DETECTION 
ASSIGNMENT
2024 Semester 1
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Version 2.2Deadline: 3rd June 2024, 23:59pm
●In this assignment, you will write a Python code pipeline to automatically detect all the coins in the 
given images. This is an individual assignment, so every student has to submit this assignment! This 
assignment is worth 15 marks.
●We have provided you with 6 images for testing your pipeline (you can find the images in the 
‘Images/easy’ folder).
○Your pipeline should be able to detect all the coins in the image labelled with easy-level. This will 
reward you with up to 10 marks.
○For extension (up to 5 marks), try images labelled as hard-level images in the “Images/hard” folder.
○Write a short reflective report about your extension. (Using Latex/Word)
●To output the images shown on the slides for checking, you may use the following code:
fig, axs = pyplot.subplots(1, 1)
# replace image with your image that you want to output
axs.imshow(image, cmap='gray')
pyplot.axis('off')
pyplot.tight_layout()
pyplot.show()
2SUBMISSION
Please upload your submission as a zipped file of the assignment folder to the UoA 
Assignment Dropbox by following this link: 
https://canvas.auckland.ac.nz/courses/103807/assignments/383790
●Don’t put any virtual environment (venv) folders into this zip file, it just adds to the size, and we 
will have our own testing environment.
●Your code for executing the main coin detection algorithm has to be located in the provided 
“CS373_coin_detection.py” file!
●You can either put all of your code into that file, or use a modular structure with additional files 
(that, of course, have to be submitted in the zip file). However, we will only execute the 
“CS373_coin_detection.py” file to see if your code works for the main component!
●The main component of the assignment (“CS373_coin_detection.py”) must not use any non-built-in 
Python packages (e.g., PIL, OpenCV, NumPy, etc.) except for Matplotlib. Ensure your IDE hasn’t 
added any of these packages to your imports.
●For the extensions, please create a new Python source file called 
‘CS373_coin_detection_extension.py’
; this will ensure your extension part doesn’t mix up with the 
main component of the assignment. Remember, your algorithm has to pass the main component 
first!
●Including a short PDF report about your extension.
●Important: Use a lab computer to test if your code works on Windows on a different machine 
(There are over 300 students, we cannot debug code for you if it doesn’t work!)
3easy_case_1 final output
easy_case_2 final output
easy_case_4 final output easy_case_6 final outputASSIGNMENT STEPS
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1. Convert to greyscale and normalize
I. Convert to grey scale image: read input image using the ‘readRGBImageToSeparatePixelArrays()’ helper 
function. Convert the RGB image to greyscale (use RGB channel ratio 0.3 x red, 0.6 x green, 0.1 x blue), 
and round the pixel values to the nearest integer value.
II. Contrast Stretching: stretch the values between 0 to 255 (using the 5-95 percentile strategy) as described 
on lecture slides ImagesAndHistograms, p20-68). Do not round your 5% and 95% cumulative histogram 
values. Your output for this step should be the same as the image shown on Fig. 2.
Hint 1: see lecture slides ImagesAndHistograms and Coderunner Programming quiz in Week 10.
Hint 2: for our example image (Fig. 1), the 5_percentile (f_min) = 86 and the 95_percentile (f_max) = 173.
Fig. 1: input Fig. 2: step 1 output
We will use this image 
(‘easy_case_1’) as an 
example on this slides2. Edge Detection
I. Apply a 3x3 Scharr filter in horizontal (x) and vertical (y) directions independently to get the edge maps (see 
Fig. 3 and Fig. 4), you should store the computed value for each individual pixel as Python float.
II. Take the absolute value of the sum between horizontal (x) and vertical (y) direction edge maps (see Hint 4). You 
do not need to round the numbers. The output for this step should be the same as the image shown on Fig. 5.
Hint 1: see lecture slides on edge detection and Coderunner Programming quiz in Week 11.
Hint 2: please use the 3x3 Scharr filter shown below for this assignment:
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Hint 4: you should use the BorderIgnore option and set border 
pixels to zero in output, as stated on the slide Filtering, p13.
Hint 5: for computing the edge strength, you may use the 
following equation:
gm
(x, y) = |gx
(x, y)| + |gy
(x, y)|
Absolute grey level 
gradient on the 
horizontal direction
Absolute grey level 
gradient on the vertical 
direction
Edge map on 
horizontal and 
vertical
Fig. 5: Step 2 
output (gm
)
Fig. 4: Edge map 
(gy
) on vertical 
direction
Fig. 3: Edge map 
(gx
) on horizontal 
direction7
3. Image Blurring
Apply 5x5 mean filter(s) to image. Your output for this step should be the same as the image shown on 
Fig. 7.
