feedc0de/arduino-esp32
1
0
Fork 0
forked from espressif/arduino-esp32
Code Pull Requests Activity

IDF release/v4.4 f23dcd3555 (#5996)

Browse Source 
esp-dsp: master 6b25cbb
esp-face: master d141502
esp-rainmaker: f1b82c7
esp32-camera: master 61400bc
esp_littlefs: master 3c29afc
This commit is contained in:
Me No Dev
2021-12-14 16:38:06 +02:00
committed by GitHub
parent 7bb30b3cf8
commit 6d400df952
348 changed files with 2934 additions and 932 deletions
Download Patch File Download Diff File Expand all files Collapse all files

52
tools/sdk/esp32s2/include/esp-face/include/image/dl_image.hpp
View File

@ -48,6 +48,15 @@ namespace dl
output[2] = input & 0xF8; // red
}
/**
* @brief Convert RGB565 image to RGB888 image.
*
* @param image ptr of RGB565 image
* @param image_shape shape of the input image
* @return Tensor<uint8_t>* output RGB88 image
*/
Tensor<uint8_t> *convert_image_rgb565_to_rgb888(uint16_t *image, std::vector<int> &image_shape);
/**
* @brief Convert RGB565 pixel to Gray.
*
@ -435,5 +444,48 @@ namespace dl
*/
Tensor<uint8_t> *rgb2hsv(Tensor<uint8_t> &image, bool bgr = false, bool fast = true);
/**
* @brief resize an image to the target shape.
*
* @param image the input image Tensor
* @param target_shape the target shape of the resized image.
* @param resize_type one of IMAGE_RESIZE_BILINEAR or IMAGE_RESIZE_MEAN or IMAGE_RESIZE_NEAREST
* @return Tensor<uint8_t>* the pointer of the resized image Tensor
*/
Tensor<uint8_t> *resize_image(Tensor<uint8_t> &image, std::vector<int> target_shape, resize_type_t resize_type);
/**
* @brief resize an image to the target shape.
*
* @param image the input image Tensor
* @param resized_image the resized image Tensor
* @param resize_type one of IMAGE_RESIZE_BILINEAR or IMAGE_RESIZE_MEAN or IMAGE_RESIZE_NEAREST
*/
void resize_image(Tensor<uint8_t> &image, Tensor<uint8_t> &resized_image, resize_type_t resize_type);
/**
* @brief resize an image to the target shape with nearest method.
*
* @tparam T
* @param image the pointer of the input image
* @param input_shape the input shape of the image
* @param target_shape the target shape of the resized image
* @return T* the pointer of the resized image
*/
template <typename T>
T *resize_image_nearest(T *image, std::vector<int> input_shape, std::vector<int> target_shape);
/**
* @brief resize an image to the target shape with nearest method.
*
* @tparam T
* @param image the pointer of the input image
* @param input_shape the input shape of the image
* @param resized_image the pointer of the resized image
* @param target_shape the target shape of the resized image
*/
template <typename T>
void resize_image_nearest(T *image, std::vector<int> input_shape, T *resized_image, std::vector<int> target_shape);
} // namespace image
} // namespace dl

4
tools/sdk/esp32s2/include/esp-face/include/layer/dl_layer_avg_pool2d.hpp
View File

@ -57,10 +57,10 @@ namespace dl
const char *name = "AvgPool2D") : Layer(name),
output_exponent(output_exponent),
filter_shape(filter_shape),
padding_type(padding_type),
padding(padding),
stride_y(stride_y),
stride_x(stride_x),
padding_type(padding_type),
padding(padding),
output_shape({})
{
this->output = new Tensor<feature_t>;

5
tools/sdk/esp32s2/include/esp-face/include/layer/dl_layer_expand_dims.hpp
View File

@ -36,7 +36,10 @@ namespace dl
* false: the output will store to a separate memory
*/
ExpandDims(std::vector<int> axis, const char *name = "ExpandDims", bool inplace = false) : Layer(name),
axis(axis), inplace(inplace), output_shape({})
output_shape({}),
axis(axis),
output(NULL),
inplace(inplace)
{
}

2
tools/sdk/esp32s2/include/esp-face/include/layer/dl_layer_flatten.hpp
View File

