I'm following the tutorial on using your own template images to do object 3D pose tracking, but I'm trying to get it working on Ubuntu 20.04 with a live webcam stream.
I was able to successfully make my index .pb file with extracted KNIFT features from my custom images.
It seems the next thing to do is load the provided template matching graph (in mediapipe/graphs/template_matching/template_matching_desktop.pbtxt) (replacing the index_proto_filename of the BoxDetectorCalculator with my own index file), and run it on a video input stream to track my custom object.
I was hoping that would be easiest to do in python, but am running into dependency problems.
(I installed mediapipe python with pip3 install mediapipe)
First, I couldn't find how to directly load a .pbtxt file as a graph in the mediapipe python API, but that's ok. I just load the text it contains and use that.
template_matching_graph_filepath=os.path.abspath("~/mediapipe/mediapipe/graphs/template_matching/template_matching_desktop.pbtxt")
graph = mp.CalculatorGraph(graph_config=open(template_matching_graph_filepath).read())
But I get missing calculator targets.
No registered object with name: OpenCvVideoDecoderCalculator; Unable to find Calculator "OpenCvVideoDecoderCalculator"
or
[libprotobuf ERROR external/com_google_protobuf/src/google/protobuf/text_format.cc:309] Error parsing text-format mediapipe.CalculatorGraphConfig: 54:70: Could not find type "type.googleapis.com/mediapipe.TfLiteInferenceCalculatorOptions" stored in google.protobuf.Any.
It seems similar to this troubleshooting case but, since I'm not trying to compile an application, I'm not sure how to link in the missing calculators.
How to I make the mediapipe python API aware of these graphs?
UPDATE:
I made decent progress by adding the graphs that the template_matching depends on to the cc_library deps of the mediapipe/python/BUILD file
cc_library(
name = "builtin_calculators",
deps = [
"//mediapipe/calculators/image:feature_detector_calculator",
"//mediapipe/calculators/image:image_properties_calculator",
"//mediapipe/calculators/video:opencv_video_decoder_calculator",
"//mediapipe/calculators/video:opencv_video_encoder_calculator",
"//mediapipe/calculators/video:box_detector_calculator",
"//mediapipe/calculators/tflite:tflite_inference_calculator",
"//mediapipe/calculators/tflite:tflite_tensors_to_floats_calculator",
"//mediapipe/calculators/util:timed_box_list_id_to_label_calculator",
"//mediapipe/calculators/util:timed_box_list_to_render_data_calculator",
"//mediapipe/calculators/util:landmarks_to_render_data_calculator",
"//mediapipe/calculators/util:annotation_overlay_calculator",
...
I also modified solution_base.py so it knows about BoxDetector's options.
from mediapipe.calculators.video import box_detector_calculator_pb2
...
CALCULATOR_TO_OPTIONS = {
'BoxDetectorCalculator':
box_detector_calculator_pb2
.BoxDetectorCalculatorOptions,
Then I rebuilt and installed mediapipe python from source with:
~/mediapipe$ python3 setup.py install --link-opencv
Then I was able to make my own class derived from SolutionBase
from mediapipe.python.solution_base import SolutionBase
class ObjectTracker(SolutionBase):
"""Process a video stream and output a video with edges of templates highlighted."""
def __init__(self,
object_knift_index_file_path):
super().__init__(binary_graph_path=object_pose_estimation_binary_file_path,
calculator_params={"BoxDetector.index_proto_filename": object_knift_index_file_path},
)
def process(self, image: np.ndarray) -> NamedTuple:
return super().process(input_data={'input_video':image})
ot = ObjectTracker(object_knift_index_file_path="/path/to/my/object_knift_index.pb")
Finally, I process a video frame from a cv2.VideoCapture
cv_video = cv2.VideoCapture(0)
result, frame = cv_video.read()
input_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
res = ot.process(image=input_frame)
So close! But I run into this error which I just don't know what to do with.
/usr/local/lib/python3.8/dist-packages/mediapipe/python/solution_base.py in process(self, input_data)
326 if data.shape[2] != RGB_CHANNELS:
327 raise ValueError('Input image must contain three channel rgb data.')
--> 328 self._graph.add_packet_to_input_stream(
329 stream=stream_name,
330 packet=self._make_packet(input_stream_type,
RuntimeError: Graph has errors:
Calculator::Open() for node "BoxDetector" failed: ; Error while reading file: /usr/local/lib/python3.8/dist-packages/
Looks like CalculatorNode::OpenNode() is trying to open the python API install path as a file. Maybe it has to do with the default_context. I have no idea where to go from here. :(
I am using tensorflow-gpu==1.10.0 and keras from tensorflow as tf.keras.
I am trying to use source code written by someone else to implement it on my network.
