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Improve scripts/amg.py: paths, progress bar, multi-GPU, resuming #398

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99 changes: 69 additions & 30 deletions scripts/amg.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,15 +4,17 @@
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

import cv2 # type: ignore

from segment_anything import SamAutomaticMaskGenerator, sam_model_registry

import argparse
import json
import os
from pathlib import Path
from typing import Any, Dict, List

import cv2 # type: ignore
import tqdm
import torch.utils.data

from segment_anything import SamAutomaticMaskGenerator, sam_model_registry

parser = argparse.ArgumentParser(
description=(
"Runs automatic mask generation on an input image or directory of images, "
Expand Down Expand Up @@ -53,6 +55,9 @@
)

parser.add_argument("--device", type=str, default="cuda", help="The device to run generation on.")
parser.add_argument("--rank", type=int, default=0, help="Rank of the current process.")
parser.add_argument("--world", type=int, default=1, help="Number of processes.")
parser.add_argument("--num-workers", type=int, default=4, help="Dataloader workers.")

parser.add_argument(
"--convert-to-rle",
Expand Down Expand Up @@ -149,13 +154,13 @@
)


def write_masks_to_folder(masks: List[Dict[str, Any]], path: str) -> None:
def write_masks_to_folder(masks: List[Dict[str, Any]], path: Path) -> None:
header = "id,area,bbox_x0,bbox_y0,bbox_w,bbox_h,point_input_x,point_input_y,predicted_iou,stability_score,crop_box_x0,crop_box_y0,crop_box_w,crop_box_h" # noqa
metadata = [header]
for i, mask_data in enumerate(masks):
mask = mask_data["segmentation"]
filename = f"{i}.png"
cv2.imwrite(os.path.join(path, filename), mask * 255)
cv2.imwrite((path / filename).as_posix(), mask * 255)
mask_metadata = [
str(i),
str(mask_data["area"]),
Expand All @@ -167,8 +172,7 @@ def write_masks_to_folder(masks: List[Dict[str, Any]], path: str) -> None:
]
row = ",".join(mask_metadata)
metadata.append(row)
metadata_path = os.path.join(path, "metadata.csv")
with open(metadata_path, "w") as f:
with open(path / "metadata.csv", "w") as f:
f.write("\n".join(metadata))

return
Expand All @@ -192,6 +196,21 @@ def get_amg_kwargs(args):
return amg_kwargs


class ImageDataset(torch.utils.data.Dataset):
def __init__(self, paths: List[Path], base: Path):
self.paths = paths
self.base = base

def __len__(self):
return len(self.paths)

def __getitem__(self, idx):
path = self.paths[idx]
image = cv2.imread((self.base / path).as_posix())
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
return path, image


def main(args: argparse.Namespace) -> None:
print("Loading model...")
sam = sam_model_registry[args.model_type](checkpoint=args.checkpoint)
Expand All @@ -200,34 +219,54 @@ def main(args: argparse.Namespace) -> None:
amg_kwargs = get_amg_kwargs(args)
generator = SamAutomaticMaskGenerator(sam, output_mode=output_mode, **amg_kwargs)

if not os.path.isdir(args.input):
targets = [args.input]
args.input = Path(args.input).expanduser().resolve()
args.output = Path(args.output).expanduser().resolve()

if not args.input.is_dir():
targets = ImageDataset([args.input.name], args.input.parent)
else:
targets = [
f for f in os.listdir(args.input) if not os.path.isdir(os.path.join(args.input, f))
f.relative_to(args.input) for f in args.input.rglob("*")
if f.is_file() and f.suffix.lower() in [".jpg", ".jpeg", ".png"]
]
targets = [os.path.join(args.input, f) for f in targets]

os.makedirs(args.output, exist_ok=True)

for t in targets:
print(f"Processing '{t}'...")
image = cv2.imread(t)
if image is None:
print(f"Could not load '{t}' as an image, skipping...")
continue
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

print(f"Found {len(targets)} images in {args.input}.")

# Per-process split
if args.world > 1:
targets = targets[args.rank::args.world]
print(f"Rank {args.rank}/{args.world} will process {len(targets)} images.")

# Skip existing
if output_mode == "binary_mask":
targets = [
f for f in targets
if not Path.is_dir(args.output / f.with_suffix(""))
]
else:
targets = [
f for f in targets
if not Path.is_file(args.output / f.with_suffix(".json"))
]
print(f"Skip already processed images, {len(targets)} remain to do.")

targets = torch.utils.data.DataLoader(
ImageDataset(targets, args.input),
batch_size=None,
shuffle=False,
num_workers=args.num_workers,
collate_fn=lambda x: x,
)

for path, image in tqdm.tqdm(targets, ncols=0):
masks = generator.generate(image)

base = os.path.basename(t)
base = os.path.splitext(base)[0]
save_base = os.path.join(args.output, base)
if output_mode == "binary_mask":
os.makedirs(save_base, exist_ok=False)
write_masks_to_folder(masks, save_base)
save_dir = args.output / path.with_suffix("")
save_dir.mkdir(parents=True, exist_ok=False)
write_masks_to_folder(masks, save_dir)
else:
save_file = save_base + ".json"
save_file = args.output / path.with_suffix(".json")
save_file.parent.mkdir(parents=True, exist_ok=True)
with open(save_file, "w") as f:
json.dump(masks, f)
print("Done!")
Expand Down