Gfile error Tensorflow in ROS

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Hello.

I am trying to create a node for image classification using ROS and Tensorflow.

If I run this script with python, everything works perfectly with some differents networks:

import pyrealsense2 as rs
import numpy as np
import cv2
import tensorflow as tf
Configure depth and color streams
pipeline = rs.pipeline()
config = rs.config()
config.enable_stream(rs.stream.color, 640, 480, rs.format.bgr8, 30)
print("[INFO] Starting streaming...")
pipeline.start(config)
print("[INFO] Camera ready.")
download model from: https://github.com/opencv/opencv/wiki/TensorFlow-Object-Detection-API#run-network-in-opencv
print("[INFO] Loading model...")
PATH_TO_CKPT = "frozen_inference_graph.pb"
Load the Tensorflow model into memory.
detection_graph = tf.Graph()
with detection_graph.as_default():
    od_graph_def = tf.compat.v1.GraphDef()
    with tf.compat.v1.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
        serialized_graph = fid.read()
        od_graph_def.ParseFromString(serialized_graph)
        tf.compat.v1.import_graph_def(od_graph_def, name='')
    sess = tf.compat.v1.Session(graph=detection_graph)
Input tensor is the image
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
Output tensors are the detection boxes, scores, and classes
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 represents level of confidence for each of the objects.
The 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')
Number of objects detected
num_detections = detection_graph.get_tensor_by_name('num_detections:0')
code source of tensorflow model loading: https://www.geeksforgeeks.org/ml-training-image-classifier-using-tensorflow-object-detection-api/
print("[INFO] Model loaded.")
colors_hash = {}
while True:
    frames = pipeline.wait_for_frames()
    color_frame = frames.get_color_frame()
# Convert images to numpy arrays
color_image = np.asanyarray(color_frame.get_data())
scaled_size = (color_frame.width, color_frame.height)
# expand image dimensions to have shape: [1, None, None, 3]
# i.e. a single-column array, where each item in the column has the pixel RGB value
image_expanded = np.expand_dims(color_image, axis=0)
# Perform the actual detection by running the model with the image as input
(boxes, scores, classes, num) = sess.run([detection_boxes, detection_scores, detection_classes, num_detections],
                                            feed_dict={image_tensor: image_expanded})

boxes = np.squeeze(boxes)
classes = np.squeeze(classes).astype(np.int32)
scores = np.squeeze(scores)

for idx in range(int(num)):
    class_ = classes[idx]
    score = scores[idx]
    box = boxes[idx]

    if class_ not in colors_hash:
        colors_hash[class_] = tuple(np.random.choice(range(256), size=3))

    if score &gt; 0.6:
        left = int(box[1] * color_frame.width)
        top = int(box[0] * color_frame.height)
        right = int(box[3] * color_frame.width)
        bottom = int(box[2] * color_frame.height)

        p1 = (left, top)
        p2 = (right, bottom)
        # draw box
        r, g, b = colors_hash[class_]

        cv2.rectangle(color_image, p1, p2, (int(r), int(g), int(b)), 2, 1)
        cv2.putText(color_image, str(class_)+&quot; &quot;+str(score), p1, cv2.FONT_HERSHEY_SIMPLEX, 0.75, (int(r), int(g), int(b)))

cv2.namedWindow('RealSense', cv2.WINDOW_AUTOSIZE)
cv2.imshow('RealSense', color_image)
cv2.waitKey(1)


print("[INFO] stop streaming ...")
pipeline.stop()

In the other hand, I have created a ROS package where I launch the following script:

#!/usr/bin/env python3
#Creador: AMC 2021
#Funcion: Nodo de clasificacion de imagen con TensorFlow
#Recibe: RealSense [Image(/camera/color/image_raw)]
#Envia: Realsense [Image(/image_classified)]
import rospy
from sensor_msgs.msg import Image
from std_msgs.msg import String
import pyrealsense2 as rs
import numpy as np
import cv2
import tensorflow as tf
import struct
class TENSORFLOW_ROS_CLASS:
Function: init

Comments
----------
Define neural network

Parameters
----------

Returns
-------
def __init__(self):

    # download model from: https://github.com/opencv/opencv/wiki/TensorFlow-Object-Detection-API#run-network-in-opencv
    print(&quot;[INFO] Loading model...&quot;)
    PATH_TO_CKPT =&quot;frozen_inference_graph.pb&quot;