I saved my network using save_model and load it using load_model. when I use model.get_config(), I expect a dictionary, but i"m getting a list. Keras source documentation also says that get_config returns a dictionary (https://keras.io/models/about-keras-models/).
I tried to check if it has to do with saving type : save_model or model.save that makes the difference in how it is saved, but both give me this error:
TypeError: list indices must be integers or slices, not str
my code block :
model_config = self.keras_model.get_config()
for layer in model_config['layers']:
name = layer['name']
if name in update_layers:
layer['config']['filters'] = update_layers[name]['filters']
my pip freeze :
absl-py==0.6.1
astor==0.7.1
bitstring==3.1.5
coverage==4.5.1
cycler==0.10.0
decorator==4.3.0
Django==2.1.3
easydict==1.7
enum34==1.1.6
futures==3.1.1
gast==0.2.0
geopy==1.11.0
grpcio==1.16.1
h5py==2.7.1
image==1.5.15
ImageHash==3.7
imageio==2.5.0
imgaug==0.2.5
Keras==2.1.3
kiwisolver==1.1.0
lxml==4.1.1
Markdown==3.0.1
matplotlib==2.1.0
networkx==2.2
nose==1.3.7
numpy==1.14.1
olefile==0.46
opencv-python==3.3.0.10
pandas==0.20.3
Pillow==4.2.1
prometheus-client==0.4.2
protobuf==3.6.1
pyparsing==2.3.0
pyquaternion==0.9.2
python-dateutil==2.7.5
pytz==2018.7
PyWavelets==1.0.1
PyYAML==3.12
Rtree==0.8.3
scikit-image==0.13.1
scikit-learn==0.19.1
scipy==0.19.1
Shapely==1.6.4.post1
six==1.11.0
sk-video==1.1.8
sklearn-porter==0.6.2
tensorboard==1.10.0
tensorflow-gpu==1.10.0
termcolor==1.1.0
tqdm==4.19.4
utm==0.4.2
vtk==8.1.0
Werkzeug==0.14.1
xlrd==1.1.0
xmltodict==0.11.0
I used the following code in Pycharm:
import tensorflow as tf
sess = tf.Session()
a = tf.constant(value=5, name='input_a')
b = tf.constant(value=3, name='input_b')
c = tf.multiply(a,b, name='mult_c')
d = tf.add(a,b, name='add_d')
e = tf.add(c,d, name='add_e')
print(sess.run(e))
writer = tf.summary.FileWriter("./tb_graph", sess.graph)
Then, I pasted following line to the Anaconda Prompt:
tensorboard --logdir=="tb_graph"
I tried both with "" and '' as there were proposed: Tensorboard: No graph definition files were found. and it does nothing for me.
I had similar issue. The issue occurred when I specified 'logdir' folder inside single quotes instead of double quotes. Hope this may be helpful to you.
egs: tensorboard --logdir='my_graph' -> Tensorboard didn't detect the graph
tensorboard --logdir="my_graph" -> Tensorboard detected the graph
I checked the code on laptop with Ubuntu 16.04 and another one with Win10, so it probably isn't system-based error.
I also tried adding and removing --host=127.0.0.1 in An Prompt and checking several times both http://localhost:6006/ and http://desktop-.......:6006/.
Still same error:
No graph definition files were found.
To store a graph, create a tf.summary.FileWriter and pass the graph either via the constructor, or by calling its add_graph() method. You may want to check out the graph visualizer tutorial.
....
Please tell me what is wrong in the code/propmp command?
EDIT: On Ubuntu I used the normal terminal, of course.
EDIT2: I used both = and == in command prompt
The answer to my question is:
1) change "./new1_dir" into ".\\new1_dir"
and
2)put full track to file to anaconda propmpt: --logdir="C:\Users\Admin\Documents\PycharmProjects\try_tb\new1_dir"
Thanks #BugKiller for your help!
EDIT: Working only on Windows for me, but still better than nothing
EDIT2: Works on Ubuntu 16.04 too
I am trying to deploy a model based on Object Detection example to do some tests and I am getting this error:
"Expects arg[0] to be uint8 but float is provided"
In that case I am using this to load my data:
request.inputs['inputs'].CopyFrom(
tf.contrib.util.make_tensor_proto({FLAGS.input_image}))
where FLAGS.input_image is my image data in bytes.
I was thinking that maybe that I should convert my image bytes to something that this input understands, but I haven't found yet.
What could I do to fix this issue?
Thanks !!!!
To convert the image to bytes, use the following in client code (python)
with open(FLAGS.image, 'rb') as f:
data = f.read()
Also please find a sample client (for inception model in python) as follows https://github.com/tensorflow/serving/blob/master/tensorflow_serving/example/inception_client.py
After following this tutorial on summaries and TensorBoard, I've been able to successfully save and look at data with TensorBoard. Is it possible to open this data with something other than TensorBoard?
