Summary

(Below is from a data in KITTI 3D Object Detection Dataset)

Run demo

1. Requirements

2. Clone this repo

git clone https://github.com/KleinYuan/tf-3d-object-detection.git

2. Install Dependencies

# Simply run this in this project root folder
cd tf-3d-object-detection
pip install -r requirements.txt

If you meet error install say opencv, do conda install opencv if you use Anaconda. Otherwise, dude, build from source and let's call it a day.

3. Pick a 2D Object Detection Model

In here we support 5 different 2D Detection models:

Model name	Speed	COCO mAP	Outputs
ssd_mobilenet_v1_coco	fast	21	Boxes
ssd_inception_v2_coco	fast	24	Boxes
rfcn_resnet101_coco	medium	30	Boxes
faster_rcnn_resnet101_coco	medium	32	Boxes
faster_rcnn_inception_resnet_v2_atrous_coco	slow	37	Boxes

Pick one of those that makes you feel good, and find it in the list -- _DETECTOR_2D_OPTIONS in configs/configs, then replace it with the value of _DETECTOR_2D_MODEL_NAME.

And by default, I use ssd_mobilenet_v1_coco_11_06_2017 due to it's fast.

4. Download Test Data

Due to the license of KITTI is waaaaaaaaaaay to long to read, I will just tell ya how to do it instead of running a risk to attach here with some data from KITTI, which when I downloaded it I clicked some button to have agreed on something that's TLTR.

# Step1 Go to http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d
# Step2 Do "Download left color images of object data set (12 GB)"
# Step3 Do "Download Velodyne point clouds, if you want to use laser information (29 GB)"
# Step4 Do "Download camera calibration matrices of object data set (16 MB)"
# Step5 Unzip all those three zip files and you will find ~7000ish training datasets, each pair include velodyne, image and calibration
# Step6 Pick one of them, copy and paste it under example_data folder, and name the image to 1.png, and velodyne file to 1.bin
# Step7 Open calibration file, find corresponding item and replace it with CALIB_PARAM in configs/configs.py, by default, it's from training/000000.txt
# Step8 Really sorry to let you go thru last 7 Steps and I think I may come up with a better idea to do it with one button

5. Download Pretrained Model

As you may see, this project combined 2 Deep Neural Networks together. Therefore, yes you need to download two pre-trained model.

2D Object Detector Model	3D Object Detector Model
Download Link	Download v1 and v2 is not supported yet (originally from here)

Then, unzip them and put them under pretrained folder. Also, renamed the checkpoint.txt file to checkpoint even though it's useless and you cannot freeze it :unhappy: .

The folder will look like this:

--tf-3d-object-detection
  |-- pretrained
      |--log_v1
          |-- checkpoint (originally named checkpoint.txt)
          |-- log_train.txt
          |-- model.ckpt.data-00000-of-00001
          |-- model.ckpt
          |-- model.ckpt.meta
      |-- ssd_mobilenet_v1_coco_11_06_2017 (or other names if you decide to use different ones)
          |-- frozen_inference_graph.pb
          |-- graph.pbtxt
          |-- model.ckpt-0.data-00000-of-00001
          |-- model.ckpt-0.index
          |-- model.ckpt-0.meta

You may realize this fact thus 3D object detection model is not really frozenable one.

(Hopefully they can disclose the original tensorflow ops for v1 so that we can remove both tf.py_func and freeze the model)

6. Run Demo

# If you use Pycharm, just click the green run button
# If not, navigate to root folder of this repo and run:
python apps/demo.py

# If it complains, yo, I cannot find some modules, yo, do:
export PYTHONPATH='.'
python apps/demo.py

# And if you still have the issue, man, you must really mess up with your python env.
# I don't wanan help you on that in this readme and don't create an issue for that as well.
# You shall either try using anaconda or find a python knower to help you with it
# Or, just do STACKOVERFLOW like other pals do

Then you should be able to see 3 Windows pop up in order, and don't forget to Press any key to continue as the terminal mention.

References

Project Template: AIInAi/tensorflow-project-template
FPNet Code: Dark-Rinnegan/frustum-pointnets

KleinYuan / tf-3d-object-detection