halcon机器视觉深度学习对象检测，物体检测

目录 效果图操作步骤软件版本halcon参考代码本地函数 get_distinct_colors()本地函数 make_neighboring_colors_distinguishable()

效果图

操作步骤

首先要在Deep Learning Tool工具里面把图片打上标注文本， 然后训练模型，导出模型文件

这个是模型 model_训练-250215-111516_opt.hdl

模型配置参数 model_训练-250215-111516_opt_dl_preprocess_params.hdict

软件版本 使用的版本 halcon 23.11Deep Learning Tool-24.05.1 halcon参考代码 * * Inference can be done on a GPU or CPU. * See the respective system requirements in the Installation Guide. * If possible a GPU is used in this example. * In case you explicitly wish to run this example on the CPU, * choose the CPU device instead. query_available_dl_devices (['runtime', 'runtime'], ['gpu', 'cpu'], DLDeviceHandles) if (|DLDeviceHandles| == 0) throw ('No supported device found to continue this example.') endif * Due to the filter used in query_available_dl_devices, the first device is a GPU, if available. *第一个设备是 GPU（如果可用） DLDevice := DLDeviceHandles[0] * * ************************************************* * ** 设置推理路径和参数 *** * ************************************************* * * 我们将对示例图像进行推理。 * 在实际应用程序中，新传入的图像（不用于训练或评估） * 将在此处使用。 * * 在此示例中，我们从 file 中读取图像。 * 用我训练的图片 ImageDir := 'G:/机器视觉_测试项目/家具目标检测/images - 副本' * * Set the paths of the retrained model and the corresponding preprocessing parameters. * Example data folder containing the outputs of the previous example series. ExampleDataDir := 'detect_pills_data' * Use the pretrained model and preprocessing parameters shipping with HALCON. *使用 HALCON 附带的预训练模型和预处理参数。 *PreprocessParamFileName := 'detect_pills_preprocess_param.hdict' * RetrainedModelFileName := 'detect_pills.hdl' *whl 测试我自己训练的模型和参数，图片配置 dir1223:='G:/机器视觉_测试项目/家具目标检测/' imgConfigHdict:='model_训练-250215-111516_opt_dl_preprocess_params.hdict' PreprocessParamFileName:= dir1223+imgConfigHdict *识别模型 * RetrainedModelFileName := dir1223+ 'best_model.hdl' RetrainedModelFileName :=dir1223+ 'model_训练-250215-111516_opt.hdl' * * Batch Size used during inference.推理批次大小 BatchSizeInference := 1 * * Postprocessing parameters for the detection model.检测模型的后处理参数。 MinConfidence := 0.6 MaxOverlap := 0.2 MaxOverlapClassAgnostic := 0.7 * * ******************** * ** 推理 *** * ******************** * * Check if all necessary files exist. *check_data_availability (ExampleDataDir, PreprocessParamFileName, RetrainedModelFileName, UsePretrainedModel) * * 读取重新训练的模型。 read_dl_model (RetrainedModelFileName, DLModelHandle) * * Set the batch size. 设置批处理大小。 set_dl_model_param (DLModelHandle, 'batch_size', BatchSizeInference) * * Initialize the model for inference.初始化模型以进行推理。 set_dl_model_param (DLModelHandle, 'device', DLDevice) * * Set postprocessing parameters for model.设置模型的后处理参数。 set_dl_model_param (DLModelHandle, 'min_confidence', MinConfidence) set_dl_model_param (DLModelHandle, 'max_overlap', MaxOverlap) set_dl_model_param (DLModelHandle, 'max_overlap_class_agnostic', MaxOverlapClassAgnostic) * * Get the parameters used for preprocessing.获取用于预处理的参数。 read_dict (PreprocessParamFileName, [], [], DLPreprocessParam) * * 使用显示所需的数据集参数创建字典。 DLDataInfo := dict{} get_dl_model_param (DLModelHandle, 'class_names', ClassNames) * 目标对象，标签名称 DLDataInfo.class_names := ClassNames get_dl_model_param (DLModelHandle, 'class_ids', ClassIDs) DLDataInfo.class_ids := ClassIDs * 设置可视化的通用参数。 