├── Intro_To_Computer_Vision
    ├── 1_ImageIO.ipynb
    ├── 2_Drawing.ipynb
    ├── 3_BasicImageOperations.ipynb
    ├── 4_Histograms.ipynb
    ├── 5_Thresholding.ipynb
    └── 6_ImageSmoothning_and_Morphing.ipynb
├── OpenCV_Brief_Guide
    ├── 1_Contours.ipynb
    ├── 2_EdgeDetection.ipynb
    ├── 3_ImagePyramids.ipynb
    ├── 4_MathematicalTransforms.ipynb
    ├── 5_FeatureDetection.ipynb
    ├── 6_MoreOnFeatures.ipynb
    ├── 7_FeatureMatching.ipynb
    ├── 8_VideoAnalysis1.ipynb
    ├── 8_VideoAnalysis2.ipynb
    ├── 8_VideoAnalysis3.ipynb
    └── 9_CameraCalibration.ipynb
├── README.md
├── demos
    ├── MeanShiftClustering.ipynb
    ├── MeanShift_Segmentation.ipynb
    ├── Panorama_Stitch.ipynb
    ├── SudokuSolver.ipynb
    └── Tracker.ipynb
└── resources
    ├── H1to3p
    ├── algo.png
    ├── apple.jpg
    ├── apples_oranges.png
    ├── bg_sub.mp4
    ├── blur_amount.jpg
    ├── box.png
    ├── box_in_scene.png
    ├── brisk.jpg
    ├── camshift_track.mp4
    ├── canny.jpg
    ├── cells.png
    ├── compass.jpg
    ├── contour.png
    ├── corner_test.jpg
    ├── dense_flow.mp4
    ├── emoji.png
    ├── fast_neighbour.jpg
    ├── feature_target_test.jpg
    ├── feature_test.jpg
    ├── flow.mp4
    ├── football.avi
    ├── fourier1.png
    ├── fourier2.png
    ├── gauss.png
    ├── grid-interpolation2d.png
    ├── hand.jpg
    ├── hand2.jpg
    ├── hand3.jpeg
    ├── harris.jpg
    ├── harris_1.png
    ├── harris_2.png
    ├── harris_3.png
    ├── hierarchy.png
    ├── hsv_flow.mp4
    ├── image.png
    ├── img1.ppm
    ├── img3.ppm
    ├── inter_nearest.png
    ├── interpolation.png
    ├── laplacian.jpg
    ├── left01.jpg
    ├── left02.jpg
    ├── left03.jpg
    ├── left04.jpg
    ├── left05.jpg
    ├── left06.jpg
    ├── left07.jpg
    ├── left08.jpg
    ├── left09.jpg
    ├── left11.jpg
    ├── left12.jpg
    ├── left13.jpg
    ├── left14.jpg
    ├── lena.jpg
    ├── letter1.png
    ├── letter2.png
    ├── meanshift_track.mp4
    ├── messi.jpg
    ├── messi2.jpg
    ├── noise.jpg
    ├── octave.jpg
    ├── optic_flow.jpg
    ├── orange.jpg
    ├── pixel_state.jpg
    ├── pyramid.png
    ├── radial_dis.png
    ├── scaling.jpg
    ├── scene.jpg
    ├── scene.png
    ├── scene_gray.png
    ├── shi-tomasi.jpg
    ├── sift_local_extrema.jpg
    ├── sobel1.jpg
    ├── sobel2.jpg
    ├── statue.jpg
    ├── stitch1.jpg
    ├── stitch2.jpg
    ├── sudoku0.jpg
    ├── sudoku1.png
    ├── sudoku2.jpg
    ├── surf_orientation.jpg
    ├── template_test.png
    ├── test_gray.jpg
    └── watershed_test.jpg


/Intro_To_Computer_Vision/1_ImageIO.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": null,
  6 |    "metadata": {
  7 |     "collapsed": true
  8 |    },
  9 |    "outputs": [],
 10 |    "source": [
 11 |     "import cv2\n",
 12 |     "import numpy as np\n",
 13 |     "from matplotlib import pyplot as plt"
 14 |    ]
 15 |   },
 16 |   {
 17 |    "cell_type": "markdown",
 18 |    "metadata": {},
 19 |    "source": [
 20 |     "## Reading/Loading an image\n",
 21 |     "Function: <b>`cv2.imread(image_path, flag)`</b>\n",
 22 |     "\n",
 23 |     "<u>Flags</u>:\n",
 24 |     "1. <b>cv2.IMREAD_COLOR</b> (default flag) - Loads a color image, neglecting any transparency.\n",
 25 |     "2. <b>cv2.IMREAD_GRAYSCALE</b> - Loads image in grayscale\n",
 26 |     "3. <b>cv2.IMREAD_UNCHANGED</b> - Loads color image without neglecting transparency. (won't be discussing)\n",
 27 |     "\n",
 28 |     "## Displaying an image\n",
 29 |     "Function: <b>`cv2.imshow(window_name, image)`</b>\n",
 30 |     "\n",
 31 |     "## Writing an image\n",
 32 |     "Function: <b>`cv2.imwrite(file_path, image)</b>"
 33 |    ]
 34 |   },
 35 |   {
 36 |    "cell_type": "code",
 37 |    "execution_count": null,
 38 |    "metadata": {},
 39 |    "outputs": [],
 40 |    "source": [
 41 |     "image_path = \"../resources/messi.jpg\"\n",
 42 |     "\n",
 43 |     "color_image = cv2.imread(image_path)  # no flag given, IMREAD_COLOR flag used by default\n",
 44 |     "cv2.imshow(\"color\", color_image)\n",
 45 |     "cv2.waitKey(0)  # keyboard hook that waits infinitely for a key stroke\n",
 46 |     "cv2.destroyWindow(\"color\")  # close the opencv window"
 47 |    ]
 48 |   },
 49 |   {
 50 |    "cell_type": "markdown",
 51 |    "metadata": {},
 52 |    "source": [
 53 |     "<img class=\"left\" src=\"../resources/messi.jpg\">"
 54 |    ]
 55 |   },
 56 |   {
 57 |    "cell_type": "code",
 58 |    "execution_count": null,
 59 |    "metadata": {
 60 |     "collapsed": true
 61 |    },
 62 |    "outputs": [],
 63 |    "source": [