@ -32,7 +32,7 @@ namespace dl
* @param inplace true: the output will store to input0
* false: the output will store to a separate memory
*/
Flatten(const char *name = "Flatten", bool inplace = false) : Layer(name), inplace(inplace), output_shape({})
Flatten(const char *name = "Flatten", bool inplace = false) : Layer(name), output(NULL), inplace(inplace), output_shape({})
{}
/**

4
tools/sdk/esp32s2/include/esp-face/include/layer/dl_layer_max_pool2d.hpp
View File

@ -53,10 +53,10 @@ namespace dl
const int stride_x = 1,
const char *name = "MaxPool2D") : Layer(name),
filter_shape(filter_shape),
padding_type(padding_type),
padding(padding),
stride_y(stride_y),
stride_x(stride_x),
padding_type(padding_type),
padding(padding),
output_shape({})
{
this->output = new Tensor<feature_t>;

6
tools/sdk/esp32s2/include/esp-face/include/layer/dl_layer_relu.hpp
View File

@ -18,7 +18,7 @@ namespace dl
* - int8_t: stands for operation in int8_t quantize
*/
template <typename feature_t>
class ReLU : public Layer
class Relu : public Layer
{
private:
Tensor<feature_t> *output; /*<! output ptr of relu >*/
@ -33,7 +33,7 @@ namespace dl
* @param inplace true: the output will store to input0
* false: the output will store to a separate memory
*/
ReLU(const char *name = "ReLU", bool inplace = false) : Layer(name),
Relu(const char *name = "Relu", bool inplace = false) : Layer(name),
output(NULL), inplace(inplace), output_shape({})
{
}
@ -42,7 +42,7 @@ namespace dl
* @brief Destroy the ReLU object
*
*/
~ReLU()
~Relu()
{
if ((!this->inplace) && (this->output != NULL))
{

4
tools/sdk/esp32s2/include/esp-face/include/layer/dl_layer_reshape.hpp
View File

@ -35,7 +35,9 @@ namespace dl
* false: the output will store to a separate memory
*/
Reshape(std::vector<int> shape, const char *name = "Reshape", bool inplace = false) : Layer(name),
output_shape(shape), inplace(inplace)
output(NULL),
inplace(inplace),
output_shape(shape)
{
}

6
tools/sdk/esp32s2/include/esp-face/include/layer/dl_layer_squeeze.hpp
View File

@ -35,7 +35,11 @@ namespace dl
* @param inplace true: the output will store to input0
* false: the output will store to a separate memory
*/
Squeeze(int axis = INT32_MAX, const char *name = "Squeeze", bool inplace = false) : Layer(name), axis(axis), inplace(inplace), output_shape({})
Squeeze(int axis = INT32_MAX, const char *name = "Squeeze", bool inplace = false) : Layer(name),
output(NULL),
inplace(inplace),
axis(axis),
output_shape({})
{
}

6
tools/sdk/esp32s2/include/esp-face/include/layer/dl_layer_sub2d.hpp
View File

@ -38,7 +38,11 @@ namespace dl
* false: the output will store to a separate memory
*/
Sub2D(const int output_exponent, const Activation<feature_t> *activation = NULL, const char *name = "Sub2D", bool inplace = false) : Layer(name),
output_exponent(output_exponent), activation(activation), output(NULL), inplace(inplace), output_shape({})
output_exponent(output_exponent),
activation(activation),
output(NULL),
inplace(inplace),
output_shape({})
{
}

6
tools/sdk/esp32s2/include/esp-face/include/layer/dl_layer_transpose.hpp
View File

@ -33,7 +33,11 @@ namespace dl
* @param inplace true: the output will store to input
* false: the output will store to a separate memory
*/
Transpose(std::vector<int> perm = {}, const char *name = "Transpose", bool inplace = false) : Layer(name), perm(perm), inplace(inplace), output_shape({})
Transpose(std::vector<int> perm = {}, const char *name = "Transpose", bool inplace = false) : Layer(name),
output(NULL),
inplace(inplace),
perm(perm),
output_shape({})
{
}

334
tools/sdk/esp32s2/include/esp-face/include/model_zoo/color_detector.hpp
View File

