diff --git a/ProjectReport.md b/ProjectReport.md
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+## Project: Search and Sample Return
+
+Source code https://github.com/boldbinarywisdom/RoboND-Rover-Project
+
+The purpose of this project is to write code to drive rover autonomoulsy in a map that is simulated environment with objects of interests and terrain where rover cannot drive. arned about in the lesson, and build upon them.
+
+
+## Steps
+
+** Training / Calibration **
+
+* Download the simulator and take data in "Training Mode"
+* Test out the functions in the Jupyter Notebook provided
+* Add functions to detect obstacles and samples of interest (golden rocks)
+* Fill in the `process_image()` function with the appropriate image processing steps (perspective transform, color threshold etc.) to get from raw images to a map. The `output_image` you create in this step should demonstrate that your mapping pipeline works.
+* Use `moviepy` to process the images in your saved dataset with the `process_image()` function. Include the video you produce as part of your submission.
+
+** Autonomous Navigation / Mapping **
+
+* `perception_step()` function within the `perception.py` has image processing functions to create a map and update `Rover()` object
+* `decision_step()` function within the `decision.py` has conditional statements that take into consideration the outputs of the `perception_step()` in deciding how to issue throttle, brake and steering commands to Rover object.
+* drive_rover.py Iterates on perception and decision functions until rover does a reasonable job of navigating and mapping.
+### More detailed steps can be found under Readme.md
+
+[//]: # (Image References)
+
+[image1]: ./output/terrain2direction.jpg
+[image2]: ./output/terrain2drivablemap.jpg
+[image3]: ./calibration_images/example_rock1.jpg
+[image4]: ./output/foundRocks.jpg
+
+
+
+## Submissions:
+
+
+### Notebook Analysis
+
+![Terrain to Direction of Robot][image1]
+
+
+1. The `process_image()` function was modified with the appropriate analysis steps to map pixels identifying navigable terrain, obstacles and rock samples into a worldmap.
+
+2. Running `process_image()` on the test data using the `moviepy` functions produces a movie which can be found in ./output folder
+
+
+And another!
+
+![Terrain to Driveable map][image2]
+
+### Autonomous Navigation and Mapping
+
+The telemetry() function in driver_rover.py receives images and calls two key functions:
+
+1. perception_step() which applies vision steps, finds navigation angle and updates Rover state
+
+Perception steps and modified code is detailed below and summary of steps added to the function are as follows:
+
+1. Applying persptive transform
+2. Applying color threshold
+3. Converting rto over centric coordinates and updating to world map
+4. obstacles are added to world map channel red which can be seen in image2
+5. Rocks are added to the green channel in the world map
+6. Navigable terrain is added to the blue channel of the world map
+7. A mosaic is created with various images. Original image, warped image, transformed image and navigable map
+8. Rover pixels are coverted to angles which are stored in Rover objects for subsequent steps used in actually driving the rover
+9. Finding rocks
+10. Rocks are brigher than the surrounding hence a RGB filter is applied to isolate the area in the map
+11. This location of the map upon doing perspective transform gives the location in the terrain
+
+2. decision_step() which decided to steer, throttle or brake
+
+#### No modifications added to drive_rover.py
+
+Updated code in perception_step() function
+
+ # Apply the above functions in succession and update the Rover state accordingly
+def perception_step(Rover):
+ # Perform perception steps to update Rover()
+ # NOTE: camera image is coming to you in Rover.img
+ # 1) Define source and destination points for perspective transform
+ # 2) Apply perspective transform
+ # Define calibration box in source (actual) and destination (desired) coordinates
+ # These source and destination points are defined to warp the image
+ # to a grid where each 10x10 pixel square represents 1 square meter
+ # The destination box will be 2*dst_size on each side
+ dst_size = 5
+ # Set a bottom offset to account for the fact that the bottom of the image
+ # is not the position of the rover but a bit in front of it
+ # this is just a rough guess, feel free to change it!
+ bottom_offset = 6
+ image = Rover.img
+ source = np.float32([[14, 140], [301 ,140],[200, 96], [118, 96]])
+ destination = np.float32([[image.shape[1]/2 - dst_size, image.shape[0] - bottom_offset],
+ [image.shape[1]/2 + dst_size, image.shape[0] - bottom_offset],
+ [image.shape[1]/2 + dst_size, image.shape[0] - 2*dst_size - bottom_offset],
+ [image.shape[1]/2 - dst_size, image.shape[0] - 2*dst_size - bottom_offset],
+ ])
+ warped, mask = perspect_transform(Rover.img, source, destination)
+
+ # 3) Apply color threshold to identify navigable terrain/obstacles/rock samples
+ threshed = color_thresh(warped)
+ obs_map = np.absolute(np.float32(threshed) -1) * mask
+ # 4) Update Rover.vision_image (this will be displayed on left side of screen)
+ # Example: Rover.vision_image[:,:,0] = obstacle color-thresholded binary image
+ # Rover.vision_image[:,:,1] = rock_sample color-thresholded binary image
+ # Rover.vision_image[:,:,2] = navigable terrain color-thresholded binary image
+
+ Rover.vision_image[:,:,2] = threshed * 255
+ Rover.vision_image[:,:,0] = obs_map * 255
+
+ # 5) Convert map image pixel values to rover-centric coords
+ xpix, ypix = rover_coords(threshed)
+
+ # 6) Convert rover-centric pixel values to world coordinates
+ world_size = Rover.worldmap.shape[0]
+ scale = 2 * dst_size
+ x_world, y_world = pix_to_world(xpix, ypix, Rover.pos[0], Rover.pos[1],
+ Rover.yaw, world_size, scale)
+ obsxpix, obsypix = rover_coords(obs_map)
+ obs_x_world, obs_y_world = pix_to_world(obsxpix, obsypix, Rover.pos[0], Rover.pos[1],
+ Rover.yaw, world_size, scale)
+
+
+ # 7) Update Rover worldmap (to be displayed on right side of screen)
+ # Example: Rover.worldmap[obstacle_y_world, obstacle_x_world, 0] += 1
+ # Rover.worldmap[rock_y_world, rock_x_world, 1] += 1
+ # Rover.worldmap[navigable_y_world, navigable_x_world, 2] += 1
+
+ Rover.worldmap[y_world, x_world, 2] += 10
+ Rover.worldmap[obs_y_world, obs_x_world, 0] += 1
+
+ # 8) Convert rover-centric pixel positions to polar coordinates
+ dist, angles = to_polar_coords(xpix, ypix)
+ # Update Rover pixel distances and angles
+ # Rover.nav_dists = rover_centric_pixel_distances
+ # Rover.nav_angles = rover_centric_angles
+ Rover.nav_angles = angles
+
+ # See if we can find some rocks
+ rock_map = find_rocks(warped, levels=(110, 110, 70))
+
+ if rock_map.any():
+ rock_x, rock_y = rover_coords(rock_map)
+
+ rock_x_world, rock_y_world = pix_to_world(rock_x, rock_y, Rover.pos[0],
+ Rover.pos[1], Rover.yaw, world_size, scale)
+ rock_dist, rock_ang = to_polar_coords(rock_x, rock_y)
+ rock_idx = np.argmin(rock_dist)
+ rock_xcen = rock_x_world[rock_idx]
+ rock_ycen = rock_y_world[rock_idx]
+
+ Rover.worldmap[rock_ycen, rock_xcen, 1] = 255
+ Rover.vision_image[:, :, 1] = rock_map * 255
+ else:
+ Rover.vision_image[:, :, 1] = 0
+
+ return Rover
+
+
+
+#### The robot navigation is controlled by below parameters
+
+ self.stop_forward = 50 # Threshold to initiate stopping --> if < 50 pixesl are present then stop going forward
+ self.go_forward = 500 # Threshold to go forward again --> > 500 is good indication of path forward
+ self.max_vel = 2 # Maximum velocity (meters/second) --> velocity value which can be tuned
+
+### Improvements
+
+Launching autonomous mode with vision processing improves Rovers navigation greatly. When world map is displayed, the red areas indicates obstacle region. The Robotics motion can be further tuned by parameters listed above.
+
+
+![And Found a Rock][image4]
+
+### Summary
+
+The Robot mapped over 75% of the terrain when image4 was captured.
