Latest Contributions by ZacharyNeumann1

I am following the automatic road extraction using deep learning sample notebook. My code is mostly the same (instead of pointing to data path in arcgis online I am pointing to folder that holds training data). I have created my own training data that I am using.   When I run: data = prepare_data(datapath, chip_size=512, batch_size=10)   I get the error: C:\Program Files\ArcGIS\Pro\bin\Python\envs\arcgispro-py3\lib\site-packages\arcgis\learn\models\_arcgis_model.py:366: UserWarning: Cuda is not available warnings.warn("Cuda is not available")   But I still get results for: data.show_batch(alpha=1)   Then when I run: model = MultiTaskRoadExtractor(data, mtl_model="hourglass")   I get the following AttributeError: --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) C:\Program Files\ArcGIS\Pro\bin\Python\envs\arcgispro-py3\lib\site-packages\PIL\Image.py in open(fp, mode) 2846 try: -> 2847 fp.seek(0) 2848 except (AttributeError, io.UnsupportedOperation): AttributeError: 'numpy.ndarray' object has no attribute 'seek' During handling of the above exception, another exception occurred: AttributeError Traceback (most recent call last) <ipython-input-5-f5e8368fe40d> in <module> ----> 1 model = MultiTaskRoadExtractor(data, mtl_model="hourglass") C:\Program Files\ArcGIS\Pro\bin\Python\envs\arcgispro-py3\lib\site-packages\arcgis\learn\models\_multi_task_road_extractor.py in __init__(self, data, backbone, pretrained_path, *args, **kwargs) 176 177 --> 178 self._orient_data=self._get_road_orient_data(data,**kwargs) 179 if len(data.classes) > 2: 180 raise Exception( C:\Program Files\ArcGIS\Pro\bin\Python\envs\arcgispro-py3\lib\site-packages\arcgis\learn\models\_multi_task_road_extractor.py in _get_road_orient_data(self, data, **kwargs) 378 dataset_type=self._data.dataset_type, resize_to=self._data.resize_to, **kwargs) 379 road_orient_obj = RoadOrientation(self._data,road_data_obj, **kwargs) --> 380 orient_data = road_orient_obj.get_databunch(data,**kwargs) 381 return orient_data 382 C:\Program Files\ArcGIS\Pro\bin\Python\envs\arcgispro-py3\lib\site-packages\arcgis\learn\_data_utils\_road_orient_data.py in get_databunch(self, orig_data, **kwargs) 262 263 --> 264 data.chip_size = data.train_ds[0][0].shape[-1] 265 data.c = len(self.class_mapping) if self.class_mapping else 2 266 C:\Program Files\ArcGIS\Pro\bin\Python\envs\arcgispro-py3\lib\site-packages\arcgis\learn\_data_utils\_road_orient_data.py in __getitem__(self, index) 344 345 def __getitem__(self, index): --> 346 image, label = self._getRoadData(index) 347 348 seed = np.random.randint(2147483647) # make a seed with numpy generator C:\Program Files\ArcGIS\Pro\bin\Python\envs\arcgispro-py3\lib\site-packages\arcgis\learn\_data_utils\_road_orient_data.py in _getRoadData(self, index) 407 # 408 #image = PILImage.fromarray(np.asarray(ArcGISMSImage.open_gdal(image_dict["image"]))).astype(np.uint8) --> 409 label = self._get_mask(np.asarray(PILImage.open(image_dict["label"]))) 410 411 return image, PILImage.fromarray(label).convert("L") C:\Program Files\ArcGIS\Pro\bin\Python\envs\arcgispro-py3\lib\site-packages\PIL\Image.py in open(fp, mode) 2847 fp.seek(0) 2848 except (AttributeError, io.UnsupportedOperation): -> 2849 fp = io.BytesIO(fp.read()) 2850 exclusive_fp = True 2851 AttributeError: 'numpy.ndarray' object has no attribute 'read'   I am running ArcGIS Pro 2.8.0, I have image analyst and spatial analyst turned on and have installed the ArcGIS Pro 2.8 deep learning libraries from github.   Photos:   ... View more

