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:
process_image() function with the appropriate steps/functions to go from a raw image to a worldmap.process_image() using moviepy functions to create video outputperception.py and decision.py to allow your rover to navigate and map in autonomous mode!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".
Run the next cell to get code highlighting in the markdown cells.
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%%HTML
<style> code {background-color : orange !important;} </style>
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%matplotlib inline
#%matplotlib qt # Choose %matplotlib qt to plot to an interactive window (note it may show up behind your browser)
# Make some of the relevant imports
import cv2 # OpenCV for perspective transform
import numpy as np
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import scipy.misc # For saving images as needed
import glob # For reading in a list of images from a folder
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.
Next, read in and display a random image from the test_dataset folder
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path = '../test_dataset/IMG/*'
img_list = glob.glob(path)
# Grab a random image and display it
idx = np.random.randint(0, len(img_list)-1)
image = mpimg.imread(img_list[idx])
plt.imshow(image)
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path = '../sim_dataset/IMG/*'
img_list = glob.glob(path)
# Grab a random image and display it
idx = np.random.randint(0, len(img_list)-1)
image = mpimg.imread(img_list[idx])
plt.imshow(image)
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# In the simulator you can toggle on a grid on the ground for calibration
# You can also toggle on the rock samples with the 0 (zero) key.
# Here's an example of the grid and one of the rocks
example_grid = '../calibration_images/example_grid1.jpg'
example_rock = '../calibration_images/example_rock1.jpg'
grid_img = mpimg.imread(example_grid)
rock_img = mpimg.imread(example_rock)
fig = plt.figure(figsize=(12,3))
plt.subplot(121)
plt.imshow(grid_img)
plt.subplot(122)
plt.imshow(rock_img)
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# Define a function to perform a perspective transform
# I've used the example grid image above to choose source points for the
# grid cell in front of the rover (each grid cell is 1 square meter in the sim)
# Define a function to perform a perspective transform
def perspect_transform(img, src, dst):
M = cv2.getPerspectiveTransform(src, dst)
warped = cv2.warpPerspective(img, M, (img.shape[1], img.shape[0]))# keep same size as input image
return warped
# 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
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 = perspect_transform(grid_img, source, destination)
plt.imshow(warped)
#scipy.misc.imsave('../output/warped_example.jpg', warped)
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Define the color thresholding function from the lesson and apply it to the warped image
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.
Hints and Suggestion:
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 using OpenCV.
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.
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# Identify pixels above the threshold
# Threshold of RGB > 160 does a nice job of identifying ground pixels only
def color_thresh(img, rgb_thresh=(160, 160, 160)):
# Create an array of zeros same xy size as img, but single channel
color_select = np.zeros_like(img[:,:,0])
# Require that each pixel be above all three threshold values in RGB
# above_thresh will now contain a boolean array with "True"
# where threshold was met
above_thresh = (img[:,:,0] > rgb_thresh[0]) \
& (img[:,:,1] > rgb_thresh[1]) \
& (img[:,:,2] > rgb_thresh[2])
# Index the array of zeros with the boolean array and set to 1
color_select[above_thresh] = 1
# Return the binary image
return color_select
def obstacle_thresh(img, rgb_thresh=(160, 160, 160)):
# Create an array of zeros same xy size as img, but single channel
color_select = np.zeros_like(img[:,:,0])
# Require that each pixel be above all three threshold values in RGB
# above_thresh will now contain a boolean array with "True"
# where threshold was met
above_thresh = (img[:,:,0] <= rgb_thresh[0]) \
& (img[:,:,1] <= rgb_thresh[1]) \
& (img[:,:,2] <= rgb_thresh[2])
# Index the array of zeros with the boolean array and set to 1
color_select[above_thresh] = 1
# Return the binary image
return color_select
def rock_thresh(img, threshold_low=(100, 100, 0), threshold_high=(160, 160, 40)):
# Create an array of zeros same xy size as img, but single channel
color_select = np.zeros_like(img[:, :, 0])
# Require that each pixel be above all three threshold values in RGB
# above_thresh will now contain a boolean array with "True"
# where threshold was met
above_thresh = ~((img[:, :, 0] > threshold_low[0]) & (img[:, :, 0] < threshold_high[0]) \
& (img[:, :, 1] > threshold_low[1]) & (img[:, :, 1] < threshold_high[1]) \
& (img[:, :, 2] > threshold_low[2]) & (img[:, :, 2] < threshold_high[2]))
# Index the array of zeros with the boolean array and set to 1
color_select[above_thresh] = 1
return color_select
threshed = color_thresh(warped)
plt.imshow(threshed, cmap='gray')
#scipy.misc.imsave('../output/warped_threshed.jpg', threshed*255)
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threshed_rock = color_thresh(rock_img, (23,40,133))
plt.imshow(threshed_rock, cmap='gray')
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def rover_coords(binary_img):
# Identify nonzero pixels
ypos, xpos = binary_img.nonzero()
