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#Navigate to the  model downloader directory and download the models
 
#Download the person detection model
python3 /opt/intel/openvino/deployment_tools/tools/model_downloader/downloader.py --name "person-detection-retail-0013"

#Download the face detection model
python /opt/intel/openvino/deployment_tools/tools/model_downloader/downloader.py --name "face-detection-adas-binary-0001"

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#Optimize the safety model
python3 /opt/intel/openvino/deployment_tools/tools/model_optimizer/mo_caffe.py --input_model <path to caffemodel file>  --input_proto <path to prototxt file>  -o <specify the output directory>  

#Optimize the mask model
python3 /opt/intel/openvino/deployment_tools/tools/model_optimizer/mo_caffe.py --input_model <path to caffemodel file>  --input_proto <path to prototxt file>  -o <specify the output directory> 

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#include "tensorflow/lite/micro/examples/micro_speech/main_functions.h"

#include "tensorflow/lite/micro/examples/micro_speech/audio_provider.h"
#include "tensorflow/lite/micro/examples/micro_speech/command_responder.h"
#include "tensorflow/lite/micro/examples/micro_speech/feature_provider.h"
#include "tensorflow/lite/micro/examples/micro_speech/micro_features/micro_model_settings.h"
#include "tensorflow/lite/micro/examples/micro_speech/micro_features/model.h"
#include "tensorflow/lite/micro/examples/micro_speech/recognize_commands.h"
#include "tensorflow/lite/micro/kernels/micro_ops.h"
#include "tensorflow/lite/micro/micro_error_reporter.h"

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#include "tensorflow/lite/micro/examples/micro_speech/micro_features/micro_model_settings.h"

const char* kCategoryLabels[kCategoryCount] = {
    "silence",
    "unknown",
    "yes",
    "no",
};

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#include "tensorflow/lite/micro/examples/micro_speech/micro_features/model.h"

// We need to keep the data array aligned on some architectures.
#ifdef __has_attribute
#define HAVE_ATTRIBUTE(x) __has_attribute(x)
#else
#define HAVE_ATTRIBUTE(x) 0
#endif
#if HAVE_ATTRIBUTE(aligned) || (defined(__GNUC__) && !defined(__clang__))
#define DATA_ALIGN_ATTRIBUTE __attribute__((aligned(4)))

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stages:
  ELT:
    cmd: python src/data_preprocessor.py -s train test valid -o "./data_store/data/American
      Sign Language Letters.v1-v1.tfrecord"
    deps:
    - src/data_preprocessor.py
    outs:
    - ./data_store/data/American Sign Language Letters.v1-v1.tfrecord
  feature_tranform-train:
    cmd: python src/train.py -dt True

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attached_datasets = experiment.list_attached_dataset_versions()
train_dataset_version = experiment.get_dataset(train_version_name)
eval_dataset_version = experiment.get_dataset(eval_version_name)
images_dir = os.path.join(os.getcwd(), train_set_local_dir)

if os.path.isdir(train_set_local_dir):
  print("dataset version has already been downloaded")
else:
  train_dataset_version.download(train_set_local_dir)

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# Initializing picsellia connection 

if 'api_token' not in os.environ:
    raise Exception("You must set an api_token to run this image")
api_token = os.environ["api_token"]
if "host" not in os.environ:
    host = "https://trial.picsellia.com"
else:
    host = os.environ["host"]
if "organization_name " not in os.environ:

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labels = train_dataset_version.list_labels()
label_names = [label.name for label in labels]

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def coco_format_loader(train_dataset_version, label_names, coco_annotations_path):
    coco_annotation = train_dataset_version.build_coco_file_locally(
            enforced_ordered_categories=label_names
        )
    annotations_dict = coco_annotation.dict()
    with open(coco_annotations_path, "w") as f:
        f.write(json.dumps(annotations_dict))
    annotations_coco = COCO(coco_annotations_path)
    return annotations_coco