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# Copyright (c) 2026 Qualcomm Technologies, Inc. and/or its subsidiaries.
# SPDX-License-Identifier: BSD-3-Clause
# ---------------------------------------------------------------------
from __future__ import annotations
from qai_hub_models.utils.args import (
demo_model_from_cli_args,
get_model_cli_parser,
get_on_device_demo_parser,
validate_on_device_demo_args,
)
from qai_hub_models.utils.asset_loaders import CachedWebModelAsset, load_image
from qai_hub_models.utils.display import display_or_save_image
from .app import DepthProApp
from .model import MODEL_ID, DepthPro
# Reuse the midas depth-estimation fixture — any indoor/outdoor natural image
# works; keeping this out-of-tree avoids uploading a fresh asset just for
# the initial recipe.
INPUT_IMAGE_ADDRESS = CachedWebModelAsset.from_asset_store(
"midas", 3, "test_input_image.jpg"
)
def main(is_test: bool = False) -> None:
parser = get_model_cli_parser(DepthPro)
parser = get_on_device_demo_parser(parser, add_output_dir=True)
parser.add_argument(
"--image",
type=str,
default=INPUT_IMAGE_ADDRESS,
help="image file path or URL",
)
args = parser.parse_args([] if is_test else None)
model = demo_model_from_cli_args(DepthPro, MODEL_ID, args)
validate_on_device_demo_args(args, MODEL_ID)
(_, _, height, width) = model.get_input_spec()["image"][0]
image = load_image(args.image)
print("Model Loaded")
app = DepthProApp(model, height, width) # type: ignore[arg-type]
prediction = app.estimate_depth(image)
print(
f"Predicted field of view: {prediction.field_of_view:.2f} deg "
f"(focal length: {prediction.focal_length_px:.1f} px)"
)
if not is_test:
display_or_save_image(
prediction.heatmap, args.output_dir, "out_heatmap.png", "heatmap"
)
if __name__ == "__main__":
main()
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