@ -7,46 +7,143 @@ typedef struct
int area; /*!< Area of connected domains >*/
std::vector<int> center; /*<! centroid of connected domains [x, y] >*/
std::vector<int> box; /*<! [left_up_x, left_up_y, right_down_x, right_down_y] >*/
} components_stats_t;
} color_detect_result_t;
typedef struct
{
std::vector<int> start_col;
std::vector<int> end_col;
std::vector<int> row;
std::vector<int> index;
std::vector<int> area;
} color_segment_result_t;
typedef struct
{
std::vector<uint8_t> color_thresh; /*!< threshold of colors, The threshold of each color is composed of 6 numbers >*/
int area_thresh; /*!< the area threshold of each color,
the area that is smaller than the threshold is filtered >*/
std::string name; /*!<name of the color>*/
} color_info_t;
class ColorDetector
{
private:
std::vector<std::vector<components_stats_t>> results; /*!< detection results >*/
std::vector<std::vector<color_detect_result_t>> detection_results; /*!< detection results >*/
std::vector<color_segment_result_t> segmentation_results; /*!< segmentation results >*/
std::vector<color_info_t> registered_colors; /*!< the infomation of registered colors >*/
std::vector<uint8_t> color_thresh_offset; /*!< HSV offset of the registered colors>*/
std::vector<int> detection_shape; /*!< the inference shape of images, the input image will be resized to this shape.
if the shape == {}, the input image will not be resized >*/
bool bgr; /*!< true: the input image is in BGR format
false: the input image is in RGB format >*/
int id_nums; /*!< the number of registered colors in history>*/
float h_ratio;
float w_ratio;
void color_detection_forward(dl::Tensor<uint8_t> &bin, int area_thresh);
public:
std::vector<std::vector<uint8_t>> color_thresh; /*!< threshold of colors, The threshold of each color is composed of 6 numbers >*/
std::vector<int> area_thresh; /*!< the area threshold of each color,
the area that is smaller than the threshold is filtered >*/
bool bgr; /*!< true: the input image is in BGR format
false: the input image is in RGB format >*/
/**
* @brief get the color threshold of rectangular region in the image
*
* @param image the input image
* @param image the input image in RGB888 format.
* @param box the coordinates of the rectanglar region : [left_up_x, left_up_y, right_down_x, right_down_y]
* @return std::vector<uint8_t> the threshold.
*/
std::vector<uint8_t> cal_color_thresh(dl::Tensor<uint8_t> &image, std::vector<int> box);
/**
* @brief get the color threshold of rectangular region in the image
*
* @param input the ptr of RGB565 image.
* @param input_shape shape of the input image.
* @param box the coordinates of the rectanglar region : [left_up_x, left_up_y, right_down_x, right_down_y]
* @return std::vector<uint8_t> the threshold.
*/
std::vector<uint8_t> cal_color_thresh(uint16_t *input, std::vector<int> input_shape, std::vector<int> box);
/**
* @brief register a new color to the color detector
*
* @param image the input image in RGB888 format.
* @param box the coordinates of the rectanglar region : [left_up_x, left_up_y, right_down_x, right_down_y]
* @param area_thresh the area threshold of the color
* @param id the index of the color
* @return int the number of the registered colors. if the id is not valid, return -1.
*/
int register_color(dl::Tensor<uint8_t> &image, std::vector<int> box, int area_thresh = 256, std::string color_name = "", int id = -1);
/**
* @brief register a new color to the color detector
*
* @param input the ptr of RGB565 image.
* @param input_shape shape of the input image.
* @param box the coordinates of the rectanglar region : [left_up_x, left_up_y, right_down_x, right_down_y]
* @param area_thresh the area threshold of the color
* @param id the index of the color
* @return int the number of the registered colors. if the id is not valid, return -1.
*/
int register_color(uint16_t *input, std::vector<int> input_shape, std::vector<int> box, int area_thresh = 256, std::string color_name = "", int id = -1);
/**
* @brief register a new color to the color detector
*
* @param color_thresh the color threshold
