mindspore::dataset

#include <lite_mat.h>
#include <image_process.h>

Functions of image_process.h

ResizeBilinear

bool ResizeBilinear(LiteMat &src, LiteMat &dst, int dst_w, int dst_h)

Resize image by bilinear algorithm, currently the data type only supports uint8, the channel only supports 3 and 1.

• Parameters

  • src: Input image data.

  • dst: Output image data.

  • dst_w: The width of the output image data.

  • dst_h: The height of the output image data.

• Returns

  Return True or False.

InitFromPixel

bool InitFromPixel(const unsigned char *data, LPixelType pixel_type, LDataType data_type, int w, int h, LiteMat &m)

Initialize LiteMat from pixel, currently the conversion supports rbgaTorgb and rgbaTobgr.

• Parameters

  • data: Input data.

  • pixel_type: The type of pixel.

  • data_type: The type of data.

  • w: The width of the output data.

  • h: The height of the output data.

  • mat: Used to store image data.

• Returns

  Return True or False.

ConvertTo

bool ConvertTo(LiteMat &src, LiteMat &dst, double scale = 1.0)

Convert the data type, currently it supports converting the data type from uint8 to float.

• Parameters

  • src: Input image data.

  • dst: Output image data.

  • scale: Scale pixel values(default=1.0).

• Returns

  Return True or False.

Crop

bool Crop(LiteMat &src, LiteMat &dst, int x, int y, int w, int h)

Crop image, the channel supports is 3 and 1.

• Parameters

  • src: Input image data.

  • dst: Output image data.

  • x: The x coordinate value of the starting point of the screenshot.

  • y: The y coordinate value of the starting point of the screenshot.

  • w: The width of the screenshot.

  • h: The height of the screenshot.

• Returns

  Return True or False.

SubStractMeanNormalize

bool SubStractMeanNormalize(const LiteMat &src, LiteMat &dst, const std::vector<float> &mean, const std::vector<float> &std)

Normalize image, currently the supports data type is float.

• Parameters

  • src: Input image data.

  • dst: Output image data.

  • mean: Mean of the data set.

  • std: Norm of the data set.

• Returns

  Return True or False.

Pad

bool Pad(const LiteMat &src, LiteMat &dst, int top, int bottom, int left, int right, PaddBorderType pad_type, uint8_t fill_b_or_gray, uint8_t fill_g, uint8_t fill_r)

Pad image, the channel supports is 3 and 1.

• Parameters

  • src: Input image data.

  • dst: Output image data.

  • top: The length of top.

  • bottom: The length of bottom.

  • left: The length of left.

  • right: The length of right.

  • pad_type: The type of pad.

  • fill_b_or_gray: B or GRAY.

  • fill_g: G.

  • fill_r: R.

• Returns

  Return True or False.

Affine

void Affine(LiteMat &src, LiteMat &out_img, double M[6], std::vector<size_t> dsize, UINT8_C1 borderValue)

Apply affine transformation for 1 channel image.

• Parameters

  • src: Input image data.

  • out_img: Output image data.

  • M[6]: Affine transformation matrix.

  • dsize: The size of the output image.

  • borderValue: The pixel value is used for filing after the image is captured.

void Affine(LiteMat &src, LiteMat &out_img, double M[6], std::vector<size_t> dsize, UINT8_C3 borderValue)

Apply affine transformation for 3 channel image.

• Parameters

  • src: Input image data.

  • out_img: Output image data.

  • M[6]: Affine transformation matrix.

  • dsize: The size of the output image.

  • borderValue: The pixel value is used for filing after the image is captured.

GetDefaultBoxes

std::vector<std::vector<float>> GetDefaultBoxes(BoxesConfig config)

Get default anchor boxes for Faster R-CNN, SSD, YOLO etc.

• Parameters

  • config: Objects of BoxesConfig structure.

• Returns

  Return the default boxes.

ConvertBoxes

void ConvertBoxes(std::vector<std::vector<float>> &boxes, std::vector<std::vector<float>> &default_boxes, BoxesConfig config)

Convert the prediction boxes to the actual boxes with (y, x, h, w).

• Parameters

  • boxes: Actual size box.

  • default_boxes: Default box.

  • config: Objects of BoxesConfig structure.

ApplyNms

std::vector<int> ApplyNms(std::vector<std::vector<float>> &all_boxes, std::vector<float> &all_scores, float thres, int max_boxes)

Real-size box non-maximum suppression.

• Parameters

  • all_boxes: All input boxes.

  • all_scores: Score after all boxes are executed through the network.

  • thres: Pre-value of IOU.

  • max_boxes: Maximum value of output box.

• Returns

  Return the id of the boxes.

 

LiteMat

Class that represents a lite Mat of a Image.

Constructors & Destructors

LiteMat

LiteMat()

LiteMat(int width, LDataType data_type = LDataType::UINT8)

LiteMat(int width, int height, LDataType data_type = LDataType::UINT8)

LiteMat(int width, int height, int channel, LDataType data_type = LDataType::UINT8)

Constructor of MindSpore dataset LiteMat using default value of parameters.

~LiteMat();

Destructor of MindSpore dataset LiteMat.

Public Member Functions

Init

void Init(int width, LDataType data_type = LDataType::UINT8)

void Init(int width, int height, LDataType data_type = LDataType::UINT8)

void Init(int width, int height, int channel, LDataType data_type = LDataType::UINT8)

The function to initialize the channel, width and height of the image, but the parameters are different.

IsEmpty

bool IsEmpty() const

A function to determine whether the object is empty.

• Returns

  Return True or False.

Release

void Release()

A function to release memory.

Private Member Functions

AlignMalloc

void *AlignMalloc(unsigned int size)

Apply for memory alignment.

• Parameters

  • size: Memory size.

• Returns

  Return the size of a pointer.

AlignFree

void AlignFree(void *ptr)

A function to release pointer memory.

void InitElemSize(LDataType data_type)

Initialize the value of elem_size_ by data_type.

• Parameters

  • data_type: Type of data.

addRef

 int addRef(int *p, int value)

A function to count the number of times the function is referenced.

• Parameters

  • p: Point to the referenced object.

  • value: Value added when quoted.