Why can't we achieve the demonstration effect? There are three tennis rackets in the incoming image, and only one mask is returned

[hjj-lmx]

How is the effect of hq_sqm achieved in this, and why can't I achieve this effect

Copyright (c) Meta Platforms, Inc. and affiliates.

All rights reserved.

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 typing import Any, Dict, List

parser = argparse.ArgumentParser(
description=(
"Runs automatic mask generation on an input image or directory of images, "
"and outputs masks as either PNGs or COCO-style RLEs. Requires open-cv, "
"as well as pycocotools if saving in RLE format."
)
)

parser.add_argument(
"--input",
type=str,
default="E:\Project\input\example0.png",
help="Path to either a single input image or folder of images.",
)

parser.add_argument(
"--output",
type=str,
default="E:\Project\output\",
help=(
"Path to the directory where masks will be output. Output will be either a folder "
"of PNGs per image or a single json with COCO-style masks."
),
)

parser.add_argument(
"--model-type",
type=str,
default="vit_h",
help="The type of model to load, in ['default', 'vit_h', 'vit_l', 'vit_b']",
)

parser.add_argument(
"--checkpoint",
type=str,
default="../pretrained_checkpoint/sam_hq_vit_h.pth",
help="The path to the SAM checkpoint to use for mask generation.",
)

parser.add_argument("--device", type=str, default="cuda", help="The device to run generation on.")

parser.add_argument(
"--convert-to-rle",
action="store_true",
help=(
"Save masks as COCO RLEs in a single json instead of as a folder of PNGs. "
"Requires pycocotools."
),
)

amg_settings = parser.add_argument_group("AMG Settings")

amg_settings.add_argument(
"--points-per-side",
type=int,
default=None,
help="Generate masks by sampling a grid over the image with this many points to a side.",
)

amg_settings.add_argument(
"--points-per-batch",
type=int,
default=None,
help="How many input points to process simultaneously in one batch.",
)

amg_settings.add_argument(
"--pred-iou-thresh",
type=float,
default=None,
help="Exclude masks with a predicted score from the model that is lower than this threshold.",
)

amg_settings.add_argument(
"--stability-score-thresh",
type=float,
default=None,
help="Exclude masks with a stability score lower than this threshold.",
)

amg_settings.add_argument(
"--stability-score-offset",
type=float,
default=None,
help="Larger values perturb the mask more when measuring stability score.",
)

amg_settings.add_argument(
"--box-nms-thresh",
type=float,
default=None,
help="The overlap threshold for excluding a duplicate mask.",
)

amg_settings.add_argument(
"--crop-n-layers",
type=int,
default=None,
help=(
"If >0, mask generation is run on smaller crops of the image to generate more masks. "
"The value sets how many different scales to crop at."
),
)

amg_settings.add_argument(
"--crop-nms-thresh",
type=float,
default=None,
help="The overlap threshold for excluding duplicate masks across different crops.",
)

amg_settings.add_argument(
"--crop-overlap-ratio",
type=int,
default=None,
help="Larger numbers mean image crops will overlap more.",
)

amg_settings.add_argument(
"--crop-n-points-downscale-factor",
type=int,
default=None,
help="The number of points-per-side in each layer of crop is reduced by this factor.",
)

amg_settings.add_argument(
"--min-mask-region-area",
type=int,
default=None,
help=(
"Disconnected mask regions or holes with area smaller than this value "
"in pixels are removed by postprocessing."
),
)

def write_masks_to_folder(masks: List[Dict[str, Any]], path: str) -> 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)
mask_metadata = [
str(i),
str(mask_data["area"]),
*[str(x) for x in mask_data["bbox"]],
*[str(x) for x in mask_data["point_coords"][0]],
str(mask_data["predicted_iou"]),
str(mask_data["stability_score"]),
*[str(x) for x in mask_data["crop_box"]],
]
row = ",".join(mask_metadata)
metadata.append(row)
metadata_path = os.path.join(path, "metadata.csv")
with open(metadata_path, "w") as f:
f.write("\n".join(metadata))

return

def get_amg_kwargs(args):
amg_kwargs = {
"points_per_side": args.points_per_side,
"points_per_batch": args.points_per_batch,
"pred_iou_thresh": args.pred_iou_thresh,
"stability_score_thresh": args.stability_score_thresh,
"stability_score_offset": args.stability_score_offset,
"box_nms_thresh": args.box_nms_thresh,
"crop_n_layers": args.crop_n_layers,
"crop_nms_thresh": args.crop_nms_thresh,
"crop_overlap_ratio": args.crop_overlap_ratio,
"crop_n_points_downscale_factor": args.crop_n_points_downscale_factor,
"min_mask_region_area": args.min_mask_region_area,
}
amg_kwargs = {k: v for k, v in amg_kwargs.items() if v is not None}
return amg_kwargs

def main(args: argparse.Namespace) -> None:
print("Loading model...")
sam = sam_model_registryargs.model_type
_ = sam.to(device=args.device)
output_mode = "coco_rle" if args.convert_to_rle else "binary_mask"
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]
else:
    targets = [
        f for f in os.listdir(args.input) if not os.path.isdir(os.path.join(args.input, f))
    ]
    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)

    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)
    else:
        save_file = save_base + ".json"
        with open(save_file, "w") as f:
            json.dump(masks, f)
print("Done!")

if name == "main":
args = parser.parse_args()
main(args)