# Calculate pixel positions with reference to the rover position being at the
# center bottom of the image.
x_pixel = np.absolute(ypos - binary_img.shape[0]).astype(np.float)
y_pixel = -(xpos - binary_img.shape[0]).astype(np.float)
return x_pixel, y_pixel
# Define a function to convert to radial coords in rover space
def to_polar_coords(x_pixel, y_pixel):
# Convert (x_pixel, y_pixel) to (distance, angle)
# in polar coordinates in rover space
# Calculate distance to each pixel
dist = np.sqrt(x_pixel**2 + y_pixel**2)
# Calculate angle away from vertical for each pixel
angles = np.arctan2(y_pixel, x_pixel)
return dist, angles
# Define a function to map rover space pixels to world space
def pix_to_world(xpix, ypix, x_rover, y_rover, yaw_rover, world_size, scale):
# Map pixels from rover space to world coords
yaw = yaw_rover * np.pi / 180
# Perform rotation, translation and clipping all at once
x_pix_world = np.clip(np.int_((((xpix * np.cos(yaw)) - (ypix * np.sin(yaw)))/scale) + x_rover),
0, world_size - 1)
y_pix_world = np.clip(np.int_((((xpix * np.sin(yaw)) + (ypix * np.cos(yaw)))/scale) + y_rover),
0, world_size - 1)
return x_pix_world, y_pix_world
# Grab another random image
idx = np.random.randint(0, len(img_list)-1)
image = mpimg.imread(img_list[idx])
warped = perspect_transform(image, source, destination)
threshed = color_thresh(warped)
# Calculate pixel values in rover-centric coords and distance/angle to all pixels
xpix, ypix = rover_coords(threshed)
dist, angles = to_polar_coords(xpix, ypix)
mean_dir = np.mean(angles)
# Do some plotting
fig = plt.figure(figsize=(12,9))
plt.subplot(221)
plt.imshow(image)
plt.subplot(222)
plt.imshow(warped)
plt.subplot(223)
plt.imshow(threshed, cmap='gray')
plt.subplot(224)
plt.plot(xpix, ypix, '.')
plt.ylim(-160, 160)
plt.xlim(0, 160)
arrow_length = 100
x_arrow = arrow_length * np.cos(mean_dir)
y_arrow = arrow_length * np.sin(mean_dir)
plt.arrow(0, 0, x_arrow, y_arrow, color='red', zorder=2, head_width=10, width=2)
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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.
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.
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# Import pandas and read in csv file as a dataframe
import pandas as pd
# Change this path to your data directory
df = pd.read_csv('../sim_2_dataset/robot_log.csv')
img_list_sorted = df["Path"].tolist() # Create list of image pathnames
# Read in ground truth map and create a 3-channel image with it
ground_truth = mpimg.imread('../calibration_images/map_bw.png')
ground_truth_3d = np.dstack((ground_truth*0, ground_truth, ground_truth*0)).astype(np.float)
# Creating a class to be the data container
# Will read in saved data from csv file and populate this object
# Worldmap is instantiated as 200 x 200 grids corresponding
# to a 200m x 200m space (same size as the ground truth map: 200 x 200 pixels)
# This encompasses the full range of output position values in x and y from the sim
class Databucket():
def __init__(self):
self.images = img_list_sorted
self.xpos = df["X_Position"].values
self.ypos = df["Y_Position"].values
self.yaw = df["Yaw"].values
self.count = 0
self.worldmap = np.zeros((200, 200, 3)).astype(np.float)
self.ground_truth = ground_truth_3d # Ground truth worldmap
# Instantiate a Databucket().. this will be a global variable/object
# that you can refer to in the process_image() function below
data = Databucket()
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.
