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Use the Tensorflow Object Detection API with Tensorflow 2

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TannerGilbert/Tensorflow-Object-Detection-with-Tensorflow-2.0

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Duckies detection

In this repository you can find some examples on how to use the Tensorflow OD API with Tensorflow 2. For more information check out my articles:

Installation

You can install the TensorFlow Object Detection API either with Python Package Installer (pip) orDocker, an open-source platform for deploying and managing containerized applications.

First clone the master branch of the Tensorflow Models repository:

git clone https://github.com/tensorflow/models.git

Docker Installation

# From the root of the git repository (inside the models directory)docker build -f research/object_detection/dockerfiles/tf2/Dockerfile -t od .docker run -it od

Python Package Installation

cd models/research# Compile protos.protoc object_detection/protos/*.proto --python_out=.# Install TensorFlow Object Detection API.cp object_detection/packages/tf2/setup.py .python -m pip install .

Note: The *.proto designating all files does not work protobuf version 3.5 and higher. If you are using version 3.5, you have to go through each file individually. To make this easier, I created a python script that loops through a directory and converts all proto files one at a time.

importosimportsysargs=sys.argvdirectory=args[1]protoc_path=args[2]forfileinos.listdir(directory):iffile.endswith(".proto"):os.system(protoc_path+" "+directory+"/"+file+" --python_out=.")
python use_protobuf.py <path to directory> <path to protoc file>

To test the installation run:

# Test the installation.python object_detection/builders/model_builder_tf2_test.py

If everything installed correctly you should see something like:

...[       OK ] ModelBuilderTF2Test.test_create_ssd_models_from_config[ RUN      ] ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update[       OK ] ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update[ RUN      ] ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold[       OK ] ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold[ RUN      ] ModelBuilderTF2Test.test_invalid_model_config_proto[       OK ] ModelBuilderTF2Test.test_invalid_model_config_proto[ RUN      ] ModelBuilderTF2Test.test_invalid_second_stage_batch_size[       OK ] ModelBuilderTF2Test.test_invalid_second_stage_batch_size[ RUN      ] ModelBuilderTF2Test.test_session[  SKIPPED ] ModelBuilderTF2Test.test_session[ RUN      ] ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor[       OK ] ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor[ RUN      ] ModelBuilderTF2Test.test_unknown_meta_architecture[       OK ] ModelBuilderTF2Test.test_unknown_meta_architecture[ RUN      ] ModelBuilderTF2Test.test_unknown_ssd_feature_extractor[       OK ] ModelBuilderTF2Test.test_unknown_ssd_feature_extractor----------------------------------------------------------------------Ran 20 tests in 91.767sOK (skipped=1)

Running a pre-trained model

Theobject_detection_tutorial.ipynb notebook walks you through the process of using a pre-trained model to detect objects in an image. To try it out, I recommend to run it insideGoogle Colab.

Person and Kites detection

Modify code to run on a video stream

The above example can be easily rewritten to work with video streams by replacing the show_inference method with:

importcv2cap=cv2.VideoCapture(0)# or cap = cv2.VideoCapture("<video-path>")defrun_inference(model,cap):whilecap.isOpened():ret,image_np=cap.read()# Actual detection.output_dict=run_inference_for_single_image(model,image_np)# Visualization of the results of a detection.vis_util.visualize_boxes_and_labels_on_image_array(image_np,output_dict['detection_boxes'],output_dict['detection_classes'],output_dict['detection_scores'],category_index,instance_masks=output_dict.get('detection_masks_reframed',None),use_normalized_coordinates=True,line_thickness=8)cv2.imshow('object_detection',cv2.resize(image_np, (800,600)))ifcv2.waitKey(25)&0xFF==ord('q'):cap.release()cv2.destroyAllWindows()breakrun_inference(detection_model,cap)

Live Object Detection Example

You can find the code as anotebook orpython file.

Few-shot learning

The new release also comes withanother notebook showing us how to fine-tune a RetinaNet pre-trained model to detect rubber duckies with only 5 images and <5 minutes of training time in Google Colab.

Duckies detection

Author

Gilbert Tanner

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