",
"\n",
"\n",
"\n",
"\n",
"A path can be shown on a grid of M rows by N columns. The image below shows two possible paths. Notice that there are many possible paths for connecting elements of the sequence A to elements of the sequence B without violating any the constraints. The cost of each path is the accumulated distance between the corresponding points. **The goal of the DTW algorithm is to find the best path which minimizes the total cost.**\n",
"<img src=\"V4VW3VNNRET6TKXJW2MR0A024BD0EMUM.png\"/>\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Finding the Best Path\n",
"To find the best path:\n",
"* Set an M by N matrix G\n",
"* Set $G\\left[1,1\\right]=d(1,1)$ where $d(i,j)$ is the distance between elements i and j in the sequences respectively.\n",
"* Calculate each element of the the matrix G as:\n",
"\n",
"$$\\large G\\left[i,j\\right]=d(i,j)+min(G\\left[i,j-1\\right],G\\left[i-1,j\\right],G\\left[i-1,j-1\\right])$$\n",
"\n",
"* Total distance will be $D(A,B)=G\\left[M,N\\right]$\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
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"source": [
"def dtw(s1, s2, window=3):\n",
" grid = np.inf*np.ones((len(s1), len(s2)))\n",
" # grid[0, :] = abs(s1[0] - s2)\n",
" for i in range(window+1):\n",
" grid[0, i] = abs(s1[0] - s2[i])\n",
" for j in range(window+1):\n",
" grid[j, 0] = abs(s2[0] - s1[j])\n",
" \n",
" for i in range(1, len(s1)):\n",
" for j in range(1, len(s2)):\n",
" if abs(i-j) > window:\n",
" continue\n",
" grid[i, j] = abs(s1[i] - s2[j]) + min(grid[i - 1, j], grid[i, j-1], grid[i-1, j-1])\n",
" \n",
" \n",
" print(grid)\n",
" print(grid[-1, -1])\n",
" \n",
"def display_two_sequences(n1,n2):\n",
" index1 = np.linspace(0,15,n1)\n",
" index2 = np.linspace(0,15,n2)\n",
" A = [5, 6, 9, 2, 6]*2\n",
" B = [5, 7, 2, 6, 9 , 2]*2\n",
" #A = 5*np.sin(index1)\n",
" #B = 3*np.sin(index2 + 1)\n",
" # s1 = [1, 2, 3, 4]\n",
" # s2 = [2, 3, 5, 6, 8]\n",
" # ob_dtw = cl_dtw()\n",
" # distance,_ = ob_dtw.calculate_dtw_distance(s1, s2)\n",
" print(A)\n",
" print(B)\n",
" \n",
" dtw(A, B)\n",
"# fig = plt.figure(figsize=(12,4))\n",
"# plt.plot(index1, A, '-ro', label='A')\n",
"# plt.plot(index2, B, '-bo' ,label='B')\n",
"# plt.ylabel('value')\n",
"# plt.xlabel('index')\n",
"# plt.legend()\n",
"\n",
"# plt.show()\n",
"# plt.pause(0.001)\n",
"controls = interact(display_two_sequences,\n",
" n1=IntSlider(min=0, \n",
" max=100, step=1,value=5,\n",
" description='# of points in sequence 1',\n",
" continuous_update=True),\n",
" n2=IntSlider(min=0, \n",
" max=100, step=1,value=7,\n",
" description='# of points in sequence 2',\n",
" continuous_update=True));\n",
"\n",
"# arrange_widgets_in_grid(controls)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Warping Window\n",
"In order to gurantee that the alignment does not get stuck in one element a warping window is defined. The warping window limits the difference between the indexes of the two sequence. In cases where the number of elements in both sequences are equal, the warping window forces the path not to wander too far from the diagnoal.\n",
"<img src=\"PMCFBXFYO2WUX5EPRCQM0K8DALBYR0KB.png\"/>\n",
"\n",
"In a given path the warping window constraints can be imposed by limiting $\\left| {i_k - j_k} \\right| \\le r,r > 0$\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Slope Constraints\n",
"In order to prevent that a very short section of one sequence match a very long section of another, a slope constraint is imposed on the path.\n",
"<img src=\"A3VPKJJ0GMVJSNXICNJ5L5Y9JT9K2YKT.png\"/>\n",
"\n",
"$$\\large {{\\left( {{j_{k + s}} - {j_k}} \\right)} \\over {\\left( {{i_{k + s}} - {i_k}} \\right)}} \\le {s_h}$$\n",
