google / ftc-object-detection Goto Github PK

Full url to that README.md: https://github.com/google/ftc-object-detection/tree/master/training.

bazel run -c opt tensorflow/contrib/lite/toco:toco --
--input_file=[PATH TO THIS REPO]/training/models/sample_mobilenet_v1_0.5_ssd_quantized/tflite/tflite_graph.pb
--output_file=[PATH TO THIS REPO]/training/models/sample_mobilenet_v1_0.5_ssd_quantized/tflite/detect.tflite
--input_shapes=1,300,300,3
--input_arrays='normalized_input_image_tenor'
--
ouptut_arrays='TFLite_Detection_PostProcess','TFLite_Detection_PostProcess:1','TFLite_Detection_PostProcess:2','TFLite_Detection_PostProcess:3'
--inference_type=QUANTIZED_UINT8
--mean_values=128
--std_values=128
--change_concat_input_ranges=false
--allow_custom_ops

Look in the middle.

tenor should be tensor.
ouptut should be output.

In documentation file https://github.com/google/ftc-object-detection/tree/master/training/README.md, toward the bottom of "Checkpoint Conversion" the bazel run path is given as

tensorflow/contrib/lite/toco:toco

But in https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib there is no lite directory. I think the bazel run path should omit the "contrib", and be

tensorflow/lite/toco:toco

This issue was automatically created by Allstar.

Security Policy Violation
Project is out of compliance with Binary Artifacts policy: binaries present in source code

Rule Description
Binary Artifacts are an increased security risk in your repository. Binary artifacts cannot be reviewed, allowing the introduction of possibly obsolete or maliciously subverted executables. For more information see the Security Scorecards Documentation for Binary Artifacts.

Remediation Steps
To remediate, remove the generated executable artifacts from the repository.

Artifacts Found

• TFObjectDetector/tfod/libs/common-1.1.0.jar
• TFObjectDetector/tfod/libs/support-annotations-27.1.1.jar

Additional Information
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I am using a Windows 10 machine with Ubuntu 18.04 LTS installed as a Linux subsystem on my windows machine.

I am using VcXsrv as the X Windows server on my laptop so I can view XWindows applications that are run on my Ubuntu subsystem.

This works well, but I had a problem with the window being too small to use when I launched labeler.py. I noticed that in the labeler.py script, the window scale is specified as ".75". I compared this to the scale specified in find_bb.py (which runs properly on my VcXsrv server). In the script find_bb.py it specifies the scale as "0.5".

I modified the labeler.py script (see below) and it works well now. The window is large and visible. It appears that for scale values < 1, the value must include a leading "0" in the script:

WINDOW = "Tracking"

# On VcXsrv servers, it seems the scale has to be specified with a leading zero for a value less than 1.
# Otherwise, the displayed window is very small.
#WINDOW_SCALE = .75
WINDOW_SCALE = 0.75

CACHE_SIZE = 150 # 5 seconds worth of frames

I am not sure if this is a problem unique to VcXsrv, but when I ran labeler.py on a Windows machine (natively, using Python3 for windows) I did not encounter this problem.

I am relatively new to using TFObjectDetector and my team found it better for the phone to be laid on its side rather than upright. I have looked through the source code and can't seem to find anyway to change the orientation from portrait to landscape. Any help would be appreciated.

"Installation", step 3.

./gradlew :tfod:asssemble
should be
./gradlew :tfod:assemble

The training tutorial https://github.com/google/ftc-object-detection/tree/master/training says. "You can now take the .record files you generated and use them in the same training pipeline you were using earlier in the tutorials. As before, you'll almost certainly want to fine tune an existing model..." I'm not quite sure what are those earlier tutorials. The only tutorial mentioned on the training tutorial is a Medium one for training on the cloud.

I have a good video of poker chips and thumb drives, good records, and a pre-trained model -- the one you supply for Gold and Silver Minerals. My aim is to convert those poker chip and thumb drive records into checkpoints using your Gold and Silver Mineral model, so my phone can recognize poker chips and thumb drives. How do I do this?

I can make a good model using Tensorflow for Poets, but that model does not work with ftc_app version 4.3 ConceptTensorFlowObjectDetection.

Directive

aaptOptions {
noCompress "tflite"
}

added to top of TeamCode's build.gradle provokes error message on project sync:

Could not find method aaptOptions() for arguments [build_e5d9rdb1nwf42enje2of40kq2$_run_closure1@3e6d5bf3] on project ':TeamCode' of type org.gradle.api.Project.