Add files via upload

Author: mac999

app.py

@@ -0,0 +1,53 @@
+# author: taewook kang
+# date: 2025-02-28
+# description: scan to model pipeline app
+# email: [email protected]
+import os, json, sys, time, shutil, subprocess, zipfile, requests, gradio as gr
+from scan_to_model_pipeline import scan_to_model_process
+
+module_path = os.path.dirname(os.path.realpath(__file__))
+
+class args_param:
+	input = ''
+	output = ''
+	pipeline = ''
+
+def process_point_cloud(pipeline_file, input_file, progress=gr.Progress()):
+    try:
+        output_dir = module_path + "/output"
+        os.makedirs(output_dir, exist_ok=True)
+
+        # Run the scan to model process
+        params = args_param()
+        params.input = input_file
+        params.output = os.path.join(output_dir, "result.las")
+        params.pipeline = pipeline_file
+        scan_to_model_process(params, progress.tqdm)
+
+        # Create a zip file of the output
+        zip_filename = module_path + "/output.zip"
+        with zipfile.ZipFile(zip_filename, 'w') as zipf:
+            for root, dirs, files in progress.tqdm(os.walk(output_dir), desc="Zipping files"):
+                for file in files:
+                    zipf.write(os.path.join(root, file), file)
+
+        # Clean up the output directory
+        shutil.rmtree(output_dir)
+    except Exception as e:
+        print(e)
+        gr.Warning(f'Error: {e}')
+        output_dir = ''
+    return zip_filename
+
+with gr.Blocks(title="Scan to Model Pipeline") as interface:
+    gr.Markdown("# Scan to Model Pipeline (ver 0.2. prototype)")
+    gr.Markdown("Upload pipeline configuration file (JSON) and point cloud data file (LAS, LAZ) to process the data and download the results as a zip file.")
+    gr.Markdown("1. [Upload pipeline configuration (JSON)](https://github.com/mac999/scan_to_model_pipeline/blob/main/pipeline.json)</br>2. Upload [point cloud data (LAS, LAZ)](https://github.com/mac999/scan_to_model_pipeline/tree/main/input)</br>3. Click 'Run Pipeline' button</br>In detail, refer to the [github page](https://github.com/mac999/scan_to_model_pipeline.git)")
+    with gr.Row(equal_height="height"):
+        input_config = gr.File(label="Pipeline Configuration File", file_types=['json'])
+        input_files = gr.File(label="Point Cloud Data File", file_types=['pcd', 'txt', 'las', 'laz'])
+    output_file = gr.File(label="Download Model Output Zip File")
+    run_button = gr.Button("Run Pipeline")
+    run_button.click(fn=process_point_cloud, inputs=[input_config, input_files], outputs=output_file)
+
+interface.launch(share=True)

