587 lines
20 KiB
Python
587 lines
20 KiB
Python
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import os
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import uuid
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from datetime import datetime
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import xml.etree.ElementTree as ET
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from django.db import transaction
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from django.db.models import Case, Count, IntegerField, Q, Sum, When
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from django.shortcuts import get_object_or_404
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from rest_framework import status
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from rest_framework.decorators import api_view, permission_classes
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from rest_framework.pagination import PageNumberPagination
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from rest_framework.permissions import IsAuthenticated
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from rest_framework.response import Response
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from rest_framework.utils import json
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from apps.common.serializers import AiAnnotateTaskSampleSerializer
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from apps.common.utils.annotation_store import (
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get_dataset_sample_annotation_content,
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split_dataset_sample_annotation_content,
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)
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from apps.core.models import (
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AiAnnotateTask,
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AiAnnotateTaskSample,
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AiDatasetSample,
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)
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from apps.datasets.dataset_stats import refresh_dataset_sample_counters
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from apps.datasets.storage_service import download_sidecar_text, upload_sidecar_bytes
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def _safe_split_wh(custom1):
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try:
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w_str, h_str = str(custom1).lower().split("x")
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return int(w_str), int(h_str)
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except Exception:
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return None, None
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def _extract_sample_index(saved_filename):
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stem = os.path.splitext(os.path.basename(str(saved_filename or "")))[0]
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tail = stem.rsplit("-", 1)[-1]
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if tail.isdigit():
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return int(tail)
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return None
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def _get_dataset_sample_summary_status(dataset_sample, task_sample_statuses):
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if dataset_sample.rectangle or dataset_sample.polygon or "06" in task_sample_statuses:
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return "02"
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if task_sample_statuses:
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return "01"
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return "00"
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def _refresh_dataset_sample_status(sample_ids):
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if not sample_ids:
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return
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sample_ids = list({sample_id for sample_id in sample_ids if sample_id})
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samples = list(
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AiDatasetSample.objects.filter(id__in=sample_ids).only("id", "status", "rectangle", "polygon")
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)
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if not samples:
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return
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task_sample_status_map = {}
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for sample_id, status_code in AiAnnotateTaskSample.objects.filter(sample_id__in=sample_ids).values_list(
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"sample_id", "status"
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):
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task_sample_status_map.setdefault(sample_id, set()).add(status_code)
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objects_to_update = []
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for sample in samples:
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new_status = _get_dataset_sample_summary_status(
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sample, task_sample_status_map.get(sample.id, set())
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)
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if sample.status != new_status:
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objects_to_update.append(AiDatasetSample(id=sample.id, status=new_status))
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if objects_to_update:
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AiDatasetSample.objects.bulk_update(objects_to_update, fields=["status"])
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def _normalize_polygon_points(region):
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if not isinstance(region, list):
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return []
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pts = []
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for p in region:
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if not isinstance(p, (list, tuple)) or len(p) != 2:
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continue
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try:
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x = float(p[0])
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y = float(p[1])
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except Exception:
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continue
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pts.append([x, y])
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return pts if len(pts) >= 3 else []
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def _shape_key(label, points):
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try:
