Пример #1
0
def save(subdir, b, p, dp, faces, gt_mesh, image, duration=5, fps=50):
    from scipy.misc import imsave
    from util3d.mesh.obj_io import write_obj
    imsave(os.path.join(subdir, 'image.png'), image)
    v, f = (np.array(gt_mesh[k]) for k in ('vertices', 'faces'))
    write_obj(os.path.join(subdir, 'gt_mesh.obj'), v, f)
    save_anim(subdir, b, p, dp, faces, duration, fps)
Пример #2
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def save(subdir, b, p, dp, faces, gt_mesh, image, duration=5, fps=50):
    from scipy.misc import imsave
    from util3d.mesh.obj_io import write_obj
    imsave(os.path.join(subdir, 'image.png'), image)
    v, f = (np.array(gt_mesh[k]) for k in ('vertices', 'faces'))
    write_obj(os.path.join(subdir, 'gt_mesh.obj'), v, f)
    save_anim(subdir, b, p, dp, faces, duration, fps)
Пример #3
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def save():
    import tensorflow as tf
    from util3d.mesh.obj_io import write_obj
    from shapenet.image import with_background
    from template_ffd.model import get_builder
    builder = get_builder(model_id)

    mesh_fn = builder.get_prediction_to_mesh_fn(0.02)
    cloud_fn = builder.get_prediction_to_cloud_fn()

    graph = tf.Graph()
    with graph.as_default():
        image = tf.placeholder(shape=(192, 256, 3), dtype=tf.uint8)
        std_image = tf.image.per_image_standardization(image)
        std_image = tf.expand_dims(std_image, axis=0)
        example_id = tf.constant(['blah'], dtype=tf.string)
        spec = builder.get_estimator_spec(
            dict(example_id=example_id, image=std_image),
            None, tf.estimator.ModeKeys.PREDICT)
        predictions = spec.predictions
        probs_tf = predictions['probs']
        dp_tf = predictions['dp']
        saver = tf.train.Saver()

    with tf.Session(graph=graph) as sess:
        saver.restore(sess, tf.train.latest_checkpoint(builder.model_dir))
        for fn in fns:
            path = os.path.join(folder, fn)
            image_data = np.array(imread(path))
            if image_data.shape[-1] == 4:
                image_data = with_background(image_data, (255, 255, 255))
            probs, dp = sess.run(
                [probs_tf, dp_tf], feed_dict={image: image_data})
            probs = probs[0]
            dp = dp[0]
            mesh = mesh_fn(probs, dp)
            cloud = cloud_fn(probs, dp)['cloud']
            v, ov, f = (
                mesh[k] for k in('vertices', 'original_vertices', 'faces'))
            path = '%s.obj' % path[:-4]
            write_obj(path, v, f)
            p2 = '%s_template.obj' % path[:-4]
            np.save('%s_cloud.npy' % path[:-4], cloud)
            write_obj(p2, ov, f)
Пример #4
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def export(cat_desc, example_ids, regime='e'):
    from scipy.misc import imsave
    from util3d.mesh.obj_io import write_obj
    import os
    all_ds = get_ds(cat_desc, regime)
    base = os.path.realpath(os.path.dirname(__file__))

    with all_ds:
        for example_id in example_ids:
            folder = os.path.join(
                base, 'big_table_results', cat_desc, example_id)
            if not os.path.isdir(folder):
                os.makedirs(folder)
            image, gt_mesh, cloud, mesh, voxels, template_mesh = \
                all_ds[example_id]
            imsave(os.path.join(folder, 'image.png'), image)
            v, f = (np.array(mesh[k]) for k in ('vertices', 'faces'))
            write_obj(os.path.join(folder, 'deformed.obj'), v, f)
            v, f = (np.array(template_mesh[k]) for k in ('vertices', 'faces'))
            write_obj(os.path.join(folder, 'template.obj'), v, f)
            v, f = (np.array(gt_mesh[k]) for k in ('vertices', 'faces'))
            write_obj(os.path.join(folder, 'model.obj'), v, f)
            np.save(os.path.join(
                folder, 'inferred_cloud.npy'), np.array(cloud))
            path = os.path.join(folder, 'deformed.binvox')
            voxels.save(path)
Пример #5
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def export(cat_desc, example_ids, regime='e'):
    from scipy.misc import imsave
    from util3d.mesh.obj_io import write_obj
    import os
    all_ds = get_ds(cat_desc, regime)
    base = os.path.realpath(os.path.dirname(__file__))