 64 |     "gray_image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)\n",
 65 |     "# cv2.imshow(\"gray\", gray_image)\n",
 66 |     "# cv2.waitKey(0)\n",
 67 |     "# cv2.destroyWindow(\"gray\")\n",
 68 |     "plt.imshow(gray_image, cmap=\"gray\")\n",
 69 |     "plt.show()\n",
 70 |     "\n",
 71 |     "cv2.imwrite(\"../resources/test_gray.jpg\", gray_image)"
 72 |    ]
 73 |   },
 74 |   {
 75 |    "cell_type": "markdown",
 76 |    "metadata": {},
 77 |    "source": [
 78 |     "### Image properties"
 79 |    ]
 80 |   },
 81 |   {
 82 |    "cell_type": "code",
 83 |    "execution_count": null,
 84 |    "metadata": {
 85 |     "collapsed": true
 86 |    },
 87 |    "outputs": [],
 88 |    "source": [
 89 |     "# Color vs Grayscale comparison\n",
 90 |     "\n",
 91 |     "print \"Colored\"\n",
 92 |     "print \"\\t\\tHeight: %d, Width: %d, Channels: %d\" % color_image.shape\n",
 93 |     "print \"\\t\\tNumer of pixels: %d\" % color_image.size\n",
 94 |     "\n",
 95 |     "print\n",
 96 |     "\n",
 97 |     "print \"Grayscale\"\n",
 98 |     "print \"\\t\\tHeight: %d, Width: %d\" % gray_image.shape\n",
 99 |     "print \"\\t\\tNumer of pixels: %d\" % gray_image.size"
100 |    ]
101 |   },
102 |   {
103 |    "cell_type": "markdown",
104 |    "metadata": {},
105 |    "source": [
106 |     "## Accessing and modifying pixels"
107 |    ]
108 |   },
109 |   {
110 |    "cell_type": "code",
111 |    "execution_count": null,
112 |    "metadata": {
113 |     "collapsed": true
114 |    },
115 |    "outputs": [],
116 |    "source": [
117 |     "pixel = color_image[100, 100]\n",
118 |     "print pixel"
119 |    ]
120 |   },
121 |   {
122 |    "cell_type": "code",
123 |    "execution_count": null,
124 |    "metadata": {
125 |     "collapsed": true
126 |    },
127 |    "outputs": [],
128 |    "source": [
129 |     "# Matplotlib uses RGB whereas CV uses BGR\n",
130 |     "def inverted(image):\n",
131 |     "    inv_image = np.zeros_like(image)\n",
132 |     "    inv_image[:,:,0] = image[:,:,2]\n",
133 |     "    inv_image[:,:,1] = image[:,:,1]\n",
134 |     "    inv_image[:,:,2] = image[:,:,0]\n",
135 |     "    return inv_image\n",
136 |     "\n",
137 |     "\n",
138 |     "copy = np.copy(color_image)\n",
139 |     "copy[100, 100] = [0, 0, 0]\n",
140 |     "\n",
141 |     "# To observe modification properly, let's modify a range of pixels\n",
142 |     "for i in range(101):\n",
143 |     "    for j in range(101):\n",
144 |     "        copy[i, j] = [200, 0, 0]\n",
145 |     "\n",
146 |     "plt.imshow(inverted(copy))\n",
147 |     "plt.show()"
148 |    ]
149 |   },
150 |   {
151 |    "cell_type": "code",
152 |    "execution_count": null,
153 |    "metadata": {
154 |     "collapsed": true
155 |    },
156 |    "outputs": [],
157 |    "source": [
158 |     "# Now, let's try shifting a region of image around\n",
159 |     "copy = np.copy(color_image)\n",
160 |     "\n",
161 |     "# Selecting a region\n",
162 |     "region = copy[50:150, 50:150]\n",
163 |     "plt.imshow(inverted(region))\n",
164 |     "plt.show()"
165 |    ]
166 |   },
167 |   {
168 |    "cell_type": "code",
169 |    "execution_count": null,
170 |    "metadata": {
171 |     "collapsed": true
172 |    },
173 |    "outputs": [],
174 |    "source": [
175 |     "# Copying the selected region over anohter region in the image\n",
176 |     "copy[200:300, 200:300] = region\n",
177 |     "plt.imshow(inverted(copy))\n",
178 |     "plt.show()"
179 |    ]
180 |   },
181 |   {
182 |    "cell_type": "code",
183 |    "execution_count": null,
184 |    "metadata": {
185 |     "collapsed": true
186 |    },
187 |    "outputs": [],
188 |    "source": [
189 |     "# Splitting and Merging pixels on basis of channels\n",
190 |     "B, G, R = cv2.split(color_image)  # another way is B = color_image[:, :, 0]\n",
191 |     "\n",
192 |     "merged = cv2.merge((B, G, R))\n",
193 |     "plt.imshow(inverted(merged))\n",
194 |     "plt.show()"
195 |    ]
196 |   },
197 |   {
198 |    "cell_type": "code",
199 |    "execution_count": null,
200 |    "metadata": {
201 |     "collapsed": true
202 |    },
203 |    "outputs": [],
204 |    "source": [
205 |     "# Removing color channels\n",
206 |     "copy = np.copy(color_image)\n",
207 |     "copy[:, :, 0] = 0  # Removed blue channel\n",
208 |     "copy[:, :, 1] = 0  # Removed green channel\n",
209 |     "\n",
210 |     "plt.imshow(inverted(copy))\n",
211 |     "plt.show()"
212 |    ]
213 |   }
214 |  ],
215 |  "metadata": {
216 |   "kernelspec": {
217 |    "display_name": "Python 2",
218 |    "language": "python",
