I am new to ArcGIS, I have downloaded a pitemx file from ArcGIS online. All of the layers import into ArcGIS correctly however, when I try to export to CAD using the Catalog > Default.gdb > Export to CAD I get the Error "this value is not a feature layer" (ERROR:840).
I exptected that I would be able to export the pitemx layers as a CAD file.
Related
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. :(
how to convert a single COCO JSON annotation file into a YOLO darknet format?? like below
each individual image has separate filename.txt file
My classmates and I have created a python package called PyLabel to help others with this task and other labelling tasks.
Our package does this conversion! You can see an example in this notebook https://github.com/pylabel-project/samples/blob/main/coco2yolov5.ipynb.
You're answer should be in there! But you should be able to do this conversion by doing something like:
!pip install pylabel
from pylabel import importer
dataset = importer.ImportCoco(path=path_to_annotations, path_to_images=path_to_images)
dataset.export.ExportToYoloV5(dataset)
You can find the source code that is used behind the scenes here https://github.com/pylabel-project/
I built a tool
https://github.com/tw-yshuang/coco2yolo
Download this repo and use the following command:
python3 coco2yolo.py [OPTIONS]
coc2yolo
Usage: coco2yolo.py [OPTIONS] [CAT_INFOS]...
Options:
-ann-path, --annotations-path TEXT
JSON file. Path for label. [required]
-img-dir, --image-download-dir TEXT
The directory of the image data place.
-task-dir, --task-categories-dir TEXT
Build a directory that follows the task-required categories.
-cat-t, --category-type TEXT Category input type. (interactive | file) [default: interactive]
-set, --set-computing-type TEXT
Set Computing for the data. (union | intersection) [default: union]
--help Show this message and exit.
There is an open-source tool called makesense.ai for annotating your images. You can download YOLO txt format once you annotate your images. But you won't be able to download the annotated images.
There is three ways.
use roboflow https://roboflow.com/formats (You can find another solution also)
You can find some usage guide for roboflow. e.g.
https://medium.com/red-buffer/roboflow-d4e8c4b52515
search 'convert coco format to yolo format' -> you will find some open-source codes to convert annotations to yolo format.
write your own code to convert coco format to yolo format
I need to run a custom GluonCV object detection module on Android.
I already fine-tuned the model (ssd_512_mobilenet1.0_custom) on a custom dataset, I tried running inference with it (loading the .params file produced during the training) and everything works perfectly on my computer. Now, I need to export this to Android.
I was referring to this answer to figure out the procedure, there are 3 suggested options:
You can use ONNX to convert models to other runtimes, for example [...] NNAPI for Android
You can use TVM
You can use SageMaker Neo + DLR runtime [...]
Regarding the first one, I converted my model to ONNX.
However, in order to use it with NNAPI, it is necessary to convert it to daq. In the repository, they provide a precomplied AppImage of onnx2daq to make the conversion, but the script returns an error. I checked the issues section, and they report that "It actually fails for all onnx object detection models".
Then, I gave a try to DLR, since it's suggested to be the easiest way.
As I understand, in order to use my custom model with DLR, I would first need to compile it with TVM (which also covers the second point mentioned in the linked post). In the repo, they provide a Docker image with some conversion scripts for different frameworks.
I modified the 'compile_gluoncv.py' script, and now I have:
#!/usr/bin/env python3
from tvm import relay
import mxnet as mx
from mxnet.gluon.model_zoo.vision import get_model
from tvm_compiler_utils import tvm_compile
shape_dict = {'data': (1, 3, 300, 300)}
dtype='float32'
ctx = [mx.cpu(0)]
classes_custom = ["CML_mug"]
block = get_model('ssd_512_mobilenet1.0_custom', classes=classes_custom, pretrained_base=False, ctx=ctx)
block.load_parameters("ep_035.params", ctx=ctx) ### this is the file produced by training on the custom dataset
for arch in ["arm64-v8a", "armeabi-v7a", "x86_64", "x86"]:
sym, params = relay.frontend.from_mxnet(block, shape=shape_dict, dtype=dtype)
func = sym["main"]
func = relay.Function(func.params, relay.nn.softmax(func.body), None, func.type_params, func.attrs)
tvm_compile(func, params, arch, dlr_model_name)
However, when I run the script it returns the error:
ValueError: Model ssd_512_mobilenet1.0_custom is not supported. Available options are
alexnet
densenet121
densenet161
densenet169
densenet201
inceptionv3
mobilenet0.25
mobilenet0.5
mobilenet0.75
mobilenet1.0
mobilenetv2_0.25
mobilenetv2_0.5
mobilenetv2_0.75
mobilenetv2_1.0
resnet101_v1
resnet101_v2
resnet152_v1
resnet152_v2
resnet18_v1
resnet18_v2
resnet34_v1
resnet34_v2
resnet50_v1
resnet50_v2
squeezenet1.0
squeezenet1.1
vgg11
vgg11_bn
vgg13
vgg13_bn
vgg16
vgg16_bn
vgg19
vgg19_bn
Am I doing something wrong? Is this thing even possible?