diff --git a/Rover_Project_Test_Notebook.ipynb b/Rover_Project_Test_Notebook.ipynb
new file mode 100644
index 0000000000..9d95e61e9e
--- /dev/null
+++ b/Rover_Project_Test_Notebook.ipynb
@@ -0,0 +1,799 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Rover Project Test Notebook\n",
+ "This notebook contains the functions from the lesson and provides the scaffolding you need to test out your mapping methods. The steps you need to complete in this notebook for the project are the following:\n",
+ "\n",
+ "* First just run each of the cells in the notebook, examine the code and the results of each.\n",
+ "* Run the simulator in \"Training Mode\" and record some data. Note: the simulator may crash if you try to record a large (longer than a few minutes) dataset, but you don't need a ton of data, just some example images to work with. \n",
+ "* Change the data directory path (2 cells below) to be the directory where you saved data\n",
+ "* Test out the functions provided on your data\n",
+ "* Write new functions (or modify existing ones) to report and map out detections of obstacles and rock samples (yellow rocks)\n",
+ "* Populate the `process_image()` function with the appropriate steps/functions to go from a raw image to a worldmap.\n",
+ "* Run the cell that calls `process_image()` using `moviepy` functions to create video output\n",
+ "* Once you have mapping working, move on to modifying `perception.py` and `decision.py` to allow your rover to navigate and map in autonomous mode!\n",
+ "\n",
+ "**Note: If, at any point, you encounter frozen display windows or other confounding issues, you can always start again with a clean slate by going to the \"Kernel\" menu above and selecting \"Restart & Clear Output\".**\n",
+ "\n",
+ "**Run the next cell to get code highlighting in the markdown cells.**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ ""
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "%%HTML\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "outputExpanded": false
+ },
+ "outputs": [],
+ "source": [
+ "%matplotlib inline\n",
+ "#%matplotlib qt # Choose %matplotlib qt to plot to an interactive window (note it may show up behind your browser)\n",
+ "# Make some of the relevant imports\n",
+ "import cv2 # OpenCV for perspective transform\n",
+ "import numpy as np\n",
+ "import matplotlib.image as mpimg\n",
+ "import matplotlib.pyplot as plt\n",
+ "import scipy.misc # For saving images as needed\n",
+ "import glob # For reading in a list of images from a folder\n",
+ "import imageio\n",
+ "imageio.plugins.ffmpeg.download()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Quick Look at the Data\n",
+ "There's some example data provided in the `test_dataset` folder. This basic dataset is enough to get you up and running but if you want to hone your methods more carefully you should record some data of your own to sample various scenarios in the simulator. \n",
+ "\n",
+ "Next, read in and display a random image from the `test_dataset` folder"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "outputExpanded": false,
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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1+LiJRtM0VGLIbLc3jDmok26TlcCRdrpraadNLPuHBxyvFmpTE0m+RKwnTVOy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CPQ4H\njzFiOO6LDqg03G638AhYdiOKXXajLXz03I9nSMZGCQ/w8Ej8KaJxXGT1mNR3P/c2gDSI2+02mW4a\nh8VIEKuFn4UdAAAgAElEQVTVEm+/+QaAKU0rB77ve8QhYhiOcKNa2bg4omoSavrurc8n9MnFx8lP\nR/MNiC6ZJxo/Rq/EAWEYr/PAO2+/ecIoKOljDIihx3q9zIhSI1H4/2azwWKxwHq9zowr+xkOBxzi\nhMZcKO2BHeO4+wFPnjzBZrPB97yTFmzTNHj48CHCMOA4ole1fbLdakMnE7u4uMhhaQxRW2+aPNZc\n/NruFy9e4N13P5dNDmRk7AcRETAxAWoXHPunT5/mg8B5ypXaUTUHDd+tHTfTsOQQUo1P0a066Pgd\ntQj+RkRsHW8XFxfwPp00xfn83Oc+h2EYioO789xJO2OMo1Y1FAhRw05pnlIGr4y+6Up2QQChB4/s\n93u8fPkSz549w/X1NW5ubvDs2TN8+OGH+Oijj/C93/sFPH36FL/5y78pC3Ca3r75zW/mNNzqIGX7\nOa7X19cFbQETUGG7gCkST/dVcL2vFuMhLOOpcI3zWG0uCg2LoCDPaUznSHvvEYd9HmeNArTjbzdu\nqdP32NeFMkuMMWuZTTOlOKn5zO4qrqYy3Ptm5/4dAP88gB7ALyCFWr6PKbzyFwD8SzHG/WwlAC5W\ni/g7v/husWhUKnPxc9HyZBUuiBAC9vsDLAPXumqe6ilscMyRDeTcNc6lQzAuLi4yI/joo4+KKBtO\nChGhRs8wnwuv4YJ4/PhxfjaAQnU8Ho84xilnBn0Q6/U6I4qPPvoIjx8/xuc+9zkAwFtvvYXNZlMQ\n1HtvvQMA+X7mk1mtUm6ei4uLIsETzSsZNXSnexpCCOPegVGD8D6j9/UypSJ4+fJlsrPf3lY3egjd\nFAxQHaj833uPl7fPTmy8Kpx5UIdVlYn62rbF0IfcL4uQN5tNdgQqzQETCiSt6e5azhWfyXmv2cD5\nu0WZ1hbLXPPU5pxzWQgPw4APPvgmlst0+PxHH32Ud10S6TO9sNK3jlWMU0isInplLhad6vqIcToh\njPS/3++x3W5xe3ubc+jz/6urK9zc3GQQQuH5wUfXuU7NgwSktAnPnj0rIs2siSalgyjT+9q0EgzO\nYH/1cBHSxX57m2mNvOXy8jKDINWoNOqGdNEfdgXdWERuzWO1OdkeQ0F3agLTd7ZZgx44Hn/jF3/5\nb8YYa+d2F+UTMfpPqzxYL+Pv/oHPn3iYOUBvvvlmcT0dZbSbbjYb7PfHvCj4GwcxJ0oT1d1qD3SO\nPX78ODPXJ0+eYL1eZyRGRHhzc4Pnz5/jxYsXePnyJa6urnB9fZ0Z68XFBS4uLvDw4cPM2NejEw+Y\nGBttgIrmXDPtHdB2chFzT4DGUFtC6VxTqHlkkqzv2bNnmWCseuu9xzGW27tzgjl5DkJilrvdLiHx\n0dFF5j8YNbRGZzaKRs0JMUYs1lPsuQoc9pNzStSlZqi8Qa2djgKkSYSIP4SAJ0+enJgxOD9t22az\nEOlRX6QfmpVqjBZIGik1JmoTypi0j+r3ofMwneV7mYXo5eVl1gK99/kgb2u60XHgOrBtq82RFRD5\nOzddp/ZsPYTk6dOneR76vsd2u8XLly8z02/Xj9H3PW5ubjJDZSjxcrnEt771rXzCmNU8+IxOAit0\nX4GaaCiIgQnR+xGcXF5eptPNRtphJk51WHNs1brAl3MOcZj2ZChNcj51HpV2CahCCDjG0nRj0TzX\nr71G5+R/+4Vfuhejfy12xi6XC/zA938fNpsNHjx4gEePHuHRo0e4vLzEcrnE933f92X0ACAv1KZp\n8PjxY7z55ps47PvMCLfbbaHGkWA4sTQNcHLbtkU3ZqO7uLjIg8qcNXRiASXKJSomOuWC43MVVZAx\naTy1XkeiiCjDrTLKjOmg7zcePcxMA5hMCm3bYjHaoh2A45Gn4yQvfd/3CDch24FTvZOtU4Us9nq2\n6xSq6l2EaxyCc9hsRi1jOKJxETEOGIYjhuE4Or9Ow8esynkzagDZbto0WC2W+XmHyEPK07mjqS6g\nbZm6d1mYTIZhsrF33YiG+8lMttvtsgDl3D5//rxgEkTVjAhRDexwOODm5gbX19eFxsZwWvbVMoa8\nEUoWL+mAwoKZJVXd19BSAHl/RYwpUOD58+d48OBBpvlyLE4PO1EbfI1xsO+kVy3OuZQvSdYB15AK\nty9/+cuZmelubq6zq+s9nj17hsVigaurK2y322y6+epXv4ovf+kLuL6+zoBNX+zP1cubk3BDtknB\nHouaLzebTQZgVrtSUMg5VWTOa5JgmdY5wZbSuraL13HM8ztKU4+1udd8IEqrOu53ldeC0b/99tv4\nIz/+44WzQtEsAMQxigMoT27pug6bzSbvLFTbt6psqnoDJfNxzmXCWDTpSL7hcMAwPmu9KBNKkYj7\nYQBGNDEMA9ZCUOrcAUYVzDzbOYcBKBYDz23lHMaULWbUKho8e/pRQRSb9SqP0eFwwHa/S3HqIwEu\nug6+6zBIVNBO7H4qUI7HI/bCuLL6LoLIIfkObm9vRuQ1ZF9JCA2AiGHoMyqbxjsxbI5FjBGbzTqP\nQ7o2pIyIXEyLMlUFP5Pgd7td4ZjjYlPU5F1XLAz29/b2Fi9fvixMAqqus51EhtbR2jRN1tLsjltt\npzUdqpYJIJstCBaoeQFTzP3FxQVevLjO5gWmS4gxReTc3NwUz2N9Nu+StpPfKXNXE47OE4syIov2\n+Zybm5sTsxeB1m63Q38A3nryJm5ubvDWkzdzpk4A+NIXvpijqVRYqHkOAHb9gPV6jYcPH2azZWF6\nHNcCS9d1Od8RkLTrw2hq0jBF5xyC5CSixhJCgKdPyaXAgr6fnKycX/6vTmEKXxW8vC4Op85XnS8V\nFpoWQunqvuW1MN38gz/42+Jf/Qt/dvb3+7XxfKfvqiPG+0vHj/uM+1wT/N3tsHXYBenCtJHHmhOA\n+sYULcuR0Z+o75Vn2mfnfoQy/FQZaM1uX7NNhmb+dyAxHpo2ABSmETLuxpcbW34jSs1cU8xHpb/2\neutfOo3CKO3/utAVyWupRYGce7dCzr7bjUrWJ2Hr0DZkf9ZQbvixJqxaOaF3nNK03s/oK9VuyLQp\nNAgm1IdgfT2qEVnBXVtDtp16f20udofTEG8LaiyQsWbG//UXfvG7x3STyjlG/Z0QRq8em3pa7m7n\nXdpWKwmbrLoHIJtmpvocnHdgHp4JuUXAuTFfSLkYibTmymE/oSHeUyBVnKJCRTJEvtoPXdTqZD/H\n7D/45odFu2pM1DI8tokO0v74nQMyc0J8rt2162qv9FsoFvwcA7eMQj/rPJ57vr2e797keqoh/3Mg\nIsYxp5RzCHCIziNWgI1l4icMFKeCtbwhjKZOhgmnz94BjU+7xodhWk9817rovLeWADL9moO15pDV\niDN7jW4Ms6CMn1UrYFHz8X3La8To//9R7pqgmm1b7fpV5GscNJokjaVAEuJzqJXVYl3cV7Td+Bb6\nGHAMA0J/LJ7BhUMEQtsn+0ibtxa7aHUnZu13PsO2MQk9j7Tl+hTp/kaUc0z8nDCrfV9D7Iroa8+e\nEzLARDcc7zk0rAxEhQKLr9CTfW4NoRdMLJYpJWrCSn+rvYd4v/GzfdOoLowpm0MT6qi7KQ981zkJ\nISCgXH/qBLf90DHVZ3ljjrHzYftj/XmvEmb52eHgn5XPymfls/L3eXlNEL2Dc+d2ed3H9n0eud2t\n6nwaMu8+ppvz7egHOQKNf/J/yA7NXGNxiEREzBFI1jHIslpuzrYh9Kdx1QCgboxjGKbNNxInTVW3\nM2ilhnrussl2i676O0stpFBVbPb9XDmHhO9b5tC8NaecM+3YSB2LUP0dvps585C+1OnIdx2fOYSY\nr/enKNMiZ40yq9URw1AifDiEwLaPYzFm3XQcG9bHOgf5bNoSY0SDiPHMFwDp0BjvHRqkZHohhCIS\nSqNtiOCbpsUwePT9ZD5JNOwQgsOxLzXvmt9CzTYs6vhv/BQ1Zvc0BEY4hZTXykcAozEq84VXIN3X\nhNGfZ5H36c8n9aV+Cuv9U/EkMLa4Zvcj8QClWqihZmofz+0yde2O548HePn0Kt+XbYRqI3bjb8rE\nTe97yatima0bo3fOOdSAKS3uXGFaC3u/MjIKxu9EmWP45+zjlqHrZ/1OzTg1005tzq2PR00ztfdP\nQ+hZ56EdhzBM+aDseFgat2OkpdZe1mfDFK3JJMaIPpa/6b4FjZLSfmgdQ5ieXbO/876aOSyv0Xg6\nn3aeWX/NJ/Aq5fVg9O4+iPt8uavfd9b/CZ//aRVr35uLl1ViIiLJiKab0LT10ocQcq7wudKFKUY4\nQHKjyCD7dkRuDAN1ZudxHKbPM+0/mlzuWpxzcCK9a07Gu9H6dx7R2/9rDNsydXtfzdlqv7Ovu4Q7\nGah97hyir423fcYcXSpDtJqVjwGAS6c6EQnn+gA4jOc+sA8oXqmdXdEHy/hsHy0jjTFiOJRnOTMk\nVM/A5b01GzvcKeOd06BYdCycczgcSr9WzclqnbSsxwrRu8rrwegBhDML8j7dCXcx+jvuj58Co7+X\n5nHH5PR9GX/PoouNzBuY0kCo2qm7AmtoRg9hqZWOJ/g0Hj5Ozp+GBO/GvQyjEBjGVRhCyIx/XLNs\nRJX5tcZpfNLn4TyDr0VnnDK3s1391EuN4XCubPv0mjk0P73Oh1/qxipAGKsw91oIprbXnq1sx1e1\nI2VAViDNhU4656AxY9pGW6fWawUM80fN9cNeP0WuADEmNN3FUlgQfWtkmK1P2+N8eZykri++2zNh\nrXZzl+DVa/SlJrL7lteG0X9WUqFax80itThfLZYItB4bbcCigqBWDseSITjnEL1Px6/5dLzgsU92\nxkHOQ+KB5GPDgGJR62JMuYW46YTP0HcA8CFWf+M7N8Ypo7B20RDuOMXyN6jUGE+NQdc+1+bU+ykz\na40JD2ISAU6ZZqrjNJqlJph4vz5P+2Hrm7tfX3nDWqwzPNZH88m58WjaOghisf1UBs7wyKWfDiuy\nv1H7YR0q/PKY4NTsxDrUVDY3v8CpmbYGAjX7rI3oeRUryGvC6COAoVjISqC9sdXawQUmdY736ztQ\nSuja/X1fqnI1lclOuJ1E7lac+z3GmLdyk5ErE48xfiwT0lw7bVtrv9dKbI0ZQN/Hn5KNveLCHi9O\njjtDuOaxoVDDTok8utPj7bTtXcfPmiOE6Pmkumo5NxZp0Z9qDbYMwzR/Ohccd0u/KfiADDl9k7KP\npnbzXNzyWadmE30O0whoO/nKuYNMmuJTYVKePcu1wPXQxsmkNwwDhmNf9DmdLxxTbiSX0mRn+h+S\nF8e3bSH0Q4wIY0x77KdzWNPz0xGUbVPSt3WkWrRs54iCRgFPF4Zi/BRcDcMAjwZAA6DMNcPPu8Op\n2bE2tnZXrObngWtPzFvKL9L3A7yL6LxDGxup+1M23Tjn/nMA/yyAb8YYf3D87gmAPw/gSwC+CuAP\nxhifudTjP410buwtgB+LMf6t+zamZme8R/tO7qkRMYvd2FPLS6H1qDpmVS3LyL/+9a8X91m0EGMs\njj206i0A+FdUyWrlHFKLMb6y2lcrNe3i0y7nmHxtzmrlrmvu7sfpbtHaZ33ZhX8XkmZ/LJpUsMEY\n+Fp/asKKdSndEvU7Vx5PyaKaXg3o6H4O51w1G6eaBWsAxKZRqPXJRu7U3nUM9Tlsl5a6cB6K+6yw\nYz+UyVPbjjEWxwXyOl4zV1TLjjGiWywLHmO1JwoG2wY+79NG9P8lgP8IgOYo+CkAfyXG+DPOuZ8a\n//9JpBOmvn98/R6ks2J/z92PmA/LAuq2xVN1rTn5Ta9R5q/OyzlzSM2JyYRSvMYSoCINZeZ6Dbdn\na1sLU8MrSOm5okyD/+vnuzZafBpM/FXQxlyZY/C/keWUAZ9+b+fdjrddrDVasc/hqUblsydGpJvD\navQzR8d6HZmDAh291u5UPldfDRhpO7TumpnBtk2/q613LWra0WKF/33mTM1Q9FHQ3GnXMYVDCAGL\nxSmjtyDT5lFSvpIcum3BzIE62FUmr2P8qSL6GOP/4pz7kvn6RwD8vvHznwHwPyMx+h8B8GdjasFf\nc849ds69G2P8xh3PwPFYxqVqWciRW7xe34HTUC47CBxMOi7ViTnnwdZB5jPmnk/EZPtgmepdscrx\nDiK/T7kPajpXvhNoHbibad9pYvoUEL0NWzt95iRs7HxbAWSZPV8WKdcKTS9zERUWQPD5iq7PjRc1\nBWVuysDUPs3rbZtruW30Gn5nx0qZ/11MHCgTAlrNGzjNsnlfdKttYooMFTJMe0AgqG2/6xlWcHC8\nLSPnc0IIOPanmoK2tSYcLQ3ct3xcG/3nyLxjjN9wzr0zfv8+gL8n1/Fw8LOM3nt/kkJ1rBsATtRW\n690GgN1uX0hMjULhoNcWoZWkiiItwapaWluINmNlDYnOLchc36eAWGto3poTvlvLp6EpvNoz5s0G\n+l2NQat5ztJVzQxlEaFd+LWXpaVzZhzLiKzWap9j66yZ/LQ/zrnZiK65dVYrytRr/jI1S1mNqfZM\n/cx3PS1LETPrV3+FjhlfTNk8N2/K6JUulNE7OeTc8hoW5XX29Z1g9HOlNnvV1jg5HPw3vfduTgeq\nTgt25vr6urqwioEfztv3a0xYzRg2T7yt5xyD5DUqrPi9JTCGwdVeYwOqz3iVcpfj+VVQ7ly5S1h8\nmiaWubbfh9DvakdNSys/1x3b5xi9Rcy6I7XGtJxzOQOnZbCWToEpikRp0tJvrd8WTStStki8xuiV\nSc/1xSYHq83RXcJpIdlT+V57zTmVKWzO0XEtV43WVeMTHHfnHBaL6YhBInab/195WY3RN+3yJJ8O\nx9jm2KkJ4FcpH5fRf0iTjHPuXQDfHL//GoDfJNfd63Dw3/FbfnP81V/91VnpaJ1ZUkf+7H1TTAjt\nbGrXmiMY4LwdmBNfOxhAC5OF2X7od2oHrNkuh0/ZPv6qjPHTKq+qSp/7fU5YfZrCpFY/UAq0Gn2y\nzCEsy6S1Hi0ECfZevqjVaiiiMhZqikrPVnOwz7bX1NaZNfXo97ZeYAp5rY2DXjsnkOz/1rfGZ8zN\ng9ZRe8/oHKc0ZQMz+AwrzEIIcM3E6FUYaJsovHXs9HqecqY2fD6bAsCGeeqzvhOI/i8iHfz9MygP\nAP+LAP5V59zPIjlhr+Id9nkgOSD7fvKCN01bJdI59VdVXyUKHcBaru2iDQat2FQDMaYj6M4xF+tQ\nnotuse3/pNLalrm84vq8c+XTMO18moxeP9f680naYTNg2rmoOccs06rFbfM9hIDVanVWG7BzU2Ng\nir5ryJsMtmbyqPW1xhwVeer3fOlJWjo+2mYLmGzahRqgst/NCRqOK30eltnxOpuls6aBcGc3r7Fr\nV02+tn0hBGz3kzlZ+67gsm2nDJiWL4UQsN2Nh5r408NzeLgSg0a0LzY+/z7lPuGV/zWS4/Ut59zX\nAPxbSAz+55xzPwHg1wH8gfHy/x4ptPLvIoVX/vh9GuGcK1LrWgKoIXm7WHRy7aKwiKq2+K2jqRYt\ncVeukJoz9hxCmRuLc+U+zO2c6cYyptpCqZ0kZDWfOZ+G/a6mtVg19T6lNp62TXMMxQqM2v9sj7V/\net8WdardVRe4Lj6LfK1At5qe7Vdts46ejToBoiZriIzmsgJEi40Kq81pDTyxr4pQ59pvo1VsGgDr\nXK2tKU0/UFvrRPS2D6yLx3vW+Ahfh8PhLK/RlCIlPaRnbDZT+KWNzrMOXM2Fo3lrusX6xEytyJ++\nRp0Dax66b7lP1M2/MPPTD1eujQD+6L2fPhaHcqBtJ2pxyHznZxvjaxHNq6DH3K4ZjaL2vzLQc/Xe\nyajvwcg/TuHCtejEvgCcpFDQUhMMtQWjwqImNGuM/lXG+67va4jc0o9+X5v/WhtrQGBOYPKzMg17\n7bSDN5x8p/XoUXIWRccYT2zjer8icjtWOiY1oaSMRcMalenYts6Nh3PJF8H1qcn47HW1d9a52WxO\n5kGZq90dbYWWLZZ+Y4yFn6AG/G62k0/FAgt+5q5WagEATuaM11qUTrMN15Fl8v9fO2M/VokRGPp5\nJ+bxmAbVbpVOk5kGa7E835W7js+bG7QaM5tjCnMbPfSaOx2ddzD6+0yutrP2ea4NNXRpiZilVkeN\nidQI0zLh+zL4+2h69t06W+3nOeRfq7Pm+ONv55isMkgrVDmOdNIrbWvblKHVkF3NlmxfvMaagBTF\n2mfqiyeTkemrycH2a25MeM6ufek19kAaOxc6D9Sw1D5e2yPAMeO4+bapjhFLLZS0AJbjzlj6SnSM\na4CGz9Zrapq1pU3+Ztt4J2A05bVg9CxzndlsNmeJGACGcCwG1i7MuwbmPsi7xsj0vhp6rPXvbDvO\n/no/Rq/PO/f9HMLWXX81wopx2uFbWwREgkrktfGtMfBzTH6OyOeYbo0h18o5Rm1v0Wt5zZwJqqbd\nsNjdrzUmrMWOIa/Tk7zm+sb3mrlN32kGssya60jBUtu2J7trrfCooU7VAmwbtR36fL0GOPULcH2z\n/Xa8VdiTHpuuPRnvmtC3z2FdunOWh5PTrq6Ckwyd6FyFcz/cDTBr7bFjc5/yWjB6Tk6NmQMo0gYA\nOLGJxRjhm9NB0MFQR9JcG+7z/Tm0riGcH1cCfxqO0FpR1KTozxJ3jLFYwHehNIvaayqqXcis0yI5\nfWfd9llkJPr7uXnXMvcMOzeKVLnjWpmkRbEWsduiOZDmrrNb6pU56ZzY9rPYLf12zmpjpH3Q0EG9\nrma6setV16Eyaa3f3jfHYNl3ZaxaPxmnomR+17btbDSK/d015fNr7TgHZO6KUGLba5oF+7LdHYvx\ns/Wx3bW5+K403QCltLSDyjj64g6D1LrFeRv9qyL6uefUrrMMZw6FclI/SXmVydU+15ib2kltnLa2\nuYbSmA7CMnldjFqfMvYao79P3+YQ+hwT03bMjcVcYZ+ZfEwXnF3cNbrQedd0tXN1KNqrLfxabhUt\n584JJgCp9UFpVJ2g6gwkg1mv18UzavOvYaCKZMngtQ6rWfLzzc3NWUbPaBYmIeNYkZZrc8A2sB3R\nnQIZO95WsGgbDv2pw500zfVld98CJhBBUiBYX0PtrFpqDt+1jD7GeeQBnG5Xto4c5xz2h22ur8bg\n77LRzzl89XMtqkaLopwaESkanCsfVyDN1VH7zLEj4dtwTLvBh98rUdoNPjXGBZT+lDmhWxvrWr8t\ng5wzAczNYW1sa/NT3j/fZqVPe5/2lXRjHXq2bRxjTUnNcaullq71T5+rc6YIk3PCtvB5q9WqqM/6\nAubs1pzvtm1zO+fWwHa7vVPbqJlAFalrygb1i2mmSC1E83RGt20L35zPv6NnCLA/6vAe4vS7ZfJ2\nLcwJRue7os1WKOv3tq334SXFGNz7ys/KZ+Wz8ln5rHxXltcC0auaw/9riEglv8aYAsimG1VXLeo4\nV6xqrM/jZ2untWillufjPmaCT3L9q9arY02kTaTJMbu5uTm5zyIm+jzmTGU2qqJmUtBix5oqc80s\ncw651+o6V85FQaWxqV+rppi5+HTWUdv5qL9r/yyKsyY/O8bW/DGntbBu2qmJhJnYr+973NzcnGhg\nqgGoZmJRddd1aJom57y3bWK7dJ1ZBG+dxTrm+t3hcID3vgiB1DZRQ1HNQJ3XTdMA/tR0pO9zm65Y\n1BlrzTraP2u20e/nNmnxM9cqn8E50Aij+5bXg9F7h7abNhWkwwjK5P3F9RVzxOFQN6sosZ4ruhnE\nlpoqZu3bfKaNCLBtIgNT05Neb8Oz7PP9jF1Tr12MYXC1OpSB5ix65hCU2gYzvtq2Rdd1VcGo/WzG\ncWnbdrKLShtIuHah6MIqbP6uksZieujs3NXmTfuTdx5yjMYX29pkc0VAqDDsGGMODZ5j9rS/a8QJ\nFznHJ42Fx6KbzAPa3iHgpP36LBuSyDHVwIW+7zOTVOav9mAy667rTuZHTXoqLGJM2/1r7WZR4T1H\nMyw28Z/9rM9V0w7r3mw2GQhyLDjWVpApbRYJ3ppSeCNGRAjjjqOJCul4ktY7WDPffr8HiTTGCBcC\nmhjhEBEdcATQtk0+oKUGYrg5jP3R1Mev4u97LRh9jLHIX2EdEzW0osQO1BG5lnM2rXOIX3+jI0nr\n1PbVdufawnaq7ZR13LUQaogQOEWl3JSibbGIRIlH0RkZOeu1TiyW3W53IpgKp6ugtTlkU8vBrv21\nCLr2uXbf3HdW2NjxqAlbZW5atB6CBIuCLfKm8FLaUEZjEa6OVTQHoNiXzrdew+9sVI2+Z2Ey0uPh\ncCgEB+tRG759ho6vBUJKe9ZXVpvLGrKvoXzLF3S86TPQ+liGYcg0rtqEMlDV0s49S3+z77XDSzgH\nMUYENCfzaNtq9yoQ1X9XInrEWN1yzFctM58ST00NqpU5VU1/m7vntMnzIXznzBXcLWcnq8aMrXAi\nAr2rjXqWak0VB0r1GyiRpTJ6RUo19ThvQJHF5ZzLCNlqY7adOmZ2fmzUgaWN2njZ8Th3WpGtrwYy\nbP1W+HBO7bzrfRwDuxO01mb2yyK8wZgGLO1Z4GOFTIwRFxcXJ4LftlMZnp2b2o7puXHVtat9Prep\nsAbaamOlDkvte22OWGz0jj6zJhStY9WOq9UqaojcMnpLY3EIiKMSGUJECBFw4/1jJGKTMFPSaJ1D\n4xuEph7pda68Fow+dbYcaC3nGP05CWs/n/t/Dj3o/ypta8+26VVt/RYtah26IHlfbRHVGKftk8Yq\na4ibMgMKRz6XG2BoZmC9DJmj2q/ppHmN1sl7F2JbtKhT+67ttP2uocra2NXedUHWFiNf504rAsqw\nRSuM2Df1L9UEk9UK9FnWtjv3DlemOLA+EGu6sYwHONWgrHAifSkI4bxYZKkanmVi/M5GjDjnCt/O\nHKOaW3/8XFvzVguy1+n8zQE0PVfWpq2w19ZAY01o2HbqNTxzV9urvMYKfV1n35WMHigRWm3z1Nx7\nbcBrk3kXUc0RVw0dzl1jd74Bp6jDopEaamV7z9nttQ0WRXaS06QmFJXR276EELIz1trvlQHQhkvh\nQPf4bK4AACAASURBVPunPqsmtOx4WVRp+z3XBx3LmpBmH23mQKst1ja5KUOvxadzbGpMXx3WNe3D\n0qYVOHO03rTN7L06Vvy9JkBqzMhqbZa+1Nyg5iY6X2sCwrZfx7O2zixj1LGtzW9tc5mdg1ob1ARp\nmaX9fBejvyvUGig1D0u/ALBw9c1Q2ic7x5au7ls+7uHgfwrAPwfgAOArAH48xvh8/O2nAfwEgAHA\nH48x/o/3aYhdDNbpVLt2rrO1CZqT4jN9nkWHtv45BKZefv1fiZmLg3byGOPJaVr2GXabusbE5wVl\nGOdc/9kOS0C3t7dFG3mvmnd04SiTrwmtWpkbt8IBW4n+sGM4Vy8/q+YxxxS1/sLPgPmcJ1brseNo\nhbM+q0abtTYU/ZJra32wkRg1ZsAdurVn8l0ZSi0jI/tAcx2fVTOLzAlP/V3fWewudsvote/sh/6v\nwp2Fwomfi7E1TFTt6Od4DNtjBZUVWHO/6/4Lmz6BfdRIJ6WxT53Ro344