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Add traveler PDF example
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‎examples/.python-version

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3.10

‎examples/JetBrainsMono-SemiBold.ttf

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‎examples/batching-with-pdfs.py

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"""
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Demo application that batches all STL files in a folder into .form files.
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Usage: python3 batching-minimal.py ~/Documents/folder-of-stl-files
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Optional flags:
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--auto-orient: Auto orient models
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--dental-mode: Use dental mode when auto orienting
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--auto-support: Auto support models
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--username: Username for login if uploading to a remote printer or Fleet Control queue
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--password: Password for login if uploading to a remote printer or Fleet Control queue
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--upload-to: Upload to a specific printer, IP Address, or Fleet Control Printer Group ID
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--traveler-pdf: Generates a traveler pdf
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"""
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import argparse
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import pathlib
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import csv
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import sys
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import tempfile
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import typing
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from functools import reduce
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import operator
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import pdf_generation
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import requests
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import formlabs_local_api_minimal as formlabs
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MACHINE_TYPE = "FRML-4-0"
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MATERIAL_CODE = "FLGPGR05"
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def list_files_in_directory(directory_path: pathlib.Path) -> list[pathlib.Path]:
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return [f for f in directory_path.iterdir() if f.is_file() and f.suffix == ".stl"]
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def create_scene():
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response = requests.post(
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"http://localhost:44388/scene/",
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json={
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"machine_type": MACHINE_TYPE,
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"material_code": MATERIAL_CODE,
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"layer_thickness_mm": 0.1,
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"print_setting": "DEFAULT",
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},
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)
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response.raise_for_status()
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return response.json()
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def get_screenshot(settings: dict) -> typing.BinaryIO:
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"""
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Calls the /scene/save-screenshot endpoint with the given settings and returns a temp file handle to the screenshot.
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:param settings: Settings to send to the /scene/save-screenshot endpoint
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:return: A temp file handle to the saved screenshot.
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"""
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temp = tempfile.NamedTemporaryFile("rb", suffix=".png", delete_on_close=False)
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screenshot_response = requests.post(
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"http://localhost:44388/scene/save-screenshot/",
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json={"file": temp.name, "image_size_px": 125} | settings,
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)
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if not screenshot_response.ok:
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print(screenshot_response.json())
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screenshot_response.raise_for_status()
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return temp
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parser = argparse.ArgumentParser(description="Process a folder path.")
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parser.add_argument("folder", type=str, help="Path to the folder")
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parser.add_argument("--auto-orient", action="store_true", help="Auto orient models")
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parser.add_argument(
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"--dental-mode", action="store_true", help="Use dental mode when auto orienting"
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)
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parser.add_argument("--auto-support", action="store_true", help="Auto support models")
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parser.add_argument("--username", type=str, help="Username for login")
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parser.add_argument("--password", type=str, help="Password for login")
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parser.add_argument(
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"--upload-to",
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type=str,
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help="Upload to a specific printer, IP Address, or Fleet Control Printer Group ID",
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)
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parser.add_argument(
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"--traveler-pdf", action="store_true", help="Generates a traveler pdf"
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)
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parser.add_argument(
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"--ps-path", type=str, help="Path to directory containing PreFormServer"
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)
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args = parser.parse_args()
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directory_path = pathlib.Path(args.folder).resolve(strict=True)
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files_to_batch = list_files_in_directory(directory_path)
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print("Files to batch:")
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print(files_to_batch)
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current_batch = 1
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models_in_current_batch = []
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model_pdf_data = []
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CSV_RESULT_FILENAME = directory_path / "summary.csv"
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pathToPreformServer = None
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ps_containing_dir = pathlib.Path(args.ps_path) if args.ps_path else pathlib.Path()
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if sys.platform == "win32":
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pathToPreformServer = (
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ps_containing_dir.resolve() / "PreFormServer/PreFormServer.exe"
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)
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elif sys.platform == "darwin":
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pathToPreformServer = (
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ps_containing_dir.resolve() / "PreFormServer.app/Contents/MacOS/PreFormServer"
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)
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else:
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print("Unsupported platform")
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sys.exit(1)
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with formlabs.PreFormApi.start_preform_server(pathToPreformServer=pathToPreformServer):
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material_response = requests.get("http://localhost:44388/list-materials")
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material_response.raise_for_status()
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material_response_json = material_response.json()
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printer_types = list(
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filter(
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lambda printer_type: MACHINE_TYPE
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in printer_type["supported_machine_type_ids"],
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material_response_json["printer_types"],
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)
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)
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MACHINE_NAME: str = printer_types[0]["label"]
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materials = dict(
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map(
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lambda material: (
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material["material_settings"][0]["scene_settings"]["material_code"],
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material["label"],
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),
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reduce(
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operator.iconcat,
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map(lambda printer_type: printer_type["materials"], printer_types),
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[],
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),
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)
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)
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MATERIAL_NAME = materials[MATERIAL_CODE]
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if args.username and args.password:
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login_response = requests.post(
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"http://localhost:44388/login/",
