ei_impulse_result_classification_t
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
| Member | Description |
|---|---|
public const char * label | Label of the detected object |
public float value | Value of the detected object |
ei_impulse_visual_ad_result_t
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
| Member | Description |
|---|---|
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
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
| Member | Description |
|---|---|
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
| Member | Description |
|---|---|
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
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
| Member | Description |
|---|---|
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
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
| Member | Description |
|---|---|
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_ptrout_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. |