DETECTION
ASSIGNMENT
2024 Semester 1
1
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/3837**
●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
“CS3**_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
“CS3**_coin_detection.py” file to see if your code works for the main component!
●The main component of the assignment (“CS3**_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
‘CS3**_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) = 1**.
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:
6
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 **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 ‘CS3**_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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