chowdr

Food recognition and volume estimation to produce caloric data.

chowdr will use a trained model on the base Faster R-CNN TensorFlow model to detect different food objects. Using this data, it estimates the volume of the food from the top and side views, then calculates the caloric amount of the food.

See the Tutorial for an end-to-end tutorial of training, testing, evaluating, and running the caloric estimator.

Set Up

1. Clone the chowdr repository.

2. Clone the ECUST food dataset into the directory above chowdr.

git clone https://github.com/Liang-yc/ECUSTFD-resized-.git ../

Set Up GCP

This guide assumes you have a GCP account created and is made specifically for the chowdr project.

1. python3 -m pip install tensorflow_cloud and install any other relevant dependencies.

2. Create a GCP Project called chowder-bucket using this guide.

3. Set your project as the active project, using the ID found here.

Optionally, using:

export PROJECT_ID=<your-project-id>
gcloud config set project $PROJECT_ID

4. Enable AI Platform Services for your project.

5. Enable Cloud Build API for your project.

6. Create a service account using this guide.

7. Generate an authentication key for the service account. Download it and create the key environment variable.

export GOOGLE_APPLICATION_CREDENTIALS=~/<key-name>.json

Now you are ready to train/test using GCP. Look at the scripts below to help you or follow the tutorial for training, evaluating, testing and using chowdr.

Tutorial

Prerequisite: Finished Set Up section from above.

1. k-fold Cross Validation Pre-processing

   Assuming you have followed the initial set up for cloning the ECUSTFD repository, we will now pre-process the images with cross-validation k-folds. This script will create folders in /workspace/ for training and testing on k folds. Run all scripts in the main directory of the chowdr repository. Let's do a 3-fold cross validation!

   python3 scripts/preprocessing/kfold_partition_dataset.py -i ../ECUSTFD-resized-/JPEGImages/ -o workspace/ -k 3 -x

2. Generating TensorFlow Records

   Since we also generated the XML files for the images using the above script's -x option, we will convert these into TFRecords that can be piped into the Tensorflow model. The training and testing XMLs will be located in the folders created for each fold (ie. /workspace/train_0fold and /workspace/test_0fold).

   For the sake of this tutorial, we will focus on fold 0, but feel free to train and test similarly on other folds.

   Before we create TFRecords, we need a label map. We can use the same one in the training_demo.

   cp workspace/annotations/label_map.pbtxt workspace/train_0fold/annotations/label_map.pbtxt

   Now that we have a label map, we can use it to generate the records required for training.

   python scripts/preprocessing/generate_tfrecord.py -x workspace/train_0fold -l workspace/train_0fold/annotations/label_map.pbtxt -o workspace/train_0fold/annotations/train0.record

3. Training a SSD Model

   We have already added a pre-trained model with the chowdr repository. We will use this as the foundation for our custom object detection model.

   mkdir workspace/train_0fold/models
   mkdir workspace/train_0fold/models/ssd
   cp pre-trained-models/ssd-mobilenet-v2-fpnlite-640/pipeline.config workspace/train_0fold/models/ssd/pipeline.config

   Within the workspace/train_0fold/models/ssd directory, we will copy the pipeline.config from the training_demo directory and change certain lines.

     # ... At the bottom of the file
     fine_tune_checkpoint: "pre-trained-models/ssd-mobilenet-v2-fpnlite-640/checkpoint/ckpt-0" # Path to pre-trained model checkpoint
     # ...
   }
   train_input_reader {
     label_map_path: "workspace/train_0fold/annotations/label_map.pbtxt"   # Path to label map
     tf_record_input_reader {
       input_path: "workspace/train_0fold/annotations/train0.record"       # Path to fold 0 training TFRecord
     }
   }
   eval_config {
     metrics_set: "coco_detection_metrics"
     use_moving_averages: false
   }
   eval_input_reader {
     label_map_path: "workspace/test_0fold/annotations/label_map.pbtxt"   # Path to label map
     shuffle: false
     num_epochs: 1
     tf_record_input_reader {
       input_path: "workspace/test_0fold/annotations/test0.record"        # Path to fold 0 test TFRecord
     }
   }
     
   

   Then we are ready to run the training. This job may take a while and outputs once every 100 steps by default.

   python3 scripts/training/create_run_model.py --model_dir=workspace/train_0fold/models/ssd --pipeline_config_path=workspace/train_0fold/models/ssd/pipeline.config

4. Evaluating the Trained Model

   Next, we will run an evaluation on the trained model. This will test us the prediction accuracy of the model on the test dataset.