+F8G8NMxxt459+8C+GkA\nP+mc++0AfhTA7wDwHoD/yTn3AzHGswdFUkrbxWwlIEuNwdrJnmMkc0x8jkD1Wru4anWr87Jmz6Vt\nW8+t1YVkT6BStOWcy4ml2C5dpPyuv4O5KQKqLWoWFSCK4rhDz457gULMWNn5q82VFu03n3Nuzu04\n155nBbbWPcdoFWHVEDqvtyGRSsc2PFjbwqIC3JouY4zoh1Onq7bJmiUs7QLIqXn5XVWgGMGlzEdN\nqIrea3Sm/awJr1r7dD7nQBifMfc7gEJg6T1E6QxRtPOu5a7c/XNts+2o/Z61nibSbg2PiHTuFeeP\ntvqQ//ceiM6hcXXt61z5WIeDxxj/kvz71wD8/vHzjwD42RjjHsCvOuf+LoB/GMDPn3uGc+WhBnME\ncRcq4vscs9dSc5rZZ9YY/TlhRHSr4Ydc5GTqz549y0ye5hC2x/spNthGvPCZeoiF7a9FAfq93mMj\nnCxzV2GizN7aqLloakKirSTymmP6c8i6hnC034yMqAl5ZU5zc6z12vm8C2BQqLOdHPcas9Poj7nF\nqd/X1HfnJyCk1/FFxqQaiBU6Sr9KVwoWVPDbOqhtWg1Q14iOgdIY67H26DmBpP/XhCIwr23ZsVCT\nDsdWtVHrb7BmE52fmlCy9/H/muZQvMJw8gz7+RxIOCfsbPk0bPR/GMCfHz+/j8T4WXg4+Elxcmbs\ne9/zTtVDrdK7JnUVcdRi7WtoXO/VwbNON4soSdA6oYrgySD7vsf19TVub29xe3uL7TalZtjv93nD\nijKhruuwXC6xXC7RdV0RwlabUGUCOgY6Fp1sWrH3A2UeDfZ9uVxitVqh67pC+JBYh2HA4XDAfr/P\npiYtFoXVMjZaAT4Mw0noJk1ZmsPECnu+NKzRzp0ycms/rjmplWnoeOppRlo3f9N3ZYC1UgMt/KzJ\nwGpaAcMrlS5DCPnAESsIday4hvQZjMm2c6JjrgK37/vqZicdL6vt6BzotVbQWPqshWBqHefSmajg\n1X7ZdtNswnGw1+nmxxqAqPl2lI5I33O8BABcMwlWavZz4FTB7asgeZZPxOidc/8mgB7An+NXlcuq\nrYpyZuzv/G0/EGsdzEReka61wTf1F5+tU9QSY41x2YHn9SSCYUiJlbbbLYZhwG63qzIWIBHOYrHI\nm1FWqxVWqxUWi8VZyW8XUW1Bs19WEGrRet58880T1MrfdLfxMAxZQJH5agieMgOLDlejVnBujiwz\ns/3QZGA1m7OOl/bjnJZmx5iLUa9RhFtzqPFay5Rr33PB19qq/9+1gH1TAhH2VbUpFcyWiSrtzI2F\nanrafrsWbLv1vWau0Bfr5Ma8mqPW+qFqc1arm99Zx3INoWup8YM5pjsnqG0fnJvMrCy2HYtu6qdG\nu2n9BDOqIX0chv+xGb1z7g8hOWl/OE5PvPfh4JX68rtFh+oBB+5mHrWJq4Vo6j21CBQ70fyeKOBw\nOGC322WUq89vmgbr9TqbYsjQN5vNiXlH7fW6sO7rfLF91UVkEVOM5ck2rJ/PV8ZHFK8InvUx6mbO\nKdv4EiHb+dO67JySmVmUbO87J9jsXKoNW5+p4/0qjE2Fjy5QZfzqkK+Ng15vad62f7FcV9ukiFsd\njpwLnYPj8XgydlYrtmuLKN8+t8b8LDCrmaqs4KGA0t9sHL1l+vYZFvzYcayhfAIVK6h5fy2MWeu9\ni/YoOG0fVAAPw6gtAoADvBc/1FjPsitz88+N613lYzF659w/BeAnAfzjMcZb+ekvAvivnHP/PpIz\n9vsB/I171FhdfHOM3S54XSjK4KyqzXutVIyxzPdd+z3GiO12mxeVqlpk3NyQQqanhzNw4XHDVN/3\n2G63xc7UGjOfWyj3+V/Rqdb3/PnzEyHC63WslMk6N+2aJUOxAlWfZTfX1OZTnWY14W39F/Z5c2NV\nQ1t6/RxCqwnX2oK2bbaCmGOoTFUXqTIuVestw9TrlqvVyXxpW2qahf5O/481JagGynZbs4QKYDuf\nOr5z42NppCZM7PO02O+0jrk6z60bHTOdL3ud3qt9tMDQFj7bhmAqLVH42Lq13TUAMUeTd5WPezj4\nTwNYAvjLYwP/WozxX4kx/p/OuZ8D8LeRTDp/NN4RcZPK6WJXqWVRuEVNnGj7mw6gqns1Rq6TUvsd\nSNv++QwyPZpjaGaoISZgcoDu9/sTW66aPtRuWlvwNYKwBK52dSJ1FSiWMVnBOGfLrm2q0jboGA4m\neqgmkPRZtcWvJwZZdMr3E0oyC/zczlzOj0WH+rKhfjUAUJsHjl+NESmjsMy01gcAGMJtbpNFht6n\n3cyKbq0ABybHtHU+6npRBljzd2g/5zQ1PsvWXfODaLGo+hzjt99bgFADFnYO9D5rqrH8Q5+t77Xn\n87fFYlEIUjWLOuewWpWJAe0YAcg5hGo0V6OTufJxDwf/z85c/ycB/Ml7twCTc6jWoRhj9VR3y8wt\n2rDmHiIWFjuwdqenvQ4AHj16VHjqdXFY4tWNM/o/k0rZqAcWm0+kNlZarO0OAIIsRBsVo4zGJhzj\nPWxDTTPScdF3+xlmkVtEbQWztqHGLNlX9RXYbKF2AVjhY5/nXBn6qG2yyPccylTNpKZW175TBgOU\nezRqPplhfyzmTscWmBiCjdJQOrNrwQq3GgNW+q/Z3/V+oH7uA4XHnFDX64AyMGIOYev7XLH0ZrV6\nO966PlQg1BD0XaYd9sfSlAWONR+X1sW9DzUQ8irltdgZG3Ea5qaltoPSMvZclxCRTuZdzthaRjx7\n3XK5LJ5Lxk3GY52HOhmsc7VaFRMLlJEzNrLBvlt7ZA1Z9SbMjyYji9p0PEuksSr6X0PTliBtURRX\nQ18WAVlBwGKZnprN7jp1iUxRn2eFLE1MyuxqqKnWNmUAOqaWfs4hL9JRDVzoOLeuRMi6XV+BjZ0D\nK2gtgrX0XpsfHTtex8/Uymq7Ynmv0myNSduxncvVpONj76sJ0trvc5/n2mQ/33WffmY/nJu0U71m\nv7sBYkAYAsIwacAcVwCjQzei8Q7eeUTvEGP9uMRz5bVg9A6lvd0idUuILBaJ6DVc5PbzvdojdXBR\nOeey2UV3sgLTgiLB2/h4YEJWiqC1zWRaNmTQFosOa0yJfgDgNKbeOYfNZlPE8pOhaFIyHYsaUZ9j\nGDFGNMb5qWiTTGEuNzlfFDg6zvQR6Pe2bVpqjF5NUBwrnVelq7mImbnFbbVOKyg5Zhbd8uSlGsJz\nzmG52pwIdGWwFuHXtN1z2klNoOtcO+eKUFMbFsvrFI2zHh1TjaiZoyVu7KrtI6mBgjlT6zkarYGw\nOeZZ+37OzGR5Bz+zKG1dXFxUtW5tnwoLpYdXtdO/FowecCe5onXRA6UabtF/6nQsCFAHuUYMgLVB\nnzIdDr7ayWpoXSeFi0IZCVDmwtZ6LGPRWOca0VrVWBexjp1vmhT5YnK1OOdwc309aSljNFCZF32e\n4MPYv4OcL1pbZANOj3y0zijusFUGq78pCrWIm2P6/7Z3bbHWXVX5G2uffS60TStysSlEWoJRYgw2\nBEk0PHhB6YPVhAeeRGNiopDoAzEYEoOPatRoYiAaScAYQVEDD5pIFOMTN7GFEoL8KJFLQyWGVvrz\n//85ew0f1hprf/NbY669z6F/9z4na5yc7L3XZc4xxxhz3OZtCtx92LJZlUZ4werFqremspb987TE\n4H/UmSn8MCrBz7YtB25jsDsMUtM0aN2xWBiA9ZhTh1sL91VxVitQLghqmn78p+1TP814Q7JQsGgM\nB4frgfYhilqd4exWOYtEPe7lcjmiBfOzq3AFM6CrvpftWA3a79S4PDla97PVKeArYODdothnPpON\nwJuVJvPFzLoy14Iy6me8PYG7w9B51Is4glAMuspItDuT38Dr6eunRZ2QraijTO9QhLeGVet91VsM\nfRLshaI3W3sgyjz2LOKTT8kJD+H0tFxNp+EZT6fSgRb39f4uARmDNCesxoPHEjTkDKWle9kA40Uw\nep+/syGofbKyzvLDOshayw9mxi2u1bzqGmh7o+OxEuB0ACv5AJUDzVNmHv0dd9wxusedjb1OjdJC\neTEtlCZ8jzt65nAwnqyQQ+4ZvzB+4dGdkfwq3my0VO5D9pumgS3WK3SVHiG/imOU3bYtVgf5trs8\nHpatCYlPM8PBYjHiKfPfbL29Ao9zFQP9bV4+X1NZULrBx5MJ9LfKH0M2BVS/x+8w5ACK77HNAddX\nw0dlP5P1KdgLRQ8RYLZmTdOMBihVeQLrjZAyz4+ViiqR6Cg8o4aBywjvMMJ/teBAfaZC4JStRszq\nq93PDFntnr7PhjIzRqo4gz5MU8WlJoCqBPle1BkrSvk+G2LGh98NyPas0Tbr2At7m6zEWHFzJKLb\n/yqOXLZ6bWw0MhyCTkpXnqExyGk73n6D7+sKX5VLd0crc8dVXpTe2q5sAFJTCDw1k/FY4zLeGkGj\nEDYy3E8H2noud/GdJzwEjkovb0vDWnMQle9c59R3pqfK3JQBUWWvfYN5fx5lPx8OPsMMM8xwxWE/\nPPrEi2dvMwsH1XM6PFymFjPzvjQk1PRQ5qkD67BLc9oBvBlT5rnVQub4Dmy34o6teebRRA6TUyE6\n2M1lqbehYS57ujzwPBVqZt6Q4qpzhKMOToNwGzXlFuGzelX8nh7vqDRgryvu1aa+TvEkuxZlRt66\n5tFl/FS+tPKMtrnm0RfleX6UYABHEYrHarUC2pKn2eAge/18L3BZnd0q6uS0TES72gbePM6928lT\n28BlxmI+lX+G1Vl9bEYjUJUdjTYzepqtT4PL+ll3fRytq87Jpv9eBPZC0TvK6Wk6iBej8Bkxg4Gx\nuVNmMIIxmvfj57OThJToqljcvUg/hDBqTjtAy8vCvmyAMVOiHG6roOp2DhktVPD4ucCFByKVpoqL\ndpSp8pgXSp9aO7gtmeFQOrJx0HeZpqzIuL3RzrvvvnvAOVIqvACNjTm/y/WF/NbSAkwzbcPA50T5\n8Lv6XRU1ANjioJCbjHbqoLBjdHgwXreg6RxNubCjYGYwlKebaV3u3Qwk1gFsMNy7Q7ZV3pgGh7Kp\nX9RRjE015T3+VDqqLJvZaAOvWh9VHMvf6+cz4w2MU49qLLaFvVD0hpKgLFzMYBWIknD5cuG4f+vW\nrYKhbFA6wavvrxFl8IwXFWT38XmwDJwnjDrU0846r7aTpwMG6BYKPC2RcVSFy3jo1M94Tg1W8Ian\nLWb80GmaoSQBDNNPM4PE/5miZ3ptM2W2ZjjjP5QCG57MOLNxi7p5NgfTWenGZTB9GZdFMkjJbd10\nVnDUkw1ox/0VxjPQtDw1wMHvtm3TvVtqkw40Yo76GpteSBefmt/m+9FXVeaYDtl7rOgPFuW2Ke7l\nJAalsfbPbSI9xinD9+wsX5jG9fIh5Zde0QPjsLzWGYHxNrsAsFyOw3jubDxIxBtyresdz45QAcrO\nF2Wh5Y3Nsvno2b8qelY22YCmepEqrFFXTQHHvRhQ5oU3UV4tXGSB1U6ReaNcBhuX6CR8fijTiOmq\ndbAyq53RW8OrRpNoTyhvlpvYZlrr5znksf1AGDPediIcAFWIbEyj/sxI6ASCGr001ZQNFt+8dYbW\nWwDWrZ5uS1qtKgbKHGhQHt3INODvGqUxX4DSWVIHJ77rthPK1ziPmK+rkVAcuP8CwPJgvHeS9keV\nIW6HbaHoOUWqbeg+63P59ZP5fF4lD+yNoi9zcUDJsOvXr486MCuq7p2y8ZweaNt2eI49V+782SpL\nFSJd4JDtqRHt0Px93NetS+N6QQ3Lp8m5e7FZWDyrnlWWEuGOD6DYQTOMFO/KyWWr8gbGHVa/65oH\noJzjnb2jn9pRtFPWPEFV4LXyg1ZaHtOdIfMSFUIm1QvTdKSmsXhfed6LP/OY4zNT5lNQc6S4fQHs\nCDEts2c5WmNFlEUw7aoZGTOuMxyxmsILmmVti0/mKb9TLExarSOz0CVM08xjP6+C5fqze8vlopAR\nbld8j+m18U7WF7eBvVD0TWOjDYDYa4/tBcLSx2ALE8i9nFYZzDs+PsZisRgd0aeQdXLtDE8++WTq\nAazb0VQ95Si3tsd64BQHc9cg6/DxnZWHvqMpj1A2kdJimmdjEfq/KW2iO/cpHdzH6xL0uZs3bxbG\nWY10bftqhmyBGf/rytl4hh2DoOkwn3y1Knh269atYYA+eM/4ZAZK28pKT8cVmqYpoix1UpqmmwbK\nUSSnO4dzBNxGcsdt1kVCSi9W+plRMFsPPEebRsrcrDo1mXFho81tjnJZXjQFyou2Mjy68suy1Tjo\nSW9qqE8TRyd0FTuXTEPlbduWMqIRbCbb2TjCNnChw8Hp3lsA/C6A57v7161rzR8CeAjAdQA/7+6f\n3FRH27ZFiNyXPRBKQ3wdze4I3J2Ac3R0NAhSLDi5efPmxsHWb35zrWCZoEzYSCGxwGR5dvWeo0yg\nU+T8TCiQGNzL5n0rXWqWPTqIesJB43g22yiJhU07dIZLlFfrsJpbzNrCqQBWgNzZlKbcMVRhZSG7\n0kFpqJ6btoN/s1FgR0Q93Szlpm3QyCCjVZTdti0OrB/kBOCtA+hSL6218FA0WKHtV8623stD28Ja\nh7WO1Vl5zF9Bi/4l6wpCf2VYEWpmQ7pClTAb0yIPnizuW52tJ03wWFnQV/mc5qR9bBwy5afOECvT\nRvqNlsP77XBENshnUrYav83R32HRVn02k6VaX9oEFz0cHGb2YgA/AeC/6fLr0O1B/zIAPwTgHf3n\nRsg8n2BAKI1sC4QQjLvuurNQXkGI5XJZpCiiLs6pdt/rBK11xqg7PE02BKxEA2dVSvHJOPMgX+Z1\nqZGqGZQp5cbCFc+xELNw1QYomV9ZCiGUooakjEtEZhl+NRlhPDg6AtadK8O1pjQyw6p0UTorf9Uw\naufkSQXKp4zX/GzQ9qBZRxbaRjZ8te0B2rbFysf7FoX8ZspOedJQFFeUS4vqOG+fOVRn5kW/Y1hP\njChXXavTZchz3zXFqkq+K2N6i+tNe8wsyfnU+9Febl+mS3Sn0QyYFior51H2FzocvIc/APDrAD5A\n1x4G8B7vMPiImd1jZve6++NTdZiNj93KGsHCH//hBRwdHQ75Zd5zJp5RJRs7T4YnvVyOBwZr9cd3\nnU/Ph4rEJxsn925GTEQakXpomvVmXZtSDZsUfUZbFcaolzs5l82HQGceLtetSp6jg6C3dpL4nQ26\ncX1sZDIDxrncGr5scJgPAdlMKq5Hec7GWGenaEoh7umZpDV8WGaVbstmepaTAm8jMDgRy3Uf476h\ns61qzgLTgunKij5ki711lr1aXYwz48d1DekoH6cUMyOe1TEo4VUpK1k/4vJ1GumKnAvdR0nxznA0\nM6xWY94pHuxEZFHstnDRE6Z+GsBX3P1RQew+AF+i33E4+EjRGx0Oft93vRDAOGwJUI9I59cCwFNP\nPVXk5SM3eePGjeGYNRY8YL1nTsfIctBQ0zGhmFRwGddvfetbI+FXIQvlr1Mwoyy28tzROaTd5NGr\nR6ReVeZpTXnfnDvMBqu4M2RpAc5fM915a1313vg7/3O7s4OxlebZLBDmaTa4HZ9cttIj/lmpaRmM\nk9JYFT0fGs/4sbedRTtTypnLdXc0B+O9bFhBaSSi+XyddRP4FZ6yWdFGlRtQm9gYBa84StJ+PhgI\nz+nL9NwEbFC0L2lUwfSIa6roeXyGZWPKOJstRvxkUP3C+N52RW9mzwHwNgCvzW4n11KMnA4H//7v\n/R6PbVozr1VDulAyxf4XFEb25Q8dkA/Y6NswfAZDlsvxAJjioocW8wKa1WoFbgOXw3XG4pnIPzOu\n7GmzMmEcshkFXA7fqylJ3m8+OjCH04yztoGvsWLN9sThqEsVfRi9KcjGSRgP7TiZpxOb1TFNORec\nRSyKA4PWEYqN8WE6MZ2zzjl4mEk0wtcbXw/mFc8Fr5FHA/zsrbPxsZGFYhK5ac26/57+N87W8+h1\njCauHx0djTYiY1xihW8YGPaUo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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "path = '../test_dataset/IMG/*'\n",
