This use case involves identifying each instance of an object within an image. Image Source: Labelme Generate Synthetic Data with Our New Free Trial. Labelme_draw_label_png data_dataset_voc/SegmentationClassPNG/2011_000003.png Use the following command to view the label PNG file: The label file will only contain low label values (i.e., 0, 4, 14), with 255 indicating the _ignore_ value (-1 in the NPY file). labelme2voc.py data_annotated data_dataset_voc –labels labels.txt # -data_dataset_voc/SegmentationClassVisualization Labelme data_annotated –labels labels.txt –nodata This use case involves segmenting images based on object classes, with every pixel assigned to a class to create fields with meaning. Image Source: Labelme Semantic Segmentation Labelme_draw_label_png apc2016_obj3_json/label.png > lbl = np.asarray((label_png))Īlternatively, you can use the following command to view a label PNG: You can avoid unexpected issues by using the command and the following script: It may be challenging to load label.png using, skimage.io.imread because it does not always work properly. Label names for PNG file values- label_names.txt.Label PNG visualization- label_viz.png.This will generate the following standard files from your JSON file: Labelme_json_to_dataset apc2016_obj3.json -o apc2016_obj3_json Run the following command to convert the JSON to an image and label dataset: You can use the following utility script to view JSON files quickly: Run the following command to annotate an image: Here are some examples of the operations associated with annotating a single image: This use case involves applying labels to a specific image. There are several ways to annotate images with Labelme, including single image annotation, semantic segmentation, and instance segmentation. Sudo apt-get install python3-pyqt5 # PyQt5įor more installation instructions, see the Labelme Github repo. Installing on Ubuntu 14.04 or 16.04 using Python 3 Pip install pyqt5 # pyqt5 can be installed via pip on python3
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