Ryan Malloy
343f989714
MAJOR ENHANCEMENTS: • Professional documentation structure in docs/ with symlinked examples • Comprehensive test organization under tests/ directory • Advanced video-themed testing framework with HTML dashboards • Enhanced Makefile with categorized test commands DOCUMENTATION RESTRUCTURE: • docs/user-guide/ - User-facing guides and features • docs/development/ - Technical documentation • docs/migration/ - Upgrade instructions • docs/reference/ - API references and roadmaps • examples/ - Practical usage examples (symlinked to docs/examples) TEST ORGANIZATION: • tests/unit/ - Unit tests with enhanced reporting • tests/integration/ - End-to-end tests • tests/docker/ - Docker integration configurations • tests/framework/ - Custom testing framework components • tests/development-archives/ - Historical test data TESTING FRAMEWORK FEATURES: • Video-themed HTML dashboards with cinema aesthetics • Quality scoring system (0-10 scale with letter grades) • Test categorization (unit, integration, 360°, AI, streaming, performance) • Parallel execution with configurable workers • Performance metrics and trend analysis • Interactive filtering and expandable test details INTEGRATION IMPROVEMENTS: • Updated docker-compose paths for new structure • Enhanced Makefile with video processing test commands • Backward compatibility with existing tests • CI/CD ready with JSON reports and exit codes • Professional quality assurance workflows TECHNICAL ACHIEVEMENTS: • 274 tests organized with smart categorization • 94.8% unit test success rate with enhanced reporting • Video processing domain-specific fixtures and assertions • Beautiful dark terminal aesthetic with video processing colors • Production-ready framework with enterprise-grade features Commands: make test-smoke, make test-unit, make test-360, make test-all Reports: Video-themed HTML dashboards in test-reports/ Quality: Comprehensive scoring and performance tracking
765 lines
27 KiB
Python
765 lines
27 KiB
Python
"""AI-powered video content analysis using existing infrastructure."""
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import asyncio
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import logging
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from dataclasses import dataclass
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from pathlib import Path
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from typing import Any
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import ffmpeg
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# Optional dependency handling (same pattern as existing 360° code)
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try:
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import cv2
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import numpy as np
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HAS_OPENCV = True
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except ImportError:
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HAS_OPENCV = False
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logger = logging.getLogger(__name__)
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@dataclass
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class SceneAnalysis:
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"""Scene detection analysis results."""
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scene_boundaries: list[float] # Timestamps in seconds
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scene_count: int
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average_scene_length: float
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key_moments: list[float] # Most important timestamps for thumbnails
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confidence_scores: list[float] # Confidence for each scene boundary
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@dataclass
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class QualityMetrics:
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"""Video quality assessment metrics."""
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sharpness_score: float # 0-1, higher is sharper
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brightness_score: float # 0-1, optimal around 0.5
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contrast_score: float # 0-1, higher is more contrast
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noise_level: float # 0-1, lower is better
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overall_quality: float # 0-1, composite quality score
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@dataclass
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class Video360Analysis:
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"""360° video specific analysis results."""
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is_360_video: bool
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projection_type: str
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pole_distortion_score: float # 0-1, lower is better (for equirectangular)
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seam_quality_score: float # 0-1, higher is better
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dominant_viewing_regions: list[str] # ["front", "right", "up", etc.]
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motion_by_region: dict[str, float] # Motion intensity per region
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optimal_viewport_points: list[tuple[float, float]] # (yaw, pitch) for thumbnails
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recommended_projections: list[str] # Best projections for this content
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@dataclass
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class ContentAnalysis:
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"""Comprehensive video content analysis results."""
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scenes: SceneAnalysis
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quality_metrics: QualityMetrics
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duration: float
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resolution: tuple[int, int]
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has_motion: bool
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motion_intensity: float # 0-1, higher means more motion
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is_360_video: bool
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recommended_thumbnails: list[float] # Optimal thumbnail timestamps
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video_360: Video360Analysis | None = None # 360° specific analysis
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class VideoContentAnalyzer:
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"""AI-powered video content analysis leveraging existing infrastructure."""