+ "img_list = glob.glob(path)\n",
+ "# Grab a random image and display it\n",
+ "idx = np.random.randint(0, len(img_list)-1)\n",
+ "image = mpimg.imread(img_list[idx])\n",
+ "plt.imshow(image)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Calibration Data\n",
+ "Read in and display example grid and rock sample calibration images. You'll use the grid for perspective transform and the rock image for creating a new color selection that identifies these samples of interest. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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WJvRxPp9jES8PBXNPlkLlbzv8xx4rOx9HAm3KHMosmaDv+z6yJAVyDeUoINfI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\nwOaIt4O+fhj9PIpiaSpAFJEZpLN8Phlr++1+v08n7gOK0eFI5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VcH/vnme9l+/73d7me9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svQ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\n7s508bkAAAAASUVORK5CYII=\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# In the simulator you can toggle on a grid on the ground for calibration\n",
+ "# You can also toggle on the rock samples with the 0 (zero) key. \n",
+ "# Here's an example of the grid and one of the rocks\n",
+ "example_grid = '../calibration_images/example_grid1.jpg'\n",
+ "example_rock = '../calibration_images/example_rock1.jpg'\n",
+ "grid_img = mpimg.imread(example_grid)\n",
+ "rock_img = mpimg.imread(example_rock)\n",
+ "\n",
+ "fig = plt.figure(figsize=(12,3))\n",
+ "plt.subplot(121)\n",
+ "plt.imshow(grid_img)\n",
+ "plt.subplot(122)\n",
+ "plt.imshow(rock_img)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Perspective Transform\n",
+ "\n",
+ "Define the perspective transform function from the lesson and test it on an image."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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V6/k3v/Yb2O+32O32uVmIqshpLLOyzkObBUzK9a59yJkgl/ebj0sisTPvMgBL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bZ/1Ppyj0SiY4w4PUGFlu/5a/8js/khs1lMBFkRYqCpPRAJ6WVaI9YT55wkd4RA27ZME7H+7NOf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\njwKv2HTsc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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Define a function to perform a perspective transform\n",
+ "# I've used the example grid image above to choose source points for the\n",
+ "# grid cell in front of the rover (each grid cell is 1 square meter in the sim)\n",
+ "# Define a function to perform a perspective transform\n",
+ "def perspect_transform(img, src, dst):\n",
+ " \n",
+ " M = cv2.getPerspectiveTransform(src, dst)\n",
+ " warped = cv2.warpPerspective(img, M, (img.shape[1], img.shape[0]))# keep same size as input image\n",
+ " mask = cv2.warpPerspective(np.ones_like(img[:,:,0]), M, (img.shape[1], img.shape[0]))\n",
+ " return warped, mask\n",
+ "\n",
+ "\n",
+ "# Define calibration box in source (actual) and destination (desired) coordinates\n",
+ "# These source and destination points are defined to warp the image\n",
+ "# to a grid where each 10x10 pixel square represents 1 square meter\n",
+ "# The destination box will be 2*dst_size on each side\n",
+ "dst_size = 5 \n",
+ "# Set a bottom offset to account for the fact that the bottom of the image \n",
+ "# is not the position of the rover but a bit in front of it\n",
+ "# this is just a rough guess, feel free to change it!\n",
+ "bottom_offset = 6\n",
+ "source = np.float32([[14, 140], [301 ,140],[200, 96], [118, 96]])\n",
+ "destination = np.float32([[image.shape[1]/2 - dst_size, image.shape[0] - bottom_offset],\n",
+ " [image.shape[1]/2 + dst_size, image.shape[0] - bottom_offset],\n",
+ " [image.shape[1]/2 + dst_size, image.shape[0] - 2*dst_size - bottom_offset], \n",
+ " [image.shape[1]/2 - dst_size, image.shape[0] - 2*dst_size - bottom_offset],\n",
+ " ])\n",
+ "warped, mask = perspect_transform(grid_img, source, destination)\n",
+ "fig = plt.figure(figsize=(12,3))\n",
+ "plt.subplot(121)\n",
+ "plt.imshow(warped)\n",
+ "plt.subplot(122)\n",
+ "plt.imshow(mask,cmap='gray')\n",
+ "#scipy.misc.imsave('../output/warped_example.jpg', warped)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Color Thresholding\n",
+ "Define the color thresholding function from the lesson and apply it to the warped image\n",
+ "\n",
+ "**TODO:** Ultimately, you want your map to not just include navigable terrain but also obstacles and the positions of the rock samples you're searching for. Modify this function or write a new function that returns the pixel locations of obstacles (areas below the threshold) and rock samples (yellow rocks in calibration images), such that you can map these areas into world coordinates as well. \n",
+ "**Hints and Suggestion:** \n",
+ "* For obstacles you can just invert your color selection that you used to detect ground pixels, i.e., if you've decided that everything above the threshold is navigable terrain, then everthing below the threshold must be an obstacle!\n",
+ "\n",
+ "\n",
+ "* For rocks, think about imposing a lower and upper boundary in your color selection to be more specific about choosing colors. You can investigate the colors of the rocks (the RGB pixel values) in an interactive matplotlib window to get a feel for the appropriate threshold range (keep in mind you may want different ranges for each of R, G and B!). Feel free to get creative and even bring in functions from other libraries. Here's an example of [color selection](http://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_imgproc/py_colorspaces/py_colorspaces.html) using OpenCV. \n",
+ "\n",
+ "* **Beware However:** if you start manipulating images with OpenCV, keep in mind that it defaults to `BGR` instead of `RGB` color space when reading/writing images, so things can get confusing."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Identify pixels above the threshold\n",
+ "# Threshold of RGB > 160 does a nice job of identifying ground pixels only\n",
+ "def color_thresh(img, rgb_thresh=(160, 160, 160)):\n",
+ " # Create an array of zeros same xy size as img, but single channel\n",
+ " color_select = np.zeros_like(img[:,:,0])\n",
+ " # Require that each pixel be above all three threshold values in RGB\n",
+ " # above_thresh will now contain a boolean array with \"True\"\n",
+ " # where threshold was met\n",
+ " above_thresh = (img[:,:,0] > rgb_thresh[0]) \\\n",
+ " & (img[:,:,1] > rgb_thresh[1]) \\\n",
+ " & (img[:,:,2] > rgb_thresh[2])\n",
+ " # Index the array of zeros with the boolean array and set to 1\n",
+ " color_select[above_thresh] = 1\n",
+ " # Return the binary image\n",
+ " return color_select\n",
+ "\n",
+ "threshed = color_thresh(warped)\n",
+ "plt.imshow(threshed, cmap='gray')\n",
+ "#scipy.misc.imsave('../output/warped_threshed.jpg', threshed*255)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Coordinate Transformations\n",
