phase 2

detect/profiles/__init__.py

@@ -0,0 +1,23 @@
+from .base import (
+    ContentTypeProfile,
+    BrandDictionary,
+    CropContext,
+    DetectionConfig,
+    FrameExtractionConfig,
+    OCRConfig,
+    ResolverConfig,
+    SceneFilterConfig,
+)
+from .soccer import SoccerBroadcastProfile
+
+__all__ = [
+    "ContentTypeProfile",
+    "BrandDictionary",
+    "CropContext",
+    "DetectionConfig",
+    "FrameExtractionConfig",
+    "OCRConfig",
+    "ResolverConfig",
+    "SceneFilterConfig",
+    "SoccerBroadcastProfile",
+]

detect/profiles/base.py

@@ -0,0 +1,71 @@
+"""
+ContentTypeProfile protocol and config dataclasses.
+
+The pipeline graph is fixed — what varies per content type is configuration
+and hooks. Each profile provides stage configs, a brand dictionary,
+VLM prompt templates, and an aggregation strategy.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import Protocol
+
+from detect.models import BrandDetection, DetectionReport
+
+
+@dataclass
+class FrameExtractionConfig:
+    fps: float = 2.0
+    max_frames: int = 500
+
+
+@dataclass
+class SceneFilterConfig:
+    hamming_threshold: int = 8
+    enabled: bool = True
+
+
+@dataclass
+class DetectionConfig:
+    model_name: str = "yolov8n.pt"
+    confidence_threshold: float = 0.3
+    target_classes: list[str] = field(default_factory=lambda: ["logo", "text"])
+
+
+@dataclass
+class OCRConfig:
+    languages: list[str] = field(default_factory=lambda: ["en"])
+    min_confidence: float = 0.5
+
+
+@dataclass
+class ResolverConfig:
+    fuzzy_threshold: int = 75
+
+
+@dataclass
+class BrandDictionary:
+    """Maps canonical brand name → list of known aliases/spellings."""
+    brands: dict[str, list[str]] = field(default_factory=dict)
+
+
+@dataclass
+class CropContext:
+    image: bytes
+    surrounding_text: str = ""
+    position_hint: str = ""
+
+
+class ContentTypeProfile(Protocol):
+    name: str
+
+    def frame_extraction_config(self) -> FrameExtractionConfig: ...
+    def scene_filter_config(self) -> SceneFilterConfig: ...
+    def detection_config(self) -> DetectionConfig: ...
+    def ocr_config(self) -> OCRConfig: ...
+    def brand_dictionary(self) -> BrandDictionary: ...
+    def resolver_config(self) -> ResolverConfig: ...
+    def vlm_prompt(self, crop_context: CropContext) -> str: ...
+    def aggregate(self, detections: list[BrandDetection]) -> DetectionReport: ...
+    def auxiliary_detections(self, source: str) -> list[BrandDetection]: ...

detect/profiles/soccer.py

@@ -0,0 +1,92 @@
+"""Soccer broadcast profile — pitch hoardings, kits, scoreboards."""
+
+from __future__ import annotations
+
+from detect.models import BrandDetection, BrandStats, DetectionReport, PipelineStats
+
+from .base import (
+    BrandDictionary,
+    CropContext,
+    DetectionConfig,
+    FrameExtractionConfig,
+    OCRConfig,
+    ResolverConfig,
+    SceneFilterConfig,
+)
+
+
+class SoccerBroadcastProfile:
+    name = "soccer_broadcast"
+
+    def frame_extraction_config(self) -> FrameExtractionConfig:
+        return FrameExtractionConfig(fps=2.0, max_frames=500)
+
+    def scene_filter_config(self) -> SceneFilterConfig:
+        return SceneFilterConfig(hamming_threshold=8, enabled=True)
+
+    def detection_config(self) -> DetectionConfig:
+        return DetectionConfig(
+            model_name="yolov8n.pt",
+            confidence_threshold=0.3,
+            target_classes=["logo", "text", "banner", "scoreboard"],
+        )
+
+    def ocr_config(self) -> OCRConfig:
+        return OCRConfig(languages=["en", "es"], min_confidence=0.5)
+
+    def brand_dictionary(self) -> BrandDictionary:
+        return BrandDictionary(brands={
+            "Nike": ["nike", "NIKE", "swoosh"],
+            "Adidas": ["adidas", "ADIDAS", "adi"],
+            "Puma": ["puma", "PUMA"],
+            "Emirates": ["emirates", "fly emirates", "EMIRATES"],
+            "Coca-Cola": ["coca-cola", "coca cola", "coke", "COCA-COLA"],
+            "Pepsi": ["pepsi", "PEPSI"],
+            "Mastercard": ["mastercard", "MASTERCARD"],
+            "Heineken": ["heineken", "HEINEKEN"],
+            "Santander": ["santander", "SANTANDER"],
+            "Gazprom": ["gazprom", "GAZPROM"],
+            "Qatar Airways": ["qatar airways", "QATAR AIRWAYS"],
+            "Lay's": ["lays", "lay's", "LAYS", "LAY'S"],
+        })
+
+    def resolver_config(self) -> ResolverConfig:
+        return ResolverConfig(fuzzy_threshold=75)
+
+    def vlm_prompt(self, crop_context: CropContext) -> str:
+        hint = f" Position: {crop_context.position_hint}." if crop_context.position_hint else ""
+        text = f" Nearby text: '{crop_context.surrounding_text}'." if crop_context.surrounding_text else ""
+        return (
+            f"Identify the brand or sponsor visible in this cropped region "
+            f"from a soccer broadcast.{hint}{text} "
+            f"Respond with: brand, confidence (0-1), reasoning."
+        )
+
+    def aggregate(self, detections: list[BrandDetection]) -> DetectionReport:
+        brands: dict[str, BrandStats] = {}
+        for d in detections:
+            if d.brand not in brands:
+                brands[d.brand] = BrandStats()
+            s = brands[d.brand]
+            s.total_appearances += 1
+            s.total_screen_time += d.duration
+            s.avg_confidence = (
+                (s.avg_confidence * (s.total_appearances - 1) + d.confidence)
+                / s.total_appearances
+            )
+            if s.first_seen == 0.0 or d.timestamp < s.first_seen:
+                s.first_seen = d.timestamp
+            if d.timestamp > s.last_seen:
+                s.last_seen = d.timestamp
+
+        return DetectionReport(
+            video_source="",
+            content_type=self.name,
+            duration_seconds=0.0,
+            brands=brands,
+            timeline=sorted(detections, key=lambda d: d.timestamp),
+            pipeline_stats=PipelineStats(),
+        )
+
+    def auxiliary_detections(self, source: str) -> list[BrandDetection]:
+        return []

