def _create_metadata(self):
"""Creates the metadata for an image classifier."""
# Creates model info.
model_meta = _metadata_fb.ModelMetadataT()
model_meta.name = self.model_info.name
model_meta.description = ("Identify the most prominent object in the "
"image from a set of %d categories." %
self.model_info.num_classes)
model_meta.version = self.model_info.version
model_meta.author = "TensorFlow"
model_meta.license = ("Apache License. Version 2.0 "
"http://www.apache.org/licenses/LICENSE-2.0.")
# Creates input info.
input_meta = _metadata_fb.TensorMetadataT()
input_meta.name = "image"
input_meta.description = (
"Input image to be classified. The expected image is {0} x {1}, with "
"three channels (red, blue, and green) per pixel. Each value in the "
"tensor is a single byte between {2} and {3}.".format(
self.model_info.image_width, self.model_info.image_height,
self.model_info.image_min, self.model_info.image_max))
input_meta.content = _metadata_fb.ContentT()
input_meta.content.contentProperties = _metadata_fb.ImagePropertiesT()
input_meta.content.contentProperties.colorSpace = (
_metadata_fb.ColorSpaceType.RGB)
input_meta.content.contentPropertiesType = (
_metadata_fb.ContentProperties.ImageProperties)
input_normalization = _metadata_fb.ProcessUnitT()
input_normalization.optionsType = (
_metadata_fb.ProcessUnitOptions.NormalizationOptions)
input_normalization.options = _metadata_fb.NormalizationOptionsT()
input_normalization.options.mean = self.model_info.mean
input_normalization.options.std = self.model_info.std
input_meta.processUnits = [input_normalization]
input_stats = _metadata_fb.StatsT()
input_stats.max = [self.model_info.image_max]
input_stats.min = [self.model_info.image_min]
input_meta.stats = input_stats
# Creates output info.
output_location_meta = _metadata_fb.TensorMetadataT()
output_location_meta.name = "location"
output_location_meta.description = "The locations of the detected boxes."
output_location_meta.content = _metadata_fb.ContentT()
output_location_meta.content.contentPropertiesType = (
_metadata_fb.ContentProperties.BoundingBoxProperties)
output_location_meta.content.contentProperties = (
_metadata_fb.BoundingBoxPropertiesT())
output_location_meta.content.contentProperties.index = [1, 0, 3, 2]
output_location_meta.content.contentProperties.type = (
_metadata_fb.BoundingBoxType.BOUNDARIES)
output_location_meta.content.contentProperties.coordinateType = (
_metadata_fb.CoordinateType.RATIO)
output_location_meta.content.range = _metadata_fb.ValueRangeT()
output_location_meta.content.range.min = 0
output_location_meta.content.range.max = 255
output_confidence_meta = _metadata_fb.TensorMetadataT()
output_confidence_meta.name = "confidence"
output_confidence_meta.description = "The confidences of the detected boxes."
output_confidence_meta.content = _metadata_fb.ContentT()
output_confidence_meta.content.contentPropertiesType = (
_metadata_fb.ContentProperties.FeatureProperties)
output_confidence_meta.content.contentProperties = (
_metadata_fb.FeaturePropertiesT())
output_confidence_meta.content.range = _metadata_fb.ValueRangeT()
output_confidence_meta.content.range.min = 0
output_confidence_meta.content.range.max = 255
# label_file = _metadata_fb.AssociatedFileT()
# label_file.name = os.path.basename("labelmap.txt")
# label_file.description = "Label of objects that this model can recognize."
# label_file.type = _metadata_fb.AssociatedFileType.TENSOR_VALUE_LABELS
# output_confidence_meta.associatedFiles = [label_file]
output_landmark_meta = _metadata_fb.TensorMetadataT()
output_landmark_meta.name = "landmark"
output_landmark_meta.description = "The landmark of the detected boxes."
output_landmark_meta.content = _metadata_fb.ContentT()
output_landmark_meta.content.contentPropertiesType = (
_metadata_fb.ContentProperties.FeatureProperties)
output_landmark_meta.content.contentProperties = (
_metadata_fb.FeaturePropertiesT())
output_landmark_meta.content.range = _metadata_fb.ValueRangeT()
output_landmark_meta.content.range.min = 0
output_landmark_meta.content.range.max = 255
# Creates subgraph info.
subgraph = _metadata_fb.SubGraphMetadataT()
subgraph.inputTensorMetadata = [input_meta]
subgraph.outputTensorMetadata = [
output_location_meta, output_confidence_meta, output_landmark_meta
]
model_meta.subgraphMetadata = [subgraph]
b = flatbuffers.Builder(0)
b.Finish(model_meta.Pack(b),
_metadata.MetadataPopulator.METADATA_FILE_IDENTIFIER)
self.metadata_buf = b.Output()
def _create_metadata(self):
"""Creates the metadata for an object detector."""
