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synced 2026-01-22 20:18:30 +03:00
Remove unused file in docker image and update model names
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@ -82,20 +82,21 @@ RUN cd /usr/local/src/ \
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RUN wget -q -O edgetpu_api.tar.gz --no-check-certificate http://storage.googleapis.com/cloud-iot-edge-pretrained-models/edgetpu_api.tar.gz
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RUN tar xzf edgetpu_api.tar.gz \
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&& rm -rf edgetpu_api.tar.gz \
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&& cd python-tflite-source \
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&& cp -p libedgetpu/libedgetpu_x86_64.so /lib/x86_64-linux-gnu/libedgetpu.so \
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&& cp edgetpu/swig/compiled_so/_edgetpu_cpp_wrapper_x86_64.so edgetpu/swig/_edgetpu_cpp_wrapper.so \
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&& cp edgetpu/swig/compiled_so/edgetpu_cpp_wrapper.py edgetpu/swig/ \
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&& python3 setup.py develop --user
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# symlink the model and labels
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RUN ln -s /python-tflite-source/edgetpu/test_data/mobilenet_ssd_v2_coco_quant_postprocess_edgetpu.tflite /mobilenet_ssd_v2_coco.tflite
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RUN ln -s /python-tflite-source/edgetpu/test_data/coco_labels.txt /coco_labels.txt
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# Minimize image size
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RUN (apt-get autoremove -y; \
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apt-get autoclean -y)
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# symlink the model and labels
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RUN ln -s /python-tflite-source/edgetpu/test_data/mobilenet_ssd_v2_coco_quant_postprocess_edgetpu.tflite /frozen_inference_graph.pb
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RUN ln -s /python-tflite-source/edgetpu/test_data/coco_labels.txt /label_map.pbtext
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# Set TF object detection available
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ENV PYTHONPATH "$PYTHONPATH:/usr/local/lib/python3.5/dist-packages/tensorflow/models/research:/usr/local/lib/python3.5/dist-packages/tensorflow/models/research/slim"
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RUN cd /usr/local/lib/python3.5/dist-packages/tensorflow/models/research && protoc object_detection/protos/*.proto --python_out=.
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@ -6,11 +6,6 @@ import numpy as np
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from edgetpu.detection.engine import DetectionEngine
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from . util import tonumpyarray
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# Path to frozen detection graph. This is the actual model that is used for the object detection.
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PATH_TO_CKPT = '/frozen_inference_graph.pb'
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# List of the strings that is used to add correct label for each box.
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PATH_TO_LABELS = '/label_map.pbtext'
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# Function to read labels from text files.
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def ReadLabelFile(file_path):
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with open(file_path, 'r') as f:
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@ -28,9 +23,10 @@ class PreppedQueueProcessor(threading.Thread):
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self.cameras = cameras
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self.prepped_frame_queue = prepped_frame_queue
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# Load the edgetpu engine and labels
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self.engine = DetectionEngine(PATH_TO_CKPT)
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self.labels = ReadLabelFile(PATH_TO_LABELS)
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# Load the edgetpu engine with the model used for object detection.
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self.engine = DetectionEngine('/mobilenet_ssd_v2_coco.tflite')
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# Load the strings used to add the correct label for each box.
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self.labels = ReadLabelFile('/coco_labels.txt')
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def run(self):
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# process queue...
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