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buenosairesam
2026-03-30 13:05:28 -03:00
parent aac27b8504
commit 55e83e4203
23 changed files with 1321 additions and 201 deletions
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16
core/api/detect/config.py
View File

@@ -30,12 +30,21 @@ class ConfigUpdate(BaseModel):
preprocessing: dict | None = None
class StageOutputHintInfo(BaseModel):
key: str
type: str
label: str = ""
default_opacity: float = 0.5
src_format: str = "png"
class StageConfigInfo(BaseModel):
name: str
label: str
description: str
category: str
config_fields: list[dict]
output_hints: list[StageOutputHintInfo] = []
reads: list[str]
writes: list[str]
@@ -121,6 +130,13 @@ def _stage_to_info(stage) -> StageConfigInfo:
}
for f in stage.config_fields
],
output_hints=[
StageOutputHintInfo(
key=h.key, type=h.type, label=h.label,
default_opacity=h.default_opacity, src_format=h.src_format,
)
for h in getattr(stage, "output_hints", [])
],
reads=stage.io.reads,
writes=stage.io.writes,
)

215
core/api/detect/replay.py
View File

@@ -41,34 +41,24 @@ class ScenarioInfo(BaseModel):
class ReplayRequest(BaseModel):
timeline_id: str
job_id: str
start_stage: str
config_overrides: dict | None = None
class ReplayResponse(BaseModel):
status: str
timeline_id: str
job_id: str
replay_job_id: str
start_stage: str
detections: int = 0
brands_found: int = 0
class RetryRequest(BaseModel):
timeline_id: str
config_overrides: dict | None = None
start_stage: str = "escalate_vlm"
schedule_seconds: float | None = None # delay before execution (off-peak)
class RetryResponse(BaseModel):
status: str
task_id: str
timeline_id: str
class ReplaySingleStageRequest(BaseModel):
timeline_id: str
job_id: str
stage: str
frame_refs: list[int] | None = None
config_overrides: dict | None = None
@@ -103,16 +93,24 @@ class ReplaySingleStageResponse(BaseModel):
# --- Endpoints ---
@router.get("/checkpoints/{timeline_id}")
def list_checkpoints(timeline_id: str) -> list[CheckpointInfo]:
"""List available checkpoint stages for a job."""
from core.detect.checkpoint import list_checkpoints as _list
def list_checkpoints_endpoint(timeline_id: str) -> list[CheckpointInfo]:
"""List available checkpoint stages for a timeline."""
from core.detect.checkpoint.storage import get_checkpoints_for_timeline
try:
stages = _list(timeline_id)
checkpoints = get_checkpoints_for_timeline(timeline_id)
except Exception as e:
raise HTTPException(status_code=404, detail=f"No checkpoints for job {timeline_id}: {e}")
raise HTTPException(status_code=404, detail=f"No checkpoints for timeline {timeline_id}: {e}")
result = [CheckpointInfo(stage=s) for s in stages]
result = [
CheckpointInfo(
stage=c["stage_name"],
is_scenario=c.get("is_scenario", False),
scenario_label=c.get("scenario_label", ""),
)
for c in checkpoints
if c["stage_name"]
]
return result
@@ -211,11 +209,11 @@ def list_scenarios_endpoint():
@router.post("/replay", response_model=ReplayResponse)
def replay(req: ReplayRequest):
"""Replay pipeline from a specific stage with optional config overrides."""
from core.detect.checkpoint import replay_from
from core.detect.checkpoint.replay import replay_from
try:
result = replay_from(
timeline_id=req.timeline_id,
job_id=req.job_id,
start_stage=req.start_stage,
config_overrides=req.config_overrides,
)
@@ -230,7 +228,8 @@ def replay(req: ReplayRequest):
response = ReplayResponse(
status="completed",
timeline_id=req.timeline_id,
job_id=req.job_id,
replay_job_id=result.get("job_id", ""),
start_stage=req.start_stage,
detections=len(detections),
brands_found=brands_found,
@@ -238,29 +237,6 @@ def replay(req: ReplayRequest):
return response
@router.post("/retry", response_model=RetryResponse)
def retry(req: RetryRequest):
"""Queue an async retry of unresolved candidates with different config."""
from core.detect.checkpoint.tasks import retry_candidates
kwargs = {
"timeline_id": req.timeline_id,
"config_overrides": req.config_overrides,
"start_stage": req.start_stage,
}
if req.schedule_seconds:
task = retry_candidates.apply_async(kwargs=kwargs, countdown=req.schedule_seconds)
else:
task = retry_candidates.delay(**kwargs)
response = RetryResponse(
status="queued",
task_id=task.id,
timeline_id=req.timeline_id,
)
return response
@router.post("/replay-stage", response_model=ReplaySingleStageResponse)
def replay_single_stage(req: ReplaySingleStageRequest):
@@ -269,7 +245,7 @@ def replay_single_stage(req: ReplaySingleStageRequest):
try:
result = _replay(
timeline_id=req.timeline_id,
job_id=req.job_id,
stage=req.stage,
