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### COPY ALL THE CODE INTO A JYPYTER NOTEBOOK ### | |
### THE JYPYTER NOTEBOOK NEEDS TO BE IN 'tensorflow\models\research\deeplab' ### | |
## Imports | |
import collections | |
import os | |
import io | |
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 | |
## Select and download models | |
_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.request.urlretrieve(config.model_url, download_path) | |
print('download completed!') | |
## Load model in TensorFlow | |
_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) | |
## Helper methods | |
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() | |
## Run on sample images | |
# 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']) | |
## Run on internet images | |
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.request.urlopen(url) | |
jpeg_str = f.read() | |
except IOError: | |
print('invalid url: ' + url) | |
return | |
orignal_im = Image.open(io.BytesIO(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='') |
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Hi
if tf.__version__ < '1.5.0'
return wrong resultmaybe use this code is a better idea :
if int(tf.__version__[2:4]) < 5