GenParam := dict{scale_windows: 1.2,display_labels:true} *读取目录里面的若干图片文件 list_files (ImageDir, ['files' ], ImageFiles) *获取图片尺寸，whl测试 read_image(img1,ImageFiles[0]) get_image_size (img1, Width, Height) * dev_open_window (1, 1, Width, Height, 'black', WindowID1) dev_open_window (1, 1, 900, 900*Height/(Width*1.0), 'black', WindowID1) *视频文件读取 *grab_image_from_video() * open_framegrabber() * 读取视频帧 *grab_image_start([]) *grab_image(Image) *grab_image_stop([]) * * 以 BatchSizeInference 大小批量循环访问所有图像以进行推理 for BatchIndex := 0 to floor(|ImageFiles| / real(BatchSizeInference)) - 1 by 1 * * Get the paths to the images of the batch. Batch := ImageFiles[BatchIndex * BatchSizeInference:(BatchIndex + 1) * BatchSizeInference - 1] * 读取图片 read_image (ImageBatch, Batch) * * Generate the DLSampleBatch. gen_dl_samples_from_images (ImageBatch, DLSampleBatch) * * Preprocess the DLSampleBatch. preprocess_dl_samples (DLSampleBatch, DLPreprocessParam) * * 在 DLSampleBatch 上应用 DL 模型。 apply_dl_model (DLModelHandle, DLSampleBatch, [], DLResultBatch) * * Postprocessing and visualization.后处理和可视化 * Loop over each sample in the batch.循环处理批次中的每个样品 for SampleIndex := 0 to BatchSizeInference - 1 by 1 * * Get sample and according results.获取样本和相应的结果。 DLSample := DLSampleBatch[SampleIndex] DLResult := DLResultBatch[SampleIndex] * *whl测试 KeysForDisplay:='bbox_result' * * 显示检测结果. * dev_display_dl_data (DLSample, DLResult, DLDataInfo, 'bbox_result', GenParam, WindowHandleDict) *whl测试，ocr_detection_score_map_character * * This procedure displays the content of the provided DLSample and/or DLResult * depending on the input string KeysForDisplay. * DLDatasetInfo is a dictionary containing the information about the dataset. * The visualization can be adapted with GenParam. * * ** Set the default values: *** Params := dict{} * * Define the screen width when a new window row is started. Params.threshold_width := 1024 * Since potentially a lot of windows are opened, * scale the windows consistently. Params.scale_windows := 0.8 * Set a font and a font size. Params.font := 'mono' Params.font_size := 14 * Params.line_width := 2 Params.map_transparency := 'cc' Params.map_color_bar_width := 140 * * Define parameter values specifically for 3d_gripping_point_detection Params.gripping_point_color := '#00FF0099' Params.gripping_point_size := 6 Params.region_color := '#FF000040' Params.gripping_point_map_color := '#83000080' Params.gripping_point_background_color := '#00007F80' * * Define parameter values specifically for anomaly detection * and Global Context Anomaly Detection. Params.anomaly_region_threshold := -1 Params.anomaly_classification_threshold := -1 Params.anomaly_region_label_color := '#40e0d0' Params.anomaly_color_transparency := '40' Params.anomaly_region_result_color := '#ff0000c0' * * Define segmentation-specific parameter values. Params.segmentation_max_weight := 0 Params.segmentation_draw := 'fill' Params.segmentation_transparency := 'aa' Params.segmentation_exclude_class_ids := [] * * Define bounding box-specific parameter values. Params.bbox_label_color := '#000000' + '99' Params.bbox_display_confidence := 1 Params.bbox_text_color := '#eeeeee' * * By default, display a description on the bottom. Params.display_bottom_desc := true * * By default, show a legend with class IDs. Params.display_legend := true * * By default, show the anomaly ground truth regions. Params.display_ground_truth_anomaly_regions := true * * By default, show class IDs and color frames for classification