19. How I Did It

Solution to Undistort and Transform quiz:

That was a tricky quiz!

Let's take a closer look at the pieces involved. These steps will all be useful when you get to the project at the end of this section.

First off, we defined a function for you, corners_unwarp() . The function accepts an image and our previously calculated values for the camera matrix, mtx , and distortion coefficients, dist .

Then you had to implement the function.

Here's how I implemented mine: 

# Define a function that takes an image, number of x and y points, 
# camera matrix and distortion coefficients
def corners_unwarp(img, nx, ny, mtx, dist):
    # Use the OpenCV undistort() function to remove distortion
    undist = cv2.undistort(img, mtx, dist, None, mtx)
    # Convert undistorted image to grayscale
    gray = cv2.cvtColor(undist, cv2.COLOR_BGR2GRAY)
    # Search for corners in the grayscaled image
    ret, corners = cv2.findChessboardCorners(gray, (nx, ny), None)

    if ret == True:
        # If we found corners, draw them! (just for fun)
        cv2.drawChessboardCorners(undist, (nx, ny), corners, ret)
        # Choose offset from image corners to plot detected corners
        # This should be chosen to present the result at the proper aspect ratio
        # My choice of 100 pixels is not exact, but close enough for our purpose here
        offset = 100 # offset for dst points
        # Grab the image shape
        img_size = (gray.shape[1], gray.shape[0])

        # For source points I'm grabbing the outer four detected corners
        src = np.float32([corners[0], corners[nx-1], corners[-1], corners[-nx]])
        # For destination points, I'm arbitrarily choosing some points to be
        # a nice fit for displaying our warped result 
        # again, not exact, but close enough for our purposes
        dst = np.float32([[offset, offset], [img_size[0]-offset, offset], 
                                     [img_size[0]-offset, img_size[1]-offset], 
                                     [offset, img_size[1]-offset]])
        # Given src and dst points, calculate the perspective transform matrix
        M = cv2.getPerspectiveTransform(src, dst)
        # Warp the image using OpenCV warpPerspective()
        warped = cv2.warpPerspective(undist, M, img_size)

    # Return the resulting image and matrix
    return warped, M

Output

Here's the output you needed to achieve:

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