40 lines
2.5 KiB
Markdown
40 lines
2.5 KiB
Markdown
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---
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title: "Week 3"
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date: 2022-02-20T12:46:55Z
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---
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# Filming footage
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At the start of the week I went into town to film some practise footage to work with later (up until this point I had been experimenting with footage limited by my bedroom walls). I took some basic vertical and horizontal footage of the town - no nature or breach footage yet.
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# Gaining more useful information
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I used the footage I had recorded at the start of the week and revised my "filesize" code. I made a new function `order_frames_by_filesize()` which orders the frames by the filesize and prints the name of size of each frame in order.
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```python {linenos=table}
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def order_frames_by_filesize():
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"""Order the frames by filesize and print the filenames and their sizes"""
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frames = os.listdir(outputfolder)
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# sort the frames by their filesize
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frames = sorted(frames,
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key = lambda x: os.stat(os.path.join(outputfolder, x)).st_size, reverse = True)
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# print every frame and it's size in a human readable format
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for frame in frames:
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filesize = os.stat(os.path.join(outputfolder, frame)).st_size
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if filesize > 1024:
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filesize = filesize / 1024
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print(frame + ": " + str(filesize) + " KB")
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else:
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print(frame + ": " + str(filesize))
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```
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# Analysing datasets
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There's been a few datasets that I've previously looked at which could be useful to use for this project. I wasn't sure how they would perform with the data I was expecting to use. AVA[^1] was trained on data that had been photographed under ideal conditions by profesionals. This won't reflect the frames extracted by the footage in this project's use case. I wasn't sure if this distinction was significant enough to effect the results of a trained model. I had previously found a project[^2] which had models pretrained using AVA[^1] I could use to predict an aesthetic value from images I provided.
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[^1]: N. Murray, L. Marchesotti and F. Perronnin, "AVA: A large-scale database for aesthetic visual analysis," 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012, pp. 2408-2415, doi: 10.1109/CVPR.2012.6247954.
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[^2]:Image Quality Assessment: https://github.com/idealo/image-quality-assessment - Idealo
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# 1:1 Weekly meeting
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- We discussed that during week 4 I should be looking at implementing CNNs as training might take a while and therefore should be a priority.
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- Look at what other people are doing with aesthetic analysis to get an idea on how the code works.
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- Attempt to get a basic CNN working.
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