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4
core/detect/inference/__init__.py
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4
core/detect/inference/__init__.py
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from .client import InferenceClient
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from .types import DetectResult, OCRResult, VLMResult
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__all__ = ["InferenceClient", "DetectResult", "OCRResult", "VLMResult"]
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262
core/detect/inference/client.py
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262
core/detect/inference/client.py
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"""
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HTTP client for the inference server.
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The pipeline stages call this instead of importing ML libraries directly.
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The inference server runs on the GPU machine (or spot instance).
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"""
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from __future__ import annotations
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import base64
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import io
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import logging
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import os
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import numpy as np
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import requests
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from PIL import Image
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from .types import DetectResult, OCRResult, RegionDebugResult, RegionResult, ServerStatus, VLMResult
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logger = logging.getLogger(__name__)
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DEFAULT_URL = os.environ.get("INFERENCE_URL", "http://localhost:8000")
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def _encode_image(image: np.ndarray) -> str:
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"""Encode numpy array as base64 JPEG."""
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img = Image.fromarray(image)
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buf = io.BytesIO()
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img.save(buf, format="JPEG", quality=85)
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return base64.b64encode(buf.getvalue()).decode()
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class InferenceClient:
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"""HTTP client for the GPU inference server."""
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def __init__(self, base_url: str | None = None, timeout: float = 60.0,
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job_id: str = "", log_level: str = "INFO"):
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self.base_url = (base_url or DEFAULT_URL).rstrip("/")
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self.timeout = timeout
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self.job_id = job_id
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self.log_level = log_level
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self.session = requests.Session()
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if job_id:
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self.session.headers["X-Job-Id"] = job_id
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self.session.headers["X-Log-Level"] = log_level
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def health(self) -> ServerStatus:
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"""Check server health and loaded models."""
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resp = self.session.get(f"{self.base_url}/health", timeout=self.timeout)
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resp.raise_for_status()
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data = resp.json()
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return ServerStatus(
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loaded_models=data.get("loaded_models", []),
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vram_used_mb=data.get("vram_used_mb", 0),
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vram_budget_mb=data.get("vram_budget_mb", 0),
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strategy=data.get("strategy", "sequential"),
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)
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def detect(
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self,
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image: np.ndarray,
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model: str = "yolov8n",
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confidence: float = 0.3,
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target_classes: list[str] | None = None,
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) -> list[DetectResult]:
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"""Run object detection on an image."""
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payload = {
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"image": _encode_image(image),
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"model": model,
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"confidence": confidence,
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}
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if target_classes:
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payload["target_classes"] = target_classes
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resp = self.session.post(
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f"{self.base_url}/detect",
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json=payload,
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timeout=self.timeout,
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)
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resp.raise_for_status()
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results = []
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for d in resp.json().get("detections", []):
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result = DetectResult(
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x=d["x"], y=d["y"], w=d["w"], h=d["h"],
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confidence=d["confidence"], label=d["label"],
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)
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results.append(result)
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return results
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def ocr(
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self,
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image: np.ndarray,
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languages: list[str] | None = None,
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) -> list[OCRResult]:
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"""Run OCR on an image region."""
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payload = {
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"image": _encode_image(image),
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}
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if languages:
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payload["languages"] = languages
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resp = self.session.post(
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f"{self.base_url}/ocr",
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json=payload,
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timeout=self.timeout,
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)
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resp.raise_for_status()
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results = []
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for d in resp.json().get("results", []):
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result = OCRResult(
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text=d["text"],
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confidence=d["confidence"],
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bbox=tuple(d["bbox"]),
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)
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results.append(result)
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return results
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def vlm(
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self,
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image: np.ndarray,
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prompt: str,
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model: str = "moondream2",
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) -> VLMResult:
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"""Query a visual language model with an image crop + prompt."""
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payload = {
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"image": _encode_image(image),
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"prompt": prompt,
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"model": model,
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}
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resp = self.session.post(
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f"{self.base_url}/vlm",
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json=payload,
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timeout=self.timeout,
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)
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resp.raise_for_status()
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data = resp.json()
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return VLMResult(
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brand=data.get("brand", ""),
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confidence=data.get("confidence", 0.0),
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reasoning=data.get("reasoning", ""),
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)
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def detect_edges(
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self,
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image: np.ndarray,
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edge_canny_low: int = 50,
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edge_canny_high: int = 150,
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edge_hough_threshold: int = 80,
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edge_hough_min_length: int = 100,
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edge_hough_max_gap: int = 10,
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edge_pair_max_distance: int = 200,
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edge_pair_min_distance: int = 15,
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) -> list[RegionResult]:
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"""Run edge detection on an image."""
