百度seo网站在线诊断,网站建设对比,layui+wordpress,wordpress生成海报图片插件1.1根据txt格式标签生成Label图片
(1) 经过测试验证#xff0c;使用python代码或者halcon代码生成的Label图片是一样的。但要注意#xff0c;最后要生成png格式的Label图片。
(2) 使用python代码生成Label图片
import cv2
import os
import numpy as npdef gen_label_img(…1.1根据txt格式标签生成Label图片
(1) 经过测试验证使用python代码或者halcon代码生成的Label图片是一样的。但要注意最后要生成png格式的Label图片。
(2) 使用python代码生成Label图片
import cv2
import os
import numpy as npdef gen_label_img(img_path, txt_label_path, png_label_path):img_file_name os.path.basename(img_path).split(.jpg)[0]print(img_file_name)img cv2.imread(img_path)h, w img.shape[:2]mask np.zeros((h, w), dtypenp.uint8)with open(txt_label_path, r) as f:for line in f.readlines():class_id, *pyly line.strip().split( )pyly [float(i) for i in pyly]pyly np.array(pyly).reshape(-1, 2)# 根据h,w进行反归一化pyly (pyly * np.array([w, h])).astype(np.int32)print()mask cv2.drawContours(mask, [pyly], -1, (int(class_id) 255, int(class_id) 255, int(class_id) 255), -1)cv2.imwrite(png_label_path / img_file_name.png, mask)if __name__ __main__:img_path1 r./DataImage/train/txt_label_path1 r./DataLabel/txtlabel/train/png_label_path r./DataLabel/pnglabel/train2print(os.listdir(img_path1))for img_file in os.listdir(img_path1):img_path2 img_path1 img_fileprint(img_path2)txt_label_path2 txt_label_path1 img_file.split(.)[0] .txtgen_label_img(img_path2, txt_label_path2, png_label_path)(3) 使用halcon代码生成Label图片
img_path:./DataImage/train
txt_path:./DataLabel/txtlabel/train
label_path:./DataLabel/pnglabel/train1
list_image_files (img_path, default, [], ImageFiles)for i: 0 to |ImageFiles|-1 by 1read_image (Image1, ImageFiles[i])get_image_size (Image1, Width, Height)gen_image_const(ImageResult,byte,Width,Height) parse_filename (ImageFiles[i], BaseName, Extension, Directory)txt_file_path: txt_path/BaseName.txtlabel_file_path:label_path/BaseNameopen_file (txt_file_path, input, FileHandle)repeatfread_line(FileHandle, oneline, IsEOF)if(IsEOF 1)break endifif(oneline or oneline\n)continueendiftuple_regexp_replace (oneline, \n, , oneline)tuple_split (oneline, , Substrings)tuple_number (Substrings, Number)Points:Number[1:|Number|-1] tuple_select (Points, [0:2:|Points|-1], Selected1)tuple_select (Points, [1:2:|Points|], Selected2)c: Selected1*Widthr: Selected2*Heightgen_region_polygon_filled (Region, r, c)paint_region (Region, ImageResult, ImageResult, 255, fill)until (IsEOF)write_image (ImageResult, png, 0,label_file_path)endfor
1.2应用示例代码
*
* *** 0) SET INPUT/OUTPUT PATHS AND DATASET PARAMETERS ***
*
ImageDir : pill
SegmentationDir : labels/pill
*
OutputDir : segment_pill_defects_data
*
ClassNames : [good, contamination, crack]
ClassIDs : [0, 1, 2]
* Set to true, if the results should be deleted after running this program.
RemoveResults : false
*
* *** 1.) PREPARE ***
*
* Read and prepare the DLDataset.
read_dl_dataset_segmentation (ImageDir, SegmentationDir, ClassNames, ClassIDs, [], [], [], DLDataset)
split_dl_dataset (DLDataset, 60, 20, [])
* Here, existing preprocessed data will be overwritten if necessary.
PreprocessSettings : dict{overwrite_files: auto}
create_dl_preprocess_param (segmentation, 400, 400, 3, -127, 128, none, full_domain, [], [], [], [], DLPreprocessParam)
preprocess_dl_dataset (DLDataset, OutputDir, DLPreprocessParam, PreprocessSettings, DLDatasetFileName)
*
* Inspect 10 randomly selected preprocessed DLSamples visually.