* @param area_thresh the area threshold of the color
* @param id the index of the color
* @return int the number of the registered colors. if the id is not valid, return -1.
*/
int register_color(std::vector<uint8_t> color_thresh, int area_thresh = 256, std::string color_name = "", int id = -1);
/**
* @brief delete a registered color
*
* @param id the index of the color
* @return int the number of the registered colors. if the id is not valid, return -1.
*/
int delete_color(int id = -1);
/**
* @brief delete a registered color
*
* @param color_name name of the registered_color
* @return int the number of the registered colors. if the id is not valid, return -1.
*/
int delete_color(std::string color_name);
/**
* @brief delete all the registered colors
*
*/
void clear_color();
/**
* @brief detect the colors based on the color thresholds
*
* @param image the input image.
* @return std::vector<std::vector<components_stats_t>>& detection result.
* @return std::vector<std::vector<color_detect_result_t>>& detection result.
*/
std::vector<std::vector<components_stats_t>> &detect(dl::Tensor<uint8_t> &image);
std::vector<std::vector<color_detect_result_t>> &detect(dl::Tensor<uint8_t> &image, std::vector<int> color_ids = {});
/**
* @brief
*
* @param input
* @param input_shape
* @return std::vector<std::vector<color_detect_result_t>>&
*/
std::vector<std::vector<color_detect_result_t>> &detect(uint16_t *input_shape, std::vector<int> shape, std::vector<int> color_ids = {});
/**
* @brief Construct a new Color Detector object
*
* @param color_thresh threshold of colors, The threshold of each color is composed of 6 numbers
* @param area_thresh the area threshold of each color,the area that is smaller than the threshold is filtered
* @param color_thresh_offset HSV offset of the registered colors>
* @param detection_shape the inference shape of images, the input image will be resized to this shape
* @param bgr true: the input image is in BGR format
* false: the input image is in RGB format
*/
ColorDetector(std::vector<std::vector<uint8_t>> color_thresh, std::vector<int> area_thresh, bool bgr = false) : color_thresh(color_thresh), area_thresh(area_thresh), bgr(bgr)
ColorDetector(std::vector<uint8_t> color_thresh_offset = {}, std::vector<int> detection_shape = {}, bool bgr = true) : color_thresh_offset(color_thresh_offset),
detection_shape(detection_shape), bgr(bgr), id_nums(0)
{
}
@ -57,12 +154,213 @@ public:
~ColorDetector() {}
/**
* @brief Get the results object
* @brief Get the detection results object
*
* @return std::vector<std::vector<components_stats_t>>& the detection result.
* @return std::vector<std::vector<color_detect_result_t>>& the detection result.
*/
std::vector<std::vector<components_stats_t>> &get_results()
std::vector<std::vector<color_detect_result_t>> &get_detection_results()
{
return this->results;
return this->detection_results;
}
/**
* @brief Get the segmentation results object
*
* @return std::vector<color_segment_result_t>& the segmentation result.
*/
std::vector<color_segment_result_t> &get_segmentation_results()
{
return this->segmentation_results;
}
/**
* @brief Get the registered colors object
*
* @return std::vector<color_info_t> the information of resgistered colors
*/
std::vector<color_info_t> get_registered_colors()
{
return this->registered_colors;
}
/**
* @brief Set the color thresh offset object
*
* @param color_thresh_offset the offset of color thresh for registered colors
* @return ColorDetector&
*/
ColorDetector &set_color_thresh_offset(std::vector<uint8_t> color_thresh_offset)
{
assert(color_thresh_offset.size() == 3);
this->color_thresh_offset = color_thresh_offset;
return *this;
}
/**
* @brief Get the color thresh offset object
*
* @return std::vector<uint8_t> color_thresh_offset
*/
std::vector<uint8_t> get_color_thresh_offset()
{
return this->color_thresh_offset;
}
/**
* @brief Set the area thresh object
*
* @param area_thresh the area thresh for each registered colors
* @return ColorDetector&
*/
ColorDetector &set_area_thresh(std::vector<int> area_thresh)
{