219 |    "name": "python2"
220 |   },
221 |   "language_info": {
222 |    "codemirror_mode": {
223 |     "name": "ipython",
224 |     "version": 2
225 |    },
226 |    "file_extension": ".py",
227 |    "mimetype": "text/x-python",
228 |    "name": "python",
229 |    "nbconvert_exporter": "python",
230 |    "pygments_lexer": "ipython2",
231 |    "version": "2.7.12"
232 |   }
233 |  },
234 |  "nbformat": 4,
235 |  "nbformat_minor": 2
236 | }
237 | 


--------------------------------------------------------------------------------
/Intro_To_Computer_Vision/3_BasicImageOperations.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": null,
  6 |    "metadata": {
  7 |     "collapsed": true,
  8 |     "slideshow": {
  9 |      "slide_type": "slide"
 10 |     }
 11 |    },
 12 |    "outputs": [],
 13 |    "source": [
 14 |     "import cv2\n",
 15 |     "import numpy as np\n",
 16 |     "from matplotlib import pyplot as plt"
 17 |    ]
 18 |   },
 19 |   {
 20 |    "cell_type": "markdown",
 21 |    "metadata": {
 22 |     "slideshow": {
 23 |      "slide_type": "fragment"
 24 |     }
 25 |    },
 26 |    "source": [
 27 |     "## Basic Image Operations"
 28 |    ]
 29 |   },
 30 |   {
 31 |    "cell_type": "code",
 32 |    "execution_count": null,
 33 |    "metadata": {
 34 |     "collapsed": true,
 35 |     "slideshow": {
 36 |      "slide_type": "fragment"
 37 |     }
 38 |    },
 39 |    "outputs": [],
 40 |    "source": [
 41 |     "image = cv2.imread(\"../resources/messi.jpg\")"
 42 |    ]
 43 |   },
 44 |   {
 45 |    "cell_type": "markdown",
 46 |    "metadata": {
 47 |     "slideshow": {
 48 |      "slide_type": "fragment"
 49 |     }
 50 |    },
 51 |    "source": [
 52 |     "### Switching Colorspace\n",
 53 |     "There are more than 150 colorspace conversion methods in OpenCV.\n",
 54 |     "\n",
 55 |     "We'll be focussing mainly on the following two conversions:\n",
 56 |     "1. BGR - Grayscale\n",
 57 |     "2. BGR - HSV"
 58 |    ]
 59 |   },
 60 |   {
 61 |    "cell_type": "code",
 62 |    "execution_count": null,
 63 |    "metadata": {
 64 |     "collapsed": true,
 65 |     "slideshow": {
 66 |      "slide_type": "slide"
 67 |     }
 68 |    },
 69 |    "outputs": [],
 70 |    "source": [
 71 |     "hsv_view = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)\n",
 72 |     "gray_view = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
 73 |     "\n",
 74 |     "fig = plt.figure()\n",
 75 |     "fig.set_size_inches(18, 10)\n",
 76 |     "\n",
 77 |     "fig.add_subplot(1, 3, 1)\n",
 78 |     "plt.imshow(hsv_view)\n",
 79 |     "\n",
 80 |     "fig.add_subplot(1, 3, 2)\n",
 81 |     "plt.imshow(cv2.cvtColor(hsv_view, cv2.COLOR_HSV2RGB))\n",
 82 |     "\n",
 83 |     "fig.add_subplot(1, 3, 3)\n",
 84 |     "plt.imshow(gray_view, cmap='gray')\n",
 85 |     "\n",
 86 |     "plt.show()"
 87 |    ]
 88 |   },
 89 |   {
 90 |    "cell_type": "markdown",
 91 |    "metadata": {
 92 |     "slideshow": {
 93 |      "slide_type": "slide"
 94 |     }
 95 |    },
 96 |    "source": [
 97 |     "### Image Scaling\n",
 98 |     "Function: <b>cv2.resize(image, new_dimensions, interpolation)</b>\n",
 99 |     "\n",
100 |     "Interpolation helps preserving the edges when resizing image. It tries to achieve a best approximation of a pixel's color and intensity based on the values at surrounding pixels when an image is resized.\n",
101 |     "\n",
102 |     "<u>Interpolation Methods</u>:\n",
103 |     "1. <b>INTER_NEAREST</b> - a nearest-neighbor interpolation.\n",
104 |     "2. <b>INTER_LINEAR</b> - a bilinear interpolation over 2x2 pixel neighborhood (used by default). Preferred for zooming.\n",
105 |     "3. <b>INTER_AREA</b> - resampling using pixel area relation. Preferred for shrinking.\n",
106 |     "4. <b>INTER_CUBIC</b> - a bicubic interpolation over 4x4 pixel neighborhood. Preferred for zooming. (slow)\n",
107 |     "5. <b>INTER_LANCZOS4</b> - a Lanczos interpolation over 8x8 pixel neighborhood. (we won't be discussing this for now)"
108 |    ]
109 |   },
110 |   {
111 |    "cell_type": "markdown",
112 |    "metadata": {
113 |     "slideshow": {
114 |      "slide_type": "slide"
115 |     }
116 |    },
117 |    "source": [
118 |     "<img src=\"../resources/inter_nearest.png\">\n",
119 |     "<img src=\"../resources/interpolation.png\">"
120 |    ]
121 |   },
122 |   {
123 |    "cell_type": "code",
124 |    "execution_count": null,
125 |    "metadata": {
126 |     "collapsed": true,
127 |     "slideshow": {
128 |      "slide_type": "slide"
129 |     }
130 |    },
131 |    "outputs": [],
132 |    "source": [