   To do this, we use the same command, with a checkpoint directory parameter.

   python3 scripts/training/create_run_model.py --model_dir=workspace/train_0fold/models/ssd --pipeline_config_path=workspace/train_0fold/models/ssd/pipeline.config --checkpoint_dir=workspace/train_0fold/models/ssd

   Running the script should generate a similar output:

    Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.786
    Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.945
    Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.942
    Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.006
    Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.596
    Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.814
    Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.815
    Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.816
    Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.816
    Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000
    Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.670
    Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.858
   

5. Exporting the Trained Model

   Now we want to export the trained model into a .pb file.

   mkdir exported-models
   python3 scripts/testing/export_model.py --input_type image_tensor --pipeline_config_path workspace/train_0fold/models/ssd/pipeline.config --trained_checkpoint_dir workspace/train_0fold/models/ssd --output_directory workspace/exported-models/ssd_fold0
   

Pre-processing Scripts

All scripts are run from the main directory.

Splitting Images Into k-folds for Cross Validation.

Will split the ECUSTFD images into train and test folders in the output directory (/workspace/) for cross validation training on k-folds. Use python3 scripts/preprocessing/kfold_partition_dataset.py -h for parameter usage.

python3 scripts/preprocessing/kfold_partition_dataset.py -i <input-dir> -o <output-dir> -k <k-folds> [-s <random-seed>] [-x]

Example:

python3 scripts/preprocessing/kfold_partition_dataset.py -i ../ECUSTFD-resized-/JPEGImages/ -o workspace/ -k 5

Create TensorFlow Records

Converts XML files to TFRecord files in preparation for training.

Use python3 scripts/preprocessing/generate_tfrecord.py -h for parameter usage.

Example:

python scripts/preprocessing/generate_tfrecord.py -x workspace/train_0fold -l workspace/train_0fold/annotations/label_map.pbtxt -o workspace/train_0fold/annotations/train0.record

Training Scripts

All scripts are run from the main directory.

Training a Custom Model

Initiates a new custom object detection training job for the base-model in model_dir.

Use python3 scripts/training/create_run_model.py -h for parameter usage.

Example:

python3 scripts/training/create_run_model.py --model_dir=models/faster_rcnn_0 --pipeline_config_path=models/faster_rcnn_0/pipeline.config

Running test training on GCP

Follow the instructions above to set up GCP, then run this script from the main directory.

scripts/gcp/requirements.txt is necessary for the running of this script. It is a list of python packages that the model depends on.

Change the GCP_BUCKET variable in the script to match your bucket name if it is not already chowdr-bucket.

python3 scripts/gcp/test_gcp_train.py

Testing Scripts

All scripts are run from the main directory.

Running an Evaluation on Trained Model

Evaluates how well the model performs in detecting objects in the test dataset that is configured in pipeline.config.

Use python3 scripts/training/create_run_model.py -h for parameter usage.

Example:

python3 scripts/training/create_run_model.py --model_dir=models/faster_rcnn_0 --pipeline_config_path=models/faster_rcnn_0/pipeline.config --checkpoint_dir=models/faster_rcnn_0

Export an Object Detection Model for Inference.

Prepares an object detection tensorflow graph for inference using model configuration and a trained checkpoint. Outputs associated checkpoint files, a SavedModel, and a copy of the model config.

Use python3 scripts/testing/export_model.py -h for parameter usage.

Example:

python3 scripts/testing/export_model.py --input_type image_tensor --pipeline_config_path models/faster_rcnn_resnet50_v1_640x640_coco17_tpu-8/pipeline.config --trained_checkpoint_dir models/faster_rcnn_resnet50_v1_640x640_coco17_tpu-8/ --output_directory exported-models/faster_rcnn_

Running Test Images on Pre-Trained Object Detectors

Use and configure this script to run a sample, pre-trained object detection model on test images.

python3 scripts/testing/test_sample.py

Running The Calorie Detector With A Test Image

Run this script to use our custom object detection model on a specified pair of test images. -s defaults to data/apple001S(1).JPG and -t defaults to data/apple001T(1).JPG.

Specify two input images:

python3 scripts/testing/calorie_detector.py -s <side view image path> -t <top view image path>

Use python3 scripts/testing/calorie_detector.py -h for parameter usage.

Example:

python3 scripts/testing/calorie_detector.py -s "data/apple001S(1).JPG" -t "data/apple001T(1).JPG"

Output:

Original Images

Detected Images

Grabcut Results:

Running Analysis Tools

Use this script to perform analysis, estimations, error and beta computations across the entire ECUSTF dataset.

Prerequisite: You must clone ECUST dataset into ./dataset/.

git clone [email protected]:Liang-yc/ECUSTFD-resized-.git ./dataset/

python3 scripts/testing/compute_betas.py