137 lines
4.2 KiB
Plaintext
137 lines
4.2 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import imageio\n",
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"import math"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"def rgb2gray(rgb):\n",
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" return np.dot(rgb[..., :3], [0.299, 0.587, 0.114]).astype(np.uint8)\n",
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"\n",
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"\n",
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"def hough_line(img, angle_step=1, lines_are_white=True, value_threshold=5):\n",
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" \"\"\"\n",
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" Hough transform for lines\n",
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"\n",
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" Input:\n",
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" img - 2D binary image with nonzeros representing edges\n",
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" angle_step - Spacing between angles to use every n-th angle\n",
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" between -90 and 90 degrees. Default step is 1.\n",
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" lines_are_white - boolean indicating whether lines to be detected are white\n",
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" value_threshold - Pixel values above or below the value_threshold are edges\n",
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"\n",
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" Returns:\n",
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" accumulator - 2D array of the hough transform accumulator\n",
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" theta - array of angles used in computation, in radians.\n",
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" rhos - array of rho values. Max size is 2 times the diagonal\n",
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" distance of the input image.\n",
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" \"\"\"\n",
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" # Rho and Theta ranges\n",
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" thetas = np.deg2rad(np.arange(-90.0, 90.0, angle_step))\n",
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" width, height = img.shape\n",
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" diag_len = int(round(math.sqrt(width * width + height * height)))\n",
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" rhos = np.linspace(-diag_len, diag_len, diag_len * 2)\n",
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"\n",
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" # Cache some resuable values\n",
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" cos_t = np.cos(thetas)\n",
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" sin_t = np.sin(thetas)\n",
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" num_thetas = len(thetas)\n",
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"\n",
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" # Hough accumulator array of theta vs rho\n",
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" accumulator = np.zeros((2 * diag_len, num_thetas), dtype=np.uint8)\n",
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" # (row, col) indexes to edges\n",
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" are_edges = img > value_threshold if lines_are_white else img < value_threshold\n",
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" y_idxs, x_idxs = np.nonzero(are_edges)\n",
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"\n",
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" # Vote in the hough accumulator\n",
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" for i in range(len(x_idxs)):\n",
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" x = x_idxs[i]\n",
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" y = y_idxs[i]\n",
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"\n",
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" for t_idx in range(num_thetas):\n",
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" # Calculate rho. diag_len is added for a positive index\n",
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" rho = diag_len + int(round(x * cos_t[t_idx] + y * sin_t[t_idx]))\n",
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" accumulator[rho, t_idx] += 1\n",
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"\n",
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" return accumulator, thetas, rhos"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"def show_hough_line(img, accumulator, thetas, rhos, save_path=None):\n",
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" import matplotlib.pyplot as plt\n",
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"\n",
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" fig, ax = plt.subplots(1, 2, figsize=(10, 10))\n",
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"\n",
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" ax[0].imshow(img, cmap=plt.cm.gray)\n",
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" ax[0].set_title('Input image')\n",
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" ax[0].axis('image')\n",
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"\n",
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" ax[1].imshow(\n",
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" accumulator, cmap='jet',\n",
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" extent=[np.rad2deg(thetas[-1]), np.rad2deg(thetas[0]), rhos[-1], rhos[0]])\n",
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" ax[1].set_aspect('equal', adjustable='box')\n",
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" ax[1].set_title('Hough transform')\n",
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" ax[1].set_xlabel('Angles (degrees)')\n",
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" ax[1].set_ylabel('Distance (pixels)')\n",
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" ax[1].axis('image')\n",
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"\n",
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" # plt.axis('off')\n",
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" if save_path is not None:\n",
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" plt.savefig(save_path, bbox_inches='tight')\n",
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" plt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"imgpath = '../images/bangdiem.png'\n",
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"img = imageio.imread(imgpath)\n",
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"if img.ndim == 3:\n",
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" img = rgb2gray(img)\n",
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"accumulator, thetas, rhos = hough_line(img)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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