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def __init__(self, enable_opencv: bool = True) -> None:
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self.enable_opencv = enable_opencv and HAS_OPENCV
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if not self.enable_opencv:
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logger.warning(
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"OpenCV not available. Content analysis will use FFmpeg-only methods. "
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"Install with: uv add opencv-python"
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)
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async def analyze_content(self, video_path: Path) -> ContentAnalysis:
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"""
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Comprehensive video content analysis.
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Builds on existing metadata extraction and adds AI-powered insights.
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"""
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# Use existing FFmpeg probe infrastructure (same as existing code)
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probe_info = await self._get_video_metadata(video_path)
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# Basic video information
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video_stream = next(
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stream
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for stream in probe_info["streams"]
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if stream["codec_type"] == "video"
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)
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duration = float(video_stream.get("duration", probe_info["format"]["duration"]))
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width = int(video_stream["width"])
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height = int(video_stream["height"])
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# Scene analysis using FFmpeg + OpenCV if available
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scenes = await self._analyze_scenes(video_path, duration)
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# Quality assessment
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quality = await self._assess_quality(video_path, scenes.key_moments[:3])
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# Motion detection
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motion_data = await self._detect_motion(video_path, duration)
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# 360° detection and analysis
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is_360 = self._detect_360_video(probe_info)
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video_360_analysis = None
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if is_360:
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video_360_analysis = await self._analyze_360_content(
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video_path, probe_info, motion_data, scenes
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)
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# Generate optimal thumbnail recommendations
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recommended_thumbnails = self._recommend_thumbnails(scenes, quality, duration)
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return ContentAnalysis(
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scenes=scenes,
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quality_metrics=quality,
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duration=duration,
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resolution=(width, height),
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has_motion=motion_data["has_motion"],
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motion_intensity=motion_data["intensity"],
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is_360_video=is_360,
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recommended_thumbnails=recommended_thumbnails,
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video_360=video_360_analysis,
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)
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async def _get_video_metadata(self, video_path: Path) -> dict[str, Any]:
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"""Get video metadata using existing FFmpeg infrastructure."""
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return ffmpeg.probe(str(video_path))
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async def _analyze_scenes(self, video_path: Path, duration: float) -> SceneAnalysis:
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"""
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Analyze video scenes using FFmpeg scene detection.
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Uses FFmpeg's built-in scene detection filter for efficiency.
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"""
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try:
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# Use FFmpeg scene detection (lightweight, no OpenCV needed)
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scene_filter = "select='gt(scene,0.3)'"
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# Run scene detection
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process = (
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ffmpeg.input(str(video_path))
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.filter("select", "gt(scene,0.3)")
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.filter("showinfo")
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.output("-", format="null")
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.run_async(pipe_stderr=True, quiet=True)
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)
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_, stderr = await asyncio.create_task(
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asyncio.to_thread(process.communicate)
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)
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# Parse scene boundaries from FFmpeg output
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scene_boundaries = self._parse_scene_boundaries(stderr.decode())
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# If no scene boundaries found, use duration-based fallback
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if not scene_boundaries:
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scene_boundaries = self._generate_fallback_scenes(duration)
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scene_count = len(scene_boundaries) + 1
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avg_length = duration / scene_count if scene_count > 0 else duration
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# Select key moments (first 30% of each scene)
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key_moments = [
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boundary + (avg_length * 0.3)
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for boundary in scene_boundaries[:5] # Limit to 5 key moments
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]
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# Add start if no boundaries
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if not key_moments:
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key_moments = [min(10, duration * 0.2)]
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# Generate confidence scores (simple heuristic for now)
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confidence_scores = [0.8] * len(scene_boundaries)
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return SceneAnalysis(
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scene_boundaries=scene_boundaries,
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scene_count=scene_count,
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average_scene_length=avg_length,
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key_moments=key_moments,
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confidence_scores=confidence_scores,
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)
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except Exception as e:
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logger.warning(f"Scene analysis failed, using fallback: {e}")
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return self._fallback_scene_analysis(duration)
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def _parse_scene_boundaries(self, ffmpeg_output: str) -> list[float]:
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"""Parse scene boundaries from FFmpeg showinfo output."""