requirements.txt

@@ -1,169 +1,17 @@
-alphashape==1.3.1
-altgraph==0.17.3
-asttokens==2.4.0
-attrs==23.1.0
-backcall==0.2.0
-blinker==1.7.0
-cachetools==5.3.1
-certifi==2023.7.22
-charset-normalizer==3.2.0
-click==8.1.7
-click-log==0.4.0
-click-plugins==1.1.1
-cligj==0.7.2
-cloth-simulation-filter==1.1.4
-colorama @ file:///home/conda/feedstock_root/build_artifacts/colorama_1666700638685/work
-comm==0.1.4
-comtypes==1.3.1
-contourpy==1.1.0
-cycler==0.11.0
-Cython==3.0.8
-dash==2.16.1
-dash-core-components==2.0.0
-dash-html-components==2.0.0
-dash-table==5.0.0
-debugpy==1.8.0
-decorator==5.1.1
-descartes==1.1.0
-durationpy==0.6
-et-xmlfile==1.1.0
-exceptiongroup==1.1.3
-executing==1.2.0
-fastjsonschema==2.18.0
-fiona==1.9.5
-Flask==3.0.2
-fonttools==4.42.1
-fpdf==1.7.2
-GDAL==3.7.2
-geopandas==0.14.0
-google-auth==2.23.0
-grpcio==1.58.0
-h5py==3.9.0
-idna==3.4
-importlib-metadata==6.8.0
-importlib-resources==6.0.1
-iniconfig==2.0.0
-ipykernel==6.25.2
-ipython==8.15.0
-ipython-genutils==0.2.0
-ipywidgets==8.0.4
-itsdangerous==2.1.2
-jedi==0.19.0
-Jinja2==3.1.3
-joblib==1.3.2
-json5==0.9.14
-jsonschema==4.19.0
-jsonschema-specifications==2023.7.1
-jupyter_client==8.3.1
-jupyter_core==5.3.1
-jupyterlab-widgets==3.0.9
-kiwisolver==1.4.5
-laspy==2.5.3
-littleutils==0.2.2
-llvmlite==0.41.1
-lorem==0.1.1
-Markdown==3.4.4
-markdown-it-py==3.0.0
-MarkupSafe==2.1.5
-matplotlib==3.7.3
-matplotlib-inline==0.1.6
-mdurl==0.1.2
-meshio==5.3.5
-mpmath==1.2.1
-nbformat==5.7.0
-nest-asyncio==1.5.7
-networkx==3.2.1
-numba==0.58.1
-numpy @ file:///D:/bld/numpy_1691056377689/work
-nvidia-smi==0.1.3
-open3d @ file:///F:/projects/Open3D/build/lib/python_package/pip_package/open3d-0.17.0%2Bb7f9f3ae6-cp39-cp39-win_amd64.whl#sha256=d324c1f12e0a051cd0c72069545e5a8d291c071a0c3a51548cc08b33467bc550
-opencv-python==4.8.1.78
-openpyxl==3.1.5
-packaging==23.1
-pandas @ file:///D:/bld/pandas_1693415295498/work
-parso==0.8.3
-pefile==2023.2.7
-pickleshare==0.7.5
-Pillow==10.0.0
-plotly==5.20.0
-pluggy==1.3.0
-pooch==1.7.0
-prompt-toolkit==3.0.39
-psutil==5.9.5
-pure-eval==0.2.2
-pyasn1==0.5.0
-pyasn1-modules==0.3.0
-pyautocad==0.2.0
-pybind11==2.11.1
-pybind11-global==2.11.1
-pydeck==0.8.0
-Pygments==2.16.1
-pyinstaller==5.13.2
-pyinstaller-hooks-contrib==2023.8
-PyOpenGL @ file:///C:/Program%20Files%20%28x86%29/ZED%20SDK/PyOpenGL-3.1.5-cp39-cp39-win_amd64.whl#sha256=4f6861cd8f7bd0d8b51fbd0afb10ce10102b79c620237480eaf638162bf40a1b
-PyOpenGL-accelerate @ file:///C:/Program%20Files%20%28x86%29/ZED%20SDK/PyOpenGL_accelerate-3.1.5-cp39-cp39-win_amd64.whl#sha256=1b9b2ccb587d1fbe39cc2f0146674b80962daf5f49bac98e1eea89e672fff8ba
-pyparsing==3.1.1
-pyproj==3.6.1
-pyransac3d==0.6.0
-pyreadline==2.1
-pytest==7.4.2
-python-dateutil @ file:///home/conda/feedstock_root/build_artifacts/python-dateutil_1626286286081/work
-pytz @ file:///home/conda/feedstock_root/build_artifacts/pytz_1693930252784/work
-pyvista==0.42.3
-pywin32==306
-pywin32-ctypes==0.2.2
-PyYAML==6.0.1
-pyzed==1.3.0
-pyzmq==24.0.1
-referencing==0.30.2
-requests==2.31.0
-retrying==1.3.4
-rfc3339-validator==0.1.4
-rfc3986-validator==0.1.1
-rich==13.7.1
-rpds-py==0.10.3
-rsa==4.9
-Rtree==1.3.0
-scikit-learn==1.3.0
-scipy==1.11.2
-scooby==0.7.4
-seaborn==0.12.2
-Send2Trash==1.8.2
-setuptools-scm==8.0.4
-shapely==2.0.1
-simplekml==1.3.6
-six @ file:///home/conda/feedstock_root/build_artifacts/six_1620240208055/work
-sniffio==1.3.0
-sorcery==0.2.2
-soupsieve==2.5
-sspilib==0.1.0
-stack-data==0.6.2
-sympy==1.11.1
-tenacity==8.2.3
-threadpoolctl==3.2.0
-tinycss2==1.2.1
-tomli==2.0.1
-toolz==0.12.0
-torch==2.0.1+cu118
-torchaudio==2.0.2+cu118
-torchvision==0.15.2+cu118
-tornado==6.3.3
-tqdm @ file:///home/conda/feedstock_root/build_artifacts/tqdm_1691671248568/work
-traitlets==5.10.0
-trimesh==4.2.2
-types-python-dateutil==2.8.19.14
-typing_extensions==4.10.0
-tzdata @ file:///home/conda/feedstock_root/build_artifacts/python-tzdata_1680081134351/work
-ujson==5.8.0
-uri-template==1.3.0
-urllib3==1.26.16
-vtk==9.2.6
-wcwidth==0.2.6
-webcolors==1.13
-webencodings==0.5.1
-websocket-client==1.6.4
-Werkzeug==3.0.1
-widgetsnbextension==4.0.9
-wrapt==1.15.0
-y-py==0.6.2
-zipp==3.16.2
+Gradio==4.16.0
+reportlab==3.6.11
+pandas==2.2.3
+openpyxl==3.1.2
+fpdf==1.7.2
+fastapi==0.112.4
+pydantic==2.10.6
+ifcopenshell
+alphashape
+shapely
+rtree
+laspy
+cloth-simulation-filter
+pyvista
+tqdm
+scipy
+scikit-learn

scan_to_model_pipeline.py

@@ -5,6 +5,7 @@
 # revision history
 #   0.1: initial implementation
 #   0.15: add pipeline architecture
+#   0.3: refactoring
 # function: clustring. filtering. make footprints. make LoD1. make spreadsheet
 # license: MIT license
 # reference:
@@ -292,7 +293,7 @@ def view_tin(ground_xyz, tri):
 	ax.plot_trisurf(ground_xyz[:,0], ground_xyz[:,1], ground_xyz[:,2], triangles=tri.simplices, cmap=plt.cm.Spectral)
 	plt.show()
 