+ "Define the functions used to do coordinate transforms and apply them to an image."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "outputExpanded": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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MLrk0UsNtSIxx7cT1aZo2pXddwyYFubRhT4oIaAPrbOyG70q4NwDwfLo5EB38qP2YSuep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q3gQLdvxg/sAmKu9XaGEMEdkfQDBTEc1lq0/jrFjTG7pmMk/NIzXxnRxjhNpHXLjla3ELCQwg16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\nuCkKa4Fxko5sT9IPa5G9lbnAiojYHRHPAN0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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Define a function to convert from image coords to rover coords\n",
+ "def rover_coords(binary_img):\n",
+ " # Identify nonzero pixels\n",
+ " ypos, xpos = binary_img.nonzero()\n",
+ " # Calculate pixel positions with reference to the rover position being at the \n",
+ " # center bottom of the image. \n",
+ " x_pixel = -(ypos - binary_img.shape[0]).astype(np.float)\n",
+ " y_pixel = -(xpos - binary_img.shape[1]/2 ).astype(np.float)\n",
+ " return x_pixel, y_pixel\n",
+ "\n",
+ "# Define a function to convert to radial coords in rover space\n",
+ "def to_polar_coords(x_pixel, y_pixel):\n",
+ " # Convert (x_pixel, y_pixel) to (distance, angle) \n",
+ " # in polar coordinates in rover space\n",
+ " # Calculate distance to each pixel\n",
+ " dist = np.sqrt(x_pixel**2 + y_pixel**2)\n",
+ " # Calculate angle away from vertical for each pixel\n",
+ " angles = np.arctan2(y_pixel, x_pixel)\n",
+ " return dist, angles\n",
+ "\n",
+ "# Define a function to map rover space pixels to world space\n",
+ "def rotate_pix(xpix, ypix, yaw):\n",
+ " # Convert yaw to radians\n",
+ " yaw_rad = yaw * np.pi / 180\n",
+ " xpix_rotated = (xpix * np.cos(yaw_rad)) - (ypix * np.sin(yaw_rad)) \n",
+ " ypix_rotated = (xpix * np.sin(yaw_rad)) + (ypix * np.cos(yaw_rad))\n",
+ " # Return the result \n",
+ " return xpix_rotated, ypix_rotated\n",
+ "\n",
+ "def translate_pix(xpix_rot, ypix_rot, xpos, ypos, scale): \n",
+ " # Apply a scaling and a translation\n",
+ " xpix_translated = (xpix_rot / scale) + xpos\n",
+ " ypix_translated = (ypix_rot / scale) + ypos\n",
+ " # Return the result \n",
+ " return xpix_translated, ypix_translated\n",
+ "\n",
+ "\n",
+ "# Define a function to apply rotation and translation (and clipping)\n",
+ "# Once you define the two functions above this function should work\n",
+ "def pix_to_world(xpix, ypix, xpos, ypos, yaw, world_size, scale):\n",
+ " # Apply rotation\n",
+ " xpix_rot, ypix_rot = rotate_pix(xpix, ypix, yaw)\n",
+ " # Apply translation\n",
+ " xpix_tran, ypix_tran = translate_pix(xpix_rot, ypix_rot, xpos, ypos, scale)\n",
+ " # Perform rotation, translation and clipping all at once\n",
+ " x_pix_world = np.clip(np.int_(xpix_tran), 0, world_size - 1)\n",
+ " y_pix_world = np.clip(np.int_(ypix_tran), 0, world_size - 1)\n",
+ " # Return the result\n",
+ " return x_pix_world, y_pix_world\n",
+ "\n",
+ "# Grab another random image\n",
+ "idx = np.random.randint(0, len(img_list)-1)\n",
+ "image = mpimg.imread(img_list[idx])\n",
+ "warped, mask = perspect_transform(image, source, destination)\n",
+ "threshed = color_thresh(warped)\n",
+ "\n",
+ "# Calculate pixel values in rover-centric coords and distance/angle to all pixels\n",
+ "xpix, ypix = rover_coords(threshed)\n",
+ "dist, angles = to_polar_coords(xpix, ypix)\n",
+ "mean_dir = np.mean(angles)\n",
+ "\n",
+ "# Do some plotting\n",
+ "fig = plt.figure(figsize=(12,9))\n",
+ "plt.subplot(221)\n",
+ "plt.imshow(image)\n",
+ "plt.subplot(222)\n",
+ "plt.imshow(warped)\n",
+ "plt.subplot(223)\n",
+ "plt.imshow(threshed, cmap='gray')\n",
+ "plt.subplot(224)\n",
+ "plt.plot(xpix, ypix, '.')\n",
+ "plt.ylim(-160, 160)\n",
+ "plt.xlim(0, 160)\n",
+ "arrow_length = 100\n",
+ "x_arrow = arrow_length * np.cos(mean_dir)\n",
+ "y_arrow = arrow_length * np.sin(mean_dir)\n",
+ "plt.arrow(0, 0, x_arrow, y_arrow, color='red', zorder=2, head_width=10, width=2)\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Read in saved data and ground truth map of the world\n",
+ "The next cell is all setup to read your saved data into a `pandas` dataframe. Here you'll also read in a \"ground truth\" map of the world, where white pixels (pixel value = 1) represent navigable terrain. \n",
+ "\n",
+ "After that, we'll define a class to store telemetry data and pathnames to images. When you instantiate this class (`data = Databucket()`) you'll have a global variable called `data` that you can refer to for telemetry and map data within the `process_image()` function in the following cell. \n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "inputHidden": false,
+ "outputHidden": false
+ },
+ "outputs": [],
+ "source": [
+ "# Import pandas and read in csv file as a dataframe\n",
+ "import pandas as pd\n",
+ "# Change the path below to your data directory\n",
+ "# If you are in a locale (e.g., Europe) that uses ',' as the decimal separator\n",
+ "# change the '.' to ','\n",
+ "df = pd.read_csv('../test_dataset/robot_log.csv', delimiter=';', decimal='.')\n",
+ "csv_img_list = df[\"Path\"].tolist() # Create list of image pathnames\n",
+ "# Read in ground truth map and create a 3-channel image with it\n",
+ "ground_truth = mpimg.imread('../calibration_images/map_bw.png')\n",
+ "ground_truth_3d = np.dstack((ground_truth*0, ground_truth*255, ground_truth*0)).astype(np.float)\n",
+ "\n",
+ "# Creating a class to be the data container\n",
+ "# Will read in saved data from csv file and populate this object\n",
+ "# Worldmap is instantiated as 200 x 200 grids corresponding \n",
+ "# to a 200m x 200m space (same size as the ground truth map: 200 x 200 pixels)\n",
+ "# This encompasses the full range of output position values in x and y from the sim\n",
+ "class Databucket():\n",
+ " def __init__(self):\n",
+ " self.images = csv_img_list \n",
+ " self.xpos = df[\"X_Position\"].values\n",
+ " self.ypos = df[\"Y_Position\"].values\n",
+ " self.yaw = df[\"Yaw\"].values\n",
+ " self.count = 0 # This will be a running index\n",
+ " self.worldmap = np.zeros((200, 200, 3)).astype(np.float)\n",
+ " self.ground_truth = ground_truth_3d # Ground truth worldmap\n",
+ "\n",
+ "# Instantiate a Databucket().. this will be a global variable/object\n",
+ "# that you can refer to in the process_image() function below\n",
+ "data = Databucket()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Write a function to process stored images\n",
+ "\n",
+ "Modify the `process_image()` function below by adding in the perception step processes (functions defined above) to perform image analysis and mapping. The following cell is all set up to use this `process_image()` function in conjunction with the `moviepy` video processing package to create a video from the images you saved taking data in the simulator. \n",
+ "\n",
+ "In short, you will be passing individual images into `process_image()` and building up an image called `output_image` that will be stored as one frame of video. You can make a mosaic of the various steps of your analysis process and add text as you like (example provided below). \n",
+ "\n",
+ "\n",
+ "\n",
+ "To start with, you can simply run the next three cells to see what happens, but then go ahead and modify them such that the output video demonstrates your mapping process. Feel free to get creative!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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NOD0YY1AahTBoklyapg0S4x4x6TwURRHCMMROv1cNMq1mp0EVeLL/1KYxHI8wLXJcvbaL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\nvQ5RCFaPT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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "def find_rocks(img, levels=(110, 110, 50)):\n",
