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[Doc] add doxygen docs for c sharp api (#1495)

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add doxygen docs for c sharp api

Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
This commit is contained in:
chenjian
2023-04-09 12:32:22 +08:00
committed by GitHub
parent ed19c759df
commit fc15124800
11 changed files with 3201 additions and 36 deletions
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55
csharp/fastdeploy/vision/result.cs
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@@ -37,9 +37,11 @@ public enum ResultType {
HEADPOSE
}
/*! Mask structure, used in DetectionResult for instance segmentation models
*/
public class Mask {
public List<byte> data;
public List<long> shape;
public List<byte> data; /// Mask data buffer
public List<long> shape; /// Shape of mask
public ResultType type;
public Mask() {
this.data = new List<byte>();
@@ -47,6 +49,7 @@ public class Mask {
this.type = ResultType.MASK;
}
/// convert the result to string to print
public override string ToString() {
string information = "Mask(" ;
int ndim = this.shape.Count;
@@ -63,16 +66,19 @@ public class Mask {
}
/*! @brief Classify result structure for all the image classify models
*/
public class ClassifyResult {
public List<int> label_ids;
public List<float> scores;
public ResultType type;
public List<int> label_ids; /// Classify result for an image
public List<float> scores; /// The confidence for each classify result
public ResultType type;
public ClassifyResult() {
this.label_ids = new List<int>();
this.scores = new List<float>();
this.type = ResultType.CLASSIFY;
}
/// convert the result to string to print
public string ToString() {
string information;
information = "ClassifyResult(\nlabel_ids: ";
@@ -89,12 +95,14 @@ public class ClassifyResult {
}
}
/*! @brief Detection result structure for all the object detection models and instance segmentation models
*/
public class DetectionResult {
public List<float[]> boxes;
public List<float> scores;
public List<int> label_ids;
public List<Mask> masks;
public bool contain_masks;
public List<float[]> boxes; /// Member variable which indicates the coordinates of all detected target boxes in a single image, each box is represented by 4 float values in order of xmin, ymin, xmax, ymax, i.e. the coordinates of the top left and bottom right corner.
public List<float> scores; /// Member variable which indicates the confidence level of all targets detected in a single image
public List<int> label_ids; /// Member variable which indicates all target categories detected in a single image
public List<Mask> masks; /// Member variable which indicates all detected instance masks of a single image
public bool contain_masks; /// Member variable which indicates whether the detected result contains instance masks
public ResultType type;
public DetectionResult() {
this.boxes = new List<float[]>();
@@ -105,7 +113,7 @@ public class DetectionResult {
this.type = ResultType.DETECTION;
}
/// convert the result to string to print
public string ToString() {
string information;
if (!contain_masks) {
@@ -130,12 +138,14 @@ public class DetectionResult {
}
/*! @brief OCR result structure for all the OCR models.
*/
public class OCRResult {
public List<int[]> boxes;
public List<string> text;
public List<float> rec_scores;
public List<float> cls_scores;
public List<int> cls_labels;
public List<int[]> boxes; /// Member variable which indicates the coordinates of all detected target boxes in a single image. Each box is represented by 8 int values to indicate the 4 coordinates of the box, in the order of lower left, lower right, upper right, upper left.
public List<string> text; /// Member variable which indicates the content of the recognized text in multiple text boxes
public List<float> rec_scores; /// Member variable which indicates the confidence level of the recognized text.
public List<float> cls_scores; /// Member variable which indicates the confidence level of the classification result of the text box
public List<int> cls_labels; /// Member variable which indicates the directional category of the textbox
public ResultType type;
public OCRResult() {
@@ -146,6 +156,8 @@ public class OCRResult {
this.cls_labels = new List<int>();
this.type = ResultType.OCR;
}
/// convert the result to string to print
public string ToString() {
string no_result = "";
if (boxes.Count > 0) {
@@ -225,11 +237,13 @@ public class OCRRecognizerResult{
public float rec_score;
}
/*! @brief Segmentation result structure for all the segmentation models
*/
public class SegmentationResult{
public List<byte> label_map;
public List<float> score_map;
public List<long> shape;
public bool contain_score_map;
public List<byte> label_map; /// `label_map` stores the pixel-level category labels for input image.
public List<float> score_map; /// `score_map` stores the probability of the predicted label for each pixel of input image.
public List<long> shape; /// The output shape, means [H, W]
public bool contain_score_map; /// SegmentationResult whether containing score_map
public ResultType type;
public SegmentationResult() {
this.label_map = new List<byte>();
@@ -239,6 +253,7 @@ public class SegmentationResult{
this.type = ResultType.SEGMENTATION;
}
/// convert the result to string to print
public string ToString() {
string information;
information = "SegmentationResult Image masks 10 rows x 10 cols: \n";