ground truth/results. Params.display_classification_ids := true Params.display_classification_color_frame := true * * By default, show class labels for detection ground truth/results. Params.display_labels := true * * By default, show direction of the ground truth/results instances for detection with instance_type 'rectangle2'. Params.display_direction := true * * By default, use color scheme 'Jet' for the heatmap display. Params.heatmap_color_scheme := 'jet' * ** Set user-defined values: *** * * Overwrite default values by given generic parameters. if (GenParam != []) get_dict_param (GenParam, 'keys', [], GenParamNames) for ParamIndex := 0 to |GenParamNames| - 1 by 1 GenParamName := GenParamNames[ParamIndex] get_dict_param (Params, 'key_exists', GenParamName, KeyExists) if (not KeyExists) throw ('Unknown generic parameter: ' + GenParamName + '.') endif Params.[GenParamName] := GenParam.[GenParamName] endfor endif * if (|DLSample| > 1 or |DLResult| > 1) throw ('Only a single dictionary for DLSample and DLResult is allowed') endif * * Get the dictionary keys. get_dict_param (DLSample, 'keys', [], SampleKeys) if (DLResult != []) get_dict_param (DLResult, 'keys', [], ResultKeys) endif * * Get image ID if it is available. get_dict_param (DLSample, 'key_exists', 'image_id', ImageIDExists) if (ImageIDExists) get_dict_tuple (DLSample, 'image_id', ImageID) ImageIDString := 'image ID ' + ImageID ImageIDStringBraces := '(image ID ' + ImageID + ')' ImageIDStringCapital := 'Image ID ' + ImageID else ImageIDString := '' ImageIDStringBraces := ImageIDString ImageIDStringCapital := ImageIDString endif * AdditionalGreenClassNames := [] KeyIndex := 0 * whl添加if * * Check if DLDatasetInfo is valid. * whl添加 DLDatasetInfo:=DLDataInfo * Check if DLDatasetInfo contains necessary keys. ClassKeys := ['class_names', 'class_ids'] get_handle_param (DLDatasetInfo, 'key_exists', ClassKeys, ClassKeysExist) if (min(ClassKeysExist) == 0) * In that case we expect that the class names and ids are never used. else get_handle_param (DLDatasetInfo, 'keys', [], DLDatasetInfoKeys) for Index := 0 to |ClassKeys| - 1 by 1 if (find_first(DLDatasetInfoKeys,ClassKeys[Index]) == -1) throw ('Key ' + ClassKeys[Index] + ' is missing in DLDatasetInfo.') endif endfor * * Get the general dataset information, if available. get_handle_tuple (DLDatasetInfo, 'class_names', ClassNames) get_handle_tuple (DLDatasetInfo, 'class_ids', ClassIDs) * * 为类定义不同的颜色 * get_dl_class_colors (ClassNames, AdditionalGreenClassNames, Colors) * 函数get_dl_class_colors 替代者，开始 * Define distinct colors for the classes. NumColors := |ClassNames| * Get distinct colors without randomness makes neighboring colors look very similar. * We use a workaround to get deterministic colors where subsequent colors are distinguishable. get_distinct_colors (NumColors, false, 0, 200, ColorsRainbow) tuple_inverse (ColorsRainbow, ColorsRainbow) make_neighboring_colors_distinguishable (ColorsRainbow, Colors) * If a class 'OK','ok', 'good' or 'GOOD' or a class specified in AdditionalGreenClassNames is present set this class to green. * Only the first occurrence found is set to a green shade. tuple_union (['good', 'GOOD', 'ok', 'OK'], AdditionalGreenClassNames, ClassNamesGood) for IndexFind := 0 to |ClassNamesGood| - 1 by 1 GoodIdx := find_first(ClassNames,ClassNamesGood[IndexFind]) if (GoodIdx != -1 and |ClassNames| <= 