Character Finder

Prerequisites

Make sure you have the prerequisites for the Object Detection API installed. The directions for installations can be found here

For Santa Claus

Download the training images in character-finder/characters directory and evaluation images in the characterfinder/eval_image directory

This only downloads the images for Santa Claus.

Run the following commands

 # Unzip the contents
 # From character-finder/characters
 unzip santa.zip  
 # From character-finder/eval_images
 unzip santa.zip
 
 # From character-finder/
 python change_csv.py
 # This will change the path in the already existing csvs to point to the images in the correct directory

For Other Characters

Make a new directory in the character-finder/characters folder and name it the character. For example
mkdir characters/foobar
Similarly do the same for the evaluation images
mkdir eval_images/foobar
Save all training images for that character in characters/foobar and the evaluation images in eval\_images/foobar
Next step is to get bounding box imformation about the characters and store it in a csv which will later be converted to tf.record file. Done for both training and evaluation images.

Run the following command

 # For Training Images
 python detect_labels.py --annotation_file PATH_TO_CSV --images characters/
 # For Evaluation Images
 python detect_labels.py --annotation_file PATH_TO_CSV --images eval_images/

Note: Already existing train.csv and eval.csv have bounding box information for images of Santa Claus

Label the images by clicking on the top left of the characters face first and then on the bottom right
Modify the characters_label_map.pbtext file depending on number of characters. For example
```
 item {
     id: 1
     name: foobar1
 }
 item {
     id: 2
     name: foobar2
 }
```

Generating record files

After generating csv, generate the record files

# Generate training record
python --csv_input PATH_TO_TRAIN_CSV --output_path PATH_TO_TRAIN.record --label_map_path characters_label_map.pbtext

 # Generate evaluation record
 python --csv_input PATH_TO_EVAL_CSV --output_path PATH_TO_EVAL.record --label_map_path characters_label_map.pbtext

Training

For training, construct an object-detection training pipeline.

Use any of the config files present in object_detection/samples/configs/ as basis
Changes in the .config file:
1. Adjust the number of classes depending on the number of characters training on 2 It is recommended to train the model from a pre-trained checkpoint. Tensorflow provides several pre-trained checkpoints which can be found here.
  Change the
  fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt" to point to the checkpoint to be used
2. In the following Code snippet
```
train_input_reader: {
  tf_record_input_reader {
    input_path: "PATH_TO_BE_CONFIGURED/train.record"
  }
  label_map_path: "characters_label_map.pbtxt"
}
```
  Change the input_path: "PATH_TO_BE_CONFIGURED/train.record" to point to the full path of the train.record file created in the previous step and the label_map_path: "PATH_TO_BE_CONFIGURED/characters_label_map.pbtxt" to point to the full path of the label map.
3. In the following Code snippet
```
eval_input_reader: {
  tf_record_input_reader {
	    input_path: "PATH_TO_BE_CONFIGURED/eval.record"
  }
  label_map_path: "PATH_TO_BE_CONFIGURED/characters_label_map.pbtxt"
  shuffle: false
  num_readers: 1
  num_epochs: 1
}
```
  Change the input_path: "PATH_TO_BE_CONFIGURED/eval.record" to point to the full path of the eval.record file created in the previous step and the label_map_path: "PATH_TO_BE_CONFIGURED/characters_label_map.pbtxt" to point to the full path of the label map.

Run the training job

 python object_detection/train.py --logtostderr --train_dir PATH_TO_SAVE_CHECKPOINTS --pipeline_config_path PATH_TO_CONFIG_FILE

It is advised to run the evaluation job simultaneously with the training.

 python object_detection/eval.py --logtostderr --checkpoint_dir PATH_TO_CHECKPOINT_DIR --eval_dir PATH_TO_EVAL_DIR --pipeline_config_path PATH_TO_CONFIG_FILE

Visualise the training and evaluation by running the folowing command
```
 tensorboard --logdir PATH_TO_CHECKPOINT_DIR
```

Exporting the model

To export the model, choose a checkpoint # to export. For example to export checkpoint no. 130912

 python object_detection/export_inference_graph --input_type image_tensor --pipeline_congif_path PATH_TO_CONFIG_FILE --trained_checkpoint_prefix PATH_TO_CHECKPOINT/model.ckpt-130912 --output_directory PATH_TO_EXPORT_MODEL

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