assert((area_thresh.size() == this->registered_colors.size()) || (area_thresh.size() == 1));
if (area_thresh.size() == 1)
{
for (int i = 0; i < this->registered_colors.size(); ++i)
{
this->registered_colors[i].area_thresh = area_thresh[0];
}
}
else
{
for (int i = 0; i < this->registered_colors.size(); ++i)
{
this->registered_colors[i].area_thresh = area_thresh[i];
}
}
return *this;
}
/**
* @brief Set the area thresh object
*
* @param area_thresh the area thresh for each registered colors
* @param id index of the registered color
* @return ColorDetector&
*/
ColorDetector &set_area_thresh(int area_thresh, int id)
{
assert((id >= 0) && (id < this->registered_colors.size()));
this->registered_colors[id].area_thresh = area_thresh;
return *this;
}
/**
* @brief Set the bgr object
*
* @param bgr
* @return ColorDetector&
*/
ColorDetector &set_bgr(bool bgr)
{
this->bgr = bgr;
return *this;
}
/**
* @brief Get the bgr object
*
* @return bool bgr flag
*/
bool get_bgr()
{
return this->bgr;
}
/**
* @brief Get the detection shape object
*
* @return std::vector<int>
*/
std::vector<int> get_detection_shape()
{
return this->detection_shape;
}
/**
* @brief Set the detection shape object
*
* @param detection_shape the inference shape of images, the input image will be resized to this shape
* @return ColorDetector&
*/
ColorDetector &set_detection_shape(std::vector<int> detection_shape)
{
assert(detection_shape.size() == 3);
this->detection_shape = detection_shape;
return *this;
}
/**
* @brief Get the registered colors num
*
* @return int the registered colors num
*/
int get_registered_colors_num()
{
return this->registered_colors.size();
}
/**
* @brief print the detection detection results
*
* @param tag
*/
void print_detection_results(const char *tag = "RGB")
{
printf("\n%s | color detection result:\n", tag);
for (int i = 0; i < this->detection_results.size(); ++i)
{
printf("color %d: detected box :%d\n", i, this->detection_results[i].size());
for (int j = 0; j < this->detection_results[i].size(); ++j)
{
printf("center: (%d, %d)\n", this->detection_results[i][j].center[0], this->detection_results[i][j].center[1]);
printf("box: (%d, %d), (%d, %d)\n", this->detection_results[i][j].box[0], this->detection_results[i][j].box[1], this->detection_results[i][j].box[2], this->detection_results[i][j].box[3]);
printf("area: %d\n", this->detection_results[i][j].area);
}
printf("\n");
}
}
/**
* @brief print the segmentation results
*
* @param tag
*/
void print_segmentation_results(const char *tag = "RGB")
{
printf("\n%s | color segmentation result:\n", tag);
for (int i = 0; i < this->segmentation_results.size(); ++i)
{
printf("color %d: detected box :%d\n", i, this->detection_results[i].size());
for (int j = 0; j < this->segmentation_results[i].index.size(); ++j)
{
printf("box_index: %d, start col: %d, end col: %d, row: %d, area: %d\n",
this->segmentation_results[i].index[j], this->segmentation_results[i].start_col[j], this->segmentation_results[i].end_col[j],
this->segmentation_results[i].row[j], this->segmentation_results[i].area[j]);
}
printf("\n");
}
}
/**
* @brief draw the color segmentation result on the input image
*
* @param image the input RGB image
* @param draw_colors RGB values for each detected colors
* @param draw_backgound draw the background if it is true
* @param background_color RGB values for the background color
*/
void draw_segmentation_results(dl::Tensor<uint8_t> &image, std::vector<std::vector<uint8_t>> draw_colors, bool draw_backgound = true, std::vector<uint8_t> background_color = {0, 0, 0});
/**
* @brief draw the color segmentation result on the input image
*
* @param image the pointer of the input RGB565 image
* @param image_shape the shape of the input image
* @param draw_colors RGB565 values for each detected colors
* @param draw_backgound draw the background if it is true
* @param background_color RGB565 values for the background color
*/
void draw_segmentation_results(uint16_t *image, std::vector<int> image_shape, std::vector<uint16_t> draw_colors, bool draw_backgound = true, uint16_t background_color = 0x0000);
};

