{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import cv2\n", "import glob\n", "import matplotlib.pyplot as plt\n", "import matplotlib.image as mpimg\n", "from moviepy.editor import VideoFileClip\n", "from IPython.display import HTML\n", "from functools import partial\n", "from collections import deque\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "class FindLanes:\n", " \n", " def __init__(self):\n", " self.search_start = -1\n", " self.search_end = -1\n", " self.error_counter = 0\n", " self.smooth_value = 10\n", " self.update_data = 10\n", " self.frame = 0\n", " self.left_curvature = 0\n", " self.right_curvature = 0\n", " self.off_center = 0\n", " self.leftx = deque(maxlen=self.smooth_value)\n", " self.lefty = deque(maxlen=self.smooth_value)\n", " self.rightx = deque(maxlen=self.smooth_value)\n", " self.righty = deque(maxlen=self.smooth_value)\n", " self.ym_per_pix = 30/720 # meters per pixel in y dimension\n", " self.xm_per_pix = 3.7/700 # meters per pixel in x dimension\n", " \n", " \n", " def compute_calibartion_points(self):\n", " # prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)\n", " objp = np.zeros((6*9,3), np.float32)\n", " objp[:,:2] = np.mgrid[0:9,0:6].T.reshape(-1,2)\n", "\n", " # Arrays to store object points and image points from all the images.\n", " objpoints = [] # 3d points in real world space\n", " imgpoints = [] # 2d points in image plane.\n", "\n", " # Make a list of calibration images\n", " images = glob.glob('./camera_cal/calibration*.jpg')\n", "\n", " # Step through the list and search for chessboard corners\n", " for fname in images:\n", " img = cv2.imread(fname)\n", " gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n", "\n", " # Find the chessboard corners\n", " ret, corners = cv2.findChessboardCorners(gray, (9,6),None)\n", "\n", " # If found, add object points, image points\n", " if ret == True:\n", " objpoints.append(objp)\n", " imgpoints.append(corners)\n", " self.objpoints = objpoints\n", " self.imgpoints = imgpoints\n", "\n", "\n", " def undistort_image(self, img):\n", " ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(self.objpoints, self.imgpoints, \n", " (img.shape[1], img.shape[0]),None,None)\n", " return cv2.undistort(img, mtx, dist, None, mtx)\n", "\n", "\n", " def binary_pipeline(self, img, s_thresh=(170, 255), sx_thresh=(20, 100)):\n", " img = np.copy(img)\n", " # Convert to HLS color space\n", " hsv = cv2.cvtColor(img, cv2.COLOR_RGB2HLS).astype(np.float)\n", " l_channel = hsv[:,:,1]\n", " s_channel = hsv[:,:,2]\n", " # Sobel x\n", " sobelx = cv2.Sobel(l_channel, cv2.CV_64F, 1, 0) # Take the derivative in x\n", " abs_sobelx = np.absolute(sobelx) # Absolute x derivative to accentuate lines away from horizontal\n", " scaled_sobel = np.uint8(255*abs_sobelx/np.max(abs_sobelx))\n", "\n", " # Threshold x gradient\n", " sxbinary = np.zeros_like(scaled_sobel)\n", " sxbinary[(scaled_sobel >= sx_thresh[0]) & (scaled_sobel <= sx_thresh[1])] = 1\n", "\n", " # Threshold color channel\n", " s_binary = np.zeros_like(s_channel)\n", " s_binary[(s_channel >= s_thresh[0]) & (s_channel <= s_thresh[1])] = 1\n", "\n", " #combine\n", " color_binary = np.zeros_like(scaled_sobel)\n", " color_binary[(sxbinary == 1) | (s_binary ==1)] = 1\n", " return color_binary\n", "\n", "\n", " def warper(self, img):\n", " img_size = (img.shape[1], img.shape[0])\n", " src = np.float32(\n", " [[(img_size[0] / 2) - 55, img_size[1] / 2 + 100],\n", " [((img_size[0] / 6) - 10), img_size[1]],\n", " [(img_size[0] * 5 / 6) + 60, img_size[1]],\n", " [(img_size[0] / 2 + 55), img_size[1] / 2 + 100]])\n", " dst = np.float32(\n", " [[(img_size[0] / 4), 0],\n", " [(img_size[0] / 4), img_size[1]],\n", " [(img_size[0] * 3 / 4), img_size[1]],\n", " [(img_size[0] * 3 / 4), 0]])\n", " M = cv2.getPerspectiveTransform(src, dst)\n", " Minv = cv2.getPerspectiveTransform(dst, src)\n", " warped = cv2.warpPerspective(img, M, img_size, flags=cv2.INTER_NEAREST) # keep same size as input image\n", " return warped, Minv\n", "\n", " \n", " def get_histogram_points(self, binary_warped):\n", " if self.search_start == -1:\n", " search_start = 150\n", " else:\n", " search_start = self.search_start\n", " if self.search_end == -1:\n", " search_end = 1150\n", " else:\n", " search_end = min(self.search_end, binary_warped.shape[1])\n", " \n", " histogram = np.sum(binary_warped[binary_warped.shape[0]/2:,search_start:search_end], axis=0)\n", "\n", " midpoint = np.int(histogram.shape[0]/2)\n", " leftx_base = search_start + np.argmax(histogram[:midpoint])\n", " rightx_base = search_start + np.argmax(histogram[midpoint:]) + midpoint\n", "\n", " return