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import json
from collections import defaultdict
from typing import Dict, List, Tuple
import torch
from pycocotools import mask as mask_util
# ============================================================================
# Utility Functions
# ============================================================================
def convert_boxlist_to_normalized_tensor(box_list, image_width, image_height):
"""
Converts a list of bounding boxes to a normalized PyTorch tensor.
Args:
box_list (list of list or tuples): Each box is [x_min, y_min, x_max, y_max].
image_width (int or float): Width of the image.
image_height (int or float): Height of the image.
Returns:
torch.Tensor: Normalized tensor of shape (N, 4), values in [0, 1].
"""
boxes = torch.tensor(box_list, dtype=torch.float32)
boxes[:, [0, 2]] /= image_width # x_min, x_max
boxes[:, [1, 3]] /= image_height # y_min, y_max
boxes = boxes.clamp(0, 1)
return boxes
def load_coco_and_group_by_image(json_path: str) -> Tuple[List[Dict], Dict[int, str]]:
"""
Load COCO JSON file and group annotations by image.
Args:
json_path (str): Path to COCO JSON file.
Returns:
Tuple containing:
- List of dicts with 'image' and 'annotations' keys
- Dict mapping category IDs to category names
"""
with open(json_path, "r") as f:
coco = json.load(f)
images = {img["id"]: img for img in coco["images"]}
anns_by_image = defaultdict(list)
for ann in coco["annotations"]:
anns_by_image[ann["image_id"]].append(ann)
sorted_image_ids = sorted(images.keys())
grouped = []
for image_id in sorted_image_ids:
image_info = images[image_id]
grouped.append(
{"image": image_info, "annotations": anns_by_image.get(image_id, [])}
)
cat_id_to_name = {cat["id"]: cat["name"] for cat in coco["categories"]}
return grouped, cat_id_to_name
def ann_to_rle(segm, im_info: Dict) -> Dict:
"""
Convert annotation which can be polygons or uncompressed RLE to RLE.
Args:
segm: Segmentation data (polygon list or RLE dict)
im_info (dict): Image info containing 'height' and 'width'
Returns:
RLE encoded segmentation
"""
h, w = im_info["height"], im_info["width"]
if isinstance(segm, list):
# Polygon - merge all parts into one mask RLE code
rles = mask_util.frPyObjects(segm, h, w)
rle = mask_util.merge(rles)
elif isinstance(segm["counts"], list):
# Uncompressed RLE
rle = mask_util.frPyObjects(segm, h, w)
else:
# Already RLE
rle = segm
return rle
# ============================================================================
# COCO Training API
# ============================================================================
class COCO_FROM_JSON:
"""
COCO training API for loading box-only annotations from JSON.
Groups all annotations per image and creates queries per category.
"""
def __init__(
self,
annotation_file,
prompts=None,
include_negatives=True,
category_chunk_size=None,
):
"""
Initialize the COCO training API.
Args:
annotation_file (str): Path to COCO JSON annotation file
prompts: Optional custom prompts for categories
include_negatives (bool): Whether to include negative examples (categories with no instances)
"""
self._raw_data, self._cat_idx_to_text = load_coco_and_group_by_image(
annotation_file
)
self._sorted_cat_ids = sorted(list(self._cat_idx_to_text.keys()))
self.prompts = None
self.include_negatives = include_negatives
self.category_chunk_size = (
category_chunk_size
if category_chunk_size is not None
else len(self._sorted_cat_ids)
)
self.category_chunks = [
self._sorted_cat_ids[i : i + self.category_chunk_size]
for i in range(0, len(self._sorted_cat_ids), self.category_chunk_size)
]
if prompts is not None:
prompts = eval(prompts)
self.prompts = {}
for loc_dict in prompts:
self.prompts[int(loc_dict["id"])] = loc_dict["name"]
assert len(self.prompts) == len(
self._sorted_cat_ids
), "Number of prompts must match number of categories"
def getDatapointIds(self):
"""Return all datapoint indices for training."""
return list(range(len(self._raw_data) * len(self.category_chunks)))
def loadQueriesAndAnnotationsFromDatapoint(self, idx):
"""
Load queries and annotations for a specific datapoint.
