In this project several computer vision techniques are applied to detect lanes on the read and draw lines fitting them.

Step 1: Set up Anaconda with OpenCV

Step 2: Open the P1.ipynb in a Jupyter Notebook (Python 3.5)

Step 3: Run the notebook

1. Apply gray scale to the initial image
2. Use Gaussian blur technique to distinct the white lanes and input to canny edge method
3. Apply Canny edge method to find the edges in the image
4. Apply a mask to spot only the lanes
5. Use Hough transformation to obtain the most suitable lines passing over the edges
6. Obtain fitting lines

In order to draw a single line on the left and right lanes, I modified the ** draw_lines()** function by separating into positive and negative slope . Besides filter into two branches of positive/negative slope, I apply a range of acceptable values for slope according to the plot of all possible hough lines. This range was within [0.4, 0.95] for positive slopes and [-0.95,-0.4] for negative slopes. Afterwards, I evaluate the average slope for left and right lines (positive/negative slopes) in order to find a single line for both cases. Finally, using simple linear equation y = mx +b , I obtained the parameter b as well as extrapolate the right and left lines.