Merge pull request #9 from mreichelt/8-add-spaces-for-github-markdown

fix #8 by using correct GitHub Markdown

Author: ryan-keenan

writeup_template.md

@@ -1,5 +1,6 @@
-##Writeup Template
-###You can use this file as a template for your writeup if you want to submit it as a markdown file, but feel free to use some other method and submit a pdf if you prefer.
+## Writeup Template
+
+### You can use this file as a template for your writeup if you want to submit it as a markdown file, but feel free to use some other method and submit a pdf if you prefer.
 
 ---
 
@@ -27,17 +28,20 @@ The goals / steps of this project are the following:
 [video1]: ./project_video.mp4 "Video"
 
 ## [Rubric](https://review.udacity.com/#!/rubrics/571/view) Points
-###Here I will consider the rubric points individually and describe how I addressed each point in my implementation.  
+
+### Here I will consider the rubric points individually and describe how I addressed each point in my implementation.  
 
 ---
-###Writeup / README
 
-####1. Provide a Writeup / README that includes all the rubric points and how you addressed each one.  You can submit your writeup as markdown or pdf.  [Here](https://github.com/udacity/CarND-Advanced-Lane-Lines/blob/master/writeup_template.md) is a template writeup for this project you can use as a guide and a starting point.  
+### Writeup / README
+
+#### 1. Provide a Writeup / README that includes all the rubric points and how you addressed each one.  You can submit your writeup as markdown or pdf.  [Here](https://github.com/udacity/CarND-Advanced-Lane-Lines/blob/master/writeup_template.md) is a template writeup for this project you can use as a guide and a starting point.  
 
 You're reading it!
-###Camera Calibration
 
-####1. Briefly state how you computed the camera matrix and distortion coefficients. Provide an example of a distortion corrected calibration image.
+### Camera Calibration
+
+#### 1. Briefly state how you computed the camera matrix and distortion coefficients. Provide an example of a distortion corrected calibration image.
 
 The code for this step is contained in the first code cell of the IPython notebook located in "./examples/example.ipynb" (or in lines # through # of the file called `some_file.py`).  
 
@@ -47,21 +51,24 @@ I then used the output `objpoints` and `imgpoints` to compute the camera calibra
 
 ![alt text][image1]
 
-###Pipeline (single images)
+### Pipeline (single images)
+
+#### 1. Provide an example of a distortion-corrected image.
 
-####1. Provide an example of a distortion-corrected image.
 To demonstrate this step, I will describe how I apply the distortion correction to one of the test images like this one:
 ![alt text][image2]
-####2. Describe how (and identify where in your code) you used color transforms, gradients or other methods to create a thresholded binary image.  Provide an example of a binary image result.
+
+#### 2. Describe how (and identify where in your code) you used color transforms, gradients or other methods to create a thresholded binary image.  Provide an example of a binary image result.
+
 I used a combination of color and gradient thresholds to generate a binary image (thresholding steps at lines # through # in `another_file.py`).  Here's an example of my output for this step.  (note: this is not actually from one of the test images)
 
 ![alt text][image3]
 
-####3. Describe how (and identify where in your code) you performed a perspective transform and provide an example of a transformed image.
+#### 3. Describe how (and identify where in your code) you performed a perspective transform and provide an example of a transformed image.
 
 The code for my perspective transform includes a function called `warper()`, which appears in lines 1 through 8 in the file `example.py` (output_images/examples/example.py) (or, for example, in the 3rd code cell of the IPython notebook).  The `warper()` function takes as inputs an image (`img`), as well as source (`src`) and destination (`dst`) points.  I chose the hardcode the source and destination points in the following manner:
 
-```
+```python
 src = np.float32(
     [[(img_size[0] / 2) - 55, img_size[1] / 2 + 100],
     [((img_size[0] / 6) - 10), img_size[1]],
@@ -72,8 +79,8 @@ dst = np.float32(
     [(img_size[0] / 4), img_size[1]],
     [(img_size[0] * 3 / 4), img_size[1]],
     [(img_size[0] * 3 / 4), 0]])
-
 ```
+
 This resulted in the following source and destination points:
 
 | Source        | Destination   | 
@@ -87,35 +94,34 @@ I verified that my perspective transform was working as expected by drawing the
 
 ![alt text][image4]
 
-####4. Describe how (and identify where in your code) you identified lane-line pixels and fit their positions with a polynomial?
+#### 4. Describe how (and identify where in your code) you identified lane-line pixels and fit their positions with a polynomial?
 
 Then I did some other stuff and fit my lane lines with a 2nd order polynomial kinda like this:
 
 ![alt text][image5]
 
-####5. Describe how (and identify where in your code) you calculated the radius of curvature of the lane and the position of the vehicle with respect to center.
+#### 5. Describe how (and identify where in your code) you calculated the radius of curvature of the lane and the position of the vehicle with respect to center.
 
 I did this in lines # through # in my code in `my_other_file.py`
 
-####6. Provide an example image of your result plotted back down onto the road such that the lane area is identified clearly.
+#### 6. Provide an example image of your result plotted back down onto the road such that the lane area is identified clearly.
 
 I implemented this step in lines # through # in my code in `yet_another_file.py` in the function `map_lane()`.  Here is an example of my result on a test image:
 
 ![alt text][image6]
 
 ---
 
-###Pipeline (video)
+### Pipeline (video)
 
-####1. Provide a link to your final video output.  Your pipeline should perform reasonably well on the entire project video (wobbly lines are ok but no catastrophic failures that would cause the car to drive off the road!).
+#### 1. Provide a link to your final video output.  Your pipeline should perform reasonably well on the entire project video (wobbly lines are ok but no catastrophic failures that would cause the car to drive off the road!).
 
 Here's a [link to my video result](./project_video.mp4)
 
 ---
 
-###Discussion
+### Discussion
 
-####1. Briefly discuss any problems / issues you faced in your implementation of this project.  Where will your pipeline likely fail?  What could you do to make it more robust?
+#### 1. Briefly discuss any problems / issues you faced in your implementation of this project.  Where will your pipeline likely fail?  What could you do to make it more robust?
 
 Here I'll talk about the approach I took, what techniques I used, what worked and why, where the pipeline might fail and how I might improve it if I were going to pursue this project further.  
-