I am following the automatic road extraction using deep learning sample notebook. My code is exactly the same. I have created my own training data that I am using.   When I run: data = prepare_data(datapath, chip_size=512, batch_size=10)   I get the error: C:\Program Files\ArcGIS\Pro\bin\Python\envs\arcgispro-py3\lib\site-packages\arcgis\learn\models\_arcgis_model.py:366: UserWarning: Cuda is not available warnings.warn("Cuda is not available")   But I still get results for: data.show_batch(alpha=1)   Then when I run: model = MultiTaskRoadExtractor(data, mtl_model="hourglass")   I get the following AttributeError: --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) C:\Program Files\ArcGIS\Pro\bin\Python\envs\arcgispro-py3\lib\site-packages\PIL\Image.py in open(fp, mode) 2846 try: -> 2847 fp.seek(0) 2848 except (AttributeError, io.UnsupportedOperation): AttributeError: 'numpy.ndarray' object has no attribute 'seek' During handling of the above exception, another exception occurred: AttributeError Traceback (most recent call last) <ipython-input-5-f5e8368fe40d> in <module> ----> 1 model = MultiTaskRoadExtractor(data, mtl_model="hourglass") C:\Program Files\ArcGIS\Pro\bin\Python\envs\arcgispro-py3\lib\site-packages\arcgis\learn\models\_multi_task_road_extractor.py in __init__(self, data, backbone, pretrained_path, *args, **kwargs) 176 177 --> 178 self._orient_data=self._get_road_orient_data(data,**kwargs) 179 if len(data.classes) > 2: 180 raise Exception( C:\Program Files\ArcGIS\Pro\bin\Python\envs\arcgispro-py3\lib\site-packages\arcgis\learn\models\_multi_task_road_extractor.py in _get_road_orient_data(self, data, **kwargs) 378 dataset_type=self._data.dataset_type, resize_to=self._data.resize_to, **kwargs) 379 road_orient_obj = RoadOrientation(self._data,road_data_obj, **kwargs) --> 380 orient_data = road_orient_obj.get_databunch(data,**kwargs) 381 return orient_data 382 C:\Program Files\ArcGIS\Pro\bin\Python\envs\arcgispro-py3\lib\site-packages\arcgis\learn\_data_utils\_road_orient_data.py in get_databunch(self, orig_data, **kwargs) 262 263 --> 264 data.chip_size = data.train_ds[0][0].shape[-1] 265 data.c = len(self.class_mapping) if self.class_mapping else 2 266 C:\Program Files\ArcGIS\Pro\bin\Python\envs\arcgispro-py3\lib\site-packages\arcgis\learn\_data_utils\_road_orient_data.py in __getitem__(self, index) 344 345 def __getitem__(self, index): --> 346 image, label = self._getRoadData(index) 347 348 seed = np.random.randint(2147483647) # make a seed with numpy generator C:\Program Files\ArcGIS\Pro\bin\Python\envs\arcgispro-py3\lib\site-packages\arcgis\learn\_data_utils\_road_orient_data.py in _getRoadData(self, index) 407 # 408 #image = PILImage.fromarray(np.asarray(ArcGISMSImage.open_gdal(image_dict["image"]))).astype(np.uint8) --> 409 label = self._get_mask(np.asarray(PILImage.open(image_dict["label"]))) 410 411 return image, PILImage.fromarray(label).convert("L") C:\Program Files\ArcGIS\Pro\bin\Python\envs\arcgispro-py3\lib\site-packages\PIL\Image.py in open(fp, mode) 2847 fp.seek(0) 2848 except (AttributeError, io.UnsupportedOperation): -> 2849 fp = io.BytesIO(fp.read()) 2850 exclusive_fp = True 2851 AttributeError: 'numpy.ndarray' object has no attribute 'read'   I am running ArcGIS Pro 2.8.0, I have image analyst and spatial analyst turned on and have installed the ArcGIS Pro 2.8 deep learning libraries from github.   Photos:   ... View more

I am building a Task Workflow to automate deployment of Crowdsource Polling. I have a task for creating a feature layer from a spreadsheet that sets up a relationship class between the feature layer and related commenting table. Once this is finished I need to symbolize the layer based on a field in the comment layer. I have created a layer file to accomplish this need by symbolizing based on layer file. I have got this to work before packaging the project but need it to work from within a packaged project. When I use the Package Project tool I have saved a the layer file (.lyrx) using the "additonal files" input: This adds the .lyrx into the commondata folder of the package.  My issue is that when I get the path for the .lyrx and plug it into my code I am getting a ValueError. When I do this before packaging the project I do not get a ValueError. I have also tried running the code from the package after I use the extract package tool and get the same error. Here is my code: p = os.path.dirname(arcpy.mp.ArcGISProject("CURRENT").defaultGeodatabase) aprx = arcpy.mp.ArcGISProject("CURRENT") m = aprx.listMaps("Map")[0] default_gdb = arcpy.mp.ArcGISProject("CURRENT").defaultGeodatabase rec_sites = os.path.join(default_gdb, "RecSitesXY") # local layer file path before packaging project #lf = arcpy.mp.LayerFile(os.path.join(p, "FeeToolRecSiteSymbols.lyrx")) # pro package layer file # layer filepath after packaging project lf = os.path.join(os.path.dirname(p), 'commondata', 'pro-recfeetool-deployment-task-workflow', 'FeeToolRecSiteSymbols.lyrx') # add coment layer and symbolize using layer file m.addDataFromPath(rec_sites) # Using insert layer instead of arcpy.ApplySymbologyFromLayer_management because # there is a known bug that this will not work from within a script m.insertLayer(m.listLayers()[0],lf) # clean up m.removeLayer(m.listLayers()[1]) aprx.save() I have also tried to search for the layer file this way and have tried using the output path with the same error The value error happens at this point in the code: m.insertLayer(m.listLayers()[0],lf) Any help would be much appreciated. ... View more

I think that when an item is created from an existing feature layer: # Create a new empty comment layer from the already existing base comment layer layer_properties={ 'title':'Test_Comment_Layer', 'description':'This is a comment layer that is being used for testing purposes only', 'tags':'Test, Python, Comment Layer', 'type':'Feature Service', 'url':'URL HERE' } base_comment_layer = gis.content.add(item_properties=layer_properties, data="URL HERE", folder=pf_name) that we should be able to choose which layers we would like to add from that Hosted Feature Layer (or Feature Layer Collection): 1) Create feature layer from existing layer: 2) Specify layers to include: The above code will create a Hosted Feature Layer with all layers included. Correct me if I'm wrong... but I would assume that this could be specified in the item_properties described in the content manager section ArcGIS API for Python guide: arcgis.gis module — arcgis 1.0.1 documentation  It seems simple to delete features of a layer, or to delete the feature layer collection itself, but to delete a layer that is a part of a feature layer collection (or specify which layers to include) is not an easy task.  ... View more

I think that when an item is created from an existing feature layer: # Create a new empty comment layer from the already existing base comment layer layer_properties={ 'title':'Test_Comment_Layer', 'description':'This is a comment layer that is being used for testing purposes only', 'tags':'Test, Python, Comment Layer', 'type':'Feature Service', 'url':'URL HERE' } base_comment_layer = gis.content.add(item_properties=layer_properties, data="URL HERE", folder=pf_name) that we should be able to choose which layers we would like to add from that Hosted Feature Layer (or Feature Layer Collection): 1) Create feature layer from existing layer: 2) Specify layers to include: The above code will create a Hosted Feature Layer with all layers included. Correct me if I'm wrong... but I would assume that this could be specified in the item_properties described in the content manager section ArcGIS API for Python guide: arcgis.gis module — arcgis 1.0.1 documentation  It seems simple to delete features of a layer, or to delete the feature layer collection itself, but to delete a layer that is a part of a feature layer collection (or specify which layers to include) is not an easy task.  ... View more