Structs
Public-facing structs for Edge Impulse C++ SDK.
ei_impulse_result_classification_t
Holds the output of inference, anomaly results, and timing information.
ei_impulse_result_t holds the output of run_classifier(). If object detection is enabled, then the output results is a pointer to an array of bounding boxes of size bounding_boxes_count, as given by ei_impulse_result_bounding_box_t. Otherwise, results are stored as an array of classification scores, as given by ei_impulse_result_classification_t.
If anomaly detection is enabled (e.g. EI_CLASSIFIER_HAS_ANOMALY == 1), then the anomaly score will be stored as a floating point value in anomaly.
Timing information is stored in an ei_impulse_result_timing_t struct.
Source: classifier/ei_classifier_types.h
Example: standalone inferencing main.cpp
public const char * label
Label of the detected object
public float value
Value of the detected object
ei_impulse_visual_ad_result_t
Holds the output of visual anomaly detection (FOMO-AD)
If visual anomaly detection is enabled (e.g. EI_CLASSIFIER_HAS_VISUAL_ANOMALY == 1), then the output results will be a pointer to an array of grid cells of size visual_ad_count, as given by ei_impulse_result_bounding_box_t.
The visual anomaly detection result is stored in visual_ad_result, which contains the mean and max values of the grid cells.
Source: classifier/ei_classifier_types.h
Example: standalone inferencing main.cpp
public float mean_value
Mean value of the grid cells
public float max_value
Max value of the grid cells
ei_impulse_result_bounding_box_t
Holds information for a single bounding box.
If object detection is enabled (i.e. EI_CLASSIFIER_OBJECT_DETECTION == 1), then inference results will be one or more bounding boxes. The bounding boxes with the highest confidence scores (assuming those scores are equal to or greater than EI_CLASSIFIER_OBJECT_DETECTION_THRESHOLD), given by the value member, are returned from inference. The total number of bounding boxes returned will be at least EI_CLASSIFIER_OBJECT_DETECTION_COUNT. The exact number of bounding boxes is stored in bounding_boxes_count field of [ei_impulse_result_t]/C++ Inference SDK Library/structs/ei_impulse_result_t.md).
A bounding box is a rectangle that ideally surrounds the identified object. The (x, y) coordinates in the struct identify the top-left corner of the box. label is the predicted class with the highest confidence score. value is the confidence score between [0.0..1.0] of the given label.
Source: classifier/ei_classifier_types.h
Example: standalone inferencing main.cpp
public const char * label
Pointer to a character array describing the associated class of the given bounding box. Taken from one of the elements of ei_classifier_inferencing_categories[].
public uint32_t x
x coordinate of the top-left corner of the bounding box
public uint32_t y
y coordinate of the top-left corner of the bounding box
public uint32_t width
Width of the bounding box
public uint32_t height
Height of the bounding box
public float value
Confidence score of the label describing the bounding box
ei_impulse_result_timing_t
Holds timing information about the processing (DSP) and inference blocks.
Records timing information during the execution of the preprocessing (DSP) and inference blocks. Can be used to determine if inference will meet timing requirements on your particular platform.
Source: classifier/ei_classifier_types.h
Example: standalone inferencing main.cpp
public int sampling
If using run_impulse() to perform sampling and inference, it is the amount of time (in milliseconds) it took to fetch raw samples. Not used for run_classifier().
public int dsp
Amount of time (in milliseconds) it took to run the preprocessing (DSP) block
public int classification
Amount of time (in milliseconds) it took to run the inference block
public int anomaly
Amount of time (in milliseconds) it took to run anomaly detection. Valid only if EI_CLASSIFIER_HAS_ANOMALY == 1.
public int64_t dsp_us
Amount of time (in milliseconds) it took to run the post-processing block
public int64_t classification_us
Amount of time (in milliseconds) it took to run the inference block
public int64_t anomaly_us
Amount of time (in microseconds) it took to run anomaly detection. Valid only if EI_CLASSIFIER_HAS_ANOMALY == 1.
ei_impulse_result_t
Holds the output of inference, anomaly results, and timing information.
ei_impulse_result_t holds the output of run_classifier(). If object detection is enabled (e.g. EI_CLASSIFIER_OBJECT_DETECTION == 1), then the output results is a pointer to an array of bounding boxes of size bounding_boxes_count, as given by ei_impulse_result_bounding_box_t. Otherwise, results are stored as an array of classification scores, as given by ei_impulse_result_classification_t.
If anomaly detection is enabled (e.g. EI_CLASSIFIER_HAS_ANOMALY == 1), then the anomaly score will be stored as a floating point value in anomaly.
Timing information is stored in an ei_impulse_result_timing_t struct.
Source: classifier/ei_classifier_types.h
Example: standalone inferencing main.cpp
public ei_impulse_result_bounding_box_t * bounding_boxes
Array of bounding boxes of the detected objects, if object detection is enabled.
public uint32_t bounding_boxes_count
Number of bounding boxes detected. If object detection is not enabled, this will be 0.
public ei_impulse_result_classification_t classification
Array of classification results. If object detection is enabled, this will be empty.
public float anomaly
Anomaly score. If anomaly detection is not enabled, this will be 0. A higher anomaly score indicates greater likelihood of an anomalous sample (e.g. it is farther away from its cluster).
public ei_impulse_result_timing_t timing
Timing information for the processing (DSP) and inference blocks.
public bool copy_output
Copy the output data to a buffer. If set to false, the output data will be returned as a pointer to the internal buffer. If set to true, the output data will be copied to the buffer provided in ei_impulse_output_t.
public ei_impulse_result_bounding_box_t * visual_ad_grid_cells
Array of grid cells of the detected visual anomalies, if visual anomaly detection is enabled.
public uint32_t visual_ad_count
Number of grid cells detected as visual anomalies, if visual anomaly detection is enabled.
public ei_impulse_visual_ad_result_t visual_ad_result
Visual anomaly detection result, if visual anomaly detection is enabled.
ei_signal_t
Holds the callback pointer for retrieving raw data and the length of data to be retrieved.
Holds the callback function, get_data(size_t offset, size_t length, float *out_ptr). This callback should be implemented by the user and fills the memory location given by *out_ptr with raw features. Features must be flattened to a 1-dimensional vector, as described in this guide.
get_data() may be called multiple times during preprocessing or inference (e.g. during execution of run_classifier() or run_classifier_continuous()). The offset argument will update to point to new data, and length data must be copied into the location specified by out_ptr. This scheme allows raw features to be stored in RAM or flash memory and paged in as necessary.
Note that get_data() (even after multiple calls during a single execution of run_classifier() or run_classifier_continuous()) will never request more than a total number of features as given by total_length.
Source: dsp/numpy_types.h
Example: standalone inferencing main.cpp
public std::function< int(size_t offset, size_t length, float *out_ptr)> get_data
Callback function to be implemented by the user. Parameters are given as get_data(size_t offset, size_t length, float *out_ptr) and should return an int (e.g. EIDSP_OK if copying completed successfully). No bytes will be requested outside of the total_length. Callback parameters:
offset: The offset in the signal
length: The number of samples to write into out_ptr
out_ptr: An out buffer to set the signal data
public size_t total_length
Total number of samples the user will provide (via get_data). This value should match either the total number of raw features required for a full window (ie, the window size in Studio, but in samples), OR, if using run_classifier_continuous(), the number of samples in a single slice) for a new slice (run_classifier_continuous()) in order to perform preprocessing and inference.
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