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import os
from pathlib import Path
from typing import List, Optional
import numpy as np
import pandas as pd
import torch
from PIL import Image
from tqdm import tqdm
from rtnls_inference.ensembles.ensemble_classification import ClassificationEnsemble
from rtnls_inference.ensembles.ensemble_heatmap_regression import (
HeatmapRegressionEnsemble,
)
from rtnls_inference.ensembles.ensemble_segmentation import SegmentationEnsemble
from rtnls_inference.utils import decollate_batch, extract_keypoints_from_heatmaps
def run_quality_estimation(fpaths, ids, device: torch.device):
ensemble_quality = ClassificationEnsemble.from_release("quality.pt").to(device)
dataloader = ensemble_quality._make_inference_dataloader(
fpaths,
ids=ids,
num_workers=8,
preprocess=False,
batch_size=16,
)
output_ids, outputs = [], []
with torch.no_grad():
for batch in tqdm(dataloader):
if len(batch) == 0:
continue
im = batch["image"].to(device)
# QUALITY
quality = ensemble_quality.predict_step(im)
quality = torch.mean(quality, dim=0)
items = {"id": batch["id"], "quality": quality}
items = decollate_batch(items)
for item in items:
output_ids.append(item["id"])
outputs.append(item["quality"].tolist())
return pd.DataFrame(
outputs,
index=output_ids,
columns=["q1", "q2", "q3"],
)
def run_segmentation_vessels_and_av(
rgb_paths: List[Path],
ce_paths: Optional[List[Path]] = None,
ids: Optional[List[str]] = None,
av_path: Optional[Path] = None,
vessels_path: Optional[Path] = None,
device: torch.device = torch.device(
"cuda:0" if torch.cuda.is_available() else "cpu"
),
) -> None:
"""
Run AV and vessel segmentation on the provided images.
Args:
rgb_paths: List of paths to RGB fundus images
ce_paths: Optional list of paths to contrast enhanced images
ids: Optional list of ids to pass to _make_inference_dataloader
av_path: Folder where to store output AV segmentations
vessels_path: Folder where to store output vessel segmentations
device: Device to run inference on
"""
# Create output directories if they don't exist
if av_path is not None:
av_path.mkdir(exist_ok=True, parents=True)
if vessels_path is not None:
vessels_path.mkdir(exist_ok=True, parents=True)
# Load models
ensemble_av = SegmentationEnsemble.from_release("av_july24.pt").to(device).eval()
ensemble_vessels = (
SegmentationEnsemble.from_release("vessels_july24.pt").to(device).eval()
)
# Prepare input paths
if ce_paths is None:
# If CE paths are not provided, use RGB paths for both inputs
fpaths = rgb_paths
else:
# If CE paths are provided, pair them with RGB paths
if len(rgb_paths) != len(ce_paths):
raise ValueError("rgb_paths and ce_paths must have the same length")
fpaths = list(zip(rgb_paths, ce_paths))
# Create dataloader
dataloader = ensemble_av._make_inference_dataloader(
fpaths,
ids=ids,
num_workers=8,
preprocess=False,
batch_size=8,
)
# Run inference
with torch.no_grad():
for batch in tqdm(dataloader):
# AV segmentation
if av_path is not None:
with torch.autocast(device_type=device.type):
proba = ensemble_av.forward(batch["image"].to(device))
proba = torch.mean(proba, dim=0) # average over models
proba = torch.permute(proba, (0, 2, 3, 1)) # NCHW -> NHWC
proba = torch.nn.functional.softmax(proba, dim=-1)
items = {
"id": batch["id"],
"image": proba,
}
items = decollate_batch(items)
for i, item in enumerate(items):
fpath = os.path.join(av_path, f"{item['id']}.png")
mask = np.argmax(item["image"], -1)
Image.fromarray(mask.squeeze().astype(np.uint8)).save(fpath)
# Vessel segmentation
if vessels_path is not None:
with torch.autocast(device_type=device.type):
proba = ensemble_vessels.forward(batch["image"].to(device))
proba = torch.mean(proba, dim=0) # average over models
proba = torch.permute(proba, (0, 2, 3, 1)) # NCHW -> NHWC
proba = torch.nn.functional.softmax(proba, dim=-1)
items = {
"id": batch["id"],
"image": proba,
}
items = decollate_batch(items)
for i, item in enumerate(items):
fpath = os.path.join(vessels_path, f"{item['id']}.png")
mask = np.argmax(item["image"], -1)
Image.fromarray(mask.squeeze().astype(np.uint8)).save(fpath)
def run_segmentation_disc(
rgb_paths: List[Path],
ce_paths: Optional[List[Path]] = None,
ids: Optional[List[str]] = None,
output_path: Optional[Path] = None,
device: torch.device = torch.device(
"cuda:0" if torch.cuda.is_available() else "cpu"
),
) -> None:
ensemble_disc = (
SegmentationEnsemble.from_release("disc_july24.pt").to(device).eval()
)
# Prepare input paths
if ce_paths is None:
# If CE paths are not provided, use RGB paths for both inputs
fpaths = rgb_paths
else:
# If CE paths are provided, pair them with RGB paths
if len(rgb_paths) != len(ce_paths):
raise ValueError("rgb_paths and ce_paths must have the same length")
fpaths = list(zip(rgb_paths, ce_paths))
dataloader = ensemble_disc._make_inference_dataloader(
fpaths,
ids=ids,
num_workers=8,
preprocess=False,
batch_size=8,
)
with torch.no_grad():
for batch in tqdm(dataloader):
# AV
with torch.autocast(device_type=device.type):
proba = ensemble_disc.forward(batch["image"].to(device))
proba = torch.mean(proba, dim=0) # average over models
proba = torch.permute(proba, (0, 2, 3, 1)) # NCHW -> NHWC
proba = torch.nn.functional.softmax(proba, dim=-1)
items = {
"id": batch["id"],
"image": proba,
}
items = decollate_batch(items)
items = [dataloader.dataset.transform.undo_item(item) for item in items]
for i, item in enumerate(items):
fpath = os.path.join(output_path, f"{item['id']}.png")
mask = np.argmax(item["image"], -1)
Image.fromarray(mask.squeeze().astype(np.uint8)).save(fpath)
def run_fovea_detection(
rgb_paths: List[Path],
ce_paths: Optional[List[Path]] = None,
ids: Optional[List[str]] = None,
device: torch.device = torch.device(
"cuda:0" if torch.cuda.is_available() else "cpu"
),
) -> None:
# def run_fovea_detection(fpaths, ids, device: torch.device):
ensemble_fovea = HeatmapRegressionEnsemble.from_release("fovea_july24.pt").to(
device
)
# Prepare input paths
if ce_paths is None:
# If CE paths are not provided, use RGB paths for both inputs
fpaths = rgb_paths
else:
# If CE paths are provided, pair them with RGB paths
if len(rgb_paths) != len(ce_paths):
raise ValueError("rgb_paths and ce_paths must have the same length")
fpaths = list(zip(rgb_paths, ce_paths))
dataloader = ensemble_fovea._make_inference_dataloader(
fpaths,
ids=ids,
num_workers=8,
preprocess=False,
batch_size=8,
)
output_ids, outputs = [], []
with torch.no_grad():
for batch in tqdm(dataloader):
if len(batch) == 0:
continue
im = batch["image"].to(device)
# FOVEA DETECTION
with torch.autocast(device_type=device.type):
heatmap = ensemble_fovea.forward(im)
keypoints = extract_keypoints_from_heatmaps(heatmap)
kp_fovea = torch.mean(keypoints, dim=0) # average over models
items = {
"id": batch["id"],
"keypoints": kp_fovea,
"metadata": batch["metadata"],
}
items = decollate_batch(items)
items = [dataloader.dataset.transform.undo_item(item) for item in items]
for item in items:
output_ids.append(item["id"])
outputs.append(
[
*item["keypoints"][0].tolist(),
]
)
return pd.DataFrame(
outputs,
index=output_ids,
columns=["x_fovea", "y_fovea"],
)
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