Args:
idx (int): Datapoint index
Returns:
Tuple of (queries, annotations) lists
"""
img_idx = idx // len(self.category_chunks)
chunk_idx = idx % len(self.category_chunks)
cat_chunk = self.category_chunks[chunk_idx]
queries = []
annotations = []
query_template = {
"id": None,
"original_cat_id": None,
"object_ids_output": None,
"query_text": None,
"query_processing_order": 0,
"ptr_x_query_id": None,
"ptr_y_query_id": None,
"image_id": 0, # Single image per datapoint
"input_box": None,
"input_box_label": None,
"input_points": None,
"is_exhaustive": True,
}
annot_template = {
"image_id": 0,
"bbox": None, # Normalized bbox in xywh
"area": None, # Unnormalized area
"segmentation": None, # RLE encoded
"object_id": None,
"is_crowd": None,
"id": None,
}
raw_annotations = self._raw_data[img_idx]["annotations"]
image_info = self._raw_data[img_idx]["image"]
width, height = image_info["width"], image_info["height"]
# Group annotations by category
cat_id_to_anns = defaultdict(list)
for ann in raw_annotations:
cat_id_to_anns[ann["category_id"]].append(ann)
annotations_by_cat_sorted = [
(cat_id, cat_id_to_anns[cat_id]) for cat_id in cat_chunk
]
for cat_id, anns in annotations_by_cat_sorted:
if len(anns) == 0 and not self.include_negatives:
continue
cur_ann_ids = []
# Create annotations for this category
for ann in anns:
annotation = annot_template.copy()
annotation["id"] = len(annotations)
annotation["object_id"] = annotation["id"]
annotation["is_crowd"] = ann["iscrowd"]
normalized_boxes = convert_boxlist_to_normalized_tensor(
[ann["bbox"]], width, height
)
bbox = normalized_boxes[0]
annotation["area"] = (bbox[2] * bbox[3]).item()
annotation["bbox"] = bbox
if (
"segmentation" in ann
and ann["segmentation"] is not None
and ann["segmentation"] != []
):
annotation["segmentation"] = ann_to_rle(
ann["segmentation"], im_info=image_info
)
annotations.append(annotation)
cur_ann_ids.append(annotation["id"])
# Create query for this category
query = query_template.copy()
query["id"] = len(queries)
query["original_cat_id"] = cat_id
query["query_text"] = (
self._cat_idx_to_text[cat_id]
if self.prompts is None
else self.prompts[cat_id]
)
query["object_ids_output"] = cur_ann_ids
queries.append(query)
return queries, annotations
def loadImagesFromDatapoint(self, idx):
"""
Load image information for a specific datapoint.
Args:
idx (int): Datapoint index
Returns:
List containing image info dict
"""
img_idx = idx // len(self.category_chunks)
img_data = self._raw_data[img_idx]["image"]
images = [
{
"id": 0,
"file_name": img_data["file_name"],
"original_img_id": img_data["id"],
"coco_img_id": img_data["id"],
}
]
return images
# ============================================================================
# SAM3 Evaluation APIs
# ============================================================================
class SAM3_EVAL_API_FROM_JSON_NP:
"""
SAM3 evaluation API for loading noun phrase queries from JSON.
"""
def __init__(self, annotation_file):
"""
Initialize the SAM3 evaluation API.
Args:
annotation_file (str): Path to SAM3 JSON annotation file
"""
with open(annotation_file, "r") as f:
data = json.load(f)
self._image_data = data["images"]
def getDatapointIds(self):
"""Return all datapoint indices."""
return list(range(len(self._image_data)))
def loadQueriesAndAnnotationsFromDatapoint(self, idx):
"""
Load queries and annotations for a specific datapoint.