detect/profiles/stubs.py

@@ -0,0 +1,108 @@
+"""Stub profiles — interfaces defined, not yet implemented."""
+
+from __future__ import annotations
+
+from detect.models import BrandDetection, DetectionReport
+
+from .base import (
+    BrandDictionary,
+    CropContext,
+    DetectionConfig,
+    FrameExtractionConfig,
+    OCRConfig,
+    ResolverConfig,
+    SceneFilterConfig,
+)
+
+
+class NewsBroadcastProfile:
+    name = "news_broadcast"
+
+    def frame_extraction_config(self) -> FrameExtractionConfig:
+        raise NotImplementedError
+
+    def scene_filter_config(self) -> SceneFilterConfig:
+        raise NotImplementedError
+
+    def detection_config(self) -> DetectionConfig:
+        raise NotImplementedError
+
+    def ocr_config(self) -> OCRConfig:
+        raise NotImplementedError
+
+    def brand_dictionary(self) -> BrandDictionary:
+        raise NotImplementedError
+
+    def resolver_config(self) -> ResolverConfig:
+        raise NotImplementedError
+
+    def vlm_prompt(self, crop_context: CropContext) -> str:
+        raise NotImplementedError
+
+    def aggregate(self, detections: list[BrandDetection]) -> DetectionReport:
+        raise NotImplementedError
+
+    def auxiliary_detections(self, source: str) -> list[BrandDetection]:
+        raise NotImplementedError
+
+
+class AdvertisingProfile:
+    name = "advertising"
+
+    def frame_extraction_config(self) -> FrameExtractionConfig:
+        raise NotImplementedError
+
+    def scene_filter_config(self) -> SceneFilterConfig:
+        raise NotImplementedError
+
+    def detection_config(self) -> DetectionConfig:
+        raise NotImplementedError
+
+    def ocr_config(self) -> OCRConfig:
+        raise NotImplementedError
+
+    def brand_dictionary(self) -> BrandDictionary:
+        raise NotImplementedError
+
+    def resolver_config(self) -> ResolverConfig:
+        raise NotImplementedError
+
+    def vlm_prompt(self, crop_context: CropContext) -> str:
+        raise NotImplementedError
+
+    def aggregate(self, detections: list[BrandDetection]) -> DetectionReport:
+        raise NotImplementedError
+
+    def auxiliary_detections(self, source: str) -> list[BrandDetection]:
+        raise NotImplementedError
+
+
+class TranscriptProfile:
+    name = "transcript"
+
+    def frame_extraction_config(self) -> FrameExtractionConfig:
+        raise NotImplementedError
+
+    def scene_filter_config(self) -> SceneFilterConfig:
+        raise NotImplementedError
+
+    def detection_config(self) -> DetectionConfig:
+        raise NotImplementedError
+
+    def ocr_config(self) -> OCRConfig:
+        raise NotImplementedError
+
+    def brand_dictionary(self) -> BrandDictionary:
+        raise NotImplementedError
+
+    def resolver_config(self) -> ResolverConfig:
+        raise NotImplementedError
+
+    def vlm_prompt(self, crop_context: CropContext) -> str:
+        raise NotImplementedError
+
+    def aggregate(self, detections: list[BrandDetection]) -> DetectionReport:
+        raise NotImplementedError
+
+    def auxiliary_detections(self, source: str) -> list[BrandDetection]:
+        raise NotImplementedError