WindowDict : dict{}
find_dl_samples (DLDataset.samples, split, train, match, TrainSampleIndices)
for Index : 0 to 9 by 1SampleIndex : TrainSampleIndices[round(rand(1) * (|TrainSampleIndices| - 1))]read_dl_samples (DLDataset, SampleIndex, DLSample)dev_display_dl_data (DLSample, [], DLDataset, [segmentation_image_ground_truth, segmentation_weight_map], [], WindowDict)dev_disp_text (Press F5 to continue, window, bottom, right, black, [], [])stop ()
endfor
dev_close_window_dict (WindowDict)
*
* *** 2.) TRAIN ***
*
* Read a pretrained model and adapt its parameters
* according to the dataset.
read_dl_model (pretrained_dl_segmentation_compact.hdl, DLModelHandle)
set_dl_model_param_based_on_preprocessing (DLModelHandle, DLPreprocessParam, ClassIDs)
set_dl_model_param (DLModelHandle, class_names, ClassNames)
* Set training related model parameters.
* Training can be performed 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.
DLDevice : DLDeviceHandles[0]
get_dl_device_param (DLDevice, type, DLDeviceType)
if (DLDeviceType cpu)* The number of used threads may have an impact* on the training duration.NumThreadsTraining : 4set_system (thread_num, NumThreadsTraining)
endif
*
* For details see the documentation of set_dl_model_param () and get_dl_model_param ().
if (DLDeviceType gpu)set_dl_model_param_max_gpu_batch_size (DLModelHandle, 50)
endif
set_dl_model_param (DLModelHandle, learning_rate, 0.0001)
set_dl_model_param (DLModelHandle, device, DLDevice)
*
* Here, we run a short training of 10 epochs.
* For better model performance increase the number of epochs
* and train as long as your compute budget allows,
* e.g., for 100, 1000 or 3000 epochs.
create_dl_train_param (DLModelHandle, 10, 1, true, 42, [], [], TrainParam)
* The training and thus the call of train_dl_model_batch ()
* is done using the following procedure.
train_dl_model (DLDataset, DLModelHandle, TrainParam, 0, TrainResults, TrainInfos, EvaluationInfos)
*
* Read the best model, which is written to file by train_dl_model.
read_dl_model (model_best.hdl, DLModelHandle)
dev_disp_text (Press F5 to continue, window, bottom, left, black, [], [])
stop ()
*
dev_close_window ()
dev_close_window ()
*
* *** 3.) EVALUATE ***
*
GenParamEval : dict{show_progress: true}
GenParamEval.measures : [mean_iou, pixel_accuracy, class_pixel_accuracy, pixel_confusion_matrix]
*
set_dl_model_param (DLModelHandle, device, DLDevice)
evaluate_dl_model (DLDataset, DLModelHandle, split, test, GenParamEval, EvaluationResult, EvalParams)
*
GenParamEvalDisplay : dict{display_mode: [measures, absolute_confusion_matrix]}
dev_display_segmentation_evaluation (EvaluationResult, EvalParams, GenParamEvalDisplay, WindowDict)
dev_disp_text (Press F5 to continue, window, bottom, right, black, [], [])
stop ()
dev_close_window_dict (WindowDict)
*
* Optimize the model for inference,
* meaning, reduce its memory consumption.
set_dl_model_param (DLModelHandle, optimize_for_inference, true)
set_dl_model_param (DLModelHandle, batch_size, 1)
* Save the model in this optimized state.
write_dl_model (DLModelHandle, model_best.hdl)
*
* *** 4.) INFER ***
*
* To demonstrate the inference steps, we apply the
* trained model to some randomly chosen example images.
list_image_files (ImageDir, default, recursive, ImageFiles)
tuple_shuffle (ImageFiles, ImageFilesShuffled)
*
* Create dictionaries used in visualization.
WindowDict : dict{}
DLDatasetInfo : dict{}
get_dl_model_param (DLModelHandle, class_ids, DLDatasetInfo.class_ids)
get_dl_model_param (DLModelHandle, class_names, DLDatasetInfo.class_names)
for IndexInference : 0 to 9 by 1read_image (Image, ImageFilesShuffled[IndexInference])gen_dl_samples_from_images (Image, DLSample)preprocess_dl_samples (DLSample, DLPreprocessParam)apply_dl_model (DLModelHandle, DLSample, [], DLResult)* dev_display_dl_data (DLSample, DLResult, DLDatasetInfo, [segmentation_image_result, segmentation_confidence_map], [], WindowDict)dev_disp_text (Press F5 to continue, window, bottom, right, black, [], [])stop ()
endfor
dev_close_window_dict (WindowDict)
*
* *** 5.) REMOVE FILES ***
*
clean_up_output (OutputDir, RemoveResults)
