Tricolops 6f2e5e49e1 Added BarcodeDecodeModel for enhanced decoding capabilities. Implemented manual intervention for barcode detection by allowing specification of general areas when automatic detection fails. Reorganized code structure to streamline the execution flow and improve maintainability of segmentation and decoding operations. преди 3 месеца
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.readme 03d7d365ce Update README images преди 5 години
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README.md 6f2e5e49e1 Added BarcodeDecodeModel for enhanced decoding capabilities. Implemented manual intervention for barcode detection by allowing specification of general areas when automatic detection fails. Reorganized code structure to streamline the execution flow and improve maintainability of segmentation and decoding operations. преди 3 месеца
labelme2voc.py aa91168e4c Export original VOC format in labelme2voc.py преди 1 година
labels.txt 6f2e5e49e1 Added BarcodeDecodeModel for enhanced decoding capabilities. Implemented manual intervention for barcode detection by allowing specification of general areas when automatic detection fails. Reorganized code structure to streamline the execution flow and improve maintainability of segmentation and decoding operations. преди 3 месеца

README.md

Semantic Segmentation Example

Annotation

labelme data_annotated --labels labels.txt --nodata --validatelabel exact --config '{shift_auto_shape_color: -2}'

labelme "/media/tricolops/New Volume/Merangue/" --labels labels.txt --nodata --validatelabel exact --config '{shift_auto_shape_color: -2}' --model /home/tricolops/PaddleDetection/Exported_model/openvino_new_50/Detection.xml --segmentation_model /home/tricolops/PaddleSeg/output_bisenetv2/openvino_2023.1_segmentaion/segmentation.xml

Convert to VOC-format Dataset

# It generates:
#   - data_dataset_voc/JPEGImages
#   - data_dataset_voc/SegmentationClass
#   - data_dataset_voc/SegmentationClassNpy
#   - data_dataset_voc/SegmentationClassVisualization
./labelme2voc.py data_annotated data_dataset_voc --labels labels.txt --noobject

Fig 1. JPEG image (left), PNG label (center), JPEG label visualization (right)

Note that the label file contains only very low label values (ex. 0, 4, 14), and 255 indicates the __ignore__ label value (-1 in the npy file).
You can see the label PNG file by following.

labelme_draw_label_png data_dataset_voc/SegmentationClass/2011_000003.png