vijaydwivedi75 / Custom-Mask-RCNN_TF

Using Custom Mask RCNN with TensorFlow Object Detection API to predict instances (masks) of targets in an image

Detecting Custom Objects with Mask RCNN using TensorFlow

Mask RCNN is used in Object Detection to predict instances (masks) of objects present in an image. Using TensorFlow Object Detection API and its pre-trained models, we can easily have our own object detection tool ready and setup in less time than you could ever expect.

Steps

Set up directories and TF Models

Create a root directory with a suitable name (say, ObjectDetection_MaskRCNN).

1. cd to the root directory: cd ./ObjectDetection_MaskRCNN
2. Download TensorFlow code base and models git clone https://github.com/tensorflow/models.git
3. After finishing download, cd to the models/research directory cd ./models/research/
4. Run these commands to set up environment and add your pwd to Python Path. Note: you have to run these commands whenver you start a terminal and want to use TF models.
   export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim
protoc object_detection/protos/*.proto --python_out=.

Creating custom data and preparation

For our custom task, we try to detect Red Stamps in attested documents. We download ~70 publicly accesible attested documents from Google Images (multiple sources) and use it for the training and validation purposes.

• Here is a snapshot of the data.
• Divide the images to JPEGImages for training and testImages for testing
• The dataset/ folder should be in the root directory ObjectDetection_MaskRCNN/ and should have the following directory setup.
  • Annotations
    • masks
    • xmls
    • < color masks files >
  • JPEGImages
    • < training image files >
  • testImages
    • < test image files >
  • label.pbtxt
  • train.record
• Use PixelAnnotationTool to create object masks. The instruction to mask an image can be seen here.
• You will get 3 outputs for every image while using the PixelAnnotationTool
  • IMAGE_mask.png
  • IMAGE_color_mask.png
  • IMAGE_watershed_mask.png

• Rename all IMAGE_watershed_mask.png to IMAGE_mask.png and place inside .dataset/Annotations/masks folder.

• Rename all IMAGE_color_mask.png to IMAGE.png and place inside ./dataset/Annotations folder

• Convert your data to TF Record format (to generate the train.record file)

  • Place the 'create_mask_rcnn_tf_record.py from the directory Imp_Files to models/research/object_detection/dataset_tools/ directory.
  • To edit class name as per you requirement, edit this python script at line 57.
  • From the directory ./models/research/ as present working directory, run the following command
    py object_detection/dataset_tools/create_mask_rcnn_tf_record.py \
--data_dir=/home/vijay/ObjectDetection_MaskRCNN/dataset \
--annotations_dir=Annotations \
--image_dir=JPEGImages \
--output_dir=/home/vijay/ObjectDetection_MaskRCNN/dataset/train.record \
--label_map_path=/home/vijay/ObjectDetection_MaskRCNN/dataset/label.pbtxt

• You will see a train.record-xxx file inside the dataset directory. Rename it to train.record

Training

• Create 3 folders inside the root directory IG, CP and pre_trained_models. The current project structure would look like.
  • ObjectDetection_MaskRCNN/
    • CP/
    • IG/
    • dataset/
    • pre_trained_models/
• Download any model starting with the name mask_rcnn from the TensorFlow Detection Model Zoo and extract the compressed file inside the pre_trained_models folder. For this project, we download mask_rcnn_inception_v2_coco
• Copy the file mask_rcnn_inception_v2_coco.config file from ./models/research/object_detection/samples/configs to the root directory ObjectDetection_MaskRCNN/ and edit PATH_TO_BE_CONFIGURED at 5 locations inside this config file. Refer to the config file in this repo for making changes. You can further explore other configurations and adjust as per your needs.
• Finally, run this command from ./models/research as present working directory to start training.
  py object_detection/legacy/train.py \
--train_dir=/home/vijay/ObjectDetection_MaskRCNN/CP \
--pipeline_config_path=/home/vijay/ObjectDetection_MaskRCNN/mask_rcnn_inception_v2_coco.config
• Keep training for a few hundred (or thousand steps) and/or till the loss comes near to 0.1 or 0.2 or something.

Export Inference Graph and Run for Test Images

• Check the step on which a .ckpt file has been saved. Let's say, the step is 230. Run the following command from ./models/research as present working directory to save frozen inference graph.
  py object_detection/export_inference_graph.py \
--input_type=image_tensor \
--pipeline_config_path=/home/vijay/ObjectDetection_MaskRCNN/mask_rcnn_inception_v2_coco.config \
--trained_checkpoint_prefix=/home/vijay/ObjectDetection_MaskRCNN/CP/model.ckpt-230 \
--output_directory=/home/vijay/ObjectDetection_MaskRCNN/IG
• Run the notebook mask_rcnn_eval.ipynb to test on the images you have kept aside in the testImages folder. Explore the notebook and make the changes as you understand.
• Please note to make changes in the visualization_utils.py file inside ./models/research/object_detection/utils folder to change the way you want to visualize the bounding boxes and object masks.