_DATASETS_INFORMATION = {
    'cityscapes': _CITYSCAPES_INFORMATION,
    'pascal_voc_seg': _PASCAL_VOC_SEG_INFORMATION,
    'ade20k': _ADE20K_INFORMATION,
    'camvid':_CAMVID_INFORMATION,
    # 'mydata':_MYDATA_INFORMATION,
    }

exclude_list = ['global_step','logits']
if not initialize_last_layer:
    exclude_list.extend(last_layers)

python export_model.py 
    --logtostderr 
    --checkpoint_path="/lwh/models/research/deeplab/exp/blackboard_train/train/model.ckpt-30000" 
    --export_path="/lwh/models/research/deeplab/exp/blackboard_train/train/frozen_inference_graph.pb"  
    --model_variant="xception_65"  
    --atrous_rates=6  
    --atrous_rates=12  
    --atrous_rates=18   
    --output_stride=16  
    --decoder_output_stride=4  
    --num_classes=3 
    --crop_size=1080 
    --crop_size=1920 
    --inference_scales=1.0

# !--*-- coding:utf-8 --*--

# Deeplab Demo

import os
import tarfile

from matplotlib import gridspec
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
import tempfile
from six.moves import urllib

import tensorflow as tf


class DeepLabModel(object):
    """
  加载 DeepLab 模型；
  推断 Inference
  """
    INPUT_TENSOR_NAME = 'ImageTensor:0'
    OUTPUT_TENSOR_NAME = 'SemanticPredictions:0'
    INPUT_SIZE = 1920
    FROZEN_GRAPH_NAME = 'frozen_inference_graph'

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

        graph_def = None
        graph_def = tf.GraphDef.FromString(open(tarball_path, 'rb').read())

        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))
        target_size = (1920,1080)
        resized_image = image.convert('RGB').resize(target_size, Image.ANTIALIAS)
        print(resized_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):
    """Visualizes input image, segmentation map and overlay view."""
    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 = label_to_color_image(seg_map).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=0.0)
    plt.grid('off')
    plt.show()

LABEL_NAMES = np.asarray(
    ['background', 'blackboard','screen'])
# LABEL_NAMES = np.asarray(
#     ['background', 'blackboard','screen'])

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



download_path =  r"D:python_projectdeeplabv3+lackboard_v2.pb"

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


##
def run_visualization(imagefile):
    """
  DeepLab 语义分割，并可视化结果.
  """
    orignal_im = Image.open(imagefile)
    print('running deeplab on image %s...' % imagefile)
    resized_im, seg_map = MODEL.run(orignal_im)
    print(seg_map.shape)

    vis_segmentation(resized_im, seg_map)


images_dir = r'D:python_projectdeeplabv3+	est_img'  # 测试图片目录所在位置
images = sorted(os.listdir(images_dir))
for imgfile in images:
    run_visualization(os.path.join(images_dir, imgfile))

print('Done.')