Hint 1: do not round your output values.
Hint 2: after computing the mean filter for one 5x5 window, you should take the absolute value of your 
result before moving to the next window.
Hint 3: you should use the BorderIgnore option and set border pixels to zero in output, as stated on the 
slide Filtering, p13.
Hint 3: try applying the filter three times to the image sequentially.
Hint 4: see lecture slides on image filtering and Coderunner Programming quiz in Week 11.
Fig. 7: Step 3 output Fig. 6: Grayscale histogram for output from step 38
4. Threshold the Image
Perform a simple thresholding operation to segment the coin(s) from the black background. After 
performing this step, you should have a binary image (see Fig. 10).
Hint 1: 22 would be a reasonable value for thresholding for our example image, set any pixel value 
smaller than 22 to 0; this represents your background (region 1) in the image, and set any pixel value 
bigger or equal to 22 to 255; which represents your foreground (region 2) – the coin.
Hint 2: see lecture slides on image segmentation (p7) and see Programming quiz on Coderunner on 
Week 10.
Fig. 9: Step 3 output Fig. 10: Step 4 output Fig. 8: Grayscale histogram for output from step 39
5. Erosion and Dilation
Perform several dilation steps followed by several erosion steps. You may need to repeat the dilation 
and erosion steps multiple times. Your output for this step should be the same as the image shown on Fig. 
11.
Hint 1: use circular 5x5 kernel, see Fig. 12 for the kernel details.
Hint 2: the filtering process has to access pixels that are outside the input image. So, please use the 
BoundaryZeroPadding option, see lecture slides Filtering, p13.
Hint 2: try to perform dilation 3-4 times first, and then erosion 3-4 times. You may need to try a couple 
of times to get the desired output.
Hint 3: see lecture slides on image morphology and Coderunner Programming quiz in Week 12.
Fig. 11: Step 5 output
Fig. 12: Circular 5x5 kernel for 
dilation and erosion10
6. Connected Component Analysis
Perform a connected component analysis to find all connected components. Your output for this 
step should be the same as the image shown on Fig. 13.
After erosion and dilation, you may find there are still some holes in the binary image. That is 
fine, as long as it is one connected component.
Hint 1: see lecture slides on Segmentation_II, p4-6, and Coderunner Programming quiz in Week 
12.
Fig. 13: Step 6 outputWe will provide code for drawing the bounding box(es) 
in the image, so please store all the bounding box 
locations in a Python list called ‘bounding_box_list’, so 
our program can loop through all the bounding boxes 
and draw them on the output image.
Below is an example of the ‘bounding_box_list’ for our 
example image on the right.
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7. Draw Bounding Box
Extract the bounding box(es) around all regions that your pipeline has found by looping over 
the image and looking for the minimum and maximum x and y coordinates of the pixels in the 
previously determined connected components. Your output for this step should be the same as 
the image shown on Fig. 14.
Make sure you record the bounding box locations for each of the connected components your 
pipeline has found.
Bounding_box_list=[[74, 68, 312, 303]]
A list of list
Bounding_box_min_x
Bounding_box_min_y Bounding_box_max_x
Bounding_box_max_y
Fig. 14: Step 7 outputInput
Drawing 
Bounding Box
Color to Gray Scale 
and Normalize
Edge 
Detection
Image 
Blurring Thresholding
Dilation and 
Erosion
Connected 
Component Analysis
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Coin Detection Full Pipelineeasy_case_1 final output easy_case_2 final output
easy_case_4 final output easy_case_6 final outputEXTENSION
For this extension (worth 5 marks), you are expected to alter some parts of the pipeline.
●Using Laplacian filter for image edge detection
○Please use the Laplacian filter kernel on the right (see Fig. 15).
○You may need to change subsequent steps as well, if you decide to
use Laplacian filter.
●Output number of coins your pipeline has detected.
●Testing your pipeline on the hard-level images we provided.
○For some hard-level images, you may need to look at the size of the connected components to decide which 
component is the coin.
●Identify the type of coins (whether it is a 1-dollar coin, 50-cent coin, etc.). 
○Since different type of coins have different sizes, you may want to compute the area of the bounding box in 
the image to identify them.
●etc.
Submissions that make the most impressive contributions will get full marks. Please create a new 
Python source file called ‘CS373_coin_detection_extension.py’ for your extension part, and include a 
short PDF report about your extension. Try to be creative!
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Fig. 15: Laplacian filter kernel

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