    # Load the Tensorflow model into memory.
    detection_graph = tf.Graph()
    with detection_graph.as_default():
        od_graph_def = tf.compat.v1.GraphDef()
        with tf.compat.v1.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
            serialized_graph = fid.read()
            od_graph_def.ParseFromString(serialized_graph)
            tf.compat.v1.import_graph_def(od_graph_def, name='')
        sess = tf.compat.v1.Session(graph=detection_graph)

    # Input tensor is the image
    image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
    # Output tensors are the detection boxes, scores, and classes
    # 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 represents level of confidence for each of the objects.
    # The 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')
    # Number of objects detected
    num_detections = detection_graph.get_tensor_by_name('num_detections:0')
    # code source of tensorflow model loading: https://www.geeksforgeeks.org/ml-training-image-classifier-using-tensorflow-object-detection-api/
    print(&quot;[INFO] Model loaded.&quot;)
    colors_hash = {}


Function: main

Comments
----------
Set up the node, the publications and subsciptions.

Parameters
----------

Returns
-------
def main(self):

    rospy.init_node('tensorflow_ros')
    rospy.loginfo(&quot;Iniciado nodo tensorflow_ros&quot;)

    #Publications
    self.classification_pub = rospy.Publisher(&quot;image_classified&quot;, Image, queue_size=100)

    #Subscriber
    rospy.Subscriber(&quot;/camera/color/image_raw&quot;, Image, self.callback_image)

    rospy.spin()


Function: callback_image

Comments
----------

Parameters
----------
msg: Data of the topic

Returns
-------
def callback_image(self,msg):
    color_image=Image()
    color_image=msg

    #Publish topic
    self.classification_pub.publish(color_image)


if name == 'main':
detector=TENSORFLOW_ROS_CLASS()
detector.main()

And the error is this one:

[INFO] Loading model...
Traceback (most recent call last):
  File "/home/sara/catkin_ws/src/tensorflow_ros/script/tensorflow_ros.py", line 106, in <module>
    detector=TENSORFLOW_ROS_CLASS()
  File "/home/sara/catkin_ws/src/tensorflow_ros/script/tensorflow_ros.py", line 40, in __init__
    serialized_graph = fid.read()
  File "/home/sara/.local/lib/python3.8/site-packages/tensorflow/python/lib/io/file_io.py", line 117, in read
    self._preread_check()
  File "/home/sara/.local/lib/python3.8/site-packages/tensorflow/python/lib/io/file_io.py", line 79, in _preread_check
    self._read_buf = _pywrap_file_io.BufferedInputStream(
tensorflow.python.framework.errors_impl.NotFoundError: frozen_inference_graph.pb; No such file or directory

Actually the frozen_inference_graph.pb file is in the same directory of the script, so I don't know what is different respect the python execution.

Any ideas or other example to run a DNN in ROS using tensorflow?

Best regards. Alessandro

Originally posted by Alessandro Melino on ROS Answers with karma: 113 on 2021-03-22

Post score: 0

score 0 · Accepted Answer · answered Mar 22 '21 at 09:56

Rosanswers logo

Actually the frozen_inference_graph.pb file is in the same directory of the script, so I don't know what is different respect the python execution.

I believe this is similar/a duplicate of #q235337.

Summarising: the CWD (current working directory) when running a node with rosrun or roslaunch is not the same as when you start a script yourself.

The CWD for all nodes is $HOME/.ros. So unless there is a file called frozen_inference_graph.pb in $HOME/.ros, the error you mention is expected.

Originally posted by gvdhoorn with karma: 86574 on 2021-03-22

This answer was ACCEPTED on the original site

Post score: 0

Original comments

Comment by Alessandro Melino on 2021-03-23:
Yes, that was the problem, I must use the whole path of the file to reference it. Thank you.

Gfile error Tensorflow in ROS

Configure depth and color streams

download model from: https://github.com/opencv/opencv/wiki/TensorFlow-Object-Detection-API#run-network-in-opencv

Load the Tensorflow model into memory.

Input tensor is the image

Output tensors are the detection boxes, scores, and classes

Each box represents a part of the image where a particular object was detected

Each score represents level of confidence for each of the objects.

The score is shown on the result image, together with the class label.

Number of objects detected

code source of tensorflow model loading: https://www.geeksforgeeks.org/ml-training-image-classifier-using-tensorflow-object-detection-api/

Function: init

Comments

----------

Define neural network

Parameters

----------

Returns

-------

Function: main

Comments

----------

Set up the node, the publications and subsciptions.

Parameters

----------

Returns

-------

Function: callback_image

Comments

----------

Parameters

----------

msg: Data of the topic

Returns

-------

1 Answers1

Original comments