8
tools/sdk/esp32s2/include/esp-face/include/model_zoo/face_recognition_tool.hpp
View File

@ -10,6 +10,7 @@
#include <algorithm>
#include <math.h>
#include <string>
#include "esp_partition.h"
/**
* @brief struct of face similarity
@ -45,6 +46,13 @@ public:
*/
FaceID(int id, dl::Tensor<feature_t> &id_emb, std::string name = "");
/**
* @brief Construct a new Face ID which is same as input face_id
*
* @param face_id input face_id
*/
FaceID(FaceID<feature_t> &face_id);
/**
* @brief Destroy the Face ID object
*

108
tools/sdk/esp32s2/include/esp-face/include/model_zoo/face_recognizer.hpp
View File

@ -148,7 +148,7 @@ class FaceRecognizer
* @param name name of the face id.
* @return int the face id index of the enrolled embedding.
*/
int enroll_id(uint16_t *image_input, std::vector<int> shape, std::vector<int> &landmarks, std::string name="");
int enroll_id(uint16_t *image_input, std::vector<int> shape, std::vector<int> &landmarks, std::string name="", bool update_flash = false);
/**
* @brief enroll face id
@ -158,9 +158,11 @@ class FaceRecognizer
* @param aligned_face the Tensor to store the intermeidate aligned face.
* @param landmarks face landmarks coordinates
* @param name name of the face id.
* @param update_flash true: the enrolled ids will be stored to flash
* false: the enrolled ids will not be stored to flash
* @return int the face id index of the enrolled embedding.
*/
int enroll_id(uint16_t *image_input, std::vector<int> shape, Tensor<uint8_t> &aligned_face, std::vector<int> &landmarks, std::string name="");
int enroll_id(uint16_t *image_input, std::vector<int> shape, Tensor<uint8_t> &aligned_face, std::vector<int> &landmarks, std::string name="", bool update_flash = false);
/**
* @brief enroll face id
@ -168,9 +170,11 @@ class FaceRecognizer
* @param image_input the Tensor of input image with format bgr888.
* @param landmarks face landmarks coordinates
* @param name name of the face id.
* @param update_flash true: the enrolled ids will be stored to flash
* false: the enrolled ids will not be stored to flash
* @return int the face id index of the enrolled embedding.
*/
int enroll_id(Tensor<uint8_t> &image_input, std::vector<int> &landmarks, std::string name="");
int enroll_id(Tensor<uint8_t> &image_input, std::vector<int> &landmarks, std::string name="", bool update_flash = false);
/**
* @brief enroll face id
@ -179,42 +183,114 @@ class FaceRecognizer
* @param aligned_face the Tensor to store the intermeidate aligned face.
* @param landmarks face landmarks coordinates
* @param name name of the face id.
* @param update_flash true: the enrolled ids will be stored to flash
* false: the enrolled ids will not be stored to flash
* @return int the face id index of the enrolled embedding.
*/
int enroll_id(Tensor<uint8_t> &image_input, Tensor<uint8_t> &aligned_face, std::vector<int> &landmarks, std::string name="");
int enroll_id(Tensor<uint8_t> &image_input, Tensor<uint8_t> &aligned_face, std::vector<int> &landmarks, std::string name="", bool update_flash = false);
/**
* @brief enroll face id
*
* @param aligned_face the Tensor of the input aligned face with format bgr888.
* @param name name of the face id.
* @param update_flash true: the enrolled ids will be stored to flash
* false: the enrolled ids will not be stored to flash
* @return int the face id index of the enrolled embedding.
*/
int enroll_id(Tensor<uint8_t> &aligned_face, std::string name="");
int enroll_id(Tensor<uint8_t> &aligned_face, std::string name="", bool update_flash = false);
/**
* @brief enroll the normalzied face embedding.
*
* @param emb the normalized face embbeding.
* @param name name of the face id.
* @return int the face id index of the enrolled embedding.
* @param emb the normalized face embbeding.
* @param name name of the face id.
* @param update_flash true: the enrolled ids will be stored to flash
* false: the enrolled ids will not be stored to flash
* @return int the face id index of the enrolled embedding.
*/
int enroll_id(Tensor<float> &emb, std::string name="");
int enroll_id(Tensor<float> &emb, std::string name="", bool update_flash = false);
/**
* @brief delete the last enrolled face id.
* @brief delete the last enrolled face id.
* @param update_flash true: the ids will be updated to flash
* false: the ids will not be stored to flash
*
* @return int the number of remained face ids.
* if the face ids list is empty, return -1
*/
int delete_id();
int delete_id(bool update_flash = false);
/**
* @brief delete the face id with id index.
* @brief delete the face id with id index.
*
* @param id face id index.
* @return int the number of remained face ids.
* if there is no matched id return -1
* @param id face id index.
* @param update_flash true: the ids will be updated to flash
* false: the ids will not be stored to flash
* @return int the number of remained face ids.
* if there is no matched id return -1
*/
int delete_id(int id);
int delete_id(int id, bool update_flash = false);
/**
* @brief Set the enrolled ids
*
* @param ids the ids to be set
* @param update_flash true: the ids will be updated to flash
* false: the ids will not be stored to flash
* @return int the number of enrolled ids.
*/
int set_ids(std::vector<FaceID<float> *> &ids, bool update_flash = false);
/**
* @brief Set the enrolled ids from flash
*
* @return int the number of enrolled ids.
*/
int set_ids_from_flash();
/**
* @brief write the enrolled ids to flash
*
* @return int the number of enrolled ids.
*/
int write_ids_to_flash();
/**
* @brief Get the enrolled ids with name object
*
* @param name
* @return std::vector<face_info_t>
*/
std::vector<face_info_t> get_enrolled_ids_with_name(std::string name);
/**
* @brief Check whether the Flash partition is available
*
* @return int -2: the partition has not been set
* -1: the data in the flash does not match the current model.
* model_check_code: the Flash partition is available.
* number of ids in flash: The IDs in Flash and RAM does not sync.
*/
int check_partition();
/**
* @brief delete all the enrolled face ids.
* @param update_flash true: the ids will be updated to flash
* false: the ids will not be stored to flash
*
*/
void clear_id(bool update_flash = false);
/**
* @brief Set the partition for saving face ids to flash or reading face ids from flash.
*
* @param type esp_partition_type
* @param subtype esp_partition_subtype
* @param label the partition label
* @return int 0: set the partition failed
* 1: set the partition successfully
*/
int set_partition(esp_partition_type_t type, esp_partition_subtype_t subtype, const char *label);
};