133 |     "height, width = image.shape[0], image.shape[1]\n",
134 |     "scaled_image = cv2.resize(image, (width//2, height//2), interpolation=cv2.INTER_AREA)\n",
135 |     "\n",
136 |     "fig = plt.figure()\n",
137 |     "fig.set_size_inches(18, 10)\n",
138 |     "\n",
139 |     "fig.add_subplot(1, 2, 1)\n",
140 |     "plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))\n",
141 |     "\n",
142 |     "fig.add_subplot(1, 2, 2)\n",
143 |     "plt.imshow(cv2.cvtColor(scaled_image, cv2.COLOR_BGR2RGB))\n",
144 |     "\n",
145 |     "plt.show()"
146 |    ]
147 |   },
148 |   {
149 |    "cell_type": "markdown",
150 |    "metadata": {
151 |     "slideshow": {
152 |      "slide_type": "slide"
153 |     }
154 |    },
155 |    "source": [
156 |     "### Image Rotation"
157 |    ]
158 |   },
159 |   {
160 |    "cell_type": "code",
161 |    "execution_count": null,
162 |    "metadata": {
163 |     "collapsed": true,
164 |     "slideshow": {
165 |      "slide_type": "fragment"
166 |     }
167 |    },
168 |    "outputs": [],
169 |    "source": [
170 |     "# 2nd parameter is angle, 3rd parameter is scaling index\n",
171 |     "matrix = cv2.getRotationMatrix2D((width/2, height/2), 90, 1)\n",
172 |     "\n",
173 |     "# Affine Transformation preserves collinearity and ratios of distances between collinear points.\n",
174 |     "# In layman terms, all parallel lines in original image will also be parallel in output image after rotation\n",
175 |     "rotated_image = cv2.warpAffine(image, matrix, (width, height))\n",
176 |     "\n",
177 |     "# cv2.warpPerspective => similar to warpAffine, but the distances between lines are not preserved.\n",
178 |     "\n",
179 |     "plt.imshow(cv2.cvtColor(rotated_image, cv2.COLOR_BGR2RGB))\n",
180 |     "plt.show()"
181 |    ]
182 |   },
183 |   {
184 |    "cell_type": "markdown",
185 |    "metadata": {
186 |     "slideshow": {
187 |      "slide_type": "slide"
188 |     }
189 |    },
190 |    "source": [
191 |     "### Flipping an image"
192 |    ]
193 |   },
194 |   {
195 |    "cell_type": "code",
196 |    "execution_count": null,
197 |    "metadata": {
198 |     "collapsed": true,
199 |     "slideshow": {
200 |      "slide_type": "fragment"
201 |     }
202 |    },
203 |    "outputs": [],
204 |    "source": [
205 |     "h_flip = cv2.flip(image, 1)  # horizontal flip\n",
206 |     "v_flip = cv2.flip(image, 0)  # vertical flip\n",
207 |     "hv_flip = cv2.flip(image, -1)  # simultaneous horizontal and vertical flip\n",
208 |     "\n",
209 |     "fig = plt.figure()\n",
210 |     "fig.set_size_inches(18, 10)\n",
211 |     "\n",
212 |     "fig.add_subplot(1, 3, 1)\n",
213 |     "plt.imshow(cv2.cvtColor(h_flip, cv2.COLOR_BGR2RGB))\n",
214 |     "\n",
215 |     "fig.add_subplot(1, 3, 2)\n",
216 |     "plt.imshow(cv2.cvtColor(v_flip, cv2.COLOR_BGR2RGB))\n",
217 |     "\n",
218 |     "fig.add_subplot(1, 3, 3)\n",
219 |     "plt.imshow(cv2.cvtColor(hv_flip, cv2.COLOR_BGR2RGB))\n",
220 |     "\n",
221 |     "plt.show()"
222 |    ]
223 |   },
224 |   {
225 |    "cell_type": "markdown",
226 |    "metadata": {
227 |     "slideshow": {
228 |      "slide_type": "slide"
229 |     }
230 |    },
231 |    "source": [
232 |     "### Blending two images"
233 |    ]
234 |   },
235 |   {
236 |    "cell_type": "code",
237 |    "execution_count": null,
238 |    "metadata": {
239 |     "collapsed": true,
240 |     "slideshow": {
241 |      "slide_type": "fragment"
242 |     }
243 |    },
244 |    "outputs": [],
245 |    "source": [
246 |     "image2 = cv2.imread('../resources/lena.jpg')\n",
247 |     "image2 = cv2.resize(image2, (image.shape[1], image.shape[0]), interpolation=cv2.INTER_AREA)\n",
248 |     "\n",
249 |     "blend = cv2.cvtColor(cv2.addWeighted(image, 0.5, image2, 0.5, 0), cv2.COLOR_BGR2RGB)\n",
250 |     "\n",
251 |     "plt.imshow(blend)\n",
252 |     "plt.show()"
253 |    ]
254 |   },
255 |   {
256 |    "cell_type": "markdown",
257 |    "metadata": {
258 |     "collapsed": true,
259 |     "slideshow": {
260 |      "slide_type": "slide"
261 |     }
262 |    },
263 |    "source": [
264 |     "### Masking and Bitwise Operations\n",
265 |     "Masking involves setting the pixel values in an image to zero, or some other \"background\" value.\n",
266 |     "\n",
267 |     "<u>Using an image as a mask</u> - A mask image is simply an image where some of the pixel intensity values are zero, and others are non-zero. Wherever the pixel intensity value is zero in the mask image, corresponding pixel intensity of the resulting masked image will be set to the background value (normally zero)."
