How to use the imgviz.rgb2gray function in imgviz

To help you get started, we’ve selected a few imgviz examples, based on popular ways it is used in public projects.

Secure your code as it's written. Use Snyk Code to scan source code in minutes - no build needed - and fix issues immediately.

github wkentaro / labelme / labelme / cli / json_to_dataset.py View on Github external
label_name_to_value = {'_background_': 0}
    for shape in sorted(data['shapes'], key=lambda x: x['label']):
        label_name = shape['label']
        if label_name in label_name_to_value:
            label_value = label_name_to_value[label_name]
        else:
            label_value = len(label_name_to_value)
            label_name_to_value[label_name] = label_value
    lbl = utils.shapes_to_label(img.shape, data['shapes'], label_name_to_value)

    label_names = [None] * (max(label_name_to_value.values()) + 1)
    for name, value in label_name_to_value.items():
        label_names[value] = name
    lbl_viz = imgviz.label2rgb(
        label=lbl, img=imgviz.rgb2gray(img), label_names=label_names, loc='rb'
    )

    PIL.Image.fromarray(img).save(osp.join(out_dir, 'img.png'))
    utils.lblsave(osp.join(out_dir, 'label.png'), lbl)
    PIL.Image.fromarray(lbl_viz).save(osp.join(out_dir, 'label_viz.png'))

    with open(osp.join(out_dir, 'label_names.txt'), 'w') as f:
        for lbl_name in label_names:
            f.write(lbl_name + '\n')

    logger.warning('info.yaml is being replaced by label_names.txt')
    info = dict(label_names=label_names)
    with open(osp.join(out_dir, 'info.yaml'), 'w') as f:
        yaml.safe_dump(info, f, default_flow_style=False)

    logger.info('Saved to: {}'.format(out_dir))
github wkentaro / labelme / examples / instance_segmentation / labelme2voc.py View on Github external
cls, ins = labelme.utils.shapes_to_label(
                img_shape=img.shape,
                shapes=data['shapes'],
                label_name_to_value=class_name_to_id,
                type='instance',
            )
            ins[cls == -1] = 0  # ignore it.

            # class label
            labelme.utils.lblsave(out_clsp_file, cls)
            np.save(out_cls_file, cls)
            if not args.noviz:
                clsv = imgviz.label2rgb(
                    label=cls,
                    img=imgviz.rgb2gray(img),
                    label_names=class_names,
                    font_size=15,
                    loc='rb',
                )
                imgviz.io.imsave(out_clsv_file, clsv)

            # instance label
            labelme.utils.lblsave(out_insp_file, ins)
            np.save(out_ins_file, ins)
            if not args.noviz:
                instance_ids = np.unique(ins)
                instance_names = [str(i) for i in range(max(instance_ids) + 1)]
                insv = imgviz.label2rgb(
                    label=ins,
                    img=imgviz.rgb2gray(img),
                    label_names=instance_names,
github wkentaro / imgviz / examples / color.py View on Github external
import os.path as osp

import matplotlib.pyplot as plt

import imgviz


here = osp.dirname(osp.abspath(__file__))


if __name__ == '__main__':
    data = imgviz.data.arc2017()

    rgb = data['rgb']
    gray = imgviz.rgb2gray(rgb)
    rgb2 = imgviz.gray2rgb(gray)

    # -------------------------------------------------------------------------

    fig = plt.figure(dpi=150)

    plt.subplot(131)
    plt.title('original')
    plt.imshow(rgb)
    plt.axis('off')

    plt.subplot(132)
    plt.title('rgb2gray:\n{}'.format(gray.shape))
    plt.imshow(gray)
    plt.axis('off')
github wkentaro / labelme / labelme / cli / draw_json.py View on Github external
label_name_to_value = {'_background_': 0}
    for shape in sorted(data['shapes'], key=lambda x: x['label']):
        label_name = shape['label']
        if label_name in label_name_to_value:
            label_value = label_name_to_value[label_name]
        else:
            label_value = len(label_name_to_value)
            label_name_to_value[label_name] = label_value
    lbl = utils.shapes_to_label(img.shape, data['shapes'], label_name_to_value)

    label_names = [None] * (max(label_name_to_value.values()) + 1)
    for name, value in label_name_to_value.items():
        label_names[value] = name
    lbl_viz = imgviz.label2rgb(
        label=lbl,
        img=imgviz.rgb2gray(img),
        label_names=label_names,
        font_size=30,
        loc='rb',
    )

    plt.subplot(121)
    plt.imshow(img)
    plt.subplot(122)
    plt.imshow(lbl_viz)
    plt.show()
github wkentaro / labelme / examples / semantic_segmentation / labelme2voc.py View on Github external
img = np.asarray(PIL.Image.open(img_file))
            PIL.Image.fromarray(img).save(out_img_file)

            lbl = labelme.utils.shapes_to_label(
                img_shape=img.shape,
                shapes=data['shapes'],
                label_name_to_value=class_name_to_id,
            )
            labelme.utils.lblsave(out_png_file, lbl)

            np.save(out_lbl_file, lbl)

            if not args.noviz:
                viz = imgviz.label2rgb(
                    label=lbl,
                    img=imgviz.rgb2gray(img),
                    font_size=15,
                    label_names=class_names,
                    loc='rb',
                )
                imgviz.io.imsave(out_viz_file, viz)