"\n",
"and \n",
"\n",
"$$\\large {{\\left( {{i_{k + s}} - {i_k}} \\right)} \\over {\\left( {{j_{k + s}} - {j_k}} \\right)}} \\le {s_v}$$\n",
"\n",
"where $S_h>0$ and $S_v>0$ are the limiting slope constants.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Time Normalized Distance Measure\n",
"The time normalized distance between two sequences $A$ and $B$ for a particular path $P$ is defined as:\n",
"\n",
"$$\\large D(A,B)= {{{\\sum\\limits_{k = 1}^q {d({p_k}) \\cdot {w_k}} } \\over {\\sum\\limits_{k = 1}^q {{w_k}} }}} $$\n",
"\n",
"$$\\large p_k=(i_k,j_k)$$\n",
"\n",
"where $d(p_k)$ is the distance between element $i_k$ in the sequence A and element $j_k$ in the sequence B.\n",
"\n",
"$w_k$ is the weighting coefficient for the connection k. \n",
"\n",
"The best path $P^*$ is found by minimizing the $D(A,B)$\n",
"\n",
"$$\\large {P^*} = \\mathop {argmin }\\limits_P (D(A,B))$$\n",
"\n",
"\n",
"The term $\\sum\\limits_{k = 1}^q {{w_k}} $ in the denominator of the $D(A,B)$ complicates the optimization of the best path because it dependes on the length of the path. It is desirable to find some weighting coefficients that are independent of the path $P$. For example if we define \n",
"$$\\large w_k= (i_k-i_{k-1})+(j_k-j_{k-1})$$\n",
"\n",
"then \n",
"$$\\large \\sum\\limits_{k = 1}^q {{w_k}}=M+N=C$$\n",
"\n",
"This means that the denominator of the $D(A,B)$ is a constant\n",
"\n",
"$$\\large D(A,B)={1 \\over C}\\mathop {\\mathop {argmin }\\limits_P \\left[ {\\sum\\limits_{s = 1}^k {d({p_s}) \\cdot {w_s}} } \\right]}\\limits_{} $$ \n",
"\n",
"A an alternative we can define \n",
"$\\large w_k= (i_k-i_{k-1})$ which implies $\\large \\sum\\limits_{k = 1}^q {{w_k}}=M=C$\n",
"\n",
"or $\\large w_k= (j_k-j_{k-1})$ which implies $\\large \\sum\\limits_{k = 1}^q {{w_k}}=N=C$\n",
"\n",
"The algorithm for finding the best path with time normalization $w_k=(ik−ik−1)+(jk−jk−1)$ will be:\n",
"\n",
"* Set an M by N matrix G\n",
"* Set $G\\left[1,1\\right]=2d(1,1)$ where $d(i,j)$ is the distance between elements i and j in the sequences respectively.\n",
"* Calculate each element of the the matrix G as:\n",
"\n",
"$$\\large G\\left[i,j\\right]=min(G\\left[i,j-1\\right],G\\left[i-1,j\\right],2d(i,j)G\\left[i-1,j-1\\right])$$\n",
"* Total distance will be:\n",
"$$\\large D(A,B)={{G\\left[M,N\\right]}\\over {(N+M)}}$$\n"
]
}
],
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================================================
FILE: 02_Canny_Edge_Detection/Canny_Edge_Detection.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Shah, Jai \n",
"1001-380-311 \n",
"2017-02-07 \n",
"Assignment_01_01"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import cv2\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.colors\n",
"import numpy as np\n",
"from PIL import Image\n",
"from skimage import data\n",
"import scipy\n",
"import math\n",
"from scipy.ndimage import measurements\n",
"from skimage import data\n",
"from ipywidgets import interact, fixed, FloatSlider, IntSlider,FloatRangeSlider, Label"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from scipy.ndimage import convolve\n",
"plt.rcParams['image.interpolation'] = 'none'\n",
"def convolve(kernel,original_image):\n",
" # Normalize the kernel\n",
" kernel_sum=abs(np.sum(kernel))\n",
" kernel= kernel/kernel_sum if kernel_sum else kernel\n",
" filtered_image = scipy.ndimage.convolve(original_image, kernel)\n",