midpoint, leftx_base, rightx_base\n", " \n", "\n", " def polynomial_pipeline(self, binary_warped, Minv, image, on_road, nwindows=9, margin=100, minpix=50):\n", " # histogram of bottom 1/2\n", " midpoint, leftx_base, rightx_base = self.get_histogram_points(binary_warped)\n", " if rightx_base - leftx_base < 700:\n", " self.search_start = -1\n", " self.search_end = -1\n", " midpoint, leftx_base, rightx_base = self.get_histogram_points(binary_warped)\n", " self.search_start = max(leftx_base - margin, 0)\n", " self.search_end = rightx_base + margin\n", "\n", " # Set height of windows\n", " window_height = np.int(binary_warped.shape[0]/nwindows)\n", " # Identify the x and y positions of all nonzero pixels in the image\n", " nonzero = binary_warped.nonzero()\n", " nonzeroy = np.array(nonzero[0])\n", " nonzerox = np.array(nonzero[1])\n", " # Current positions to be updated for each window\n", " leftx_current = leftx_base\n", " rightx_current = rightx_base\n", " # Create empty lists to receive left and right lane pixel indices\n", " left_lane_inds = []\n", " right_lane_inds = []\n", "\n", " # Step through the windows one by one\n", " for window in range(nwindows):\n", " # Identify window boundaries in x and y (and right and left)\n", " win_y_low = binary_warped.shape[0] - (window+1)*window_height\n", " win_y_high = binary_warped.shape[0] - window*window_height\n", " win_xleft_low = leftx_current - margin\n", " win_xleft_high = leftx_current + margin\n", " win_xright_low = rightx_current - margin\n", " win_xright_high = rightx_current + margin\n", " # Identify the nonzero pixels in x and y within the window\n", " good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xleft_low) & (nonzerox < win_xleft_high)).nonzero()[0]\n", " good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xright_low) & (nonzerox < win_xright_high)).nonzero()[0]\n", " # Append these indices to the lists\n", " left_lane_inds.append(good_left_inds)\n", " right_lane_inds.append(good_right_inds)\n", " # If you found > minpix pixels, recenter next window on their mean position\n", " if len(good_left_inds) > minpix:\n", " leftx_current = np.int(np.mean(nonzerox[good_left_inds]))\n", " if len(good_right_inds) > minpix: \n", " rightx_current = np.int(np.mean(nonzerox[good_right_inds]))\n", "\n", " # Concatenate the arrays of indices\n", " left_lane_inds = np.concatenate(left_lane_inds)\n", " right_lane_inds = np.concatenate(right_lane_inds)\n", "\n", " # Extract left and right line pixel positions. x and y values\n", " self.leftx.append(nonzerox[left_lane_inds])\n", " self.lefty.append(nonzeroy[left_lane_inds]) \n", " self.rightx.append(nonzerox[right_lane_inds])\n", " self.righty.append(nonzeroy[right_lane_inds])\n", " \n", " leftx = self.get_all_pixels(self.leftx)\n", " lefty = self.get_all_pixels(self.lefty)\n", " rightx = self.get_all_pixels(self.rightx)\n", " righty = self.get_all_pixels(self.righty)\n", "\n", " # Fit a second order polynomial to each\n", " left_fit = np.polyfit(lefty, leftx, 2)\n", " right_fit = np.polyfit(righty, rightx, 2)\n", "\n", " ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )\n", " left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]\n", " right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]\n", " \n", " if (self.frame % self.update_data == 0):\n", " self.get_curvature(binary_warped, lefty, leftx, righty, rightx)\n", " y_eval = binary_warped.shape[0]\n", " left_bot = left_fit[0]*y_eval**2 + left_fit[1]*y_eval + left_fit[2]\n", " right_bot = right_fit[0]*y_eval**2 + right_fit[1]*y_eval + right_fit[2]\n", " mid_bot = (left_bot + right_bot) / 2\n", " mid_photo = image.shape[1] / 2\n", " #positive is right of center. in meters\n", " self.off_center = (mid_bot - mid_photo) * self.xm_per_pix\n", "\n", " font = cv2.FONT_HERSHEY_SIMPLEX\n", " cv2.putText(image,'Left Curvature: ' + str(int(self.left_curvature)),(10,60), font, 2,(255,255,255),2,cv2.LINE_AA)\n", " cv2.putText(image,'Right Curvature: ' + str(int(self.right_curvature)),(10,125), font, 2,(255,255,255),2,cv2.LINE_AA)\n", " cv2.putText(image,'Center offset: ' + str(round(self.off_center,2)),(10,190), font, 2,(255,255,255),2,cv2.LINE_AA)\n", " \n", " if on_road:\n", " return self.plot_on_road(binary_warped, image, left_fitx, right_fitx, ploty, Minv)\n", " else:\n", " return left_fitx, right_fitx, ploty\n", " \n", "\n", " def get_all_pixels(self, q):\n", " all_pixels = []\n", " for v in q:\n", " all_pixels.extend(v)\n", " return np.array(all_pixels)\n", " \n", " \n", " def plot_on_road(self, binary_warped, image, left_fitx, right_fitx, ploty, Minv):\n", " # Create