268 |    ]
269 |   },
270 |   {
271 |    "cell_type": "code",
272 |    "execution_count": null,
273 |    "metadata": {
274 |     "collapsed": true,
275 |     "slideshow": {
276 |      "slide_type": "subslide"
277 |     }
278 |    },
279 |    "outputs": [],
280 |    "source": [
281 |     "# hsv values\n",
282 |     "blue_lower = np.array([110,50,50])\n",
283 |     "blue_upper = np.array([130,255,255])\n",
284 |     "\n",
285 |     "messi = image[50:335, 48:477]\n",
286 |     "\n",
287 |     "image_hsv = cv2.cvtColor(messi, cv2.COLOR_BGR2HSV)\n",
288 |     "\n",
289 |     "mask = cv2.inRange(image_hsv, blue_lower, blue_upper)\n",
290 |     "result = cv2.bitwise_and(messi, messi, mask=mask)\n",
291 |     "\n",
292 |     "fig = plt.figure()\n",
293 |     "fig.set_size_inches(18, 10)\n",
294 |     "\n",
295 |     "fig.add_subplot(1, 3, 1)\n",
296 |     "plt.imshow(cv2.cvtColor(messi, cv2.COLOR_BGR2RGB))\n",
297 |     "\n",
298 |     "fig.add_subplot(1, 3, 2)\n",
299 |     "plt.imshow(mask, cmap='gray')\n",
300 |     "\n",
301 |     "fig.add_subplot(1, 3, 3)\n",
302 |     "plt.imshow(cv2.cvtColor(result, cv2.COLOR_BGR2RGB))\n",
303 |     "\n",
304 |     "plt.show()"
305 |    ]
306 |   }
307 |  ],
308 |  "metadata": {
309 |   "kernelspec": {
310 |    "display_name": "Python 2",
311 |    "language": "python",
312 |    "name": "python2"
313 |   },
314 |   "language_info": {
315 |    "codemirror_mode": {
316 |     "name": "ipython",
317 |     "version": 2
318 |    },
319 |    "file_extension": ".py",
320 |    "mimetype": "text/x-python",
321 |    "name": "python",
322 |    "nbconvert_exporter": "python",
323 |    "pygments_lexer": "ipython2",
324 |    "version": "2.7.12"
325 |   }
326 |  },
327 |  "nbformat": 4,
328 |  "nbformat_minor": 2
329 | }
330 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
 1 | # pydata-talk
 2 | Jupyter Notebooks for PyData talks
 3 | 
 4 | - For SudokuSolver, dowload the pre-trained model and labels into the resources folder. Download the content from [here](https://drive.google.com/drive/folders/0BzEeBAqEX505bU1kTW1WOENKZ3c?usp=sharing).
 5 | 
 6 | ## Installation Instructions For OpenCV (Linux)
 7 | 
 8 | Run the following commands
 9 | 
10 | 1. Installing required packages (last command is optional, run only if you want to test python version)
11 |   
12 |   ```
13 |   sudo apt-get install build-essential
14 |   sudo apt-get install cmake git libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev libswscale-dev
15 |   sudo apt-get install python-dev python-numpy libtbb2 libtbb-dev libjpeg-dev libpng-dev libtiff-dev libjasper-dev libdc1394-22-dev
16 |   ```
17 | 2. Downloading cmake
18 |   
19 |   ```
20 |   sudo add-apt-repository ppa:george-edison55/cmake-3.x
21 |   sudo apt-get update
22 |   sudo apt-get install cmake
23 |   ```
24 | 3. cd ~
25 | 4. Downloading opencv - wget https://github.com/Itseez/opencv/archive/2.4.13.zip
26 | 5. Building OpenCV
27 |   
28 |   ```
29 |   unzip 2.4.13.zip
30 |   cd opencv-2.4.13
31 |   mkdir release
32 |   cd release
33 |   cmake -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local ..
34 |   make
35 |   sudo make install
36 |   ```
37 | 
38 | 
39 | For Tensorflow, follow instructions [here](https://www.tensorflow.org/install/install_linux)
40 | 


--------------------------------------------------------------------------------
/demos/MeanShiftClustering.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {
  7 |     "collapsed": true
  8 |    },
  9 |    "outputs": [],
 10 |    "source": [
 11 |     "import cv2\n",
 12 |     "import numpy as np\n",
 13 |     "import matplotlib.pyplot as plt\n",
 14 |     "from sklearn.cluster import MeanShift\n",
 15 |     "\n",
 16 |     "import matplotlib\n",
 17 |     "matplotlib.rcParams['figure.figsize'] = 10, 8"
 18 |    ]
 19 |   },
 20 |   {
 21 |    "cell_type": "code",
 22 |    "execution_count": 2,
 23 |    "metadata": {
 24 |     "collapsed": true
 25 |    },
 26 |    "outputs": [],
 27 |    "source": [
 28 |     "def apply_mean_shift(img, with_edges):\n",
 29 |     "    n, m = img.shape[:2]\n",
 30 |     "\n",
 31 |     "    if with_edges: edges = cv2.Canny(img, threshold1=10, threshold2=70)\n",
 32 |     "\n",
 33 |     "    features = np.zeros((n * m, 5), np.uint8)\n",
 34 |     "    for i in range(n):\n",
 35 |     "        for j in range(m):\n",
 36 |     "            features[i * m + j] = (i, j, img[i, j, 0] / 2, img[i, j, 1] / 2, img[i, j, 2] / 2)\n",
 37 |     "            if (with_edges and edges[i, j]): features[i * m + j] = (0, 0, 255, 255, 255)\n",
 38 |     "\n",
 39 |     "    MS = MeanShift(bandwidth=17, bin_seeding=True)\n",
 40 |     "    MS.fit(features)\n",
 41 |     "    labels = MS.labels_\n",
 42 |     "\n",
 43 |     "    cluster_label = np.zeros((n, m), np.uint8)\n",
 44 |     "    unique_labels = np.unique(labels)\n",
 45 |     "    sums = np.zeros((len(unique_labels), 3), np.float32)\n",
 46 |     "    how_many = np.zeros((len(unique_labels)), np.float32)\n",
 47 |     "    color = np.zeros((len(unique_labels), 3), np.uint8)\n",
 48 |     "\n",
