90 lines
1.7 KiB
Markdown
90 lines
1.7 KiB
Markdown
# Pyrometry image processing
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## Interface Screenshots
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| Input View | Results |
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| --- | --- |
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## Using the web version
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1. Go to [pyro.turtlebasket.ml](https://pyro.turtlebasket.ml).
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2. Select an input image.
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3. Enter your DSLR camera settings.
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4. Click "Generate Heatmap".
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## Using the local (batch) version
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Create a new config file:
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```
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cp config.example.yaml config.yaml
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```
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...then edit the values to match your DSLR camera settings. Standard config syntax is:
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```yaml
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---
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# camera settings
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iso: 64
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i-darkcurrent: 7.7
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exposure-time: 0.5
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f-stop: 2.4
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# pyrometry settings
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min-temp: 600
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max-temp: 1200
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scaling-factor: 0.55
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# output settings
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smoothing-radius: 2
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```
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Install dependencies:
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```
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pipenv install
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```
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...then load images into `images-input`. Run:
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```
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pipenv run python3 batch-process.py
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```
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...and find outputs in `images-output`.
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## Developing the web frontend
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To serve in production:
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```
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gunicorn flask_frontend:app
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```
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To autoreload on source file changes:
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```
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gunicorn flask_frontend:app --reload
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```
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## Temperature maps
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**Grayscale pyrometry:** currently basic; uses grayscale opencv import, then just applies a jet filter. Doesn't yet copy the full impl in the paper.
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**Ratio pyrometry:** pretty damn close to what's in the paper but it's very broken atm
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**Test image:**
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**Ratio pyrometry result (with convolutional smoothing):**
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According to general researcher consensus, ratio pyrometry is supposed to be more accurate.
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**Grayscale pyrometry result:**
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