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norm = [(round(float(x), 2), round(float(y), 2)) for x, y in points]
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except Exception:
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return None
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return str(label), json.dumps(norm, separators=(",", ":"), ensure_ascii=False)
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def _is_polygon_items(items):
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if not isinstance(items, list):
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return False
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for it in items:
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if not isinstance(it, dict):
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continue
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region = it.get("region")
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if isinstance(region, list) and region and isinstance(region[0], (list, tuple)):
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return True
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return False
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def _bbox_key(item):
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try:
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label = str(item.get("label"))
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region = item.get("region")
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if not isinstance(region, list) or len(region) != 4:
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return None
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vals = [round(float(x), 2) for x in region]
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return label, json.dumps(vals, separators=(",", ":"), ensure_ascii=False)
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except Exception:
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return None
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def _shapes_from_items(items):
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if not isinstance(items, list):
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return []
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shapes = []
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for it in items:
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if not isinstance(it, dict):
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continue
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label = it.get("label")
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if not label:
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continue
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points = _normalize_polygon_points(it.get("region"))
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if not points:
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continue
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shapes.append(
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{
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"label": str(label),
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"points": points,
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"group_id": None,
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"shape_type": "polygon",
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"flags": {},
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"mask": None,
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}
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)
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return shapes
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def _shapes_from_labelme(labelme_obj):
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if not isinstance(labelme_obj, dict):
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return []
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shapes_obj = labelme_obj.get("shapes")
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if not isinstance(shapes_obj, list):
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return []
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shapes = []
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for sh in shapes_obj:
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if not isinstance(sh, dict):
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continue
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if sh.get("shape_type") != "polygon":
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continue
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label = sh.get("label")
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points = _normalize_polygon_points(sh.get("points"))
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if not label or not points:
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continue
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shapes.append(
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{
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"label": str(label),
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"points": points,
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"group_id": None,
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"shape_type": "polygon",
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"flags": {},
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"mask": None,
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}
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)
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return shapes
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def _load_polygon_shapes_from_dataset_sample(dataset_sample):
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content = get_dataset_sample_annotation_content(dataset_sample)
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if not content:
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return []
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try:
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parsed = json.loads(content)
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except Exception:
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return []
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if isinstance(parsed, dict) and parsed.get("type") == "polygon":
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try:
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json_text = download_sidecar_text(dataset_sample.saved_path, dataset_sample.saved_filename, ".json")
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if not json_text:
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return []
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labelme_obj = json.loads(json_text)
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except Exception:
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return []
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return _shapes_from_labelme(labelme_obj)
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if isinstance(parsed, list):
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return _shapes_from_items(parsed)