26
tools/sdk/esp32s2/include/esp-face/include/typedef/dl_constant.hpp
View File

@ -2,6 +2,7 @@
#include "dl_define.hpp"
#include <vector>
#include <stdint.h>
namespace dl
{
@ -48,10 +49,11 @@ namespace dl
/*<! - 2D: [dilation_in_height, dilation_in_width] >*/
std::vector<int> shape_with_dilation; /*<! - 1D: reserved >*/
/*<! - 2D: [filter_height_with_dilation, filter_width_with_dilation, input_channel, output_channel] >*/
std::vector<int> channel_exponent; /*<! exponent for per-channel >*/
const int8_t* channel_exponent; /*<! exponent for per-channel >*/
const int channel_exponent_size;
/**
* @brief Construct a new Filter object.
* @brief Construct a new Filter object.
*
* @param element point to element
* @param exponent exponent of element
@ -66,16 +68,17 @@ namespace dl
Filter(const T *element, const int exponent, const std::vector<int> shape, const std::vector<int> dilation = {1, 1});
/**
* @brief Construct a new Filter object.
* @brief Construct a new Filter object. it is only avaliable to int16_t
*
* @param element point to element
* @param channel_exponent exponent for per-channel
* @param shape shape of element
* @param dilation dilation of Filter
* - 1D: reserved
* - 2D: [dilation_in_height, dilation_in_width]
* @param element point to element
* @param channel_exponent exponent for per-channel
* @param channel_exponent_size size of exponent
* @param shape shape of element
* @param dilation dilation of Filter
* - 1D: reserved
* - 2D: [dilation_in_height, dilation_in_width]
*/
Filter(const T *element, const std::vector<int> channel_exponent, const std::vector<int> shape, const std::vector<int> dilation = {1, 1});
Filter(const T *element, const int8_t* channel_exponent, const int channel_exponent_size, const std::vector<int> shape, const std::vector<int> dilation = {1, 1});
/**
* @brief Print the n-th filter.
@ -98,9 +101,6 @@ namespace dl
{
public:
using Constant<T>::Constant;
std::vector<int> channel_exponent; /*<! exponent for per-channel >*/
Bias(const T *element, const std::vector<int> channel_exponent, const std::vector<int> shape);
};
/**