 49 |     "    for i in range(n):\n",
 50 |     "        for j in range(m):\n",
 51 |     "            cur_label = labels[i * m + j]\n",
 52 |     "            cluster_label[i, j] = cur_label\n",
 53 |     "            sums[cur_label, 0] += img[i, j, 0]\n",
 54 |     "            sums[cur_label, 1] += img[i, j, 1]\n",
 55 |     "            sums[cur_label, 2] += img[i, j, 2]\n",
 56 |     "            how_many[cur_label] += 1\n",
 57 |     "\n",
 58 |     "    for i in range(len(color)):\n",
 59 |     "        if (how_many[i] < 1): continue\n",
 60 |     "        sums[i] //= how_many[i]\n",
 61 |     "        color[i] = sums[i]\n",
 62 |     "\n",
 63 |     "    clusters = np.zeros((n, m, 3), np.uint8)\n",
 64 |     "    for i in range(n):\n",
 65 |     "        for j in range(m):\n",
 66 |     "            if (with_edges and edges[i, j]): clusters[i, j] = (255, 255, 255)\n",
 67 |     "            else: clusters[i, j] = color[cluster_label[i, j]]\n",
 68 |     "\n",
 69 |     "    plt.subplot(1, 2, 1)\n",
 70 |     "    plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))\n",
 71 |     "    plt.subplot(1, 2, 2)\n",
 72 |     "    plt.imshow(cv2.cvtColor(clusters, cv2.COLOR_BGR2RGB))\n",
 73 |     "    plt.show()"
 74 |    ]
 75 |   },
 76 |   {
 77 |    "cell_type": "code",
 78 |    "execution_count": 3,
 79 |    "metadata": {},
 80 |    "outputs": [
 81 |     {
 82 |      "data": {
 83 |       "image/png": 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U948YvP+AwmWM3Iz+NDDyGaNRDw4rsqOAFyE7yWWYNvgTbhb4/jdvNeu+9Jnn05ZHgRU8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SepaYJ8Q0ZZ1jP0+kI+gK33YbQB9a7Grcyqhm2sQtpwCVZvWG5/IuHVT6fc+QRcu11z6UrNypJj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eEPCoG3Fx37sBr70hcPXuZx1334Dda7Itle5/q40zyEKPQ/DE4nR7XwkRenoSDMXZgTa4F2lS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\nRU5ErkJggg==\n",
 84 |       "text/plain": [
 85 |        "<matplotlib.figure.Figure at 0x7fe7dc4b8ac8>"
 86 |       ]
 87 |      },
 88 |      "metadata": {},
 89 |      "output_type": "display_data"
 90 |     }
 91 |    ],
 92 |    "source": [
 93 |     "image = cv2.imread(\"../resources/apples_oranges.png\")\n",
 94 |     "\n",
 95 |     "ratio = 250.0 / image.shape[1]\n",
 96 |     "rows, cols = 250, int(ratio * image.shape[0])\n",
 97 |     "\n",
 98 |     "image = cv2.resize(image, (rows, cols), interpolation=cv2.INTER_AREA)\n",
 99 |     "image = cv2.GaussianBlur(image, (7, 7), 1)\n",
100 |     "\n",
101 |     "apply_mean_shift(image, False)"
102 |    ]
103 |   },
104 |   {
105 |    "cell_type": "code",
106 |    "execution_count": null,
107 |    "metadata": {
108 |     "collapsed": true
109 |    },
110 |    "outputs": [],
111 |    "source": []
112 |   }
113 |  ],
114 |  "metadata": {
115 |   "kernelspec": {
116 |    "display_name": "Python 3",
117 |    "language": "python",
118 |    "name": "python3"
119 |   },
120 |   "language_info": {
121 |    "codemirror_mode": {
122 |     "name": "ipython",
123 |     "version": 3
124 |    },
125 |    "file_extension": ".py",
126 |    "mimetype": "text/x-python",
127 |    "name": "python",
128 |    "nbconvert_exporter": "python",
129 |    "pygments_lexer": "ipython3",
130 |    "version": "3.5.2"
131 |   }
132 |  },
133 |  "nbformat": 4,
134 |  "nbformat_minor": 2
135 | }
136 | 


--------------------------------------------------------------------------------
/demos/Tracker.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {
  7 |     "collapsed": true
  8 |    },
  9 |    "outputs": [],
 10 |    "source": [
 11 |     "from collections import namedtuple\n",
 12 |     "import cv2\n",
 13 |     "import numpy as np\n",
 14 |     "cv2.ocl.setUseOpenCL(False)"
 15 |    ]
 16 |   },
 17 |   {
 18 |    "cell_type": "code",
 19 |    "execution_count": 2,
 20 |    "metadata": {
 21 |     "collapsed": true
 22 |    },
 23 |    "outputs": [],
 24 |    "source": [
 25 |     "class PoseEstimator(object):\n",
 26 |     "    def __init__(self):\n",
 27 |     "        # Use locality sensitive hashing algorithm\n",
 28 |     "        flann_params = dict(algorithm=6, table_number=6, key_size=12, multi_probe_level=1)\n",
 29 |     "        self.min_matches = 10\n",
 30 |     "        self.cur_target = namedtuple('Current', 'image, rect, keypoints, descriptors, data')\n",
 31 |     "        self.tracked_target = namedtuple('Tracked', 'target, points_prev, points_cur, H, quad')\n",
 32 |     "\n",
 33 |     "        self.feature_detector = cv2.ORB_create()\n",
 34 |     "        self.feature_detector.setMaxFeatures(1000)\n",
 35 |     "        self.feature_matcher = cv2.FlannBasedMatcher(flann_params, {})\n",
 36 |     "        self.tracking_targets = []\n",
 37 |     "\n",
 38 |     "    # Function to add a new target for tracking\n",
 39 |     "    def add_target(self, image, rect, data=None):\n",
 40 |     "        x_start, y_start, x_end, y_end = rect\n",
 41 |     "        keypoints, descriptors = [], []\n",
 42 |     "        for keypoint, descriptor in zip(*self.detect_features(image)):\n",
 43 |     "            x, y = keypoint.pt\n",