+ " rockpix = ((img[:,:,0] > levels[0]) \\\n",
+ " & (img[:,:,1] > levels[1]) \\\n",
+ " & (img[:,:,2] < levels[2]))\n",
+ " \n",
+ " color_select = np.zeros_like(img[:,:,0])\n",
+ " color_select[rockpix] = 1\n",
+ " \n",
+ " return color_select\n",
+ " \n",
+ "rock_map = find_rocks(rock_img)\n",
+ "fig = plt.figure(figsize=(12,3))\n",
+ "plt.subplot(121)\n",
+ "plt.imshow(rock_img)\n",
+ "plt.subplot(122)\n",
+ "plt.imshow(rock_img, cmap='gray')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "# Define a function to pass stored images to\n",
+ "# reading rover position and yaw angle from csv file\n",
+ "# This function will be used by moviepy to create an output video\n",
+ "def process_image(img):\n",
+ " # Example of how to use the Databucket() object defined above\n",
+ " # to print the current x, y and yaw values \n",
+ " # print(data.xpos[data.count], data.ypos[data.count], data.yaw[data.count])\n",
+ "\n",
+ " # TODO: \n",
+ " # 1) Define source and destination points for perspective transform\n",
+ " # 2) Apply perspective transform\n",
+ " # 3) Apply color threshold to identify navigable terrain/obstacles/rock samples\n",
+ " # 4) Convert thresholded image pixel values to rover-centric coords\n",
+ " # 5) Convert rover-centric pixel values to world coords\n",
+ " # 6) Update worldmap (to be displayed on right side of screen)\n",
+ " # Example: data.worldmap[obstacle_y_world, obstacle_x_world, 0] += 1\n",
+ " # data.worldmap[rock_y_world, rock_x_world, 1] += 1\n",
+ " # data.worldmap[navigable_y_world, navigable_x_world, 2] += 1\n",
+ "\n",
+ " warped, mask = perspective_transform(img, source, destination)\n",
+ " # Apply color threshold to identify navigable terrain+obstacles+rock samples\n",
+ " threshold = color_thresh(warped)\n",
+ " obs_map = np.absolute(np.float32(threshold) -1) * mask\n",
+ " xpix, ypix = rover_coords(threshed)\n",
+ " \n",
+ " # 6) Conver rover coordinates to world coordinates\n",
+ " world_size = data.worldmap.shape[0]\n",
+ " scale = 2 * dst_size\n",
+ " xpos = data.xpos[data.count]\n",
+ " ypos = data.ypos[data.count]\n",
+ " yaw = data.yaw[data.count]\n",
+ " \n",
+ " x_world, y_world = pix_to_world(xpix, ypix, xpos, ypos, yaw, world_size, scale)\n",
+ " \n",
+ " obsxpix, obsypix = rover_coords(obs_map)\n",
+ " \n",
+ " obs_x_world, obs_y_world = pix_to_world(obsxpix, obsypix, xpos, ypos, yaw, world_size, scale)\n",
+ " \n",
+ " #7 Update Rover worldmap (to be displayed on the right side of screen)\n",
+ " \n",
+ " data.worldmap[y_world, x_world, 2] = 255\n",
+ " data.worldmap[obs_y_world, obs_x_world, 0] = 255\n",
+ " nav_pix = data.worldmap[:,:,2] > 2\n",
+ " data.worldmap[nav_pix, 0] = 0\n",
+ " \n",
+ " # See if we can find some rocks\n",
+ " \n",
+ " rock_map = find_rocks(wrapped, levels=(110, 110, 50))\n",
+ " \n",
+ " if rock.map.any():\n",
+ " rock_x, rock_y = rover_coords(rock_map)\n",
+ " rock_x_world, rock_y_world = pix_to_world(rock_x, rock_y, xpos, ypos, yaw, world_size, scale)\n",
+ " \n",
+ " data.worldmap[rock_y_world, rock_x_world, :] = 255\n",
+ " \n",
+ " \n",
+ " # 7) Make a mosaic image, below is some example code\n",
+ " # First create a blank image (can be whateve shape you like)\n",
+ " \n",
+ " output_image = np.zeros((img.shape[0] + data.worldmap.shape[0], img.shape[1]*2, 3))\n",
+ " # Next you can populate regions of the image with various output\n",
+ " # Here I'm putting the original image in the upper left hand corner\n",
+ " output_image[0:img.shape[0], 0:img.shape[1]] = img\n",
+ "\n",
+ " # Let's create more images to add to the mosaic, first a warped image\n",
+ " warped = perspect_transform(img, source, destination)\n",
+ " # Add the warped image in the upper right hand corner\n",
+ " output_image[0:img.shape[0], img.shape[1]:] = warped\n",
+ "\n",
+ " # Overlay worldmap with ground truth map\n",
+ " map_add = cv2.addWeighted(data.worldmap, 1, data.ground_truth, 0.5, 0)\n",
+ " # Flip map overlay so y-axis points upward and add to output_image \n",
+ " output_image[img.shape[0]:, 0:data.worldmap.shape[1]] = np.flipud(map_add)\n",
+ "\n",
+ "\n",
+ " # Then putting some text over the image\n",
+ " cv2.putText(output_image,\"Populate this image with your analyses to make a video!\", (20, 20), \n",
+ " cv2.FONT_HERSHEY_COMPLEX, 0.4, (255, 255, 255), 1)\n",
+ " if data.count < len(data.images) - 1:\n",
+ " data.count += 1 # Keep track of the index in the Databucket()\n",
+ " \n",
+ " return output_image"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Make a video from processed image data\n",
+ "Use the [moviepy](https://zulko.github.io/moviepy/) library to process images and create a video.\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from moviepy.editor import *\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "ename": "NameError",
+ "evalue": "name 'perspective_transform' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0mclip\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImageSequenceClip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimages\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m60\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# Note: output video will be sped up because\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;31m# recording rate in simulator is fps=25\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0mnew_clip\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclip\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfl_image\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprocess_image\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#NOTE: this function expects color images!!\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 14\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'time'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'new_clip.write_videofile(output, audio=False)'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m~/anaconda3/envs/RoboND/lib/python3.5/site-packages/moviepy/video/VideoClip.py\u001b[0m in \u001b[0;36mfl_image\u001b[0;34m(self, image_func, apply_to)\u001b[0m\n\u001b[1;32m 531\u001b[0m \u001b[0;31m`\u001b[0m\u001b[0mget_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;31m`\u001b[0m \u001b[0mby\u001b[0m \u001b[0manother\u001b[0m \u001b[0mframe\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mimage_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mget_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 532\u001b[0m \"\"\"\n\u001b[0;32m--> 533\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mgf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mimage_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mapply_to\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 534\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 535\u001b[0m \u001b[0;31m# --------------------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m~/anaconda3/envs/RoboND/lib/python3.5/site-packages/moviepy/Clip.py\u001b[0m in \u001b[0;36mfl\u001b[0;34m(self, fun, apply_to, keep_duration)\u001b[0m\n\u001b[1;32m 134\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 135\u001b[0m \u001b[0;31m#mf = copy(self.make_frame)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 136\u001b[0;31m \u001b[0mnewclip\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_make_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_frame\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 137\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 138\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mkeep_duration\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m\u001b[0m in \u001b[0;36mset_make_frame\u001b[0;34m(self, mf)\u001b[0m\n",