8) * If number of classes is <= 8, swap color with a green color. CurrentColor := Colors[GoodIdx] GreenIdx := floor(|ClassNames| / 2.0) * Set to pure green. Colors[GoodIdx] := '#00ff00' * Write original color to a green entry. Colors[GreenIdx] := CurrentColor break elseif (GoodIdx != -1 and |ClassNames| > 8) * If number of classes is larger than 8, set the respective color to green. Colors[GoodIdx] := '#00ff00' break endif endfor * 函数get_dl_class_colors 替代者，结束 endif * * ** Set window parameters: *** * * * ** Display the data: *** * * Display data dictionaries. KeyIndex := 0 *while (KeyIndex < |KeysForDisplay|) * * if (KeysForDisplay[KeyIndex] == 'bbox_result' or KeysForDisplay[KeyIndex] == 'ocr_detection_result') * * Result bounding boxes on image.图像上的结果边界框。 get_dl_sample_image (Image, SampleKeys, DLSample, 'image') * get_dl_sample_image (ImageBatch, SampleKeys, DLSample, 'image') * * Get or open next window.训练时的图片宽高 get_image_size (Image, WidthImage, HeightImage) * get_next_window (Params.font, Params.font_size, Params.display_bottom_desc, WidthImage, HeightImage, 0, Params.scale_windows, Params.threshold_width, PrevWindowCoordinates, WindowHandleDict, KeysForDisplay[KeyIndex], CurrentWindowHandle, WindowImageRatio, PrevWindowCoordinates) *原始代码，whl测试注释，训练时的压缩后图片 * dev_display (Image) *whl添加，获取窗口尺寸 * get_window_extents(WindowID1,Row,Column,Window_Width,Window_Height) *图片原图本身尺寸，非训练设置压缩的图片尺寸 get_image_size (ImageBatch, WidthBig, HeightBig) *whl添加，比值 *应该先把训练时图片的原始框点转换图片本身尺寸时的坐标就可以了 imgRate:=1 imgHeightBeiWidth:=1 if(1) *宽度，乘以1.0转为小数，可以让除得到小数结果 imgRate:=WidthBig/(WidthImage*1.0) * 高度占宽度的比值 imgHeightBeiWidth:=HeightBig/(HeightImage*1.0) endif *whl 添加，显示原图片 * 调整图像尺寸 * zoom_image_size(ImageBatch,imgZoom,800,800*HeightBig/(WidthBig*1.0),'constant') * zoom_image_size(ImageBatch,imgZoom,800,800*HeightBig/(WidthBig*1.0),'constant') dev_clear_window() *whl 添加，显示原图片 dev_display(ImageBatch) *让窗口适应图片的尺寸，窗口跟图片一样大 * dev_resize_window_fit_image (ImageBatch, 0, 0, -1, -1) * dev_re * dev_open_window_fit_image (ImageBatch, 0, 0, -1, -1, WindowID1) *dev_resize_window_fit_size (0, 0, -1, -1, -1, -1) *full_domain(ImageBatch,ImageBatch） * dev_set_window(WindowID1) * dev_set_part *whl 添加测试 WindowImageRatio:=1 CurrentWindowHandle:=WindowID1 *目标对象分类文本 * className:=DLResult.bbox_class_name *显示目标对象框 *dev_display_result_detection (DLResult, ResultKeys, Params.line_width, ClassIDs, TextConf, Colors, Params.bbox_label_color, WindowImageRatio, 'top', Params.bbox_text_color, Params.display_labels, DisplayDirectionTemp, CurrentWindowHandle, BboxClassIndex) *dev_display_result_detection (DLResult, ResultKeys, Params.line_width, ClassIDs, TextConf, Colors, Params.bbox_label_color, WindowImageRatio, 'top', Params.bbox_text_color, Params.display_labels, DisplayDirectionTemp, CurrentWindowHandle, BboxClassIndex) *目标文本显示 set_display_font (WindowID1, 12, 'mono', 'false', 'false') *提取函数，显示目标对象框，识别分类文本，开始 InstanceType := '' MaskExists := false if (find(ResultKeys,'bbox_row1') != -1) *进这个 get_dict_tuple (DLResult, 'bbox_row1', BboxRow1) get_dict_tuple (DLResult, 'bbox_col1', BboxCol1) get_dict_tuple (DLResult, 'bbox_row2', BboxRow2) get_dict_tuple (DLResult, 'bbox_col2', BboxCol2) InstanceType := 'rectangle1' *1进入，0不进入 if(1) *whl 添加，乘以系数 *高度 BboxRow1:=BboxRow1*imgHeightBeiWidth BboxRow2:=BboxRow2*imgHeightBeiWidth *宽度 BboxCol1:=BboxCol1*imgRate