aws-samples / aws-autonomous-driving-data-lake-image-extraction-pipeline-from-ros-bagfiles
This workshop will familiarize you with some of the key steps towards building an autonomous driving data lake and extracting images from ROS bag files. Using these images, you will be able label them using SageMaker Ground Truth and fine-tuning a Machine Learning Model to detect cars.
Summary
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| Main Code: 20,275 LOC (112 files) = PY (99%) + LAUNCH (<1%) + YAML (<1%) Secondary code: Test: 0 LOC (0); Generated: 0 LOC (0); Build & Deploy: 36 LOC (3); Other: 1,130 LOC (5); |
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| Duplication: 12% | |||
| File Size: 24% long (>1000 LOC), 29% short (<= 200 LOC) | |||
| Unit Size: 11% long (>100 LOC), 43% short (<= 10 LOC) | |||
| Conditional Complexity: 11% complex (McCabe index > 50), 41% simple (McCabe index <= 5) | |||
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Logical Component Decomposition: primary (12 components) | ||
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6 months old
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0% of code updated more than 50 times Also see temporal dependencies for files frequently changed in same commits. |
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| Goals: Keep the system simple and easy to change (4) | |||
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Features of interest:
TODOs
34 files |
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Latest commit date: 2021-09-29
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