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import os
import re
from types import SimpleNamespace
from typing import Any
import gradio as gr
import numpy as np
from detectron2 import engine
from inference import main, setup_cfg
# internal settings
NUM_PROCESSES = 1
CROP = False
SCORE_THRESHOLD = 0.8
MAX_PARTS = 5
ARGS = SimpleNamespace(
config_file="configs/coco/instance-segmentation/swin/opd_v1_real.yaml",
model="...",
input_format="RGB",
output=".output",
cpu=True,
)
def predict(rgb_image: str, depth_image: str, intrinsics: np.ndarray, num_samples: int) -> list[Any]:
def find_gifs(path: str) -> list[str]:
"""Scrape folders for all generated gif files."""
for file in os.listdir(path):
sub_path = os.path.join(path, file)
if os.path.isdir(sub_path):
for image_file in os.listdir(sub_path):
if re.match(r".*\.gif$", image_file):
yield os.path.join(sub_path, image_file)
cfg = setup_cfg(ARGS)
engine.launch(
main,
NUM_PROCESSES,
args=(
cfg,
rgb_image,
depth_image,
intrinsics,
num_samples,
CROP,
SCORE_THRESHOLD,
),
)
# process output
# TODO: may want to select these in decreasing order of score
pre_outputs = list(find_gifs(ARGS.output))
outputs = []
for idx in range(MAX_PARTS): # hide unused components
if idx < len(pre_outputs):
outputs.append(gr.update(value=pre_outputs[idx], visible=True))
else:
outputs.append(gr.update(visible=False))
return outputs
def variable_outputs(idx):
idx = int(idx)
with gr.Blocks() as app:
gr.Markdown(
"""
# OPDMulti Demo
Upload an image to see its range of motion.
"""
)
# TODO: add gr.Examples
with gr.Row():
rgb_image = gr.Image(
image_mode="RGB", source="upload", type="filepath", label="RGB Image", show_label=True, interactive=True
)
depth_image = gr.Image(
image_mode="L", source="upload", type="filepath", label="Depth Image", show_label=True, interactive=True
)
intrinsics = gr.Dataframe(
value=[
[
214.85935872395834,
0.0,
0.0,
],
[
0.0,
214.85935872395834,
0.0,
],
[
125.90160319010417,
95.13726399739583,
1.0,
],
],
row_count=(3, "fixed"),
col_count=(3, "fixed"),
datatype="number",
type="numpy",
label="Intrinsics matrix",
show_label=True,
interactive=True,
)
num_samples = gr.Number(
value=10,
label="Number of samples",
show_label=True,
interactive=True,
precision=0,
minimum=3,
maximum=20,
)
submit_btn = gr.Button("Run model")
# TODO: do we want to set a maximum limit on how many parts we render? We could also show the number of components
# identified.
outputs = [gr.Image(type="filepath", label=f"Part {idx + 1}", visible=False) for idx in range(MAX_PARTS)]
# TODO: maybe need to use a queue here so we don't overload the instance
submit_btn.click(
fn=predict, inputs=[rgb_image, depth_image, intrinsics, num_samples], outputs=outputs, api_name="run_model"
)
app.launch()
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