an image to draw the lines on\n", " warp_zero = np.zeros_like(binary_warped).astype(np.uint8)\n", " color_warp = np.dstack((warp_zero, warp_zero, warp_zero))\n", "\n", " # Recast the x and y points into usable format for cv2.fillPoly()\n", " pts_left = np.array([np.transpose(np.vstack([left_fitx, ploty]))])\n", " pts_right = np.array([np.flipud(np.transpose(np.vstack([right_fitx, ploty])))])\n", " pts = np.hstack((pts_left, pts_right))\n", "\n", " # Draw the lane onto the warped blank image\n", " cv2.fillPoly(color_warp, np.int_([pts]), (0,255, 0))\n", "\n", " # Warp the blank back to original image space using inverse perspective matrix (Minv)\n", " newwarp = cv2.warpPerspective(color_warp, Minv, (image.shape[1], image.shape[0])) \n", " # Combine the result with the original image\n", " return cv2.addWeighted(image, 1, newwarp, 0.3, 0)\n", " \n", " \n", " def process_image(self, img):\n", " undistort = self.undistort_image(img)\n", " binary = self.binary_pipeline(undistort)\n", " warped, Minv = self.warper(binary)\n", " on_road = self.polynomial_pipeline(warped, Minv, img, True)\n", " self.frame = self.frame + 1\n", " return on_road\n", " \n", " \n", " def get_binary_warped(self, img):\n", " undistort = self.undistort_image(img)\n", " binary = self.binary_pipeline(undistort)\n", " return self.warper(binary)\n", " \n", " \n", " def get_curvature(self, binary_warped, lefty, leftx, righty, rightx):\n", " ym_per_pix = self.ym_per_pix\n", " xm_per_pix = self.xm_per_pix\n", " y_eval = binary_warped.shape[0]/2\n", "\n", " # Fit new polynomials to x,y in world space\n", " left_fit_cr = np.polyfit(lefty*ym_per_pix, leftx*xm_per_pix, 2)\n", " right_fit_cr = np.polyfit(righty*ym_per_pix, rightx*xm_per_pix, 2)\n", " # Calculate the new radii of curvature\n", " left_curverad = ((1 + (2*left_fit_cr[0]*y_eval*ym_per_pix + left_fit_cr[1])**2)**1.5) / np.absolute(2*left_fit_cr[0])\n", " right_curverad = ((1 + (2*right_fit_cr[0]*y_eval*ym_per_pix + right_fit_cr[1])**2)**1.5) / np.absolute(2*right_fit_cr[0])\n", " # Now our radius of curvature is in meters\n", " \n", " self.left_curvature = left_curverad\n", " self.right_curvature = right_curverad" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "find_lanes = FindLanes()\n", "find_lanes.compute_calibartion_points()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "undistorted = find_lanes.undistort_image(plt.imread(\"./camera_cal/calibration1.jpg\"))\n", "plt.imsave(\"./readme_images/undistorted.jpg\", undistorted)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "image = plt.imread(\"test_images/test2.jpg\")\n", "undistorted = find_lanes.undistort_image(image)\n", "plt.imsave(\"./readme_images/undistorted_car.jpg\", undistorted)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "binary = find_lanes.binary_pipeline(undistorted)\n", "plt.imsave(\"./readme_images/binary.jpg\", binary, cmap=\"gray\")" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ "warped, Minv = find_lanes.warper(undistorted)\n", "plt.imsave(\"./readme_images/warped.jpg\", warped, cmap=\"gray\")" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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vIAc+ZenQFksLrq2+/MyxeF+pa77udbb6JCmaTb7iJZcagcegSRbl1bEZCQwr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\n08bBxNIoyDhybm+PO57yFG6/9RYWs47l0aERSNNIiMq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i/z5wz9bjLwVGthZozITvAtD5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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "binary_warped, Minv = find_lanes.get_binary_warped(image)\n", "left_fitx, right_fitx, ploty = find_lanes.polynomial_pipeline(binary_warped, Minv, image, False)\n", "plt.imshow(warped)\n", "plt.plot(left_fitx, ploty, color='yellow')\n", "plt.plot(right_fitx, ploty, color='yellow')\n", "plt.savefig(\"./readme_images/plotted_lines.jpg\")" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [], "source": [ "on_road = find_lanes.process_image(image)\n", "plt.imsave(\"./readme_images/final.jpg\", on_road)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## video" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "process_video = False\n", "lanes_output = 'project_video_lines.mp4'\n", "if process_video:\n", " clip1 = VideoFileClip(\"project_video.mp4\")\n", " lanes_clip = clip1.fl_image(find_lanes.process_image) #NOTE: this function expects color images!!\n", " %time lanes_clip.write_videofile(lanes_output, audio=False)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "HTML(\"\"\"\n", "\n", "\"\"\".format(lanes_output))" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }