An object detection python library predicting object coordinates and orientation angles on 2d images.
Home Page: https://ivan-alles.github.io/localizer/
License: MIT License
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(py36) (localizer-main-VK8Pfd4G) C:\Users\internship\Desktop\localizer-main>python localizer\train.py models\maps\config.json
Filter "validate" contains 4 data elements
Traceback (most recent call last):
File "localizer\train.py", line 981, in <module>
trainer.run()
File "localizer\train.py", line 374, in run
self._read_dataset()
File "localizer\train.py", line 441, in _read_dataset
total_obj_count_by_cat[obj.category] += 1
KeyError: 1
Thank @ivan-alles for sharing a great work.
Iam quite curious about the method you used for detecting the orientation of the objects.
Can you explain it a bit or give me a reference to look at ?
Thank you in advance.
Got this error:
(py36) (localizer-main-VK8Pfd4G) C:\Users\internship\Desktop\localizer-main>python localizer\predict_for_images.py models\tools\config.json datasets\tools
2021-08-30 07:56:08.984216: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2021-08-30 07:56:09.538818: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 10815 MB memory: -> device: 0, name: Tesla K80, pci bus id: 0001:00:00.0, compute capability: 3.7
WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.
2021-08-30 07:56:10.462987: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)
2021-08-30 07:56:11.098206: I tensorflow/stream_executor/cuda/cuda_dnn.cc:369] Loaded cuDNN version 8202
Traceback (most recent call last):
File "localizer\predict_for_images.py", line 31, in <module>
predictions = localizer.predict(image)
File "C:\Users\internship\Desktop\localizer-main\localizer\predict.py", line 264, in predict
result = self._model.predict(batch.inputs)
File "C:\Users\internship\.virtualenvs\localizer-main-VK8Pfd4G\lib\site-packages\keras\engine\training.py", line 1751, in predict
tmp_batch_outputs = self.predict_function(iterator)
File "C:\Users\internship\.virtualenvs\localizer-main-VK8Pfd4G\lib\site-packages\tensorflow\python\eager\def_function.py", line 885, in __call__
result = self._call(*args, **kwds)
File "C:\Users\internship\.virtualenvs\localizer-main-VK8Pfd4G\lib\site-packages\tensorflow\python\eager\def_function.py", line 933, in _call
self._initialize(args, kwds, add_initializers_to=initializers)
File "C:\Users\internship\.virtualenvs\localizer-main-VK8Pfd4G\lib\site-packages\tensorflow\python\eager\def_function.py", line 760, in _initialize
*args, **kwds))
File "C:\Users\internship\.virtualenvs\localizer-main-VK8Pfd4G\lib\site-packages\tensorflow\python\eager\function.py", line 3066, in _get_concrete_function_internal_garbage_collected
graph_function, _ = self._maybe_define_function(args, kwargs)
File "C:\Users\internship\.virtualenvs\localizer-main-VK8Pfd4G\lib\site-packages\tensorflow\python\eager\function.py", line 3463, in _maybe_define_function
graph_function = self._create_graph_function(args, kwargs)
File "C:\Users\internship\.virtualenvs\localizer-main-VK8Pfd4G\lib\site-packages\tensorflow\python\eager\function.py", line 3308, in _create_graph_function
capture_by_value=self._capture_by_value),
File "C:\Users\internship\.virtualenvs\localizer-main-VK8Pfd4G\lib\site-packages\tensorflow\python\framework\func_graph.py", line 1007, in func_graph_from_py_func
func_outputs = python_func(*func_args, **func_kwargs)
File "C:\Users\internship\.virtualenvs\localizer-main-VK8Pfd4G\lib\site-packages\tensorflow\python\eager\def_function.py", line 668, in wrapped_fn
out = weak_wrapped_fn().__wrapped__(*args, **kwds)
File "C:\Users\internship\.virtualenvs\localizer-main-VK8Pfd4G\lib\site-packages\tensorflow\python\framework\func_graph.py", line 994, in wrapper
raise e.ag_error_metadata.to_exception(e)
TypeError: in user code:
C:\Users\internship\.virtualenvs\localizer-main-VK8Pfd4G\lib\site-packages\keras\engine\training.py:1586 predict_function *
return step_function(self, iterator)
C:\Users\internship\.virtualenvs\localizer-main-VK8Pfd4G\lib\site-packages\keras\engine\training.py:1576 step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
C:\Users\internship\.virtualenvs\localizer-main-VK8Pfd4G\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:1286 run
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
C:\Users\internship\.virtualenvs\localizer-main-VK8Pfd4G\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2849 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
C:\Users\internship\.virtualenvs\localizer-main-VK8Pfd4G\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:3632 _call_for_each_replica
return fn(*args, **kwargs)
C:\Users\internship\.virtualenvs\localizer-main-VK8Pfd4G\lib\site-packages\keras\engine\training.py:1569 run_step **
outputs = model.predict_step(data)
C:\Users\internship\.virtualenvs\localizer-main-VK8Pfd4G\lib\site-packages\keras\engine\training.py:1537 predict_step
return self(x, training=False)
C:\Users\internship\.virtualenvs\localizer-main-VK8Pfd4G\lib\site-packages\keras\engine\base_layer.py:1037 __call__
outputs = call_fn(inputs, *args, **kwargs)
C:\Users\internship\.virtualenvs\localizer-main-VK8Pfd4G\lib\site-packages\keras\engine\functional.py:415 call
inputs, training=training, mask=mask)
C:\Users\internship\.virtualenvs\localizer-main-VK8Pfd4G\lib\site-packages\keras\engine\functional.py:550 _run_internal_graph
