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jyuko/deeplab_sample_video_capture.py

Last active June 3, 2019 00:30
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  1. jyuko revised this gist Nov 17, 2018. 1 changed file with 0 additions and 2 deletions.
    2 changes: 0 additions & 2 deletions deeplab_sample_video_capture.py
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    import tempfile
    from six.moves import urllib

    from matplotlib import gridspec
    from matplotlib import pyplot as plt
    import numpy as np
    from PIL import Image

  2. jyuko created this gist Nov 17, 2018.
    171 changes: 171 additions & 0 deletions deeplab_sample_video_capture.py
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    import os
    from io import BytesIO
    import tarfile
    import tempfile
    from six.moves import urllib

    from matplotlib import gridspec
    from matplotlib import pyplot as plt
    import numpy as np
    from PIL import Image

    import tensorflow as tf

    import cv2
    import time

    class DeepLabModel(object):
    """Class to load deeplab model and run inference."""

    INPUT_TENSOR_NAME = 'ImageTensor:0'
    OUTPUT_TENSOR_NAME = 'SemanticPredictions:0'
    INPUT_SIZE = 513
    FROZEN_GRAPH_NAME = 'frozen_inference_graph'

    def __init__(self, tarball_path):
    """Creates and loads pretrained deeplab model."""
    self.graph = tf.Graph()

    graph_def = None
    # Extract frozen graph from tar archive.
    tar_file = tarfile.open(tarball_path)
    for tar_info in tar_file.getmembers():
    if self.FROZEN_GRAPH_NAME in os.path.basename(tar_info.name):
    file_handle = tar_file.extractfile(tar_info)
    graph_def = tf.GraphDef.FromString(file_handle.read())
    break

    tar_file.close()

    if graph_def is None:
    raise RuntimeError('Cannot find inference graph in tar archive.')

    with self.graph.as_default():
    tf.import_graph_def(graph_def, name='')

    self.sess = tf.Session(graph=self.graph)

    def run(self, image):
    """Runs inference on a single image.
    Args:
    image: A PIL.Image object, raw input image.
    Returns:
    resized_image: RGB image resized from original input image.
    seg_map: Segmentation map of `resized_image`.
    """
    resized_image = image
    batch_seg_map = self.sess.run(
    self.OUTPUT_TENSOR_NAME,
    feed_dict={self.INPUT_TENSOR_NAME: [np.asarray(resized_image)]})
    seg_map = batch_seg_map[0]
    return resized_image, seg_map


    def create_pascal_label_colormap():
    """Creates a label colormap used in PASCAL VOC segmentation benchmark.
    Returns:
    A Colormap for visualizing segmentation results.
    """
    colormap = np.zeros((256, 3), dtype=int)
    ind = np.arange(256, dtype=int)

    for shift in reversed(range(8)):
    for channel in range(3):
    colormap[:, channel] |= ((ind >> channel) & 1) << shift
    ind >>= 3

    return colormap


    def label_to_color_image(label):
    """Adds color defined by the dataset colormap to the label.
    Args:
    label: A 2D array with integer type, storing the segmentation label.
    Returns:
    result: A 2D array with floating type. The element of the array
    is the color indexed by the corresponding element in the input label
    to the PASCAL color map.
    Raises:
    ValueError: If label is not of rank 2 or its value is larger than color
    map maximum entry.
    """
    if label.ndim != 2:
    raise ValueError('Expect 2-D input label')

    colormap = create_pascal_label_colormap()

    if np.max(label) >= len(colormap):
    raise ValueError('label value too large.')

    return colormap[label]


    def vis_segmentation(image, seg_map):
    seg_image = label_to_color_image(seg_map).astype(np.uint8)

    result = cv2.add(image, seg_image)
    cv2.imshow("camera window", result)

    LABEL_NAMES = np.asarray([
    'background', 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus',
    'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike',
    'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tv'
    ])

    FULL_LABEL_MAP = np.arange(len(LABEL_NAMES)).reshape(len(LABEL_NAMES), 1)
    FULL_COLOR_MAP = label_to_color_image(FULL_LABEL_MAP)

    MODEL_NAME = 'mobilenetv2_coco_voctrainaug' # @param ['mobilenetv2_coco_voctrainaug', 'mobilenetv2_coco_voctrainval', 'xception_coco_voctrainaug', 'xception_coco_voctrainval']

    _DOWNLOAD_URL_PREFIX = 'http://download.tensorflow.org/models/'
    _MODEL_URLS = {
    'mobilenetv2_coco_voctrainaug':
    'deeplabv3_mnv2_pascal_train_aug_2018_01_29.tar.gz',
    'mobilenetv2_coco_voctrainval':
    'deeplabv3_mnv2_pascal_trainval_2018_01_29.tar.gz',
    'xception_coco_voctrainaug':
    'deeplabv3_pascal_train_aug_2018_01_04.tar.gz',
    'xception_coco_voctrainval':
    'deeplabv3_pascal_trainval_2018_01_04.tar.gz',
    }
    _TARBALL_NAME = 'deeplab_model.tar.gz'

    model_dir = tempfile.mkdtemp()
    tf.gfile.MakeDirs(model_dir)

    download_path = os.path.join(model_dir, _TARBALL_NAME)
    print('downloading model, this might take a while...')
    urllib.request.urlretrieve(_DOWNLOAD_URL_PREFIX + _MODEL_URLS[MODEL_NAME],
    download_path)
    print('download completed! loading DeepLab model...')

    MODEL = DeepLabModel(download_path)
    print('model loaded successfully!')

    capure = cv2.VideoCapture(0)

    def run_visualization():
    while(True):
    ret, frame = capure.read()
    original_im = cv2.resize(frame,(480,320))

    start_time = time.time()

    resized_im, seg_map = MODEL.run(original_im)
    vis_segmentation(resized_im, seg_map)

    elapsed_time = time.time() - start_time
    print(elapsed_time)

    if cv2.waitKey(1) == 27:
    break
    capure.release()
    cv2.destroyAllWindows()

    run_visualization()