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/* The Clear BSD License
*
* Copyright (c) 2025 EdgeImpulse Inc.
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted (subject to the limitations in the disclaimer
* below) provided that the following conditions are met:
*
* * Redistributions of source code must retain the above copyright notice,
* this list of conditions and the following disclaimer.
*
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
*
* * Neither the name of the copyright holder nor the names of its
* contributors may be used to endorse or promote products derived from this
* software without specific prior written permission.
*
* NO EXPRESS OR IMPLIED LICENSES TO ANY PARTY'S PATENT RIGHTS ARE GRANTED BY
* THIS LICENSE. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND
* CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
* PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR
* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
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* IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
* ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
#ifndef EI_POSTPROCESSING_THRESHOLDS_H
#define EI_POSTPROCESSING_THRESHOLDS_H
#include "model-parameters/model_metadata.h"
#include "edge-impulse-sdk/classifier/postprocessing/ei_postprocessing_types.h"
#include "edge-impulse-sdk/classifier/postprocessing/ei_postprocessing_ai_hub.h"
#include "edge-impulse-sdk/classifier/ei_model_types.h"
#include "edge-impulse-sdk/classifier/ei_classifier_types.h"
#include <string>
#include "edge-impulse-sdk/classifier/postprocessing/ei_postprocessing_common.h"
#if EI_CLASSIFIER_OBJECT_TRACKING_ENABLED == 1
#include "edge-impulse-sdk/classifier/postprocessing/ei_object_tracking.h"
#endif // EI_CLASSIFIER_OBJECT_TRACKING_ENABLED == 1
#if EI_CLASSIFIER_CALIBRATION_ENABLED == 1
#include "edge-impulse-sdk/classifier/postprocessing/ei_performance_calibration.h"
#endif // EI_CLASSIFIER_CALIBRATION_ENABLED == 1
/**
* @brief Get all configurable thresholds for a postprocessing block
* @param pp_block Pointer to a postprocessing block
* @param out_thresholds Vector of thresholds
* @return EI_IMPULSE_OK
*/
EI_IMPULSE_ERROR get_thresholds_postprocessing(const ei_postprocessing_block_t *pp_block, std::vector<ei_threshold_desc_t>& out_thresholds) {
switch (pp_block->type) {
case EI_CLASSIFIER_MODE_OBJECT_DETECTION: {
if (pp_block->postprocess_fn == process_paddleocr_f32) {
ei_fill_result_paddleocr_f32_config_t *config = (ei_fill_result_paddleocr_f32_config_t*)pp_block->config;
out_thresholds.push_back({
"object_detection", /* type */
"min_score_pixel", /* name */
config->min_score_pixel, /* value */
[config](float v) {
config->min_score_pixel = v;
}
});
out_thresholds.push_back({
"object_detection", /* type */
"min_score_box", /* name */
config->min_score_box, /* value */
[config](float v) {
config->min_score_box = v;
}
});
out_thresholds.push_back({
"object_detection", /* type */
"unclip_ratio", /* name */
config->unclip_ratio, /* value */
[config](float v) {
config->unclip_ratio = v;
}
});
}
else if (pp_block->postprocess_fn == process_paddleocr_i8) {
ei_fill_result_paddleocr_i8_config_t *config = (ei_fill_result_paddleocr_i8_config_t*)pp_block->config;
out_thresholds.push_back({
"object_detection", /* type */
"min_score_pixel", /* name */
config->min_score_pixel, /* value */
[config](float v) {
config->min_score_pixel = v;
}
});
out_thresholds.push_back({
"object_detection", /* type */
"min_score_box", /* name */
config->min_score_box, /* value */
[config](float v) {
config->min_score_box = v;
}
});
out_thresholds.push_back({
"object_detection", /* type */
"unclip_ratio", /* name */