model_meta = _metadata_fb.ModelMetadataT()
model_meta.name = self.model_info.name
model_meta.description = (
"Identify which of a known set of objects might be present and provide "
"information about their positions within the given image or a video "
"stream.")
model_meta.version = self.model_info.version
model_meta.author = "TensorFlow Lite Model Maker"
model_meta.license = ("Apache License. Version 2.0 "
"http://www.apache.org/licenses/LICENSE-2.0.")
# Creates input info.
input_meta = _metadata_fb.TensorMetadataT()
input_meta.name = "image"
input_meta.description = (
"Input image to be detected. The expected image is {0} x {1}, with "
"three channels (red, blue, and green) per pixel. Each value in the "
"tensor is a single byte between 0 and 255.".format(
self.model_info.image_width, self.model_info.image_height))
input_meta.content = _metadata_fb.ContentT()
input_meta.content.contentProperties = _metadata_fb.ImagePropertiesT()
input_meta.content.contentProperties.colorSpace = (
_metadata_fb.ColorSpaceType.RGB)
input_meta.content.contentPropertiesType = (
_metadata_fb.ContentProperties.ImageProperties)
input_normalization = _metadata_fb.ProcessUnitT()
input_normalization.optionsType = (
_metadata_fb.ProcessUnitOptions.NormalizationOptions)
input_normalization.options = _metadata_fb.NormalizationOptionsT()
input_normalization.options.mean = self.model_info.mean
input_normalization.options.std = self.model_info.std
input_meta.processUnits = [input_normalization]
input_stats = _metadata_fb.StatsT()
input_stats.max = [self.model_info.image_max]
input_stats.min = [self.model_info.image_min]
input_meta.stats = input_stats
# Creates outputs info.
output_location_meta = _metadata_fb.TensorMetadataT()
output_location_meta.name = "location"
output_location_meta.description = "The locations of the detected boxes."
output_location_meta.content = _metadata_fb.ContentT()
output_location_meta.content.contentPropertiesType = (
_metadata_fb.ContentProperties.BoundingBoxProperties)
output_location_meta.content.contentProperties = (
_metadata_fb.BoundingBoxPropertiesT())
output_location_meta.content.contentProperties.index = [1, 0, 3, 2]
output_location_meta.content.contentProperties.type = (
_metadata_fb.BoundingBoxType.BOUNDARIES)
output_location_meta.content.contentProperties.coordinateType = (
_metadata_fb.CoordinateType.RATIO)
output_location_meta.content.range = _metadata_fb.ValueRangeT()
output_location_meta.content.range.min = 2
output_location_meta.content.range.max = 2
output_class_meta = _metadata_fb.TensorMetadataT()
output_class_meta.name = "category"
output_class_meta.description = "The categories of the detected boxes."
output_class_meta.content = _metadata_fb.ContentT()
output_class_meta.content.contentPropertiesType = (
_metadata_fb.ContentProperties.FeatureProperties)
output_class_meta.content.contentProperties = (
_metadata_fb.FeaturePropertiesT())
output_class_meta.content.range = _metadata_fb.ValueRangeT()
output_class_meta.content.range.min = 2
output_class_meta.content.range.max = 2
label_file = _metadata_fb.AssociatedFileT()
label_file.name = os.path.basename(self.associated_files[0])
label_file.description = "Label of objects that this model can recognize."
label_file.type = _metadata_fb.AssociatedFileType.TENSOR_VALUE_LABELS
output_class_meta.associatedFiles = [label_file]
output_score_meta = _metadata_fb.TensorMetadataT()
output_score_meta.name = "score"
output_score_meta.description = "The scores of the detected boxes."
output_score_meta.content = _metadata_fb.ContentT()
output_score_meta.content.contentPropertiesType = (
_metadata_fb.ContentProperties.FeatureProperties)
output_score_meta.content.contentProperties = (
_metadata_fb.FeaturePropertiesT())
output_score_meta.content.range = _metadata_fb.ValueRangeT()
output_score_meta.content.range.min = 2
output_score_meta.content.range.max = 2
output_number_meta = _metadata_fb.TensorMetadataT()
output_number_meta.name = "number of detections"
output_number_meta.description = "The number of the detected boxes."