" return filtered_image"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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YFHR0sPheYanP2f3TVkmy4M+2tZOA/aZ7mZWUHx0H1vqxY0XLsONB+0qtaSpI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T45DhpZTemD40dA/2Gl7/1IvjpbbaPe9/a63vcK8BsbG8jlcgGh/E7q6qsH62CT+tH4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\neTpMmHNDcwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2392c2defd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def gaussian_filter(size=7, sigma=5.0):\n",
" gaussian_filter.filtered_image = cv2.GaussianBlur(cameraman_image,(size,size), sigma, sigma)\n",
" fig1, axes_array = plt.subplots(1, 2)\n",
" fig1.set_size_inches(8,4)\n",
" image_plot = axes_array[0].imshow(cameraman_image,cmap=plt.cm.gray) \n",
" axes_array[0].axis('off')\n",
" axes_array[0].set(title='Original Image')\n",
" image_plot = axes_array[1].imshow(gaussian_filter.filtered_image,cmap=plt.cm.gray)\n",
" axes_array[1].axis('off')\n",
" axes_array[1].set(title='Filtered Image')\n",
" plt.show() \n",
"\n",
"cameraman_image = data.camera()/255.\n",
"interact(gaussian_filter,size=IntSlider(min=1., max=11., step=2, value=7),\n",
" sigma=FloatSlider(min=0.1, max=10, step=0.1, value=5.0));\n"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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nK3JqPG8ktFaKGHMdZtJrZb22Iv5mQmluR5U3Kk6Uds5cv/G70oqaYSS2Riym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\ndoaGhgxAl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gitextract_3swu7okc/ ├── 01_Dynamic_Time_Warping/ │ └── Dynamic_Time_Warping.ipynb ├── 02_Canny_Edge_Detection/ │ └── Canny_Edge_Detection.ipynb ├── 03_Eigen_Faces/ │ └── Eigen_Faces.ipynb ├── 04_Sobel_Filter/ │ └── Sobel_Operator.ipynb ├── 05_Hough_Line_Detection/ │ └── Hough_Lines.ipynb ├── 06_Feature_Detection/ │ └── Feature_Detection.ipynb ├── 07_Image_Manipulation/ │ └── Image_Manipulation_and_Filtering.ipynb ├── LICENSE └── README.md
Condensed preview — 9 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (7,689K chars).
[
{
"path": "01_Dynamic_Time_Warping/Dynamic_Time_Warping.ipynb",
"chars": 13606,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Dynamic Time Warping (DTW)\\n\",\n "
},
{
"path": "02_Canny_Edge_Detection/Canny_Edge_Detection.ipynb",
"chars": 601467,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Shah, Jai \\n\",\n \"1001-380-311 "
},
{
"path": "03_Eigen_Faces/Eigen_Faces.ipynb",
"chars": 5476330,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Shah, Jai\\n\",\n \"### 1001-380"
},
{
"path": "04_Sobel_Filter/Sobel_Operator.ipynb",
"chars": 645197,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Sobel Operator\\n\",\n \" - T"
},
{
"path": "05_Hough_Line_Detection/Hough_Lines.ipynb",
"chars": 6702,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Hough Lines\"\n ]\n },\n {\n \""
},
{
"path": "06_Feature_Detection/Feature_Detection.ipynb",
"chars": 22548,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Feature Detection\\n\",\n \"This n"
},
{
"path": "07_Image_Manipulation/Image_Manipulation_and_Filtering.ipynb",
"chars": 803522,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Image Manipulation and Filtering\\"
},
{
"path": "LICENSE",
"chars": 1096,
"preview": "MIT License\n\nCopyright (c) 2021 Milaan Parmar / Милан пармар / _米兰 帕尔马\n\nPermission is hereby granted, free of charge, t"
},
{
"path": "README.md",
"chars": 7382,
"preview": "<p align=\"center\"> \n<a href=\"https://github.com/milaan9\"><img src=\"https://img.shields.io/static/v1?logo=github&label=ma"
}
]
About this extraction
This page contains the full source code of the milaan9/Python_Computer_Vision_from_Scratch GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 9 files (7.2 MB), approximately 1.9M tokens. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.