 44 |     "            if x_start <= x <= x_end and y_start <= y <= y_end:\n",
 45 |     "                keypoints.append(keypoint)\n",
 46 |     "                descriptors.append(descriptor)\n",
 47 |     "\n",
 48 |     "        descriptors = np.array(descriptors, dtype='uint8')\n",
 49 |     "        self.feature_matcher.add([descriptors])\n",
 50 |     "        target = self.cur_target(image=image, rect=rect, keypoints=keypoints, descriptors=descriptors, data=None)\n",
 51 |     "        self.tracking_targets.append(target)\n",
 52 |     "\n",
 53 |     "    # To get a list of detected objects\n",
 54 |     "    def track_target(self, frame):\n",
 55 |     "        self.cur_keypoints, self.cur_descriptors = self.detect_features(frame)\n",
 56 |     "\n",
 57 |     "        if len(self.cur_keypoints) < self.min_matches: return []\n",
 58 |     "        try: matches = self.feature_matcher.knnMatch(self.cur_descriptors, k=2)\n",
 59 |     "        except Exception:\n",
 60 |     "            print('Invalid target, please select another with features to extract')\n",
 61 |     "            return []\n",
 62 |     "        \n",
 63 |     "        matches = [m[0] for m in matches if len(m) == 2 and m[0].distance < m[1].distance * 0.75]\n",
 64 |     "        if len(matches) < self.min_matches: return []\n",
 65 |     "\n",
 66 |     "        matches_using_index = [[] for _ in range(len(self.tracking_targets))]\n",
 67 |     "        for match in matches:\n",
 68 |     "            matches_using_index[match.imgIdx].append(match)\n",
 69 |     "\n",
 70 |     "        tracked = []\n",
 71 |     "        for image_index, matches in enumerate(matches_using_index):\n",
 72 |     "            if len(matches) < self.min_matches: continue\n",
 73 |     "\n",
 74 |     "            target = self.tracking_targets[image_index]\n",
 75 |     "            points_prev = [target.keypoints[m.trainIdx].pt for m in matches]\n",
 76 |     "            points_cur = [self.cur_keypoints[m.queryIdx].pt for m in matches]\n",
 77 |     "            points_prev, points_cur = np.float32((points_prev, points_cur))\n",
 78 |     "            H, status = cv2.findHomography(points_prev, points_cur, cv2.RANSAC, 3.0)\n",
 79 |     "            status = (status.ravel() != 0)\n",
 80 |     "\n",
 81 |     "            if status.sum() < self.min_matches: continue\n",
 82 |     "\n",
 83 |     "            points_prev, points_cur = points_prev[status], points_cur[status]\n",
 84 |     "\n",
 85 |     "            x_start, y_start, x_end, y_end = target.rect\n",
 86 |     "            \n",
 87 |     "            quad = np.float32([[x_start, y_start], [x_end, y_start], [x_end, y_end], [x_start, y_end]])\n",
 88 |     "            quad = cv2.perspectiveTransform(quad.reshape(1, -1, 2), H).reshape(-1, 2)\n",
 89 |     "            \n",
 90 |     "            track = self.tracked_target(\n",
 91 |     "                target=target, points_prev=points_prev, points_cur=points_cur, H=H, quad=quad\n",
 92 |     "            )\n",
 93 |     "            tracked.append(track)\n",
 94 |     "\n",
 95 |     "        tracked.sort(key=lambda x: len(x.points_prev), reverse=True)\n",
 96 |     "        return tracked\n",
 97 |     "\n",
 98 |     "    # Detect features in the selected ROIs and return the keypoints and descriptors\n",
 99 |     "    def detect_features(self, frame):\n",
100 |     "        keypoints, descriptors = self.feature_detector.detectAndCompute(frame, None)\n",
101 |     "        \n",
102 |     "        if descriptors is None: descriptors = []\n",
103 |     "        return keypoints, descriptors\n",
104 |     "\n",
105 |     "    # Function to clear all the existing targets\n",
106 |     "    def clear_targets(self):\n",
107 |     "        self.feature_matcher.clear()\n",
108 |     "        self.tracking_targets = []"
109 |    ]
110 |   },
111 |   {
112 |    "cell_type": "code",
113 |    "execution_count": 3,
114 |    "metadata": {
115 |     "collapsed": true
116 |    },
117 |    "outputs": [],
118 |    "source": [
119 |     "class ROISelector(object):\n",
120 |     "    def __init__(self, win_name, init_frame, callback_func):\n",
121 |     "        self.callback_func = callback_func\n",
122 |     "        self.selected_rect = None\n",
123 |     "        self.drag_start = None\n",
124 |     "        self.tracking_state = 0\n",
125 |     "        event_params = {\"frame\": init_frame}\n",
126 |     "        cv2.namedWindow(win_name)\n",
127 |     "        cv2.setMouseCallback(win_name, self.mouse_event, event_params)\n",
128 |     "\n",
129 |     "    def mouse_event(self, event, x, y, flags, param):\n",
130 |     "        x, y = np.int16([x, y])\n",
131 |     "\n",
132 |     "        # Detecting the mouse button down event\n",
133 |     "        if event == cv2.EVENT_LBUTTONDOWN:\n",
134 |     "            self.drag_start = (x, y)\n",
135 |     "            self.tracking_state = 0\n",
136 |     "\n",
137 |     "        if self.drag_start:\n",
138 |     "            if event == cv2.EVENT_MOUSEMOVE:\n",
139 |     "                h, w = param[\"frame\"].shape[:2]\n",
