{"id":18801773,"url":"https://github.com/cloudwiser/objectdetectionrpi","last_synced_at":"2026-04-10T22:46:57.036Z","repository":{"id":83242599,"uuid":"182878975","full_name":"cloudwiser/ObjectDetectionRPi","owner":"cloudwiser","description":"TensorFlow Lite object detection example for Raspberry Pi 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align=\"left\" width=\"120\" height=\"120\" src=\"https://avatars0.githubusercontent.com/u/15658638?s=200\u0026v=4\"\u003e\n\n### TensorFlow Lite : label_image object detection on Raspberry Pi\n\nThis is a follow-on to compiling TensorFlow Lite for the Raspberry Pi as detailed at https://www.tensorflow.org/lite/guide/build_rpi\n\nHaving 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\n\nCopy 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)\n\nPlease 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\n\nIf 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\n\nOnce you have installed (and amended) the code, build it via...\n\n```sh\n$ cd ~/tensorflow/tensorflow/lite/tools/make/\n\n$ ./build_rpi_armv7l_label_image.sh\n```\n\nand 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_\u003cinput_filename\u003e` \n\n```sh\n$ cd ~/tensorflow/tensorflow/lite/tools/make/gen/rpi_armv7l/bin\n\n$ ./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\n\n$ ./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\n```\n\nRun `label_image` with `-h` and/or read the source code for the complete set of command line options\n\nThis 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 \n \n\n\u003cp align=\"center\"\u003e\n  \u003cimg height=\"186px\" width=\"300px\" border=\"2\" src=\"./assets/out_orange_banana_apple.bmp\"/\u003e\n\u003c/p\u003e\n\u003cp align=\"center\"\u003eSample BMP output showing objects detected above the threshold\u003cp align=\"center\"\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcloudwiser%2Fobjectdetectionrpi","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcloudwiser%2Fobjectdetectionrpi","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcloudwiser%2Fobjectdetectionrpi/lists"}