Python skimage.io.show() Examples

The following are 5 code examples of skimage.io.show(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module skimage.io , or try the search function .
Example #1
Source File: predict.py    From c3d-pytorch with MIT License 5 votes vote down vote up 
def get_sport_clip(clip_name, verbose=True):
    """
    Loads a clip to be fed to C3D for classification.
    TODO: should I remove mean here?
    
    Parameters
    ----------
    clip_name: str
        the name of the clip (subfolder in 'data').
    verbose: bool
        if True, shows the unrolled clip (default is True).

    Returns
    -------
    Tensor
        a pytorch batch (n, ch, fr, h, w).
    """

    clip = sorted(glob(join('data', clip_name, '*.png')))
    clip = np.array([resize(io.imread(frame), output_shape=(112, 200), preserve_range=True) for frame in clip])
    clip = clip[:, :, 44:44+112, :]  # crop centrally

    if verbose:
        clip_img = np.reshape(clip.transpose(1, 0, 2, 3), (112, 16 * 112, 3))
        io.imshow(clip_img.astype(np.uint8))
        io.show()

    clip = clip.transpose(3, 0, 1, 2)  # ch, fr, h, w
    clip = np.expand_dims(clip, axis=0)  # batch axis
    clip = np.float32(clip)

    return torch.from_numpy(clip)
Example #2
Source File: RAG_threshold.py    From Pic-Numero with MIT License 5 votes vote down vote up 
def main():
    img = misc.imread("wheat.png")

    # labels1 = segmentation.slic(img, compactness=100, n_segments=9)
    labels1 = segmentation.slic(img, compactness=50, n_segments=4)
    out1 = color.label2rgb(labels1, img, kind='overlay')
    print(labels1.shape)

    g = graph.rag_mean_color(img, labels1)
    labels2 = graph.cut_threshold(labels1, g, 29)
    out2 = color.label2rgb(labels2, img, kind='overlay')

    # get roi
    # logicalIndex = (labels2 != 1)
    # gray = rgb2gray(img);
    # gray[logicalIndex] = 0;


    plt.figure()
    io.imshow(out1)
    plt.figure()
    io.imshow(out2)
    io.show()
Example #3
Source File: RAG_threshold.py    From Pic-Numero with MIT License 5 votes vote down vote up 
def spectral_cluster(filename, compactness_val=30, n=6):
    img = misc.imread(filename)
    labels1 = segmentation.slic(img, compactness=compactness_val, n_segments=n)
    out1 = color.label2rgb(labels1, img, kind='overlay', colors=['red','green','blue','cyan','magenta','yellow'])

    fig, ax = plt.subplots()
    ax.imshow(out1, interpolation='nearest')
    ax.set_title("Compactness: {} | Segments: {}".format(compactness_val, n))
    plt.show()
Example #4
Source File: dataset.py    From deep-high-resolution-net.TensorFlow with MIT License 5 votes vote down vote up 
def draw_points_on_img(img, point_ver, point_hor, point_class):
    for i in range(len(point_class)):
        if point_class[i] != 3:
            rr, cc = draw.circle(point_ver[i], point_hor[i], 10, (256, 192))
            #draw.set_color(img, [rr, cc], [0., 0., 0.], alpha=5)
            img[rr, cc, :] = 0
    #io.imshow(img)
    #io.show()

    return img
Example #5
Source File: dataset.py    From deep-high-resolution-net.TensorFlow with MIT License 5 votes vote down vote up 
def mytest():
    tfrecord_file = '../dataset/train.tfrecords'

    filename_queue = tf.train.string_input_producer([tfrecord_file], num_epochs=None)
    image_name, image, keypoints_ver, keypoints_hor, keypoints_class = decode_tfrecord(filename_queue)

    with tf.Session() as sess:
        init_op = tf.global_variables_initializer()
        sess.run(init_op)
        coord = tf.train.Coordinator()
        threads = tf.train.start_queue_runners(coord=coord)
        try:
            # while not coord.should_stop():
            for i in range(10):
                img_name, img, point_ver, point_hor, point_class = sess.run([image_name, image, keypoints_ver,
                                                                             keypoints_hor, keypoints_class])

                print(img_name, point_hor, point_ver, point_class)

                for i in range(len(point_class)):
                    if point_class[i] > 0:
                        rr, cc = draw.circle(point_ver[i], point_hor[i], 10, (256, 192))
                        img[rr, cc, :] = 0

                io.imshow(img)
                io.show()

        except tf.errors.OutOfRangeError:
            print('Done reading')
        finally:
            coord.request_stop()