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return []
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@api_view(["POST"])
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@permission_classes([IsAuthenticated])
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def set_person_task(request):
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data = request.data
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task_id = data["task_id"]
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task = AiAnnotateTask.objects.get(id=task_id)
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dataset_id = task.dataset_id
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status_code = data.get("status")
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task_range = task.task_range
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query_condition = Q(dataset_id=dataset_id)
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if status_code:
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query_condition &= Q(status=status_code)
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all_samples = list(AiDatasetSample.objects.filter(query_condition).order_by("saved_filename"))
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existing_sample_ids = set(
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AiAnnotateTaskSample.objects.filter(task_id=task_id).values_list("sample_id", flat=True)
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)
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if existing_sample_ids:
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all_samples = [sample for sample in all_samples if sample.id not in existing_sample_ids]
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if task_range:
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start, end = map(int, task_range.split("-"))
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all_samples = [
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sample
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for sample in all_samples
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if (sample_index := _extract_sample_index(sample.saved_filename)) is not None
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and start <= sample_index <= end
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]
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all_samples_length = len(all_samples)
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rule_type = data["rule_type"]
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task_rules = json.loads(data["task_rules"])
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total_percent = 0
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last_person = ""
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for key, value in task_rules.items():
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person = key
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if rule_type == "percent":
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percent = value
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total_percent += percent
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last_person = person
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num_samples_to_assign = int(all_samples_length * percent / 100)
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create_person_samples(task_id, person, all_samples[:num_samples_to_assign])
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all_samples = all_samples[num_samples_to_assign:]
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elif rule_type == "number":
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number = value
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create_person_samples(task_id, person, all_samples[:number])
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all_samples = all_samples[number:]
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elif rule_type == "range":
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start, end = map(int, value.split("-"))
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matching_samples = [
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sample
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for sample in all_samples
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if (sample_index := _extract_sample_index(sample.saved_filename)) is not None
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and start <= sample_index <= end
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]
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create_person_samples(task_id, person, matching_samples)
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all_samples = list(set(all_samples) - set(matching_samples))
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if rule_type == "percent":
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remaining_samples = len(all_samples)
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if total_percent == 100 and remaining_samples > 0:
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create_person_samples(task_id, last_person, all_samples)
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return Response({"message": "人员标注任务分配成功"})
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@transaction.atomic
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def create_person_samples(task_id, person, assigned_samples):
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if not assigned_samples:
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return
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objects_to_save = [
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AiAnnotateTaskSample(
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id=str(uuid.uuid4()),
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task_id=task_id,
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executor=person,
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sample_id=sample.id,
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sample_path=sample.saved_path,
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sample_name=sample.saved_filename,
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status="01",
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custom1=sample.custom1,
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)
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for sample in assigned_samples
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]
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AiAnnotateTaskSample.objects.bulk_create(objects_to_save)
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_refresh_dataset_sample_status([sample.id for sample in assigned_samples])
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@api_view(["POST"])
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@permission_classes([IsAuthenticated])
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def annotate_task_sample(request):
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data = request.data