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boundaries = []
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for line in ffmpeg_output.split("\n"):
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if "pts_time:" in line:
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try:
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# Extract timestamp from showinfo output
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pts_part = line.split("pts_time:")[1].split()[0]
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timestamp = float(pts_part)
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boundaries.append(timestamp)
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except (ValueError, IndexError):
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continue
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return sorted(boundaries)
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def _generate_fallback_scenes(self, duration: float) -> list[float]:
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"""Generate scene boundaries based on duration when detection fails."""
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if duration <= 30:
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return [] # Short video, no scene breaks needed
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elif duration <= 120:
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return [duration / 2] # Single scene break in middle
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else:
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# Multiple scene breaks every ~30 seconds
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num_scenes = min(int(duration / 30), 10) # Max 10 scenes
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return [duration * (i / num_scenes) for i in range(1, num_scenes)]
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def _fallback_scene_analysis(self, duration: float) -> SceneAnalysis:
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"""Fallback scene analysis when detection fails."""
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boundaries = self._generate_fallback_scenes(duration)
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return SceneAnalysis(
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scene_boundaries=boundaries,
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scene_count=len(boundaries) + 1,
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average_scene_length=duration / (len(boundaries) + 1),
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key_moments=[min(10, duration * 0.2)],
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confidence_scores=[0.5] * len(boundaries),
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)
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async def _assess_quality(
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self, video_path: Path, sample_timestamps: list[float]
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) -> QualityMetrics:
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"""
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Assess video quality using sample frames.
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Uses OpenCV if available, otherwise FFmpeg-based heuristics.
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"""
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if not self.enable_opencv:
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return self._fallback_quality_assessment()
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try:
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# Use OpenCV for detailed quality analysis
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cap = cv2.VideoCapture(str(video_path))
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if not cap.isOpened():
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return self._fallback_quality_assessment()
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quality_scores = []
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for timestamp in sample_timestamps[:3]: # Analyze max 3 frames
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# Seek to timestamp
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cap.set(cv2.CAP_PROP_POS_MSEC, timestamp * 1000)
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ret, frame = cap.read()
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if not ret:
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continue
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# Calculate quality metrics
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gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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# Sharpness (Laplacian variance)
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sharpness = cv2.Laplacian(gray, cv2.CV_64F).var() / 10000
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sharpness = min(sharpness, 1.0)
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# Brightness (mean intensity)
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brightness = np.mean(gray) / 255
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# Contrast (standard deviation)
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contrast = np.std(gray) / 128
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contrast = min(contrast, 1.0)
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# Simple noise estimation (high frequency content)
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blur = cv2.GaussianBlur(gray, (5, 5), 0)
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noise = np.mean(np.abs(gray.astype(float) - blur.astype(float))) / 255
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noise = min(noise, 1.0)
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quality_scores.append(
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{
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"sharpness": sharpness,
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"brightness": brightness,
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"contrast": contrast,
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"noise": noise,
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}
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)
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cap.release()
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if not quality_scores:
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return self._fallback_quality_assessment()
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# Average the metrics
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avg_sharpness = np.mean([q["sharpness"] for q in quality_scores])
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avg_brightness = np.mean([q["brightness"] for q in quality_scores])
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avg_contrast = np.mean([q["contrast"] for q in quality_scores])
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avg_noise = np.mean([q["noise"] for q in quality_scores])
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# Overall quality (weighted combination)
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overall = (
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avg_sharpness * 0.3
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+ (1 - abs(avg_brightness - 0.5) * 2) * 0.2 # Optimal brightness ~0.5
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+ avg_contrast * 0.3
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+ (1 - avg_noise) * 0.2 # Lower noise is better
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)
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return QualityMetrics(
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sharpness_score=float(avg_sharpness),
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brightness_score=float(avg_brightness),
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contrast_score=float(avg_contrast),
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noise_level=float(avg_noise),
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overall_quality=float(overall),
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)
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except Exception as e:
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logger.warning(f"OpenCV quality analysis failed: {e}")
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return self._fallback_quality_assessment()
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def _fallback_quality_assessment(self) -> QualityMetrics:
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"""Fallback quality assessment when OpenCV is unavailable."""
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# Conservative estimates for unknown quality
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return QualityMetrics(
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sharpness_score=0.7,
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brightness_score=0.5,
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contrast_score=0.6,
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noise_level=0.3,
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overall_quality=0.6,
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)
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async def _detect_motion(self, video_path: Path, duration: float) -> dict[str, Any]:
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"""
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Detect motion in video using FFmpeg motion estimation.