140 |     "                xo, yo = self.drag_start\n",
141 |     "                x0, y0 = np.maximum(0, np.minimum([xo, yo], [x, y]))\n",
142 |     "                x1, y1 = np.minimum([w, h], np.maximum([xo, yo], [x, y]))\n",
143 |     "                self.selected_rect = None\n",
144 |     "\n",
145 |     "                if x1 - x0 > 0 and y1 - y0 > 0: self.selected_rect = (x0, y0, x1, y1)\n",
146 |     "\n",
147 |     "            elif event == cv2.EVENT_LBUTTONUP:\n",
148 |     "                self.drag_start = None\n",
149 |     "                if self.selected_rect is not None:\n",
150 |     "                    self.callback_func(self.selected_rect)\n",
151 |     "                    self.selected_rect = None\n",
152 |     "                    self.tracking_state = 1\n",
153 |     "\n",
154 |     "    def draw_rect(self, img, rect):\n",
155 |     "        if not rect: return False\n",
156 |     "        x_start, y_start, x_end, y_end = rect\n",
157 |     "        cv2.rectangle(img, (x_start, y_start), (x_end, y_end), (0, 255, 0), 2)\n",
158 |     "        return True"
159 |    ]
160 |   },
161 |   {
162 |    "cell_type": "code",
163 |    "execution_count": 4,
164 |    "metadata": {
165 |     "collapsed": true
166 |    },
167 |    "outputs": [],
168 |    "source": [
169 |     "class VideoHandler(object):\n",
170 |     "    def __init__(self, scaling_factor, win_name):\n",
171 |     "        self.cap = cv2.VideoCapture(0)\n",
172 |     "        self.pose_tracker = PoseEstimator()\n",
173 |     "        self.win_name = win_name\n",
174 |     "        self.scaling_factor = scaling_factor\n",
175 |     "\n",
176 |     "        ret, frame = self.cap.read()\n",
177 |     "        self.rect = None\n",
178 |     "        self.frame = cv2.resize(frame, None, fx=scaling_factor, fy=scaling_factor, interpolation=cv2.INTER_AREA)\n",
179 |     "        self.roi_selector = ROISelector(win_name, self.frame, self.set_rect)\n",
180 |     "\n",
181 |     "    def set_rect(self, rect):\n",
182 |     "        self.rect = rect\n",
183 |     "        self.pose_tracker.add_target(self.frame, rect)\n",
184 |     "\n",
185 |     "    def start(self):\n",
186 |     "        paused = False\n",
187 |     "        while True:\n",
188 |     "            if not paused or self.frame is None:\n",
189 |     "                ret, frame = self.cap.read()\n",
190 |     "                if frame is None: break\n",
191 |     "                \n",
192 |     "                frame = cv2.flip(frame, 1)\n",
193 |     "                scaling_factor = self.scaling_factor\n",
194 |     "                frame = cv2.resize(frame, None, fx=scaling_factor, fy=scaling_factor, interpolation=cv2.INTER_AREA)\n",
195 |     "                self.frame = frame.copy()\n",
196 |     "\n",
197 |     "            img = self.frame.copy()\n",
198 |     "            if not paused and self.rect is not None:\n",
199 |     "                tracked = self.pose_tracker.track_target(self.frame)\n",
200 |     "                for item in tracked:\n",
201 |     "                    cv2.polylines(img, [np.int32(item.quad)], True, (0, 0, 255), 2)\n",
202 |     "                    for (x, y) in np.int32(item.points_cur): cv2.circle(img, (x, y), 2, (255, 0, 0))\n",
203 |     "\n",
204 |     "            self.roi_selector.draw_rect(img, self.rect)\n",
205 |     "            cv2.imshow(self.win_name, img)\n",
206 |     "            ch = cv2.waitKey(1)\n",
207 |     "            if ch & 255 == ord(' '): paused = not paused\n",
208 |     "            if ch & 255 == ord('c'): self.pose_tracker.clear_targets()\n",
209 |     "            if ch & 255 == ord('q'): break\n",
210 |     "\n",
211 |     "        self.cap.release()\n",
212 |     "        cv2.destroyAllWindows()"
213 |    ]
214 |   },
215 |   {
216 |    "cell_type": "code",
217 |    "execution_count": 5,
218 |    "metadata": {},
219 |    "outputs": [],
220 |    "source": [
221 |     "v = VideoHandler(1.0, 'Tracker')\n",
222 |     "v.start()"
223 |    ]
224 |   },
225 |   {
226 |    "cell_type": "code",
227 |    "execution_count": null,
228 |    "metadata": {
229 |     "collapsed": true
230 |    },
231 |    "outputs": [],
232 |    "source": []
233 |   }
234 |  ],
235 |  "metadata": {
236 |   "kernelspec": {
237 |    "display_name": "Python 3",
238 |    "language": "python",
239 |    "name": "python3"
240 |   },
241 |   "language_info": {
242 |    "codemirror_mode": {
243 |     "name": "ipython",
244 |     "version": 3
245 |    },
246 |    "file_extension": ".py",
247 |    "mimetype": "text/x-python",
248 |    "name": "python",
249 |    "nbconvert_exporter": "python",
250 |    "pygments_lexer": "ipython3",
251 |    "version": "3.5.2"
252 |   }
253 |  },
254 |  "nbformat": 4,
255 |  "nbformat_minor": 2
256 | }
257 | 


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1 |    7.6285898e-01  -2.9922929e-01   2.2567123e+02
2 |    3.3443473e-01   1.0143901e+00  -7.6999973e+01
3 |    3.4663091e-04  -1.4364524e-05   1.0000000e+00
4 | 


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