config->unclip_ratio, /* value */
[config](float v) {
config->unclip_ratio = v;
}
});
}
else {
ei_fill_result_object_detection_threshold_config_t *config = (ei_fill_result_object_detection_threshold_config_t*)pp_block->config;
out_thresholds.push_back({
"object_detection", /* type */
"min_score", /* name */
config->threshold, /* value */
[config](float v) {
config->threshold = v;
}
});
}
break;
}
case EI_CLASSIFIER_MODE_VISUAL_ANOMALY: {
ei_fill_result_visual_ad_f32_config_t *config = (ei_fill_result_visual_ad_f32_config_t*)pp_block->config;
out_thresholds.push_back({
"anomaly_gmm", /* type */
"min_anomaly_score", /* name */
config->threshold, /* value */
[config](float v) {
config->threshold = v;
}
});
break;
}
}
#if EI_CLASSIFIER_OBJECT_TRACKING_ENABLED == 1
if (pp_block->init_fn == init_object_tracking) {
ei_object_tracking_config_t *config = (ei_object_tracking_config_t*)pp_block->config;
out_thresholds.push_back({
"object_tracking", /* type */
"keep_grace", /* name */
static_cast<float>(config->keep_grace), /* value */
[config](float v) {
config->keep_grace = static_cast<uint32_t>(v);
}
});
out_thresholds.push_back({
"object_tracking", /* type */
"max_observations", /* name */
static_cast<float>(config->max_observations), /* value */
[config](float v) {
config->max_observations = static_cast<uint16_t>(v);
}
});
out_thresholds.push_back({
"object_tracking", /* type */
"threshold", /* name */
config->threshold, /* value */
[config](float v) {
config->threshold = v;
}
});
}
#endif // EI_CLASSIFIER_OBJECT_TRACKING_ENABLED == 1
#if EI_CLASSIFIER_CALIBRATION_ENABLED == 1
if (pp_block->init_fn == init_perfcal) {
ei_performance_calibration_config_t *config = (ei_performance_calibration_config_t*)pp_block->config;
out_thresholds.push_back({
"performance_calibration", /* type */
"average_window_duration_ms", /* name */
static_cast<float>(config->average_window_duration_ms), /* value */
[config](float v) {
config->average_window_duration_ms = static_cast<uint32_t>(v);
}
});
out_thresholds.push_back({
"performance_calibration", /* type */
"detection_threshold", /* name */
config->detection_threshold, /* value */
[config](float v) {
config->detection_threshold = v;
}
});
out_thresholds.push_back({
"performance_calibration", /* type */
"suppression_ms", /* name */
static_cast<float>(config->suppression_ms), /* value */
[config](float v) {
config->suppression_ms = static_cast<uint32_t>(v);
}
});
out_thresholds.push_back({
"performance_calibration", /* type */
"suppression_flags", /* name */
static_cast<float>(config->suppression_flags), /* value */
[config](float v) {
config->suppression_flags = static_cast<uint32_t>(v);
}
});
}
#endif // EI_CLASSIFIER_CALIBRATION_ENABLED == 1
return EI_IMPULSE_OK;
}
/**
* @brief Set a threshold for a postprocessing block (view available thresholds via get_thresholds_postprocessing)
* @param pp_block Pointer to a postprocessing block
* @param name Name of the threshold
* @param value New value of the threshold
* @return EI_IMPULSE_OK if threshold was set; EI_IMPULSE_POSTPROCESSING_THRESHOLD_KEY_NOT_FOUND
* if threshold key was not found for this block.
*/
EI_IMPULSE_ERROR set_threshold_postprocessing(const ei_postprocessing_block_t *pp_block, std::string name, float value) {
std::vector<ei_threshold_desc_t> thresholds;
EI_IMPULSE_ERROR res = get_thresholds_postprocessing(pp_block, thresholds);
if (res != EI_IMPULSE_OK) {
return res;
}
bool found_threshold = false;
for (auto threshold : thresholds) {
if (threshold.name != name) continue;
threshold.set_value(value);
found_threshold = true;
break;
}
if (!found_threshold) {
return EI_IMPULSE_POSTPROCESSING_THRESHOLD_KEY_NOT_FOUND;
}
return EI_IMPULSE_OK;
}
#endif // EI_POSTPROCESSING_THRESHOLDS_H