BboxCol2:=BboxCol2*imgRate *whl 添加，重置为1 imgRate:=1 endif elseif (find(ResultKeys,'bbox_phi') != -1) get_dict_tuple (DLResult, 'bbox_row', BboxRow) get_dict_tuple (DLResult, 'bbox_col', BboxCol) get_dict_tuple (DLResult, 'bbox_length1', BboxLength1) get_dict_tuple (DLResult, 'bbox_length2', BboxLength2) get_dict_tuple (DLResult, 'bbox_phi', BboxPhi) get_dict_tuple (DLResult, 'bbox_class_id', BboxClasses) InstanceType := 'rectangle2' else throw ('Result bounding box data could not be found in DLResult.') endif if (find(ResultKeys,'mask') != -1) get_dict_object (InstanceMask, DLResult, 'mask') MaskExists := true endif if (InstanceType != 'rectangle1' and InstanceType != 'rectangle2' and not MaskExists) throw ('Result bounding box or mask data could not be found in DLSample.') endif *whl注释 get_dict_tuple (DLResult, 'bbox_class_id', BboxClasses) * whl 添加，显示检测对象名称 *whl添加 ShowLabels:=true ShowDirection:=true TextColor:='#eeeeee' TextConf:='' if (|BboxClasses| > 0) * * Get text and text size for correct positioning of result class IDs. if (ShowLabels) Text := BboxClasses + TextConf get_string_extents (CurrentWindowHandle, Text, Ascent, Descent, _, _) TextOffset := (Ascent + Descent) / WindowImageRatio endif * * Generate bounding box XLDs. if (InstanceType == 'rectangle1') tuple_gen_const (|BboxRow1|, 0.0, BboxPhi) *画目标框线，乘以 imgRate gen_rectangle2_contour_xld (BboxRectangle, 0.5 * (BboxRow1 + BboxRow2), 0.5 * (BboxCol1 + BboxCol2), BboxPhi, 0.5 * (BboxCol2 - BboxCol1), 0.5 * (BboxRow2 - BboxRow1)) * gen_rectangle2_contour_xld (BboxRectangle, 0.5 * (BboxRow1 + BboxRow2)*imgRate, 0.5 * (BboxCol1 + BboxCol2)*imgRate, BboxPhi, 0.5 * (BboxCol2 - BboxCol1)*imgRate, 0.5 * (BboxRow2 - BboxRow1)*imgRate) if (ShowLabels) LabelRowTop := BboxRow1 LabelRowBottom := BboxRow2 - TextOffset LabelCol := BboxCol1 endif elseif (InstanceType == 'rectangle2') gen_rectangle2_contour_xld (BboxRectangle, BboxRow, BboxCol, BboxPhi, BboxLength1, BboxLength2) if (ShowLabels) LabelRowTop := BboxRow - TextOffset LabelRowBottom := BboxRow LabelCol := BboxCol endif if (ShowDirection) if (ShowDirection == -1) ArrowSizeFactorLength := 0.4 ArrowSizeFactorHead := 0.2 MaxLengthArrow := 20 HalfLengthArrow := min2(MaxLengthArrow,BboxLength1 * ArrowSizeFactorLength) ArrowBaseRow := BboxRow - (BboxLength1 - HalfLengthArrow) * sin(BboxPhi) ArrowBaseCol := BboxCol + (BboxLength1 - HalfLengthArrow) * cos(BboxPhi) ArrowHeadRow := BboxRow - (BboxLength1 + HalfLengthArrow) * sin(BboxPhi) ArrowHeadCol := BboxCol + (BboxLength1 + HalfLengthArrow) * cos(BboxPhi) ArrowHeadSize := min2(MaxLengthArrow,min2(BboxLength1,BboxLength2)) * ArrowSizeFactorHead else ArrowHeadSize := 20.0 ArrowBaseRow := BboxRow ArrowBaseCol := BboxCol ArrowHeadRow := BboxRow - (BboxLength1 + ArrowHeadSize) * sin(BboxPhi) ArrowHeadCol := BboxCol + (BboxLength1 + ArrowHeadSize) * cos(BboxPhi) endif gen_arrow_contour_xld (OrientationArrows, ArrowBaseRow, ArrowBaseCol, ArrowHeadRow, ArrowHeadCol, ArrowHeadSize, ArrowHeadSize) endif elseif (MaskExists) area_center (InstanceMask, _, MaskRow, MaskCol) LabelRowTop := MaskRow - TextOffset LabelRowBottom := MaskRow LabelCol := MaskCol else throw ('Unknown instance_type: ' + InstanceType) endif * get_contour_style (CurrentWindowHandle, ContourStyle) dev_set_contour_style ('stroke') get_line_style (CurrentWindowHandle, Style) *whl添加 LineWidthBbox:=1 LineWidths := [LineWidthBbox + 2,LineWidthBbox] dev_set_line_width (LineWidthBbox) * * Collect ClassIDs of the bounding boxes. tuple_gen_const (|BboxClasses|, 0, BboxClassIndices) * * Draw bounding boxes. for IndexBbox := 0 to |BboxClasses| - 1 by 1 ClassID := find(ClassIDs,BboxClasses[IndexBbox]) BboxClassIndices[IndexBbox] := ClassID * First draw in black to make the class-color visible. CurrentColors := ['black',Colors[ClassID]] if (MaskExists) select_obj (InstanceMask, MaskSelected, IndexBbox + 1) dev_set_draw ('fill') dev_set_color (Colors[ClassID] + '80') dev_display (MaskSelected) dev_set_draw ('margin') endif for IndexStyle := 0 to |CurrentColors| - 1 by 1 dev_set_color (CurrentColors[IndexStyle]) dev_set_line_width (LineWidths[IndexStyle]) if (InstanceType != '') select_obj (BboxRectangle, RectangleSelected, IndexBbox + 1) dev_display (RectangleSelected) if (InstanceType == 'rectangle2' and ShowDirection) select_obj (OrientationArrows, ArrowSelected, IndexBbox + 1) dev_display (ArrowSelected) endif endif endfor endfor * * Draw text of bounding boxes. if (ShowLabels) * For better visibility the text is displayed after all bounding boxes are drawn. * Get text and text size for correct positioning of result class IDs. * Text := BboxClasses + TextConf *whl 对象文本 *bbox_class_name标签，bbox_confidence置信度得分 whlObjectClassName:=DLResult.bbox_class_name *四舍五入，保留10位小数 tuple_string(DLResult.bbox_confidence, '.10f', StringConfidence) *截取字符串 tuple_substr (StringConfidence, 0, 3, Substring) Text :=whlObjectClassName+ Substring * Select text color. if (TextColor == '') TextColorClasses := Colors[BboxClassIndices] else tuple_gen_const (|BboxClassIndices|, TextColor, TextColorClasses) endif * Select correct position of the text. LabelRow := LabelRowTop *whl注释 * if (TextPositionRow == 'bottom') * LabelRow := LabelRowBottom * endif *whl添加，标签字体背景色 BoxLabelColor:='#00000099' * BoxLabelColor:='#05E600' * Display text.显示对象标签文本 dev_disp_text (Text, 'image', LabelRow, LabelCol, TextColorClasses, ['box_color', 'shadow', 'border_radius'], [BoxLabelColor,'false', 0]) endif * dev_set_contour_style (ContourStyle) set_line_style (CurrentWindowHandle, Style) else * Do nothing if no results are present. BboxClassIndices := [] endif *显示目标对象框，识别分类文本，结束 *whl 注释，不执行if代码里面的代码 endif * KeyIndex := KeyIndex + 1 *endwhile * whl测试，目标框显示，结束 *whl注释 * WindowHandles := WindowHandleDict.bbox_result * dev_set_window (WindowHandles[0]) * set_display_font (WindowHandles[0], 16, 'mono', 'true', 'false') * whl测试 * set_display_font (WindowID1, 16, 'mono', 'true', 'false') *whl注释，不显示绿色的检测文本列表 * dev_disp_text (Text, 'window', 'top', 'left', TextColor, ['box_color', 'shadow'], [TextBoxColor,'false']) set_display_font (WindowID1, 16, 'mono', 'true', 'false') * dev_disp_text ('Press Run (F5) to continue', 'window', 'bottom', 'right', 'black', [], []) * 拆分字符串，图片路径 tuple_split(Batch,'\\',fileWordArr) Wordlength:=|fileWordArr| *取最后一个字符串 fileShortName:=fileWordArr[Wordlength-1] *显示文件名 dev_disp_text (fileShortName, 'window', 'bottom', 'left', 'magenta', [], []) *将窗口保存为本地图片文件 * dump_window(WindowID1,'png','G:/机器视觉_测试项目/家具目标检测/videoImages/2') stop () endfor endfor * * Close windows used for visualization.