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).
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!
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# Define a function to pass stored images to
# reading rover position and yaw angle from csv file
# This function will be used by moviepy to create an output video
def process_image(img):
# Example of how to use the Databucket() object defined in the previous cell
# print(data.xpos[0], data.ypos[0], data.yaw[0])
warp = perspect_transform(img, source, destination)
color_thre = color_thresh(warp, rgb_thresh=(160, 160, 160))
obstacle_thre = obstacle_thresh(warp)
rock_thre = rock_thresh(warp)
xpix0, ypix0 = rover_coords(obstacle_thre)
xpix1, ypix1 = rover_coords(rock_thre)
xpix2, ypix2 = rover_coords(color_thre)
scale = 20
worldsize = 200
rover_obstacle = pix_to_world(xpix0, ypix0, data.xpos[data.count], data.ypos[data.count],data.yaw[data.count], worldsize, scale)
rover_rock = pix_to_world(xpix1, ypix1, data.xpos[data.count], data.ypos[data.count],data.yaw[data.count], worldsize, scale)
rover_terrain = pix_to_world(xpix2, ypix2, data.xpos[data.count], data.ypos[data.count],data.yaw[data.count], worldsize, scale)
dist, angles = to_polar_coords(xpix, ypix)
color_thre = np.dstack((color_thre, color_thre, color_thre)).astype(np.float)
x_rover = data.xpos[data.count]
y_rover = data.ypos[data.count]
yaw_rover = data.yaw[data.count]
data.worldmap[rover_obstacle[1], rover_obstacle[0], :] = [255,0,0]
data.worldmap[rover_rock[1] , rover_rock[0] , :] = [0,255,255]
data.worldmap[rover_terrain[1] , rover_terrain[0] , :] = [0,255,0]
output_image = np.zeros((img.shape[0] + data.worldmap.shape[0], img.shape[1]*2, 3))
output_image[0:img.shape[0], 0:img.shape[1]] = img
output_image[0:img.shape[0], img.shape[1]:2*img.shape[1]] = warp
output_image[img.shape[0]:img.shape[0]*2, 0:img.shape[1]] = color_thre *255 #bottom left
output_image[img.shape[0]:img.shape[0]+data.worldmap.shape[0], img.shape[1]:img.shape[1]+ data.worldmap.shape[1],:] = data.worldmap #bottom right
cv2.putText(output_image, "Rover Camera View", (20, 20),
cv2.FONT_HERSHEY_COMPLEX, 0.4, (255, 255, 255), 1)
cv2.putText(output_image, "Birds Eye View", (350, 20),
cv2.FONT_HERSHEY_COMPLEX, 0.4, (255, 255, 255), 1)
cv2.putText(output_image, "Threshold", (20, 180),
cv2.FONT_HERSHEY_COMPLEX, 0.4, (255, 255, 255), 1)
cv2.putText(output_image, "WorldMap", (350, 180),
cv2.FONT_HERSHEY_COMPLEX, 0.4, (255, 255, 255), 1)
data.count += 1 # Keep track of the index in the Databucket()
if data.count == 921: # to overcome some error for index out of bounds
data.count =0
return output_image
Use the moviepy library to process images and create a video.
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# Import everything needed to edit/save/watch video clips
from moviepy.editor import VideoFileClip
from moviepy.editor import ImageSequenceClip
# Define pathname to save the output video
output = '../output/test_mapping2.mp4'
data = Databucket() # Re-initialize data in case you're running this cell multiple times
clip = ImageSequenceClip(data.images, fps=60) # Note: output video will be sped up because
# recording rate in simulator is fps=25
new_clip = clip.fl_image(process_image) #NOTE: this function expects color images!!
%time new_clip.write_videofile(output, audio=False)
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from IPython.display import HTML
HTML("""
<video width="960" height="540" controls>
<source src="{0}">
</video>
""".format(output))
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import io
import base64
video = io.open(output, 'r+b').read()
encoded_video = base64.b64encode(video)
HTML(data='''<video alt="test" controls>
<source src="data:video/mp4;base64,{0}" type="video/mp4" />
</video>'''.format(encoded_video.decode('ascii')))
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