- Evan Lee
- Theodore Koveos
- Ian Ng
- Rohan Maloney
- Andrew Timkov
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Copy
velocity.ipynb
into Google Colab (will not work on Jupyter Notebook) -
Setup a folder in your Google Drive with the path
Colab Notebooks/comp9517/
-
In this folder ensure there are the following files:
condensed_data.zip
- a zip folder with all of the velocity training and testing data foldersmask_rcnn_coco.h5
- pretrained weights which can be downloaded from herecar_mask_rcnn.h5
- pretrained weights fitted for the TuSimple dataset. This is optional as the model training will create this filemrcnn
folder - contains source code for Matterport's Mask R-CNN implementation with some editsevaluate
folder - contains source code for TuSimple's evaluation code with some edits
-
Run all code cells
-
Execution calls has the structure of
make_predictions(TESTING_DIR, detection_mode=["distance", "velocity"], show_img=True, show_all=False, my_evaluate=True, save=True, subset=[10, 23])
TESTING_DIR
- directory with testing datadetection_mode
-distance
to calculate distance,velocity
to calculate velocityshow_img
- set to True if you want the images to be shownshow_all
- set to True if you want False positives (un-annotated vehicles) to be shownmy_evaluate
- set to True if you want each input to be evaluated for accuracysave
- set to True if you want images to be saved to a filesubset
- define a list of clip number that you want to run predictions for. If this is set to[]
or ommitted, then predictions will be run for every clip inTESTING_DIR
- Open
lane_detection.ipynb
- choose which dataset you wish to run the code on in cell 2
- choose which clip from the dataset you would like to run the code on