deeplabv3+ demo测试图像分割

#直接复制本代码,存为.py文件,  在大概204行左右更换模型地址,在223左右更换图片路径,直接执行即可得出简单的分割效果
#!--*-- 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 = 513
    FROZEN_GRAPH_NAME = 'frozen_inference_graph'

    def __init__(self, tarball_path):
        """
        加载预训练模型
        """
        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):
        """

        Args:
        image:  转换为PIL.Image 类,不能直接用图片，原始图片

        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


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, imagefile):
    """可视化三种图像."""
    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)
    # seg_image = label_to_color_image(seg_map)
    # seg_image.save('/str(ss)+imagefile')
    plt.imshow(seg_image)
    plt.savefig('./'+imagefile+'.png')

    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', '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)


## Tensorflow 提供的模型下载
MODEL_NAME = 'xception_coco_voctrainval'
# ['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()
model_dir = './'
# 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_dir = '/‘

# download_path = os.path.join(model_dir, _MODEL_URLS[MODEL_NAME])
MODEL = DeepLabModel('./deeplab_model.tar.gz')
# MODEL = './deeplab_model.tar.gz'
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(type(orignal_im))
    # orignal_im.show()
    print('running deeplab on image %s...' % imagefile)
    resized_im, seg_map = MODEL.run(Image.open(imagefile))


    vis_segmentation(resized_im, seg_map,imagefile)

images_dir = './pictures'
images = sorted(os.listdir(images_dir))
print(images)
# img='205729y9fodss9ao6ol5921-150x150.jpg'
# img.show()
for imgfile in images:
# img.show()
    run_visualization(os.path.join(images_dir, imgfile))

print('Done.')

所使用的是deeplab_model.tar.gz，也可以修改代码使用在标准数据集上预训练过的模型；代码在182行附近。

1.修改模型保存路径

2.修改图片路径

3.运行即可

参考自：https://www.aiuai.cn/aifarm252.html