As a side note, after this I'd need to deploy on Android a pose detection model (simple_pose_resnet18_v1b) and an activity recognition one (i3d_nl10_resnet101_v1_kinetics400) as well.
You actually can run GluonCV model directly on Android with Deep Java Library (DJL)
What you need to do is:
hyridize your GluonCV model and save as MXNet model
Build MXNet engine for android, MXNET already support Android build
Include MXNet shared library into your android project
Use DJL in your android project, you can follow this DJL Android demo for PyTorch
The error message is self-explanatory - there is no model "ssd_512_mobilenet1.0_custom" supported by mxnet.gluon.model_zoo.vision.get_model. You are confusing GluonCV's get_model with MXNet Gluon's get_model.
Replace
block = get_model('ssd_512_mobilenet1.0_custom',
classes=classes_custom, pretrained_base=False, ctx=ctx)
with
import gluoncv
block = gluoncv.model_zoo.get_model('ssd_512_mobilenet1.0_custom',
classes=classes_custom, pretrained_base=False, ctx=ctx)
I am trying to work with the quite recently published tensorflow_dataset API to train a Keras model on the Open Images Dataset. The dataset is about 570 GB in size. I downloaded the data with the following code:
import tensorflow_datasets as tfds
import tensorflow as tf
open_images_dataset = tfds.image.OpenImagesV4()
open_images_dataset.download_and_prepare(download_dir="/notebooks/dataset/")
After the download was complete, the connection to my jupyter notebook somehow interrupted but the extraction seemed to be finished as well, at least all downloaded files had a counterpart in the "extracted" folder. However, I am not able to access the downloaded data now:
tfds.load(name="open_images_v4", data_dir="/notebooks/open_images_dataset/extracted/", download=False)
This only gives the following error:
AssertionError: Dataset open_images_v4: could not find data in /notebooks/open_images_dataset/extracted/. Please make sure to call dataset_builder.download_and_prepare(), or pass download=True to tfds.load() before trying to access the tf.data.Dataset object.
When I call the function download_and_prepare() it only downloads the whole dataset again.
Am I missing something here?
Edit:
After the download the folder under "extracted" has 18 .tar.gz files.
This is with tensorflow-datasets 1.0.1 and tensorflow 2.0.
The folder hierarchy should be like this:
/notebooks/open_images_dataset/extracted/open_images_v4/0.1.0
All the datasets have a version. Then the data could be loaded like this.
ds = tf.load('open_images_v4', data_dir='/notebooks/open_images_dataset/extracted', download=False)
I didn't have open_images_v4 data. I put cifar10 data into a folder named open_images_v4 to check what folder structure tensorflow_datasets was expecting.
The solution to this was to also use the "data_dir" parameter when initializing the dataset:
builder = tfds.image.OpenImagesV4(data_dir="/raid/openimages/dataset")
builder.download_and_prepare(download_dir="/raid/openimages/dataset")
This way the dataset is donwloaded and extracted in the same directory. Before, it was (for me unnoticeably) extracting to the default directory, which is under /home/.../. That's what caused the error, as there wasn't enough space left under my home directory.
After the extraction, the folder structure is exactly as Manoj-Mohan described.
Above solution haven't worked for me.
builder = tfds.builder(name='folder_name', data_dir=data_dir)
builder.download_and_prepare(download_dir="/home/...")
ds = builder.as_dataset()
In the following Tweet spacy dependency tagger states that disrupt (VB) is a dobj of healthcare market (NN). As these two terms are connected I would like to extract them as one phrase. Is there any way to navigate the parse tree so I can extract the dobj of a word? If I do the folllowing I get market but not 'heathcare market'
from spacy.en import English
from spacy.symbols import nsubj, VERB,dobj
nlp = English()
doc = nlp('Juniper Research: AI start-ups set to disrupt healthcare market, with $800 million to be spent on CAD Systems by 2022')
for possible_subject in doc:
if possible_subject.dep == dobj:
print(possible_subject.text)
You can do this as below using noun chunks
for np in doc.noun_chunks:
if np.root.dep == dobj:
print(np.root.text)
print(np.text)