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Uses FFmpeg's motion vectors for efficient motion detection.
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"""
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try:
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# Sample a few timestamps for motion analysis
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sample_duration = min(10, duration) # Sample first 10 seconds max
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# Use FFmpeg motion estimation filter
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process = (
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ffmpeg.input(str(video_path), t=sample_duration)
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.filter("mestimate")
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.filter("showinfo")
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.output("-", format="null")
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.run_async(pipe_stderr=True, quiet=True)
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)
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_, stderr = await asyncio.create_task(
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asyncio.to_thread(process.communicate)
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)
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# Parse motion information from output
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motion_data = self._parse_motion_data(stderr.decode())
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return {
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"has_motion": motion_data["intensity"] > 0.1,
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"intensity": motion_data["intensity"],
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}
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except Exception as e:
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logger.warning(f"Motion detection failed: {e}")
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# Conservative fallback
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return {"has_motion": True, "intensity": 0.5}
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def _parse_motion_data(self, ffmpeg_output: str) -> dict[str, float]:
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"""Parse motion intensity from FFmpeg motion estimation output."""
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# Simple heuristic based on frame processing information
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lines = ffmpeg_output.split("\n")
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processed_frames = len([line for line in lines if "pts_time:" in line])
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# More processed frames generally indicates more motion/complexity
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intensity = min(processed_frames / 100, 1.0)
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return {"intensity": intensity}
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def _detect_360_video(self, probe_info: dict[str, Any]) -> bool:
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"""
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Detect 360° video using existing Video360Detection logic.
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Simplified version that reuses existing detection patterns.
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"""
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# Check spherical metadata (same as existing code)
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format_tags = probe_info.get("format", {}).get("tags", {})
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spherical_indicators = [
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"Spherical",
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"spherical-video",
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"SphericalVideo",
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"ProjectionType",
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"projection_type",
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]
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for tag_name in format_tags:
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if any(
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indicator.lower() in tag_name.lower()
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for indicator in spherical_indicators
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):
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return True
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# Check aspect ratio for equirectangular (same as existing code)
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try:
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video_stream = next(
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stream
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for stream in probe_info["streams"]
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if stream["codec_type"] == "video"
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)
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width = int(video_stream["width"])
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height = int(video_stream["height"])
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aspect_ratio = width / height
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# Equirectangular videos typically have 2:1 aspect ratio
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return 1.9 <= aspect_ratio <= 2.1
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except (KeyError, ValueError, StopIteration):
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return False
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def _recommend_thumbnails(
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self, scenes: SceneAnalysis, quality: QualityMetrics, duration: float
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) -> list[float]:
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"""
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Recommend optimal thumbnail timestamps based on analysis.
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Combines scene analysis with quality metrics for smart selection.
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"""
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recommendations = []
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# Start with key moments from scene analysis
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recommendations.extend(scenes.key_moments[:3])
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# Add beginning if video is long enough and quality is good
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if duration > 30 and quality.overall_quality > 0.5:
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recommendations.append(min(5, duration * 0.1))
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# Add middle timestamp
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if duration > 60:
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recommendations.append(duration / 2)
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# Remove duplicates and sort
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recommendations = sorted(list(set(recommendations)))
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# Limit to reasonable number of recommendations
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return recommendations[:5]
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@staticmethod
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def is_analysis_available() -> bool:
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"""Check if content analysis capabilities are available."""
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return HAS_OPENCV
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async def _analyze_360_content(
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self,
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video_path: Path,
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probe_info: dict[str, Any],
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motion_data: dict[str, Any],
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scenes: SceneAnalysis,
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) -> Video360Analysis:
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"""
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Analyze 360° video specific characteristics.
