Tensorflow object detection UnicodeEncodeError - tensorflow

Question

I am trying to use the pretrained faster_rcnn_inception_resnet_v2_atrous_oid. The code is modified from the official Quick Start notebook. When I use other models like faster_rcnn_nas_coco_2017_11_08, everything works. However, when I change to faster_rcnn_inception_resnet_v2_atrous_oid, I got the following error:
runfile('D:/python/tf/models-master/research/object_detection/Learn_faster.py', wdir='D:/python/tf/models-master/research/object_detection')
Reloaded modules: utils, utils.label_map_util, utils.visualization_utils
downloaded
Traceback (most recent call last):
File "e:\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2898, in run_code
self.showtraceback()
File "e:\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 1826, in showtraceback
self._showtraceback(etype, value, stb)
File "e:\Anaconda3\lib\site-packages\ipykernel\zmqshell.py", line 554, in _showtraceback
dh.parent_header, ident=topic)
File "e:\Anaconda3\lib\site-packages\jupyter_client\session.py", line 712, in send
to_send = self.serialize(msg, ident)
File "e:\Anaconda3\lib\site-packages\jupyter_client\session.py", line 607, in serialize
content = self.pack(content)
File "e:\Anaconda3\lib\site-packages\jupyter_client\session.py", line 103, in <lambda>
ensure_ascii=False, allow_nan=False,
File "e:\Anaconda3\lib\site-packages\zmq\utils\jsonapi.py", line 43, in dumps
s = s.encode('utf8')
UnicodeEncodeError: 'utf-8' codec can't encode character '\udcd5' in position 2098: surrogates not allowed
The code is:
import numpy as np
import os
import six.moves.urllib as urllib
import tarfile
import tensorflow as tf
from matplotlib import pyplot as plt
from PIL import Image
if tf.__version__ != '1.4.0':
raise ImportError('Please upgrade your tensorflow installation to v1.4.0!')
from utils import label_map_util
from utils import visualization_utils as vis_util
# What model to download.
MODEL_NAME = 'faster_rcnn_inception_resnet_v2_atrous_oid_2017_11_08'#'faster_rcnn_nas_coco_2017_11_08'#'faster_rcnn_resnet101_coco_2017_11_08' #'faster_rcnn_nas_coco_2017_11_08' 'rfcn_resnet101_coco_2017_11_08'# , , 'ssd_inception_v2_coco_2017_11_08'
MODEL_FILE = MODEL_NAME + '.tar.gz'
DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/'
# Path to frozen detection graph. This is the actual model that is used for the object detection.
PATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb'
# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = os.path.join('data', 'oid_bbox_trainable_label_map')#'mscoco_label_map.pbtxt')
NUM_CLASSES = 545
opener = urllib.request.URLopener()
opener.retrieve(DOWNLOAD_BASE + MODEL_FILE, MODEL_FILE)
print("downloaded")
tar_file = tarfile.open(MODEL_FILE)
for file in tar_file.getmembers():
file_name = os.path.basename(file.name)
if 'frozen_inference_graph.pb' in file_name:
tar_file.extract(file, os.getcwd())
detection_graph = tf.Graph()
with detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
category_index = label_map_util.create_category_index(categories)
def load_image_into_numpy_array(image):
(im_width, im_height) = image.size
return np.array(image.getdata()).reshape(
(im_height, im_width, 3)).astype(np.uint8)
# For the sake of simplicity we will use only 2 images:
# image1.jpg
# image2.jpg
# If you want to test the code with your images, just add path to the images to the TEST_IMAGE_PATHS.
PATH_TO_TEST_IMAGES_DIR = 'test_images'
TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.jpg'.format(i)) for i in range(1, 7) ]
# Size, in inches, of the output images.
IMAGE_SIZE = (12, 8)
with detection_graph.as_default():
with tf.Session(graph=detection_graph) as sess:
# Definite input and output Tensors for detection_graph
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
# Each box represents a part of the image where a particular object was detected.
detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
# Each score represent how level of confidence for each of the objects.
# Score is shown on the result image, together with the class label.
detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
num_detections = detection_graph.get_tensor_by_name('num_detections:0')
for image_path in TEST_IMAGE_PATHS:
image = Image.open(image_path)
# the array based representation of the image will be used later in order to prepare the
# result image with boxes and labels on it.
image_np = load_image_into_numpy_array(image)
# Expand dimensions since the model expects images to have shape: [1, None, None, 3]
image_np_expanded = np.expand_dims(image_np, axis=0)
# Actual detection.
(boxes, scores, classes, num) = sess.run(
[detection_boxes, detection_scores, detection_classes, num_detections],
feed_dict={image_tensor: image_np_expanded})
# Visualization of the results of a detection.
vis_util.visualize_boxes_and_labels_on_image_array(
image_np,
np.squeeze(boxes),
np.squeeze(classes).astype(np.int32),
np.squeeze(scores),
category_index,
use_normalized_coordinates=True,
line_thickness=8)
plt.figure(figsize=IMAGE_SIZE)
plt.imshow(image_np)

Answer 1

PATH_TO_LABELS = os.path.join('data', 'oid_bbox_trainable_label_map')
should be
PATH_TO_LABELS = os.path.join('data', 'oid_bbox_trainable_label_map.pbtxt')