frame_refs=req.frame_refs,
config_overrides=req.config_overrides,
@@ -324,6 +300,151 @@ def _gpu_url() -> str:
return url.rstrip("/")
# --- Overlay cache — save/load debug overlay images ---
class SaveOverlaysRequest(BaseModel):
timeline_id: str
job_id: str
stage: str
seq: int
overlays: dict[str, str] # {overlay_key: base64_png}
@router.post("/overlays")
def save_overlays_endpoint(req: SaveOverlaysRequest):
"""Save debug overlay images to blob storage cache."""
from core.detect.checkpoint.frames import save_overlays
save_overlays(req.timeline_id, req.job_id, req.stage, req.seq, req.overlays)
return {"status": "saved", "count": len(req.overlays)}
@router.get("/overlays/{timeline_id}/{job_id}/{stage}/{seq}")
def load_overlays_endpoint(timeline_id: str, job_id: str, stage: str, seq: int):
"""Load cached debug overlay images."""
from core.detect.checkpoint.frames import load_overlays
overlays = load_overlays(timeline_id, job_id, stage, seq)
return {"overlays": overlays or {}}
def _generate_debug_overlays(job_id: str, stage: str, frame) -> dict[str, str] | None:
"""Generate debug overlay images for a single frame."""
import os
inference_url = os.environ.get("INFERENCE_URL")
if stage == "detect_edges":
from core.detect.profile import get_profile, get_stage_config
from core.detect.stages.models import RegionAnalysisConfig
from core.db.connection import get_session
from core.db.job import get_job
from uuid import UUID
with get_session() as session:
job = get_job(session, UUID(job_id))
if not job:
return None
profile = get_profile(job.profile_name)
config = RegionAnalysisConfig(**get_stage_config(profile, "detect_edges"))
if inference_url:
from core.detect.inference import InferenceClient
client = InferenceClient(base_url=inference_url, job_id=job_id)
dr = client.detect_edges_debug(
image=frame.image,
edge_canny_low=config.edge_canny_low,
edge_canny_high=config.edge_canny_high,
edge_hough_threshold=config.edge_hough_threshold,
edge_hough_min_length=config.edge_hough_min_length,
edge_hough_max_gap=config.edge_hough_max_gap,
edge_pair_max_distance=config.edge_pair_max_distance,
edge_pair_min_distance=config.edge_pair_min_distance,
)
return {
"edge_overlay_b64": dr.edge_overlay_b64,
"lines_overlay_b64": dr.lines_overlay_b64,
}
else:
from core.detect.stages.edge_detector import _load_cv_edges
edges_mod = _load_cv_edges()
dr = edges_mod.detect_edges_debug(
frame.image,
canny_low=config.edge_canny_low,
canny_high=config.edge_canny_high,
hough_threshold=config.edge_hough_threshold,
hough_min_length=config.edge_hough_min_length,
hough_max_gap=config.edge_hough_max_gap,
pair_max_distance=config.edge_pair_max_distance,
pair_min_distance=config.edge_pair_min_distance,
)
return {
"edge_overlay_b64": dr["edge_overlay_b64"],
"lines_overlay_b64": dr["lines_overlay_b64"],
}
elif stage == "field_segmentation":
from core.detect.profile import get_profile, get_stage_config
from core.detect.stages.models import FieldSegmentationConfig
from core.db.connection import get_session
from core.db.job import get_job
from uuid import UUID
with get_session() as session:
job = get_job(session, UUID(job_id))
if not job:
return None
profile = get_profile(job.profile_name)
config = FieldSegmentationConfig(**get_stage_config(profile, "field_segmentation"))
if inference_url:
import httpx, json, base64, io
from PIL import Image
import numpy as np
buf = io.BytesIO()
Image.fromarray(frame.image).save(buf, format="JPEG", quality=85)
img_b64 = base64.b64encode(buf.getvalue()).decode()
resp = httpx.post(
f"{inference_url.rstrip('/')}/segment_field/debug",
json={
"image_b64": img_b64,
"hue_low": config.hue_low,
"hue_high": config.hue_high,
"sat_low": config.sat_low,
"sat_high": config.sat_high,
"val_low": config.val_low,
"val_high": config.val_high,
"morph_kernel": config.morph_kernel,
"min_area_ratio": config.min_area_ratio,
},
timeout=30.0,
)
if resp.status_code == 200:
data = resp.json()
return {"mask_overlay_b64": data.get("mask_b64", "")}
return None
return None
@router.get("/overlays/{timeline_id}/{job_id}/{stage}")
def list_overlay_frames_endpoint(timeline_id: str, job_id: str, stage: str):
"""List frame sequences that have cached overlays."""
from core.detect.checkpoint.frames import list_overlay_frames
seqs = list_overlay_frames(timeline_id, job_id, stage)
return {"frames": seqs}
# --- GPU proxy — thin passthrough to inference server for interactive editor ---
@router.post("/gpu/detect_edges")
async def gpu_detect_edges(request: Request):
"""Proxy to GPU inference server — browser can't reach it directly."""