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import streamlit as st
import torch
from dnafiber.ui.utils import get_image, get_multifile_image
from dnafiber.deployment import MODELS_ZOO
import pandas as pd
import plotly.express as px
from dnafiber.postprocess import refine_segmentation
import torch.nn.functional as F
from joblib import Parallel, delayed
import time
from catppuccin import PALETTE
from dnafiber.deployment import _get_model
from dnafiber.ui.inference import ui_inference_cacheless
def plot_result(seleted_category=None):
if st.session_state.get("results", None) is None or selected_category is None:
return
only_bilateral = st.checkbox(
"Show only bicolor fibers",
value=False,
)
remove_outliers = st.checkbox(
"Remove outliers",
value=True,
help="Remove outliers from the data",
)
reorder = st.checkbox(
"Reorder groups by median ratio",
value=True,
)
if remove_outliers:
min_ratio, max_ratio = st.slider(
"Ratio range",
min_value=0.0,
max_value=10.0,
value=(0.0, 5.0),
step=0.1,
help="Select the ratio range to display",
)
df = st.session_state.results.copy()
clean_df = df[["ratio", "image_name", "fiber_type"]].copy()
clean_df["Image"] = clean_df["image_name"]
clean_df["Fiber Type"] = clean_df["fiber_type"]
clean_df["Ratio"] = clean_df["ratio"]
if only_bilateral:
clean_df = clean_df[clean_df["Fiber Type"] == "double"]
if remove_outliers:
clean_df = clean_df[
(clean_df["Ratio"] >= min_ratio) & (clean_df["Ratio"] <= max_ratio)
]
if selected_category:
clean_df = clean_df[clean_df["Image"].isin(selected_category)]
if not reorder:
clean_df["Image"] = pd.Categorical(
clean_df["Image"], categories=selected_category, ordered=True
)
clean_df.sort_values("Image", inplace=True)
if reorder:
image_order = (
clean_df.groupby("Image")["Ratio"]
.median()
.sort_values(ascending=True)
.index
)
clean_df["Image"] = pd.Categorical(
clean_df["Image"], categories=image_order, ordered=True
)
clean_df.sort_values("Image", inplace=True)
palette = [c.hex for c in PALETTE.latte.colors]
fig = px.violin(
clean_df,
y="Ratio",
x="Image",
color="Image",
box=True, # draw box plot inside the violin
points="all", # can be 'outliers', or False
color_discrete_sequence=palette,
)
# Set y-axis to log scale
st.plotly_chart(
fig,
use_container_width=True,
)
def run_inference(model_name, pixel_size):
is_cuda_available = torch.cuda.is_available()
if "ensemble" in model_name:
model = [
_ + "_finetuned" if "finetuned" in model_name else ""
for _ in MODELS_ZOO.values()
if _ != "ensemble"
]
else:
model = _get_model(
revision=model_name,
device="cuda" if is_cuda_available else "cpu",
)
my_bar = st.progress(0, text="Running segmentation...")
all_files = st.session_state.files_uploaded
all_results = dict(
FirstAnalog=[],
SecondAnalog=[],
length=[],
ratio=[],
image_name=[],
fiber_type=[],
)
for i, file in enumerate(all_files):
if isinstance(file, tuple):
if file[0] is None:
filename = file[1].name
if file[1] is None:
filename = file[0].name
image = get_multifile_image(file)
else:
filename = file.name
image = get_image(
file, st.session_state.get("reverse_channels", False), file.file_id
)
start = time.time()
prediction = ui_inference_cacheless(
_model=model,
_image=image,
_device="cuda" if is_cuda_available else "cpu",
postprocess=False,
)
print(f"Prediction time: {time.time() - start:.2f} seconds for {file.name}")
h, w = prediction.shape
start = time.time()
if h > 2048 or w > 2048:
# Extract blocks from the prediction
blocks = F.unfold(
torch.from_numpy(prediction).unsqueeze(0).float(),
kernel_size=(4096, 4096),
stride=(4096, 4096),
)
blocks = blocks.view(4096, 4096, -1).permute(2, 0, 1).byte().numpy()
results = Parallel(n_jobs=4)(
delayed(refine_segmentation)(block) for block in blocks
)
results = [x for xs in results for x in xs]
else:
results = refine_segmentation(prediction, fix_junctions=True)
print(f"Refinement time: {time.time() - start:.2f} seconds for {filename}")
results = [fiber for fiber in results if fiber.is_valid]
all_results["FirstAnalog"].extend([fiber.red * pixel_size for fiber in results])
all_results["SecondAnalog"].extend(
[fiber.green * pixel_size for fiber in results]
)
all_results["length"].extend(
[fiber.red * pixel_size + fiber.green * pixel_size for fiber in results]
)
all_results["ratio"].extend([fiber.ratio for fiber in results])
all_results["image_name"].extend([filename.split("-")[0] for fiber in results])
all_results["fiber_type"].extend([fiber.fiber_type for fiber in results])
my_bar.progress(i / len(all_files), text=f"{filename} done")
st.session_state.results = pd.DataFrame.from_dict(all_results)
my_bar.empty()
if st.session_state.get("files_uploaded", None):
run_segmentation = st.button("Run Segmentation", use_container_width=True)
with st.sidebar:
st.metric(
"Pixel size (µm)",
st.session_state.get("pixel_size", 0.13),
)
with st.expander("Model", expanded=True):
model_name = st.selectbox(
"Select a model",
list(MODELS_ZOO.keys()),
index=0,
help="Select a model to use for inference",
)
finetuned = st.checkbox(
"Use finetuned model",
value=True,
help="Use a finetuned model for inference",
)
col1, col2 = st.columns(2)
with col1:
st.write("Running on:")
with col2:
st.button(
"GPU" if torch.cuda.is_available() else "CPU",
disabled=True,
)
tab_segmentation, tab_charts = st.tabs(["Segmentation", "Charts"])
with tab_segmentation:
st.subheader("Segmentation")
if run_segmentation:
run_inference(
model_name=MODELS_ZOO[model_name] + "_finetuned"
if finetuned
else MODELS_ZOO[model_name],
pixel_size=st.session_state.get("pixel_size", 0.13),
)
st.balloons()
if st.session_state.get("results", None) is not None:
st.write(
st.session_state.results,
)
st.download_button(
label="Download results",
data=st.session_state.results.to_csv(index=False).encode("utf-8"),
file_name="results.csv",
mime="text/csv",
use_container_width=True,
)
with tab_charts:
if st.session_state.get("results", None) is not None:
results = st.session_state.results
categories = results["image_name"].unique()
selected_category = st.multiselect(
"Select a category", categories, default=categories
)
plot_result(selected_category)
else:
st.switch_page("pages/1_Load.py")
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