-import pyvista as pv, pydeck as pdk, meshio
+import pyvista as pv # , pydeck as pdk, meshio
 from shapely.geometry import Polygon, MultiPolygon, mapping
 
 def extrude_polygon(poly, height):  
@@ -561,7 +562,7 @@ def get_pipeline_stage(pipeline, name):
 			return stage
 	return None
 
-def scan_to_model_process(args):
+def scan_to_model_process(args, progress_tqdm=tqdm):
 	function_map = {
 		'csf': filtering_csf,
 		'color': filtering_color,
@@ -574,60 +575,60 @@ def scan_to_model_process(args):
 
 	outputs_result = []
 
-	try:	
-		pipeline = load_pipeline(args.pipeline)
-		make_folders(args.output)
-
-		dataset = [{
-			"input": args.input,
-			"output": args.output,
-			"active": True}]
-
-		outputs_result = []
-		output = dataset
-		for index, stage in enumerate(pipeline):
-			name = stage['name']
-			output_tag = ''
-			if 'output_tag' in stage:
-				output_tag = stage['output_tag']
-			input_filter = ''
-			if 'input_filter' in stage:
-				input_filter = stage['input_filter']
-
-			if index == 0:
-				dataset = update_module_output(name, output_tag, output)
-			else:
-				dataset = update_output_to_input(name, output_tag, output)
-			if len(input_filter):
-				dataset = update_active_inputs(dataset, 'name', input_filter, True)
-
-			config = stage['config']
-			if 'csf.ground' in config:
-				ground_fname = get_value_from_name(outputs_result[0]['dataset'], 'name', 'ground', 'input') # TBD. should be generized.
-				config['ground'] = ground_fname
-
-			output = function_map[name](dataset, config)
-			result = {
-				'name': name,
-				'dataset': output.copy()
-			}
-			outputs_result.append(result)
-
-	except Exception as e:
-		print(traceback.format_exc())
-		pass
+	pipeline = load_pipeline(args.pipeline)
+	make_folders(args.output)
+
+	dataset = [{
+		"input": args.input,
+		"output": args.output,
+		"active": True}]
+
+	outputs_result = []
+	output = dataset
+	index = 0
+	for stage in progress_tqdm(pipeline, desc='scan to model processing...'):
+		name = stage['name']
+		output_tag = ''
+		if 'output_tag' in stage:
+			output_tag = stage['output_tag']
+		input_filter = ''
+		if 'input_filter' in stage:
+			input_filter = stage['input_filter']
+
+		if index == 0:
+			dataset = update_module_output(name, output_tag, output)
+		else:
+			dataset = update_output_to_input(name, output_tag, output)
+		if len(input_filter):
+			dataset = update_active_inputs(dataset, 'name', input_filter, True)
+
+		config = stage['config']
+		if 'csf.ground' in config:
+			ground_fname = get_value_from_name(outputs_result[0]['dataset'], 'name', 'ground', 'input') # TBD. should be generized.
+			config['ground'] = ground_fname
+
+		output = function_map[name](dataset, config)
+		result = {
+			'name': name,
+			'dataset': output.copy()
+		}
+		outputs_result.append(result)
+		index += 1
 
 	return outputs_result
 
 def main():
+	# get current module's path
+	module_path = os.path.dirname(os.path.abspath(__file__))
+
 	argparser = argparse.ArgumentParser(description="CSF Filtering")
 	# argparser.add_argument("--input", default="./input/belleview_group.las", required=False, help="Input file name")
 	# argparser.add_argument("--output", default="./output/belleview/belleview.las", required=False, help="Output file name")
 	# argparser.add_argument("--input", default="./input/downsampledlesscloudEURO3.las", required=False, help="Input file name")
 	# argparser.add_argument("--output", default="./output/euro3/EURO3.las", required=False, help="Output file name")
-	argparser.add_argument("--input", default="./input/OTP_EPSG26910_5703_38_-122_ca_sunrise_memorial.las", required=False, help="Input file name")
-	argparser.add_argument("--output", default="./output/opt/sunrise.las", required=False, help="Output file name")
-	argparser.add_argument("--pipeline", default="pipeline.json", required=False, help="pipeline file name")
+	argparser.add_argument("--input", default=f"{module_path}/input/OTP_EPSG26910_5703_38_-122_ca_sunrise_memorial.las", required=False, help="Input file name")
+	argparser.add_argument("--output", default=f"{module_path}/output/opt/sunrise.las", required=False, help="Output file name")
+	argparser.add_argument("--pipeline", default=f"{module_path}/pipeline.json", required=False, help="pipeline file name")
 	args = argparser.parse_args()
 
 	scan_to_model_process(args)