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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 DeepLab Demo
This demo will demostrate the steps to run deeplab semantic segmentation model on sample input images.
Prerequisites
Running this demo requires the following libraries
Step1:  Select and download models
Step5:  Load model in TensorFlow
Step6:  Helper methods
Step7:  Run on sample images
Step8:  Run on internet images
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Python Code:
import collections
import os
import StringIO
import sys
import tarfile
import tempfile
import urllib
from IPython import display
from ipywidgets import interact
from ipywidgets import interactive
from matplotlib import gridspec
from matplotlib import pyplot as plt
import numpy as np
from PIL import Image
import tensorflow as tf
if tf.__version__ < '1.5.0':
    raise ImportError('Please upgrade your tensorflow installation to v1.5.0 or newer!')
# Needed to show segmentation colormap labels
sys.path.append('utils')
import get_dataset_colormap
Explanation: DeepLab Demo
This demo will demostrate the steps to run deeplab semantic segmentation model on sample input images.
Prerequisites
Running this demo requires the following libraries:
Jupyter notebook (Python 2)
Tensorflow (>= v1.5.0)
Matplotlib
Pillow
numpy
ipywidgets (follow the setup here)
Imports
End of explanation
_MODEL_URLS = {
    'xception_coco_voctrainaug': 'http://download.tensorflow.org/models/deeplabv3_pascal_train_aug_2018_01_04.tar.gz',
    'xception_coco_voctrainval': 'http://download.tensorflow.org/models/deeplabv3_pascal_trainval_2018_01_04.tar.gz',
}
Config = collections.namedtuple('Config', 'model_url, model_dir')
def get_config(model_name, model_dir):
    return Config(_MODEL_URLS[model_name], model_dir)
config_widget = interactive(get_config, model_name=_MODEL_URLS.keys(), model_dir='')
display.display(config_widget)
# Check configuration and download the model
_TARBALL_NAME = 'deeplab_model.tar.gz'
config = config_widget.result
model_dir = config.model_dir or tempfile.mkdtemp()
tf.gfile.MakeDirs(model_dir)
download_path = os.path.join(model_dir, _TARBALL_NAME)
print 'downloading model to %s, this might take a while...' % download_path
urllib.urlretrieve(config.model_url, download_path)
print 'download completed!'
Explanation: Select and download models
End of explanation
_FROZEN_GRAPH_NAME = 'frozen_inference_graph'
class DeepLabModel(object):
    Class to load deeplab model and run inference.
    
    INPUT_TENSOR_NAME = 'ImageTensor:0'
    OUTPUT_TENSOR_NAME = 'SemanticPredictions:0'
    INPUT_SIZE = 513
    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 _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`.
        
        width, height = image.size
        resize_ratio = 1.0 * self.INPUT_SIZE / max(width, height)
        target_size = (int(resize_ratio * width), int(resize_ratio * height))
        resized_image = image.convert('RGB').resize(target_size, Image.ANTIALIAS)
        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
model = DeepLabModel(download_path)
Explanation: Load model in TensorFlow
End of explanation
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 = get_dataset_colormap.label_to_color_image(FULL_LABEL_MAP)
def vis_segmentation(image, seg_map):
    plt.figure(figsize=(15, 5))
    grid_spec = gridspec.GridSpec(1, 4, width_ratios=[6, 6, 6, 1])
    plt.subplot(grid_spec[0])
    plt.imshow(image)
    plt.axis('off')
    plt.title('input image')
    
    plt.subplot(grid_spec[1])
    seg_image = get_dataset_colormap.label_to_color_image(
        seg_map, get_dataset_colormap.get_pascal_name()).astype(np.uint8)
    plt.imshow(seg_image)
    plt.axis('off')
    plt.title('segmentation map')
    plt.subplot(grid_spec[2])
    plt.imshow(image)
    plt.imshow(seg_image, alpha=0.7)
    plt.axis('off')
    plt.title('segmentation overlay')
    
    unique_labels = np.unique(seg_map)
    ax = plt.subplot(grid_spec[3])
    plt.imshow(FULL_COLOR_MAP[unique_labels].astype(np.uint8), interpolation='nearest')
    ax.yaxis.tick_right()
    plt.yticks(range(len(unique_labels)), LABEL_NAMES[unique_labels])
    plt.xticks([], [])
    ax.tick_params(width=0)
    plt.show()
Explanation: Helper methods
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# Note that we are using single scale inference in the demo for fast
# computation, so the results may slightly differ from the visualizations
# in README, which uses multi-scale and left-right flipped inputs.
IMAGE_DIR = 'g3doc/img'
def run_demo_image(image_name):
    try:
        image_path = os.path.join(IMAGE_DIR, image_name)
        orignal_im = Image.open(image_path)
    except IOError:
        print 'Failed to read image from %s.' % image_path 
        return 
    print 'running deeplab on image %s...' % image_name
    resized_im, seg_map = model.run(orignal_im)
    
    vis_segmentation(resized_im, seg_map)
_ = interact(run_demo_image, image_name=['image1.jpg', 'image2.jpg', 'image3.jpg'])
Explanation: Run on sample images
End of explanation
def get_an_internet_image(url):
    if not url:
        return
    try:
        # Prefix with 'file://' for local file.
        if os.path.exists(url):
            url = 'file://' + url
        f = urllib.urlopen(url)
        jpeg_str = f.read()
    except IOError:
        print 'invalid url: ' + url
        return
    orignal_im = Image.open(StringIO.StringIO(jpeg_str))
    print 'running deeplab on image %s...' % url
    resized_im, seg_map = model.run(orignal_im)
    
    vis_segmentation(resized_im, seg_map)
_ = interact(get_an_internet_image, url='')
Explanation: Run on internet images
End of explanation
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