Args:
idx (int): Datapoint index
Returns:
Tuple of (queries, annotations) lists
"""
cur_img_data = self._image_data[idx]
queries = []
annotations = []
query_template = {
"id": None,
"original_cat_id": None,
"object_ids_output": None,
"query_text": None,
"query_processing_order": 0,
"ptr_x_query_id": None,
"ptr_y_query_id": None,
"image_id": 0,
"input_box": None,
"input_box_label": None,
"input_points": None,
"is_exhaustive": True,
}
# Create query
query = query_template.copy()
query["id"] = len(queries)
query["original_cat_id"] = int(cur_img_data["queried_category"])
query["query_text"] = cur_img_data["text_input"]
query["object_ids_output"] = []
queries.append(query)
return queries, annotations
def loadImagesFromDatapoint(self, idx):
"""
Load image information for a specific datapoint.
Args:
idx (int): Datapoint index
Returns:
List containing image info dict
"""
img_data = self._image_data[idx]
images = [
{
"id": 0,
"file_name": img_data["file_name"],
"original_img_id": img_data["id"],
"coco_img_id": img_data["id"],
}
]
return images
class SAM3_VEVAL_API_FROM_JSON_NP:
"""
SAM3 video evaluation API for loading noun phrase queries from JSON.
"""
def __init__(self, annotation_file):
"""
Initialize the SAM3 video evaluation API.
Args:
annotation_file (str): Path to SAM3 video JSON annotation file
"""
with open(annotation_file, "r") as f:
data = json.load(f)
assert "video_np_pairs" in data, "Incorrect data format"
self._video_data = data["videos"]
self._video_id_to_np_ids = defaultdict(list)
self._cat_id_to_np = {}
for cat_dict in data["categories"]:
self._cat_id_to_np[cat_dict["id"]] = cat_dict["name"]
for video_np_dict in data["video_np_pairs"]:
self._video_id_to_np_ids[video_np_dict["video_id"]].append(
video_np_dict["category_id"]
)
assert (
self._cat_id_to_np[video_np_dict["category_id"]]
== video_np_dict["noun_phrase"]
), "Category name does not match text input"
def getDatapointIds(self):
"""Return all datapoint indices."""
return list(range(len(self._video_data)))
def loadQueriesAndAnnotationsFromDatapoint(self, idx):
"""
Load queries and annotations for a specific video datapoint.
Args:
idx (int): Datapoint index
Returns:
Tuple of (queries, annotations) lists
"""
cur_vid_data = self._video_data[idx]
queries = []
annotations = []
query_template = {
"id": None,
"original_cat_id": None,
"object_ids_output": None,
"query_text": None,
"query_processing_order": 0,
"ptr_x_query_id": None,
"ptr_y_query_id": None,
"image_id": 0,
"input_box": None,
"input_box_label": None,
"input_points": None,
"is_exhaustive": True,
}
all_np_ids = self._video_id_to_np_ids[cur_vid_data["id"]]
for np_id in all_np_ids:
text_input = self._cat_id_to_np[np_id]
for i, image_path in enumerate(cur_vid_data["file_names"]):
query = query_template.copy()
query["id"] = len(queries)
query["original_cat_id"] = np_id
query["query_text"] = text_input
query["image_id"] = i
query["query_processing_order"] = i
query["object_ids_output"] = []
queries.append(query)
return queries, annotations
def loadImagesFromDatapoint(self, idx):
"""
Load image information for a specific video datapoint.
Args:
idx (int): Datapoint index
Returns:
List containing image info dicts for all frames
"""
video_data = self._video_data[idx]
images = [
{
"id": i,
"file_name": file_name,
"original_img_id": video_data["id"],
"coco_img_id": video_data["id"],
}
for i, file_name in enumerate(video_data["file_names"])
]
return images
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