output_number_meta.content = _metadata_fb.ContentT()
output_number_meta.content.contentPropertiesType = (
_metadata_fb.ContentProperties.FeatureProperties)
output_number_meta.content.contentProperties = (
_metadata_fb.FeaturePropertiesT())
# Creates subgraph info.
group = _metadata_fb.TensorGroupT()
group.name = "detection result"
group.tensorNames = [
output_location_meta.name, output_class_meta.name,
output_score_meta.name
]
subgraph = _metadata_fb.SubGraphMetadataT()
subgraph.inputTensorMetadata = [input_meta]
subgraph.outputTensorMetadata = [
output_location_meta, output_class_meta, output_score_meta,
output_number_meta
]
subgraph.outputTensorGroups = [group]
model_meta.subgraphMetadata = [subgraph]
b = flatbuffers.Builder(0)
b.Finish(model_meta.Pack(b),
_metadata.MetadataPopulator.METADATA_FILE_IDENTIFIER)
self.metadata_buf = b.Output()
input_normalization.options.std = [127.5]
input_meta.processUnits = [input_normalization]
input_stats = _metadata_fb.StatsT()
input_stats.max = [255]
input_stats.min = [0]
input_meta.stats = input_stats
# Creates outputs info.
output_location_meta = _metadata_fb.TensorMetadataT()
output_location_meta.name = "location"
output_location_meta.description = "The locations of the detected boxes."
output_location_meta.content = _metadata_fb.ContentT()
output_location_meta.content.contentPropertiesType = (
_metadata_fb.ContentProperties.BoundingBoxProperties)
output_location_meta.content.contentProperties = (
_metadata_fb.BoundingBoxPropertiesT())
output_location_meta.content.contentProperties.index = [1, 0, 3, 2]
output_location_meta.content.contentProperties.type = (
_metadata_fb.BoundingBoxType.BOUNDARIES)
output_location_meta.content.contentProperties.coordinateType = (
_metadata_fb.CoordinateType.RATIO)
output_location_meta.content.range = _metadata_fb.ValueRangeT()
output_location_meta.content.range.min = 2
output_location_meta.content.range.max = 2
output_class_meta = _metadata_fb.TensorMetadataT()
output_class_meta.name = "category"
output_class_meta.description = "The categories of the detected boxes."
output_class_meta.content = _metadata_fb.ContentT()
output_class_meta.content.contentPropertiesType = (
_metadata_fb.ContentProperties.FeatureProperties)
output_class_meta.content.contentProperties = (
input_stats.max = [255] input_stats.min = [0] input_meta.stats = input_stats labelmap_file = "F:/Android/object_detection/android/app/src/main/assets/labelmap.txt" exported_model_path = "F:/Android/object_detection/android/app/src/main/assets/model_fpnlite640.tflite" output_tensorgroups = _metadata_fb.TensorGroupT() output_tensorgroups.name = "detection result" output_tensorgroups.tensorNames = ["location", "category", "score"] location = _metadata_fb.TensorMetadataT() location.name = "location" location.description = "The location of the detected boxes. " location.content = _metadata_fb.ContentT() location.content.contentProperties = (_metadata_fb.BoundingBoxPropertiesT()) location.content.contentProperties.index = [1,0,3,2] location.content.contentProperties.type = (_metadata_fb.BoundingBoxType.BOUNDARIES) location.content.contentProperties.coordinateType = (_metadata_fb.CoordinateType.RATIO) location.content.contentPropertiesType = (_metadata_fb.ContentProperties.BoundingBoxProperties) location.content.range = _metadata_fb.ValueRangeT() location.content.range.min = 2 location.content.range.max = 2 category = _metadata_fb.TensorMetadataT() category.name = "category" category.description = "The categories of the detected boxes. " category.content = _metadata_fb.ContentT() category.content.contentPropertiesType = (_metadata_fb.ContentProperties.FeatureProperties) category.content.contentProperties = (_metadata_fb.FeaturePropertiesT()) category.content.range = _metadata_fb.ValueRangeT()
def _create_metadata(self):
"""Creates the metadata for an image classifier."""
# Creates model info.
model_meta = _metadata_fb.ModelMetadataT()
model_meta.name = self.model_info.name
model_meta.description = ("Equipment.")
model_meta.version = self.model_info.version
model_meta.author = "TensorFlow"
model_meta.license = ("Apache License. Version 2.0 "
"http://www.apache.org/licenses/LICENSE-2.0.")