    with all_ds:
        for example_id in example_ids:
            folder = os.path.join(
                base, 'big_table_results', cat_desc, example_id)
            if not os.path.isdir(folder):
                os.makedirs(folder)
            image, gt_mesh, cloud, mesh, voxels, template_mesh = \
                all_ds[example_id]
            imsave(os.path.join(folder, 'image.png'), image)
            v, f = (np.array(mesh[k]) for k in ('vertices', 'faces'))
            write_obj(os.path.join(folder, 'deformed.obj'), v, f)
            v, f = (np.array(template_mesh[k]) for k in ('vertices', 'faces'))
            write_obj(os.path.join(folder, 'template.obj'), v, f)
            v, f = (np.array(gt_mesh[k]) for k in ('vertices', 'faces'))
            write_obj(os.path.join(folder, 'model.obj'), v, f)
            np.save(os.path.join(
                folder, 'inferred_cloud.npy'), np.array(cloud))
            path = os.path.join(folder, 'deformed.binvox')
            voxels.save(path)
Пример #6
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def export(example_id):
    import os
    from util3d.mesh.obj_io import write_obj
    from scipy.misc import imsave
    save_dir = os.path.join(os.path.realpath(os.path.dirname(__file__)),
                            'top_k_results', example_id)
    if not os.path.isdir(save_dir):
        os.makedirs(save_dir)
    with all_ds:
        print(example_id)
        image, gt_mesh, predictions = all_ds[example_id]
        meshes = top_k_mesh_fn(
            *(np.array(predictions[k]) for k in ('probs', 'dp')))
        for i, mesh in enumerate(meshes):
            ov, v, f = (
                mesh[k] for k in ('original_vertices', 'vertices', 'faces'))
            write_obj(os.path.join(save_dir, 'template%d.obj' % i), ov, f)
            write_obj(os.path.join(save_dir, 'deformed%d.obj' % i), v, f)
        v, f = (np.array(gt_mesh[k]) for k in ('vertices', 'faces'))
        write_obj(os.path.join(save_dir, 'ground_truth.obj'), v, f)
        imsave(os.path.join(save_dir, 'image.png'), image)
Пример #7
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def export(example_id):
    import os
    from util3d.mesh.obj_io import write_obj
    from scipy.misc import imsave
    save_dir = os.path.join(os.path.realpath(os.path.dirname(__file__)),
                            'top_k_results', example_id)
    if not os.path.isdir(save_dir):
        os.makedirs(save_dir)
    with all_ds:
        print(example_id)
        image, gt_mesh, predictions = all_ds[example_id]
        meshes = top_k_mesh_fn(*(np.array(predictions[k])
                                 for k in ('probs', 'dp')))
        for i, mesh in enumerate(meshes):
            ov, v, f = (mesh[k]
                        for k in ('original_vertices', 'vertices', 'faces'))
            write_obj(os.path.join(save_dir, 'template%d.obj' % i), ov, f)
            write_obj(os.path.join(save_dir, 'deformed%d.obj' % i), v, f)
        v, f = (np.array(gt_mesh[k]) for k in ('vertices', 'faces'))
        write_obj(os.path.join(save_dir, 'ground_truth.obj'), v, f)
        imsave(os.path.join(save_dir, 'image.png'), image)
Пример #8
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    def get_mesh():
        import numpy as np
        from modelnet.parsed import get_saved_dataset
        dataset = get_saved_dataset('ModelNet40', 'train', 'toilet')
        with dataset as ds:
            key = tuple(ds.keys())[2]
            mesh = ds[key]
            vertices, faces = (np.array(mesh[k])
                               for k in ('vertices', 'faces'))
        return vertices, faces

    vertices, faces = get_mesh()

    v2, f2 = clean(vertices, faces)
    write_obj('/tmp/original.obj', vertices, faces)
    write_obj('/tmp/cleaned.obj', v2, f2)
    print(vertices.shape)
    print(faces.shape)
    print(v2.shape)
    print(f2.shape)

    # def vis(v0, f0, v1, f1):
    #     from mayavi import mlab
    #     from util3d.mayavi_vis import vis_mesh
    #     mlab.figure()
    #     vis_mesh(v0, f0, color=(0, 0, 1))
    #     mlab.figure()
    #     vis_mesh(v1, f1, color=(0, 1, 0))
    #     mlab.show()
    #