By the way, my application is to do off-policy learning. I'm currently saving each state-action-reward tuple using SummaryWriter. I know I could manually store/train on this data, but I thought it'd be nice to use TensorFlow's built in logging features to store/load this data.
As of March 2017, the EventAccumulator tool has been moved from Tensorflow core to the Tensorboard Backend. You can still use it to extract data from Tensorboard log files as follows:
from tensorboard.backend.event_processing.event_accumulator import EventAccumulator
event_acc = EventAccumulator('/path/to/summary/folder')
event_acc.Reload()
# Show all tags in the log file
print(event_acc.Tags())
# E. g. get wall clock, number of steps and value for a scalar 'Accuracy'
w_times, step_nums, vals = zip(*event_acc.Scalars('Accuracy'))
Easy, the data can actually be exported to a .csv file within TensorBoard under the Events tab, which can e.g. be loaded in a Pandas dataframe in Python. Make sure you check the Data download links box.
For a more automated approach, check out the TensorBoard readme:
If you'd like to export data to visualize elsewhere (e.g. iPython
Notebook), that's possible too. You can directly depend on the
underlying classes that TensorBoard uses for loading data:
python/summary/event_accumulator.py (for loading data from a single
run) or python/summary/event_multiplexer.py (for loading data from
multiple runs, and keeping it organized). These classes load groups of
event files, discard data that was "orphaned" by TensorFlow crashes,
and organize the data by tag.
As another option, there is a script
(tensorboard/scripts/serialize_tensorboard.py) which will load a
logdir just like TensorBoard does, but write all of the data out to
disk as json instead of starting a server. This script is setup to
make "fake TensorBoard backends" for testing, so it is a bit rough
around the edges.
I think the data are encoded protobufs RecordReader format. To get serialized strings out of files you can use py_record_reader or build a graph with TFRecordReader op, and to deserialize those strings to protobuf use Event schema. If you get a working example, please update this q, since we seem to be missing documentation on this.
I did something along these lines for a previous project. As mentioned by others, the main ingredient is tensorflows event accumulator
from tensorflow.python.summary import event_accumulator as ea
acc = ea.EventAccumulator("folder/containing/summaries/")
acc.Reload()
# Print tags of contained entities, use these names to retrieve entities as below
print(acc.Tags())
# E. g. get all values and steps of a scalar called 'l2_loss'
xy_l2_loss = [(s.step, s.value) for s in acc.Scalars('l2_loss')]
# Retrieve images, e. g. first labeled as 'generator'
img = acc.Images('generator/image/0')
with open('img_{}.png'.format(img.step), 'wb') as f:
f.write(img.encoded_image_string)
You can also use the tf.train.summaryiterator: To extract events in a ./logs-Folder where only classic scalars lr, acc, loss, val_acc and val_loss are present you can use this GIST: tensorboard_to_csv.py
Chris Cundy's answer works well when you have less than 10000 data points in your tfevent file. However, when you have a large file with over 10000 data points, Tensorboard will automatically sampling them and only gives you at most 10000 points. It is a quite annoying underlying behavior as it is not well-documented. See https://github.com/tensorflow/tensorboard/blob/master/tensorboard/backend/event_processing/event_accumulator.py#L186.
To get around it and get all data points, a bit hacky way is to:
from tensorboard.backend.event_processing.event_accumulator import EventAccumulator
class FalseDict(object):
def __getitem__(self,key):
return 0
def __contains__(self, key):
return True
event_acc = EventAccumulator('path/to/your/tfevents',size_guidance=FalseDict())
It looks like for tb version >=2.3 you can streamline the process of converting your tb events to a pandas dataframe using tensorboard.data.experimental.ExperimentFromDev().
It requires you to upload your logs to TensorBoard.dev, though, which is public. There are plans to expand the capability to locally stored logs in the future.
https://www.tensorflow.org/tensorboard/dataframe_api
You can also use the EventFileLoader to iterate through a tensorboard file
from tensorboard.backend.event_processing.event_file_loader import EventFileLoader
for event in EventFileLoader('path/to/events.out.tfevents.xxx').Load():
print(event)
Surprisingly, the python package tb_parse has not been mentioned yet.
From documentation:
Installation:
pip install tensorflow # or tensorflow-cpu pip install -U tbparse # requires Python >= 3.7
Note: If you don't want to install TensorFlow, see Installing without TensorFlow.
We suggest using an additional virtual environment for parsing and plotting the tensorboard events. So no worries if your training code uses Python 3.6 or older versions.
Reading one or more event files with tbparse only requires 5 lines of code:
from tbparse import SummaryReader
log_dir = "<PATH_TO_EVENT_FILE_OR_DIRECTORY>"
reader = SummaryReader(log_dir)
df = reader.scalars
print(df)