https://github.com/cloudwiser/objectdetectionrpi
TensorFlow Lite object detection example for Raspberry Pi 2+
https://github.com/cloudwiser/objectdetectionrpi
cnn-for-visual-recognition object-detection raspberry-pi ssd-mobilenet tensorflow-examples tensorflow-lite
Last synced: 2 months ago
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TensorFlow Lite object detection example for Raspberry Pi 2+
- Host: GitHub
- URL: https://github.com/cloudwiser/objectdetectionrpi
- Owner: cloudwiser
- License: apache-2.0
- Created: 2019-04-22T22:43:04.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2019-04-22T22:43:14.000Z (over 6 years ago)
- Last Synced: 2025-05-21T21:40:59.618Z (5 months ago)
- Topics: cnn-for-visual-recognition, object-detection, raspberry-pi, ssd-mobilenet, tensorflow-examples, tensorflow-lite
- Language: C++
- Homepage:
- Size: 4.82 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
### TensorFlow Lite : label_image object detection on Raspberry Pi
This is a follow-on to compiling TensorFlow Lite for the Raspberry Pi as detailed at https://www.tensorflow.org/lite/guide/build_rpi
Having got the C++ `minimal` example code compiled, this extends the `label_image` example to demonstrate object detection and bounding box extraction using the SSD Mobilenet model trained against the COCO traning set
Copy the content in the `./example`, `./make` and `./bin` sub-directories of this repo to the equivalent paths in your local `./tensorflow/tensorflow/lite/...` directory structure (after backing up the original content)
Please note that `label_image` has not been tested with other quantized or non-quantized models and it expects 1 input tensor and 4 output tensors...so running it with other models may either fail this test and/or cause a runtime error during model load or invocation
If this does occur, it should be a case of adjusting the input and output tensor size checks and ensuring the correct handling of TfLite `float` or `uint8_t` output tensors
Once you have installed (and amended) the code, build it via...
```sh
$ cd ~/tensorflow/tensorflow/lite/tools/make/
$ ./build_rpi_armv7l_label_image.sh
```
and then run `label_image` as follows. Note: the `-o 1` option writes the output image (with any above-threshold bounding boxes rendered) to an out file of the format `out_`
```sh
$ cd ~/tensorflow/tensorflow/lite/tools/make/gen/rpi_armv7l/bin
$ ./label_image -i fruit.bmp -m ./models/coco_ssd_mobilenet_v1_1.0_quant_2018_06_29/detect.tflite -l ./models/coco_ssd_mobilenet_v1_1.0_quant_2018_06_29/labelmap.txt -v 1
$ ./label_image -i orange_banana_apple.bmp -m ./models/coco_ssd_mobilenet_v1_1.0_quant_2018_06_29/detect.tflite -l ./models/coco_ssd_mobilenet_v1_1.0_quant_2018_06_29/labelmap.txt -o 1
```
Run `label_image` with `-h` and/or read the source code for the complete set of command line options
This application is based on the`label_image` example code in the Tensorflow Lite repo at https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/examples/label_image
Sample BMP output showing objects detected above the threshold