# Creates input info.
input_meta = _metadata_fb.TensorMetadataT()
input_meta.name = "image"
input_meta.description = (
"The expected image is 300 x 300, with three channels "
"(re, blue, and green) per pixel. Each value in the tensor is between"
" 0 and 1.")
input_meta.content = _metadata_fb.ContentT()
input_meta.content.contentProperties = _metadata_fb.ImagePropertiesT()
input_meta.content.contentProperties.colorSpace = (
_metadata_fb.ColorSpaceType.RGB)
input_meta.content.contentPropertiesType = (
_metadata_fb.ContentProperties.ImageProperties)
input_normalization = _metadata_fb.ProcessUnitT()
input_normalization.optionsType = (
_metadata_fb.ProcessUnitOptions.NormalizationOptions)
input_normalization.options = _metadata_fb.NormalizationOptionsT()
input_normalization.options.mean = self.model_info.mean
input_normalization.options.std = self.model_info.std
input_meta.processUnits = [input_normalization]
input_stats = _metadata_fb.StatsT()
input_stats.max = [self.model_info.image_max]
input_stats.min = [self.model_info.image_min]
input_meta.stats = input_stats
# Creates output info.
output_location_meta = _metadata_fb.TensorMetadataT()
output_location_meta.name = "location"
output_location_meta.description = "The locations of the detected boxes."
output_location_meta.content = _metadata_fb.ContentT()
output_location_meta.content.contentPropertiesType = (
_metadata_fb.ContentProperties.BoundingBoxProperties)
output_location_meta.content.contentProperties = (
_metadata_fb.BoundingBoxPropertiesT())
output_location_meta.content.contentProperties.index = [1, 0, 3, 2]
output_location_meta.content.contentProperties.type = (
_metadata_fb.BoundingBoxType.BOUNDARIES)
output_location_meta.content.contentProperties.coordinateType = (
_metadata_fb.CoordinateType.RATIO)
output_location_meta.content.range = _metadata_fb.ValueRangeT()
output_location_meta.content.range.min = 2
output_location_meta.content.range.max = 2
output_class_meta = _metadata_fb.TensorMetadataT()
output_class_meta.name = "category"
output_class_meta.description = "The categories of the detected boxes."
output_class_meta.content = _metadata_fb.ContentT()
output_class_meta.content.contentPropertiesType = (
_metadata_fb.ContentProperties.FeatureProperties)
output_class_meta.content.contentProperties = (
_metadata_fb.FeaturePropertiesT())
output_class_meta.content.range = _metadata_fb.ValueRangeT()
output_class_meta.content.range.min = 2
output_class_meta.content.range.max = 2
label_file = _metadata_fb.AssociatedFileT()
label_file.name = os.path.basename(self.label_file_path)
label_file.description = "Label of objects that this model can recognize."
label_file.type = _metadata_fb.AssociatedFileType.TENSOR_VALUE_LABELS
output_class_meta.associatedFiles = [label_file]
output_score_meta = _metadata_fb.TensorMetadataT()
output_score_meta.name = "score"
output_score_meta.description = "The scores of the detected boxes."
output_score_meta.content = _metadata_fb.ContentT()
output_score_meta.content.contentPropertiesType = (
_metadata_fb.ContentProperties.FeatureProperties)
output_score_meta.content.contentProperties = (
_metadata_fb.FeaturePropertiesT())
output_score_meta.content.range = _metadata_fb.ValueRangeT()
output_score_meta.content.range.min = 2
output_score_meta.content.range.max = 2
output_number_meta = _metadata_fb.TensorMetadataT()
output_number_meta.name = "number of detections"
output_number_meta.description = "The number of the detected boxes."
output_number_meta.content = _metadata_fb.ContentT()
output_number_meta.content.contentPropertiesType = (
_metadata_fb.ContentProperties.FeatureProperties)
output_number_meta.content.contentProperties = (
_metadata_fb.FeaturePropertiesT())
# Creates subgraph info.
group = _metadata_fb.TensorGroupT()
group.name = "detection result"
group.tensorNames = [
output_location_meta.name, output_class_meta.name,
output_score_meta.name
]
subgraph = _metadata_fb.SubGraphMetadataT()
subgraph.inputTensorMetadata = [input_meta]
subgraph.outputTensorMetadata = [
output_location_meta, output_class_meta, output_score_meta,
output_number_meta
]
subgraph.outputTensorGroups = [group]
model_meta.subgraphMetadata = [subgraph]
b = flatbuffers.Builder(0)
b.Finish(model_meta.Pack(b),
_metadata.MetadataPopulator.METADATA_FILE_IDENTIFIER)
self.metadata_buf = b.Output()