edgetpu.detection.engine

An inference engine that performs object detection.

class edgetpu.detection.engine.DetectionCandidate(label_id, score, x1, y1, x2, y2)

A data structure that represents one detection candidate (label id, score, and bounding box).

This is returned by methods detect_with_image() and detect_with_input_tensor().

bounding_box

A 2-D aray (numpy.ndarray) that describes the bounding box for the detected object.

The format is [[x1, y1], [x2, y2]], where [x1, y1] is the top-left corner and [x2, y2] is the bottom-right corner of the bounding box. The values can be either floats (relative coordinates) or integers (pixel coordinates), depending on the relative_coord bool you pass to the detect_with_image() or detect_with_input_tensor() method. [0, 0] is always the top-left corner.

label_id

An int for the label id.

score

A float for the confidence score.

class edgetpu.detection.engine.DetectionEngine(model_path, device_path=None)

Extends BasicEngine to perform object detection with a given model.

This API assumes the given model is trained for object detection. Now this engine only supports SSD model with postprocessing operator.

Parameters:
  • model_path (str) – Path to a TensorFlow Lite (.tflite) file. This model must be compiled for the Edge TPU; otherwise, it simply executes on the host CPU.
  • device_path (str) – The device path for the Edge TPU this engine should use. This argument is needed only when you have multiple Edge TPUs and more inference engines than available Edge TPUs. For details, read how to use multiple Edge TPUs.
Raises:

ValueError – If the model’s output tensor size is not 4.

detect_with_image(img, threshold=0.1, top_k=3, keep_aspect_ratio=False, relative_coord=True, resample=0)

Performs object detection with an image.

Parameters:
  • img (PIL.Image) – The image you want to process.
  • threshold (float) – Minimum confidence threshold for detected objects. For example, use 0.5 to receive only detected objects with a confidence equal-to or higher-than 0.5.
  • top_k (int) – The maximum number of detected objects to return.
  • keep_aspect_ratio (bool) – If True, keep the image aspect ratio the same when down-sampling the image (by adding black pixel padding so it fits the input tensor’s dimensions, via the resampling_with_original_ratio() function). If False, resize and reshape the image (without cropping) to match the input tensor’s dimensions. (Note: This option should be the same as what is applied on input images during model training. Otherwise, the accuracy might be affected and the bounding box of detection result might be stretched.)
  • relative_coord (bool) – If True, provide coordinates as float values between 0 and 1, representing each position relative to the total image width/height. If False, provide coordinates as integers, representing pixel positions in the original image. [0, 0] is always the top-left corner.
  • resample (int) – A resampling filter for image resizing. This can be one of PIL.Image.NEAREST, PIL.Image.BOX, PIL.Image.BILINEAR, PIL.Image.HAMMING, PIL.Image.BICUBIC, or PIL.Image.LANCZOS. Default is PIL.Image.NEAREST. See Pillow filters. (Note: A complex filter such as PIL.Image.BICUBIC may create slightly better accuracy but it also causes higher latency.)
Returns:

A list of detected objects as DetectionCandidate objects.

Raises:
  • RuntimeError – If the model’s input tensor shape doesn’t match the shape expected for an object detection model, which is [1, height, width, 3].
  • ValueError – If argument values are invalid.
detect_with_input_tensor(input_tensor, threshold=0.1, top_k=3)

Performs object detection with a raw input tensor.

This requires you to process the input data (the image) and convert it to the appropriately formatted input tensor for your model.

Parameters:
  • input_tensor (numpy.ndarray) – A 1-D array as the input tensor.
  • threshold (float) – Minimum confidence threshold for detected objects. For example, use 0.5 to receive only detected objects with a confidence equal-to or higher-than 0.5.
  • top_k (int) – The maximum number of detected objects to return.
Returns:

A list of detected objects as DetectionCandidate objects.

Raises:

ValueError – If argument values are invalid.

device_path()

Gets the path for the Edge TPU that’s associated with this inference engine.

See how to run multiple models with multiple Edge TPUs.

Returns:A string representing this engine’s Edge TPU device path.
get_all_output_tensors_sizes()

Gets the size of each output tensor.

A model may output several tensors, but the return from run_inference() and get_raw_output() concatenates them together into a 1-D array. So this function provides the size for each original output tensor, allowing you to calculate the offset for each tensor within the concatenated array.

Returns:An array (numpy.ndarray) with the length of each output tensor (this assumes that all output tensors are 1-D).
get_inference_time()

Gets the latency of the most recent inference.

This can be used by higher level engines for debugging.

Returns:A float representing the inference latency (in milliseconds).
get_input_tensor_shape()

Gets the shape required for the input tensor.

For models trained for image classification / detection, the shape is always [1, height, width, channels]. To be used as input for run_inference(), this tensor shape must be flattened into a 1-D array with size height * width * channels. To instead get that 1-D array size, use required_input_array_size().

Returns:A 1-D array (numpy.ndarray) representing the required input tensor shape.
get_num_of_output_tensors()

Gets the number of output tensors.

Returns:An integer representing number of output tensors.
get_output_tensor_size(index)

Gets the size of a specific output tensor.

Parameters:index (int) – The index position of the output tensor.
Returns:An integer representing the size of the output tensor.
get_raw_output()

Gets the output of the most recent inference.

This can be used by higher level engines for debugging.

Returns:A 1-D array (numpy.ndarray) representing the output tensor. If there are multiple output tensors, they are compressed into a single 1-D array. (Same as what’s returned by run_inference().)
model_path()

Gets the file path for model loaded by this inference engine.

Returns:A string representing the model file’s path.
required_input_array_size()

Gets the required size for the input_tensor given to run_inference().

This is the total size of the 1-D array, once the tensor shape is flattened.

Returns:An integer representing the required input tensor size.
run_inference(input)

Performs inference with a raw input tensor.

Parameters:input – (numpy.ndarray): A 1-D array as the input tensor. You can query the required size for this array with required_input_array_size().
Returns:A 2-tuple with the inference latency in milliseconds (float) and a 1-D array (numpy.ndarray) representing the output tensor. If there are multiple output tensors, they are compressed into a single 1-D array. For example, if the model outputs 2 tensors with values [1, 2, 3] and [0.1, 0.4, 0.9], the returned 1-D array is [1, 2, 3, 0.1, 0.4, 0.9]. You can calculate the size and offset for each tensor using get_all_output_tensors_sizes(), get_num_of_output_tensors(), and get_output_tensor_size().
total_output_array_size()

Gets the expected size of the 1-D output array returned by run_inference() and get_raw_output().

Returns:An integer representing the output tensor size.