Python
OpenCV
Tutorials

Individual Report

Due date: 23 Oct 2024

Include Code, figures

Submit: PDF file only to Blackboard

Q&A: Contact at the Bottom of this web page

Questions Image Resources: Link

Question 1

Give the codes and answers to the following questions about Numpy, use only one line code

1. How to find the maximum value of each row in a 2-dimensional NumPy array? (Find the maximum value of each row in a given array)

   
   np.random.seed(100)
   a = np.random.randint(1,10, [5,3])
   #> array([[9, 9, 4],
   #> [8, 8, 1],
   #> [5, 3, 6],
   #> [3, 3, 3],
   #> [2, 1, 9]])
                   

2. How to compute the result of dividing the min-by-max of each row of a 2-dimensional NumPy array? (Given a 2-dimensional NumPy array, compute min-by-max for each row)

   
   np.random.seed(100)
   a = np.random.randint(1,10, [5,3])
   a
   #> array([[9, 9, 4],
   #> [8, 8, 1],
   #> [5, 3, 6],
   #> [3, 3, 3],
   #> [2, 1, 9]])
                   

3. How to remove all missing values from a NumPy array? (Remove all nan values from a 1-dimensional NumPy array)

   
   # input
   np.array([1,2,3,np.nan,5,6,7,np.nan])
   # expect output
   array([ 1., 2., 3., 5., 6., 7.])
                   

4. How to find all local maxima (peaks) in a 1-dimensional array? (Find all the peaks in a 1-dimensional array a. A peak is a number that is larger than the numbers on either side of it.)

   
   # input
   a = np.array([1, 3, 7, 1, 2, 6, 0, 1])
   # expect output
   array([2, 5])
                   

5. How to invert all columns of a 2D array? (Reverse all columns in 2D array arr)

   
   # input
   arr = np.arange(9).reshape(3,3)
                   

Question 2

Calculate and report the camera's intrinsics and distortion coefficient of the provided images on a checkerboard (The checkerboards square actual size is 20x20 mm. The images of checkerboards are in the folder Q2_calibration).

Question 3

Using the provided set of 9 images (The images are in the folder Q3_stitching):

• Implement an image stitching algorithm using OpenCV.
• Detect and match keypoints between adjacent images.
• Estimate homographies between image pairs.

Question 4

Implement a stereo matching algorithm to generate a disparity map from the provided pair of stereo images (The images are in the folder Q4_disparity).

Question 5

Leaf Contour Extraction: Extract the contour of the green leaf in the image (The image in the folder Q5_contour), and discuss potential challenges and solutions for extracting contours from more complex backgrounds than the simple single-colored desktop.