Assignment #1

Due: Tue 10/03/17 11:59 PM

1. Resampling:

(6 pts.) Write code for zooming and shrinking an image using the nearest neighbor and bilinear interpolation. The input to your program is: (i) image, (ii) transformation parameters, and (iii) interpolation method.

• Starter code available in directory resize/
• resize/resample.py: One is required to edit the functions "nearest_neighbor" and "bilinear", you are welcome to add more function. Do not edit the function "resize"
• resize/interpolate.py: Write code for linear and bilinear interpolation in there respective function definitions, you are welcome to write new functions and call them from these functions
• Describe your method and findings in the report.md file
• This part of the assignment can be run using dip_hw1_resize.py (there is no need to edit this file)
• Usage: ./dip_hw1_resize.py -i image-name -fx scalex -fy scaley -m method
  • image-name: name of the image
  • scalex, scaley: scale to resize the image (eg. fx 0.5, fy 0.5 to make it half the original size)
  • method: "nearest_neightbor" or "bilinear"
• Example: ./dip_hw1_resize.py -i cell2.jpg -fx 0.75 -fy 0.75 -m nearest_neighbor
• Please make sure your code runs when you run the above command from prompt/Terminal
• Any output images or files must be saved to "output/" folder

2. Region Counting:

a. (5 pts.) Write a program to binarize a gray-level image based on the assumption that the image has a bimodal histogram. You are to implement the method to estimate the optimal threshold required to binarize the image. The threshold is to be computed using the average of the expectation of the two distributions. Your code should report both the binarized image and the optimal threshold value. Also assume that foreground objects are darker than background objects in the input gray-level image.

• Starter code available in directory region_analysis/
• region_analysis/binary_image.py:
  • compute_histogram: write your code to compute the histogram in this function, If you return a list it will automatically save the graph in output folder
  • find_optimal_threshold: Write your code to compute the optimal threshold using the expected values of the bimodal histograms
  • binarize: write your code to threshold the input image to create a binary image here. This function should return a binary image which will automatically be saved in output folder. For visualization one can use intensity value of 255 instead of 0 in the binay image and and 0 instead of 1 in binay images. That way the objects appear black over white background
• Describe your method and findings in the report.md file
• Any output images or files must be saved to "output/" folder

b. (7 Pts) Write a program to perform blobcoloring. The input to your code should be a binary image (0's, and 255's) and the output should be a list of objects or regions in the image.

• region_analysis/cell_counting.py:
  • blob_coloring: write your code for blob coloring here, takes as input a binary image and returns a list of objects or regions.
• Describe your method and findings in the report.md fil
• Any output images or files must be saved to "output/" folder

c. (5 Pts) Ignore cells smaller than 15 pixels in area and generate a report of the remaining cells (Cell Number, Area, Location)

• region_analysis/cell_counting.py:
  • compute_statistics: write your code for computing the statistics of each object/region, i.e area and location(centroid) here. Print out the statistics to stdout (using print function print one row for each region).
  • Example: region number, area and centroid (Region: 1, Area: 1000, Centroid: (10,22))
  • mark_regions_image: write your code to create a final cell labeled image. The final image should include an astrix representing the centroid of each cell and two numbers, one representing its Cell Number and another its area. Please see sample output below.
• Usage: ./dip_hw1_region_analysis.py -i image-name
  • image-name: name of the image
• example: ./dip_hw1_region_analysis.py -i cell2.jpg
• Please make sure your code runs when you run the above command from prompt
• Describe your method and findings in the report.md file
• Any output images or files must be saved to "output/" folder

3. (2 Pts.) Describe your method and report you findings in report.md for each problem of the assignemnt.

Two images are provided for testing: cells.png and cell2.jpg

PS. Files not to be changed: requirements.txt and .circleci directory

If you do not like the structure, you are welcome to change the over all code, under two stipulations:

1. the first part has to run using command

./dip_hw1_resize.py -i image-name -fx scalex -fy scaley -m method

and the second part using

./dip_hw1_region_analysis.py -i image-name

2. Any output file or image should be written to output/ folder

The TA will only be able to see your results if these two conditions are met

1. Resampling - 6 Pts.

2. Region Counting - 17 Pts.

3. Report - 2 Pts

   Total - 25 Pts.