+ "\u001b[0;32m~/anaconda3/envs/RoboND/lib/python3.5/site-packages/moviepy/decorators.py\u001b[0m in \u001b[0;36moutplace\u001b[0;34m(f, clip, *a, **k)\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;34m\"\"\" Applies f(clip.copy(), *a, **k) and returns clip.copy()\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0mnewclip\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclip\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnewclip\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 15\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mnewclip\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m~/anaconda3/envs/RoboND/lib/python3.5/site-packages/moviepy/video/VideoClip.py\u001b[0m in \u001b[0;36mset_make_frame\u001b[0;34m(self, mf)\u001b[0m\n\u001b[1;32m 692\u001b[0m \"\"\"\n\u001b[1;32m 693\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_frame\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 694\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 695\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 696\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m\u001b[0m in \u001b[0;36mget_frame\u001b[0;34m(self, t)\u001b[0m\n",
+ "\u001b[0;32m~/anaconda3/envs/RoboND/lib/python3.5/site-packages/moviepy/decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(f, *a, **kw)\u001b[0m\n\u001b[1;32m 87\u001b[0m new_kw = {k: fun(v) if k in varnames else v\n\u001b[1;32m 88\u001b[0m for (k,v) in kw.items()}\n\u001b[0;32m---> 89\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mnew_a\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mnew_kw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 90\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mdecorator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecorator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwrapper\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 91\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m~/anaconda3/envs/RoboND/lib/python3.5/site-packages/moviepy/Clip.py\u001b[0m in \u001b[0;36mget_frame\u001b[0;34m(self, t)\u001b[0m\n\u001b[1;32m 93\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mframe\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 94\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 95\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 96\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mfl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfun\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mapply_to\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeep_duration\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m~/anaconda3/envs/RoboND/lib/python3.5/site-packages/moviepy/Clip.py\u001b[0m in \u001b[0;36m\u001b[0;34m(t)\u001b[0m\n\u001b[1;32m 134\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 135\u001b[0m \u001b[0;31m#mf = copy(self.make_frame)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 136\u001b[0;31m \u001b[0mnewclip\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_make_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_frame\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 137\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 138\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mkeep_duration\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m~/anaconda3/envs/RoboND/lib/python3.5/site-packages/moviepy/video/VideoClip.py\u001b[0m in \u001b[0;36m\u001b[0;34m(gf, t)\u001b[0m\n\u001b[1;32m 531\u001b[0m \u001b[0;31m`\u001b[0m\u001b[0mget_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;31m`\u001b[0m \u001b[0mby\u001b[0m \u001b[0manother\u001b[0m \u001b[0mframe\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mimage_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mget_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 532\u001b[0m \"\"\"\n\u001b[0;32m--> 533\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mgf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mimage_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mapply_to\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 534\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 535\u001b[0m \u001b[0;31m# --------------------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m\u001b[0m in \u001b[0;36mprocess_image\u001b[0;34m(img)\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[0;31m# data.worldmap[navigable_y_world, navigable_x_world, 2] += 1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m \u001b[0mwarped\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mperspective_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msource\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdestination\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 22\u001b[0m \u001b[0;31m# Apply color threshold to identify navigable terrain+obstacles+rock samples\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 23\u001b[0m \u001b[0mthreshold\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcolor_thresh\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwarped\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mNameError\u001b[0m: name 'perspective_transform' is not defined"
+ ]
+ }
+ ],
+ "source": [
+ "# Import everything needed to edit/save/watch video clips\n",
+ "#from moviepy.editor import VideoFileClip\n",
+ "#from moviepy.editor import ImageSequenceClip\n",
+ "from moviepy.editor import *\n",
+ "\n",
+ "\n",
+ "\n",
+ "# Define pathname to save the output video\n",
+ "output = '../output/test_mapping.mp4'\n",
+ "data = Databucket() # Re-initialize data in case you're running this cell multiple times\n",
+ "clip = ImageSequenceClip(data.images, fps=60) # Note: output video will be sped up because \n",
+ " # recording rate in simulator is fps=25\n",
+ "new_clip = clip.fl_image(process_image) #NOTE: this function expects color images!!\n",
+ "%time new_clip.write_videofile(output, audio=False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### This next cell should function as an inline video player\n",
+ "If this fails to render the video, try running the following cell (alternative video rendering method). You can also simply have a look at the saved mp4 in your `/output` folder"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 32,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "\n",
+ "from IPython.display import HTML\n",
+ "HTML(\"\"\"\n",
+ "\n",
+ "\"\"\".format(output))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Below is an alternative way to create a video in case the above cell did not work."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "FileNotFoundError",
+ "evalue": "[Errno 2] No such file or directory: '../output/test_mapping.mp4'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mio\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mbase64\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mvideo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'r+b'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mencoded_video\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbase64\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mb64encode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvideo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m HTML(data='''