outputs = node.layer(*args, **kwargs)
C:\Users\internship\.virtualenvs\localizer-main-VK8Pfd4G\lib\site-packages\keras\engine\base_layer.py:1044 __call__
self._set_save_spec(inputs, args, kwargs)
C:\Users\internship\.virtualenvs\localizer-main-VK8Pfd4G\lib\site-packages\tensorflow\python\training\tracking\base.py:530 _method_wrapper
result = method(self, *args, **kwargs)
C:\Users\internship\.virtualenvs\localizer-main-VK8Pfd4G\lib\site-packages\keras\engine\base_layer.py:3040 _set_save_spec
flat_specs = [tf_utils.get_tensor_spec(x) for x in flat_arg]
C:\Users\internship\.virtualenvs\localizer-main-VK8Pfd4G\lib\site-packages\keras\engine\base_layer.py:3040 <listcomp>
flat_specs = [tf_utils.get_tensor_spec(x) for x in flat_arg]
C:\Users\internship\.virtualenvs\localizer-main-VK8Pfd4G\lib\site-packages\keras\utils\tf_utils.py:467 get_tensor_spec
spec = tf.TensorSpec(shape=t.shape, dtype=t.dtype, name=name)
C:\Users\internship\.virtualenvs\localizer-main-VK8Pfd4G\lib\site-packages\tensorflow\python\framework\tensor_spec.py:51 __init__
self._shape = tensor_shape.TensorShape(shape)
C:\Users\internship\.virtualenvs\localizer-main-VK8Pfd4G\lib\site-packages\tensorflow\python\framework\tensor_shape.py:784 __init__
self._dims = [as_dimension(dims)]
C:\Users\internship\.virtualenvs\localizer-main-VK8Pfd4G\lib\site-packages\tensorflow\python\framework\tensor_shape.py:729 as_dimension
return Dimension(value)
C:\Users\internship\.virtualenvs\localizer-main-VK8Pfd4G\lib\site-packages\tensorflow\python\framework\tensor_shape.py:209 __init__
.format(value, type(value))), None)
<string>:3 raise_from
TypeError: Dimension value must be integer or None or have an __index__ method, got value '<attribute 'shape' of 'numpy.generic' objects>' with type '<class 'getset_descriptor'>'
Any ideas why this is occuring?
Thank you for the great work. I just tried out the localizer with the hands_on_demo.py. Capturing the dataset and training seems to work, but during the prediction it seems to have problems (maybe related to #3). I am using the tensorflow metal package and running it on an M1. And yes, I am using a standard RGB webcam.
Would be great if you could give me a hint what could be the problem here:
Backend MacOSX is interactive backend. Turning interactive mode on.
Metal device set to: Apple M1 Max
systemMemory: 64.00 GB
maxCacheSize: 24.00 GB
2022-08-26 12:14:50.225504: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.
2022-08-26 12:14:50.225657: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)
WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.
2022-08-26 12:15:35.760479: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz
2022-08-26 12:15:35.892807: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.
1/1 [==============================] - 0s 206ms/step
Traceback (most recent call last):
File "tmp/localizer/localizer/hands_on_demo.py", line 111, in _detect
objects = self._localizer.predict(input)
File "tmp/localizer/localizer/predict.py", line 250, in predict
self._update_input_image_parameters(image.shape)
File "tmp/localizer/localizer/predict.py", line 109, in _update_input_image_parameters
self._create_model()
File "tmp/localizer/localizer/predict.py", line 212, in _create_model
yx = tf.gather_nd(average_pos, local_max_idx) + tf.cast(local_max_idx[:, 2:], np.float32)
File "tmp/localizer/venv/lib/python3.9/site-packages/tensorflow/python/util/traceback_utils.py", line 153, in error_handler
raise e.with_traceback(filtered_tb) from None
File "tmp/localizer/venv/lib/python3.9/site-packages/keras/layers/core/tf_op_layer.py", line 107, in handle
return TFOpLambda(op)(*args, **kwargs)
File "tmp/localizer/venv/lib/python3.9/site-packages/keras/utils/traceback_utils.py", line 67, in error_handler
raise e.with_traceback(filtered_tb) from None
File "<string>", line 3, in raise_from
TypeError: Dimension value must be integer or None or have an __index__ method, got value '<attribute 'shape' of 'numpy.generic' objects>' with type '<class 'getset_descriptor'>'Hey @ivan-alles ,
Nice work! I'm trying to use the package on a ubuntu 20.04, with python 3.9. However there are some dependencies that are deprecated, e.g., the robogym.
I also see that you just use limited functions from robogym, so do you think it's possible to remove the dependency on robogym?
Thanks in advance.
Thanks for your good work. I want to know if it is possible to use a localizer with YOLO?
I want to detect an object (bounding boxes) and its orientation at the same time.
I got this error when I have tried this command "!python localizer/predict_for_images.py models/tools/config.json datasets/tools". Could you please help me?
I am unable to train models using these files.
for reference:ML program
Getting the following error:
(localizer-main-VK8Pfd4G) (py36) C:\Users\internship\Desktop\localizer-main>python localizer\train.py models\maps\config.json 2021-11-16 09:45:50.877785: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll Filter "validate" contains 3 data elements Traceback (most recent call last): File "localizer\train.py", line 981, in <module> trainer.run() File "localizer\train.py", line 374, in run self._read_dataset() File "localizer\train.py", line 456, in _read_dataset assert np.allclose(1, flt.data_element_weights.sum()), flt_name AssertionError: validate
is there a particular reason?
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