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sample_id = data.get("id")
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hard = data.get("hard")
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task_sample = get_object_or_404(AiAnnotateTaskSample, id=sample_id)
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AiAnnotateTaskSample.objects.filter(id=sample_id).update(
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annotation_content=data.get("annotation_content"),
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status="02",
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hard=hard,
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annotation_time=datetime.now(),
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)
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_refresh_dataset_sample_status([task_sample.sample_id])
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return Response({"message": "ok"})
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@api_view(["POST"])
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@permission_classes([IsAuthenticated])
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def submit_task_samples(request):
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data = request.data
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task_id = data.get("task_id")
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person = data.get("person")
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ids = [id for id in data.get("ids", "").split(",") if id]
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queryset = AiAnnotateTaskSample.objects.filter(task_id=task_id, executor=person, status="02")
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if ids:
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queryset = queryset.filter(id__in=ids)
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sample_ids = list(queryset.values_list("sample_id", flat=True))
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queryset.update(status="03")
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_refresh_dataset_sample_status(sample_ids)
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return Response({"message": "ok"})
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@api_view(["GET"])
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@permission_classes([IsAuthenticated])
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def read_task_sample(request):
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sample_id = request.GET.get("id", None)
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sample = get_object_or_404(AiAnnotateTaskSample, id=sample_id)
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serializer = AiAnnotateTaskSampleSerializer(sample)
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return Response(serializer.data)
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@api_view(["POST"])
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@permission_classes([IsAuthenticated])
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def audit_task_samples(request):
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data = request.data
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task_id = data.get("task_id")
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person = request.user.username
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feedback = data.get("feedback")
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status_code = data.get("status")
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ids = [id for id in data.get("ids", "").split(",") if id]
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queryset = AiAnnotateTaskSample.objects.filter(task_id=task_id, status="03")
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if ids:
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queryset = queryset.filter(id__in=ids)
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if status_code == "04":
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sample_ids = list(queryset.values_list("sample_id", flat=True))
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queryset.update(status="04", auditor=person)
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_refresh_dataset_sample_status(sample_ids)
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elif status_code == "05":
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sample_ids = list(queryset.values_list("sample_id", flat=True))
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queryset.update(status="05", auditor=person, feedback=feedback)
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_refresh_dataset_sample_status(sample_ids)
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return Response({"message": "ok"})
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@api_view(["POST"])
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@permission_classes([IsAuthenticated])
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@transaction.atomic
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def merge_sample_content(request):
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try:
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data = request.data
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task_id = data.get("task_id")
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if not task_id:
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return Response({"error": "缺少 task_id"}, status=status.HTTP_400_BAD_REQUEST)
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datasettask = AiAnnotateTask.objects.get(id=task_id)
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executor = data.get("executor", None)
|
||
|
|
filter_kwargs = {"task_id": task_id}
|
||
|
|
if executor:
|
||
|
|
filter_kwargs["executor"] = executor
|
||
|
|
filter_kwargs["status"] = "04"
|
||
|
|
queryset = AiAnnotateTaskSample.objects.filter(**filter_kwargs)
|
||
|
|
samples = list(queryset)
|
||
|
|
sample_ids = [sample.sample_id for sample in samples]
|
||
|
|
with transaction.atomic():
|
||
|
|
for sample in samples:
|
||
|
|
if sample.annotation_content:
|
||
|
|
person_items = json.loads(sample.annotation_content)
|
||
|
|
dataset_sample = AiDatasetSample.objects.get(id=sample.sample_id)
|
||
|
|
if datasettask.annotate_type == "03" or _is_polygon_items(person_items):
|
||
|
|
annotation_fields = split_dataset_sample_annotation_content(sample.annotation_content)
|
||
|
|
AiDatasetSample.objects.filter(id=sample.sample_id).update(
|
||
|
|
rectangle=annotation_fields["rectangle"],
|
||
|
|
polygon=annotation_fields["polygon"],
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
dataset_annotation = get_dataset_sample_annotation_content(dataset_sample)
|
||
|
|
dataset_items = json.loads(dataset_annotation) if dataset_annotation else []
|
||
|
|
merged_data = person_items + dataset_items
|
||
|
|
unique_annotations = {}
|
||
|
|
for item in merged_data:
|
||
|
|
key = _bbox_key(item)
|
||
|
|
if not key:
|
||
|
|
continue
|
||
|
|
if key not in unique_annotations:
|
||
|
|
unique_annotations[key] = item
|
||
|
|
final_merged_data = list(unique_annotations.values())
|
||
|
|
imgwidth, imgheight = _safe_split_wh(sample.custom1)
|
||
|
|
xml_bytes = _build_voc_xml_bytes(
|
||
|
|
final_merged_data,
|
||
|
|
sample.sample_path,
|
||
|
|
sample.sample_name,
|
||
|
|
imgwidth,
|
||
|
|
imgheight,
|
||
|
|
)
|
||
|
|
upload_sidecar_bytes(
|
||
|
|
sample.sample_path,
|
||
|
|
sample.sample_name,
|
||
|
|
".xml",
|
||
|
|
xml_bytes,
|
||
|
|
"application/xml",
|
||
|
|
)
|
||
|
|
merged_json = json.dumps(final_merged_data, ensure_ascii=False)
|
||
|
|
annotation_fields = split_dataset_sample_annotation_content(merged_json)
|
||
|
|
AiDatasetSample.objects.filter(id=sample.sample_id).update(
|
||
|
|
rectangle=annotation_fields["rectangle"],
|
||
|
|
polygon=annotation_fields["polygon"],
|
||
|
|
)
|
||
|
|
|
||
|
|
queryset.update(status="06")
|
||
|
|
_refresh_dataset_sample_status(sample_ids)
|
||
|
|
refresh_dataset_sample_counters(datasettask.dataset_id)
|
||
|
|
sample_count = AiAnnotateTaskSample.objects.filter(task_id=task_id).count()
|
||
|
|
audited_count = AiAnnotateTaskSample.objects.filter(
|
||
|
|
task_id=task_id, status__in=["04", "06"]
|
||
|
|
).count()
|
||
|
|
if sample_count == audited_count:
|
||
|
|
AiAnnotateTask.objects.filter(id=task_id).update(status="03")
|
||
|
|
return Response({"message": "任务样本成功合并到数据集中"})
|
||
|
|
except Exception as e:
|
||
|
|
return Response(
|
||
|
|
{"error": "合并入库失败", "details": str(e)},
|
||
|
|
status=status.HTTP_500_INTERNAL_SERVER_ERROR,
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
def _build_voc_xml_bytes(data, sample_path, filename, width, height):
|
||
|
|
normalized_path = str(sample_path or "").strip("/").replace("\\", "/")
|
||
|
|
image_path = f"{normalized_path}/{filename}" if normalized_path else filename
|
||
|
|
annotation = ET.Element("annotation")
|
||
|
|
|
||
|
|
folder = ET.SubElement(annotation, "folder")
|
||
|
|
folder.text = normalized_path
|
||
|
|
|
||
|
|
filename_elem = ET.SubElement(annotation, "filename")
|
||
|
|
filename_elem.text = filename
|
||
|
|
|
||
|
|
path = ET.SubElement(annotation, "path")
|
||
|
|
path.text = image_path
|
||
|
|
|
||
|
|
source = ET.SubElement(annotation, "source")
|
||
|
|
database = ET.SubElement(source, "database")
|
||
|
|
database.text = "Unknown"
|
||
|
|
|
||
|
|
size = ET.SubElement(annotation, "size")
|
||
|
|
width_elem = ET.SubElement(size, "width")
|
||
|
|
width_elem.text = str(int(width) if width is not None else 0)
|
||
|
|
height_elem = ET.SubElement(size, "height")
|
||
|
|
height_elem.text = str(int(height) if height is not None else 0)
|
||
|
|
depth = ET.SubElement(size, "depth")
|
||
|
|
depth.text = "3"
|
||
|
|
|
||
|
|
segmented = ET.SubElement(annotation, "segmented")
|
||
|
|
segmented.text = "0"
|
||
|
|
|
||
|
|
for obj in data:
|
||
|
|
object_elem = ET.SubElement(annotation, "object")
|
||
|
|
|
||
|
|
name = ET.SubElement(object_elem, "name")
|
||
|
|
name.text = obj["label"]
|
||
|
|
|
||
|
|
pose = ET.SubElement(object_elem, "pose")
|
||
|
|
pose.text = "Unspecified"
|
||
|
|
|
||
|
|
truncated = ET.SubElement(object_elem, "truncated")
|
||
|
|
truncated.text = "0"
|
||
|
|
|
||
|
|
difficult = ET.SubElement(object_elem, "difficult")
|
||
|
|
difficult.text = "0"
|
||
|
|
|
||
|
|
bndbox = ET.SubElement(object_elem, "bndbox")
|
||
|
|
|
||
|
|
xmin, ymin, xmax, ymax = map(float, obj["region"])
|
||
|
|
|
||
|
|
xmin_elem = ET.SubElement(bndbox, "xmin")
|
||
|
|
xmin_elem.text = str(int(xmin))
|
||
|
|
ymin_elem = ET.SubElement(bndbox, "ymin")
|
||
|
|
ymin_elem.text = str(int(ymin))
|
||
|
|
xmax_elem = ET.SubElement(bndbox, "xmax")
|
||
|
|
xmax_elem.text = str(int(xmax))
|
||
|
|
ymax_elem = ET.SubElement(bndbox, "ymax")
|
||
|
|
ymax_elem.text = str(int(ymax))
|
||
|
|
|
||
|
|
return ET.tostring(annotation, encoding="utf-8", xml_declaration=True)
|
||
|
|
|
||
|
|
|
||
|
|
@api_view(["DELETE"])
|
||
|
|
@permission_classes([IsAuthenticated])
|
||
|
|
@transaction.atomic
|
||
|
|
def delete_task_samples(request):
|
||
|
|
data = request.data
|
||
|
|
task_id = data.get("task_id")
|
||
|
|
person = data.get("person")
|
||
|
|
ids = [id for id in data.get("ids", "").split(",") if id]
|
||
|
|
|
||
|
|
try:
|
||
|
|
queryset = AiAnnotateTaskSample.objects.filter(task_id=task_id, executor=person)
|
||
|
|
if ids:
|
||
|
|
queryset = queryset.filter(id__in=ids)
|
||
|
|
samples = list(queryset)
|
||
|
|
except AiAnnotateTaskSample.DoesNotExist:
|
||
|
|
return Response({"message": "没有选择对应的样本数据！"}, status=status.HTTP_404_NOT_FOUND)
|
||
|
|
|
||
|
|
with transaction.atomic():
|
||
|
|
sample_ids = [sample.sample_id for sample in samples]
|
||
|
|
queryset.delete()
|
||
|
|
_refresh_dataset_sample_status(sample_ids)
|
||
|
|
|
||
|
|
return Response({"message": "任务样本删除成功"})
|
||
|
|
|
||
|
|
|
||
|
|
@api_view(["GET"])
|
||
|
|
@permission_classes([IsAuthenticated])
|
||
|
|
def search_task_samples(request):
|
||
|
|
task_id = request.GET.get("task_id", None)
|
||
|
|
executor = request.GET.get("executor", None)
|
||
|
|
status_code = request.GET.get("status", "")
|
||
|
|
status_in = status_code.split(",") if status_code else []
|
||
|
|
sample_name = request.GET.get("sample_name", None)
|
||
|
|
is_hard = request.GET.get("hard", None)
|
||
|
|
page_size = request.GET.get("page_size", 10)
|
||
|
|
filter_kwargs = {}
|
||
|
|
if task_id:
|
||
|
|
filter_kwargs["task_id"] = task_id
|
||
|
|
if executor:
|
||
|
|
filter_kwargs["executor"] = executor
|
||
|
|
if status_code:
|
||
|
|
filter_kwargs["status__in"] = status_in
|
||
|
|
if sample_name:
|
||
|
|
filter_kwargs["sample_name__contains"] = sample_name
|
||
|
|
if is_hard:
|
||
|
|
filter_kwargs["hard"] = is_hard
|
||
|
|
|
||
|
|
samples = AiAnnotateTaskSample.objects.filter(**filter_kwargs).order_by("sample_name")
|
||
|
|
paginator = PageNumberPagination()
|
||
|
|
paginator.page_size = page_size
|
||
|
|
result_page = paginator.paginate_queryset(samples, request)
|
||
|
|
formatted_samples = []
|
||
|
|
for sample in result_page:
|
||
|
|
sample.annotation_time = (
|
||
|
|
sample.annotation_time.strftime("%Y-%m-%d %H:%M:%S")
|
||
|
|
if sample.annotation_time
|
||
|
|
else None
|
||
|
|
)
|
||
|
|
formatted_samples.append(sample)
|
||
|
|
serializer = AiAnnotateTaskSampleSerializer(formatted_samples, many=True)
|
||
|
|
return paginator.get_paginated_response(serializer.data)
|
||
|
|
|
||
|
|
|
||
|
|
@api_view(["GET"])
|
||
|
|
@permission_classes([IsAuthenticated])
|
||
|
|
def query_person_tasks(request):
|
||
|
|
task_id = request.GET.get("task_id", None)
|
||
|
|
STATUSES = ["01", "02", "03", "04", "05", "06"]
|
||
|
|
|
||
|
|
annotate_dict = {}
|
||
|
|
for status_code in STATUSES:
|
||
|
|
annotate_dict[f"count_{status_code}"] = Sum(
|
||
|
|
Case(When(status=status_code, then=1), default=0, output_field=IntegerField())
|
||
|
|
)
|
||
|
|
|
||
|
|
annotate_dict["total"] = Count("id")
|
||
|
|
|
||
|
|
executor_status_counts = (
|
||
|
|
AiAnnotateTaskSample.objects.values("executor")
|
||
|
|
.annotate(**annotate_dict)
|
||
|
|
.filter(task_id=task_id)
|
||
|
|
.order_by("executor")
|
||
|
|
)
|
||
|
|
|
||
|
|
return Response(executor_status_counts)
|