READ ME This is the summarised version of the information relevant to this assignment. I kept a log of my progress as I completed the assignment, LOG.txt, which is more detailed but also more descriptive of my thought processes, in case that is relevant. KEY STEPS: - Looked up tutorial on blurry image detection - Re-familiarised myself with python I/O, string concatenation and openCV - Wrote code to take in the image as specified and return the variance of the laplacian of the image - Determined threshold value for an image to be blurry [see algorithm validation] - Applied this to the code. ALGORITHM VALIDATION: - Calculated mean and standard deviation of variance for each set of images - Applied the formula for 95% confidence interval to obtain threshold value of 155.76 to pass blur detection - Calculated the percentage of images in each set which passed this test Blurry data: 92% failed 8% passed Good data: 20% failed 80% passed ASSUMPTIONS MADE: - The key variable here is the threshold value, it is calculated with many similar images, but if this code is tested on a very different set of images, it could be much less accurate. CURRENT LIMITATIONS: - My theoretical knowledge of blur detection is low (see line 5 of LOG.txt) WHAT I WOULD DO WITH MORE TIME: - Learn more about blur detection - Try to apply other techniques to determine blurriness and combine them with the current algorithm to increase accuracy