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json={
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"username": args.username,
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"password": args.password,
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},
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)
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login_response.raise_for_status()
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with open(CSV_RESULT_FILENAME, "w", newline="") as csvfile:
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csvwriter = csv.writer(csvfile)
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csvwriter.writerow(
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["Batch Number", "Batch Print Filename", "Model Source Filename"]
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)
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def save_batch_form():
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global current_batch, models_in_current_batch, model_pdf_data
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form_file_name = f"batch_{current_batch}.form"
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save_path = directory_path / form_file_name
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save_form_response = requests.post(
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"http://localhost:44388/scene/save-form/",
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json={
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"file": str(save_path),
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},
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)
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save_form_response.raise_for_status()
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print(f"Saving batch {current_batch} to {save_path}")
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for i, model in enumerate(models_in_current_batch):
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print(f"{i + 1}. {model['file_name']}")
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csvwriter.writerow([current_batch, form_file_name, model["file_name"]])
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if args.traveler_pdf:
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pdf = pdf_generation.TravelerPDF(
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directory_path / f"summary-batch-{current_batch}.pdf",
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form_file_name,
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)
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build_volume_image = get_screenshot({})
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get_scene_response = requests.get("http://localhost:44388/scene")
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get_scene_response.raise_for_status()
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get_scene_response_json = get_scene_response.json()
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print_setting = get_scene_response_json["scene_settings"][
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"print_setting"
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]
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layer_thickness_mm = get_scene_response_json["scene_settings"][
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"layer_thickness_mm"
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]
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pdf.add_header(
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MACHINE_NAME,
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MATERIAL_NAME,
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print_setting,
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layer_thickness_mm,
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build_volume_image,
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)
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pdf.add_parts(model_pdf_data)
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pdf.save()
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model_pdf_data = []
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current_batch += 1
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models_in_current_batch = []
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if args.upload_to:
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print(f"Uploading batch to {args.upload_to}")
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print_response = requests.post(
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"http://localhost:44388/scene/print/",
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json={
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"printer": args.upload_to,
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"job_name": form_file_name,
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},
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)
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print_response.raise_for_status()
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create_scene()
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while len(files_to_batch) > 0:
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next_file = files_to_batch.pop()
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print(f"Importing {next_file}")
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import_model_response = requests.post(
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"http://localhost:44388/scene/import-model/",
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json={"file": str(directory_path / next_file)},
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)
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if not import_model_response.ok:
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continue
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import_model_response.raise_for_status()
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new_model_id = import_model_response.json()["id"]
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models_in_current_batch.append(
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{"model_id": new_model_id, "file_name": next_file}
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)
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if args.auto_orient:
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print(f"Auto orienting {new_model_id}")
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auto_orient_params = {"models": [new_model_id]}
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if args.dental_mode:
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auto_orient_params["mode"] = "DENTAL"
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auto_orient_params["tilt"] = 0
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auto_orient_response = requests.post(
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"http://localhost:44388/scene/auto-orient/",
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json=auto_orient_params,
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)
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auto_orient_response.raise_for_status()
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if args.traveler_pdf:
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image = get_screenshot({"models": [new_model_id]})
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import_model_response_json = import_model_response.json()
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model_dimensions = tuple(
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map(
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lambda dim: float(
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import_model_response_json["bounding_box"]["max_corner"][
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dim
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]
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)
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- float(
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import_model_response_json["bounding_box"]["min_corner"][
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dim
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]
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),
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["x", "y", "z"],
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)
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)
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model_pdf_data.append(
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pdf_generation.Model(next_file, image, model_dimensions)
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)
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if args.auto_support:
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print(f"Auto supporting {new_model_id}")
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auto_support_response = requests.post(
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"http://localhost:44388/scene/auto-support/",
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json={
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"models": [new_model_id],
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},
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)
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auto_support_response.raise_for_status()
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print(f"Auto layouting all")
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layout_response = requests.post(
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"http://localhost:44388/scene/auto-layout/",
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json={
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"models": "ALL",
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},
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)
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if layout_response.status_code != 200:
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print("Not all models can fit, removing model")
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model_data = models_in_current_batch.pop()
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model_pdf_data.pop()
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delete_response = requests.delete(
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f"http://localhost:44388/scene/models/{model_data['model_id']}/",
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)
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delete_response.raise_for_status()
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files_to_batch.append(model_data["file_name"])
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save_batch_form()
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print("Clearing scene")
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create_scene()
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if len(models_in_current_batch) > 0:
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save_batch_form()

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