关闭用于可视化的窗口 *dev_close_window_dict (WindowHandleDict) * * set_display_font (WindowID1, 24, 'mono', 'true', 'false') dev_disp_text ('程序结束', 'window', 'bottom', 'right', 'green', ['box_color'], [ 'blue']) 本地函数 get_distinct_colors() * * We get distinct color-values first in HLS color-space. * Assumes hue [0, EndColor), lightness [0, 1), saturation [0, 1). * * Parameter checks. * NumColors. if (NumColors < 1) throw ('NumColors should be at least 1') endif if (not is_int(NumColors)) throw ('NumColors should be of type int') endif if (|NumColors| != 1) throw ('NumColors should have length 1') endif * Random. if (Random != 0 and Random != 1) tuple_is_string (Random, IsString) if (IsString) Random := Random == 'true' or 'false' else throw ('Random should be either true or false') endif endif * StartColor. if (|StartColor| != 1) throw ('StartColor should have length 1') endif if (StartColor < 0 or StartColor > 255) throw ('StartColor should be in the range [0, 255]') endif if (not is_int(StartColor)) throw ('StartColor should be of type int') endif * EndColor. if (|EndColor| != 1) throw ('EndColor should have length 1') endif if (EndColor < 0 or EndColor > 255) throw ('EndColor should be in the range [0, 255]') endif if (not is_int(EndColor)) throw ('EndColor should be of type int') endif * * Color generation. if (StartColor > EndColor) EndColor := EndColor + 255 endif if (NumColors != 1) Hue := (StartColor + int((EndColor - StartColor) * real([0:NumColors - 1]) / real(NumColors - 1))) % 255 else Hue := mean([StartColor,EndColor]) endif if (Random) Hue := Hue[sort_index(rand(NumColors))] Lightness := int((5.0 + rand(NumColors)) * 255.0 / 10.0) Saturation := int((9.0 + rand(NumColors)) * 255.0 / 10.0) else Lightness := int(gen_tuple_const(NumColors,0.55) * 255.0) Saturation := int(gen_tuple_const(NumColors,0.95) * 255.0) endif * * Write colors to a 3-channel image in order to transform easier. gen_image_const (HLSImageH, 'byte', 1, NumColors) gen_image_const (HLSImageL, 'byte', 1, NumColors) gen_image_const (HLSImageS, 'byte', 1, NumColors) get_region_points (HLSImageH, Rows, Columns) set_grayval (HLSImageH, Rows, Columns, Hue) set_grayval (HLSImageL, Rows, Columns, Lightness) set_grayval (HLSImageS, Rows, Columns, Saturation) * * Convert from HLS to RGB. trans_to_rgb (HLSImageH, HLSImageL, HLSImageS, ImageR, ImageG, ImageB, 'hls') * * Get RGB-values and transform to Hex. get_grayval (ImageR, Rows, Columns, Red) get_grayval (ImageG, Rows, Columns, Green) get_grayval (ImageB, Rows, Columns, Blue) Colors := '#' + Red$'02x' + Green$'02x' + Blue$'02x' return () * 本地函数 make_neighboring_colors_distinguishable() * * Shuffle the input colors in a deterministic way * to make adjacent colors more distinguishable. * Neighboring colors from the input are distributed to every NumChunks * position in the output. * Depending on the number of colors, increase NumChunks. NumColors := |ColorsRainbow| if (NumColors >= 8) NumChunks := 3 if (NumColors >= 40) NumChunks := 6 elseif (NumColors >= 20) NumChunks := 4 endif Colors := gen_tuple_const(NumColors,-1) * Check if the Number of Colors is dividable by NumChunks. NumLeftOver := NumColors % NumChunks ColorsPerChunk := int(NumColors / NumChunks) StartIdx := 0 for S := 0 to NumChunks - 1 by 1 EndIdx := StartIdx + ColorsPerChunk - 1 if (S < NumLeftOver) EndIdx := EndIdx + 1 endif IdxsLeft := [S:NumChunks:NumColors - 1] IdxsRight := [StartIdx:EndIdx] Colors[S:NumChunks:NumColors - 1] := ColorsRainbow[StartIdx:EndIdx] StartIdx := EndIdx + 1 endfor else Colors := ColorsRainbow endif return ()