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payload = {
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"image": _encode_image(image),
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"edge_canny_low": edge_canny_low,
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"edge_canny_high": edge_canny_high,
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"edge_hough_threshold": edge_hough_threshold,
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"edge_hough_min_length": edge_hough_min_length,
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"edge_hough_max_gap": edge_hough_max_gap,
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"edge_pair_max_distance": edge_pair_max_distance,
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"edge_pair_min_distance": edge_pair_min_distance,
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}
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resp = self.session.post(
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f"{self.base_url}/detect_edges",
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json=payload,
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timeout=self.timeout,
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)
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resp.raise_for_status()
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results = []
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for r in resp.json().get("regions", []):
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result = RegionResult(
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x=r["x"], y=r["y"], w=r["w"], h=r["h"],
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confidence=r["confidence"], label=r["label"],
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)
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results.append(result)
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return results
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def detect_edges_debug(
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self,
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image: np.ndarray,
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edge_canny_low: int = 50,
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edge_canny_high: int = 150,
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edge_hough_threshold: int = 80,
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edge_hough_min_length: int = 100,
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edge_hough_max_gap: int = 10,
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edge_pair_max_distance: int = 200,
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edge_pair_min_distance: int = 15,
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) -> RegionDebugResult:
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"""Run edge detection with debug overlays."""
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payload = {
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"image": _encode_image(image),
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"edge_canny_low": edge_canny_low,
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"edge_canny_high": edge_canny_high,
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"edge_hough_threshold": edge_hough_threshold,
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"edge_hough_min_length": edge_hough_min_length,
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"edge_hough_max_gap": edge_hough_max_gap,
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"edge_pair_max_distance": edge_pair_max_distance,
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"edge_pair_min_distance": edge_pair_min_distance,
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}
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resp = self.session.post(
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f"{self.base_url}/detect_edges/debug",
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json=payload,
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timeout=self.timeout,
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)
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resp.raise_for_status()
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data = resp.json()
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regions = []
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for r in data.get("regions", []):
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region = RegionResult(
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x=r["x"], y=r["y"], w=r["w"], h=r["h"],
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confidence=r["confidence"], label=r["label"],
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)
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regions.append(region)
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return RegionDebugResult(
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regions=regions,
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edge_overlay_b64=data.get("edge_overlay_b64", ""),
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lines_overlay_b64=data.get("lines_overlay_b64", ""),
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horizontal_count=data.get("horizontal_count", 0),
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pair_count=data.get("pair_count", 0),
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)
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def post(self, path: str, payload: dict) -> dict | None:
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"""Generic POST to the inference server. Returns JSON response or None on error."""
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try:
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resp = self.session.post(
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f"{self.base_url}{path}",
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json=payload,
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timeout=self.timeout,
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)
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resp.raise_for_status()
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return resp.json()
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except Exception as e:
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logger.warning("Inference POST %s failed: %s", path, e)
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return None
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def load_model(self, model: str, quantization: str = "fp16") -> None:
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"""Request the server to load a model into VRAM."""
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self.session.post(
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f"{self.base_url}/models/load",
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json={"model": model, "quantization": quantization},
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timeout=self.timeout,
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).raise_for_status()
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def unload_model(self, model: str) -> None:
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"""Request the server to unload a model from VRAM."""
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self.session.post(
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f"{self.base_url}/models/unload",
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json={"model": model},
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timeout=self.timeout,
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).raise_for_status()
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76
core/detect/inference/types.py
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76
core/detect/inference/types.py
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"""
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Inference response types.
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These are the shapes returned by the inference server.
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Kept separate from core.detect.models to avoid coupling the
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inference protocol to pipeline internals.
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"""
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from __future__ import annotations
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from dataclasses import dataclass, field
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@dataclass
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class DetectResult:
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"""Single object detection from YOLO or similar."""
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x: int
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y: int
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w: int
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h: int
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confidence: float
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label: str
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@dataclass
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class OCRResult:
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"""Text extracted from a region."""
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text: str
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confidence: float
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bbox: tuple[int, int, int, int] # x, y, w, h
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@dataclass
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class VLMResult:
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"""Visual language model response for a crop."""
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brand: str
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confidence: float
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reasoning: str
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@dataclass
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class RegionResult:
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"""A candidate region from CV analysis."""
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x: int
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y: int
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w: int
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h: int
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confidence: float
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label: str
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@dataclass
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class RegionDebugResult:
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"""CV region analysis with debug overlays."""
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regions: list[RegionResult] = field(default_factory=list)
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edge_overlay_b64: str = ""
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lines_overlay_b64: str = ""
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horizontal_count: int = 0
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pair_count: int = 0
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@dataclass
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class ModelInfo:
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"""Info about a loaded model."""
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name: str
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vram_mb: float
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quantization: str # fp32, fp16, int8, int4
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@dataclass
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class ServerStatus:
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"""Inference server health response."""
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loaded_models: list[ModelInfo] = field(default_factory=list)
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vram_used_mb: float = 0.0
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vram_budget_mb: float = 0.0
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strategy: str = "sequential" # sequential, concurrent, auto
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