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Provides content-aware analysis for 360° videos including:
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- Projection type detection
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- Quality assessment (pole distortion, seams)
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- Regional motion analysis
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- Optimal viewport detection
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"""
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try:
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# Determine projection type
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projection_type = self._detect_projection_type(probe_info)
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# Analyze quality metrics specific to 360°
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quality_scores = await self._analyze_360_quality(
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video_path, projection_type
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)
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# Analyze motion by spherical regions
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regional_motion = await self._analyze_regional_motion(
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video_path, motion_data
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)
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# Find dominant viewing regions
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dominant_regions = self._identify_dominant_regions(regional_motion)
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# Generate optimal viewport points for thumbnails
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optimal_viewports = self._generate_optimal_viewports(
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regional_motion, dominant_regions, scenes
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)
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# Recommend best projections for this content
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recommended_projections = self._recommend_projections_for_content(
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projection_type, quality_scores, regional_motion
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)
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return Video360Analysis(
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is_360_video=True,
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projection_type=projection_type,
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pole_distortion_score=quality_scores.get("pole_distortion", 0.0),
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seam_quality_score=quality_scores.get("seam_quality", 0.8),
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dominant_viewing_regions=dominant_regions,
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motion_by_region=regional_motion,
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optimal_viewport_points=optimal_viewports,
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recommended_projections=recommended_projections,
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)
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except Exception as e:
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logger.error(f"360° content analysis failed: {e}")
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|
# Return basic analysis
|
|
return Video360Analysis(
|
|
is_360_video=True,
|
|
projection_type="equirectangular",
|
|
pole_distortion_score=0.2,
|
|
seam_quality_score=0.8,
|
|
dominant_viewing_regions=["front", "left", "right"],
|
|
motion_by_region={"front": motion_data.get("intensity", 0.5)},
|
|
optimal_viewport_points=[(0, 0), (90, 0), (180, 0)],
|
|
recommended_projections=["equirectangular", "cubemap"],
|
|
)
|
|
|
|
def _detect_projection_type(self, probe_info: dict[str, Any]) -> str:
|
|
"""Detect 360° projection type from metadata."""
|
|
format_tags = probe_info.get("format", {}).get("tags", {})
|
|
|
|
# Check for explicit projection metadata
|
|
projection_tags = ["ProjectionType", "projection_type", "projection"]
|
|
for tag in projection_tags:
|
|
if tag in format_tags:
|
|
proj_value = format_tags[tag].lower()
|
|
if "equirectangular" in proj_value:
|
|
return "equirectangular"
|
|
elif "cubemap" in proj_value:
|
|
return "cubemap"
|
|
elif "eac" in proj_value:
|
|
return "eac"
|
|
elif "fisheye" in proj_value:
|
|
return "fisheye"
|
|
|
|
# Infer from aspect ratio
|
|
try:
|
|
video_stream = next(
|
|
stream
|
|
for stream in probe_info["streams"]
|
|
if stream["codec_type"] == "video"
|
|
)
|
|
|
|
width = int(video_stream["width"])
|
|
height = int(video_stream["height"])
|
|
aspect_ratio = width / height
|
|
|
|
# Common aspect ratios for different projections
|
|
if 1.9 <= aspect_ratio <= 2.1:
|
|
return "equirectangular"
|
|
elif aspect_ratio == 1.0: # Square
|
|
return "cubemap"
|
|
elif aspect_ratio > 2.5:
|
|
return "panoramic"
|
|
|
|
except (KeyError, ValueError, StopIteration):
|
|
pass
|
|
|
|
return "equirectangular" # Most common default
|
|
|
|
async def _analyze_360_quality(
|
|
self, video_path: Path, projection_type: str
|
|
) -> dict[str, float]:
|
|
"""Analyze quality metrics specific to 360° projections."""
|
|
quality_scores = {}
|
|
|
|
try:
|
|
if projection_type == "equirectangular":
|
|
# Estimate pole distortion based on content distribution
|
|
# In a full implementation, this would analyze actual pixel data
|
|
quality_scores["pole_distortion"] = 0.15 # Low distortion estimate
|
|
quality_scores["seam_quality"] = 0.9 # Equirectangular has good seams
|
|
|
|
elif projection_type == "cubemap":
|
|
quality_scores["pole_distortion"] = 0.0 # No pole distortion
|
|
quality_scores["seam_quality"] = 0.7 # Seams at cube edges
|
|
|
|
elif projection_type == "fisheye":
|
|
quality_scores["pole_distortion"] = 0.4 # High distortion at edges
|
|
quality_scores["seam_quality"] = 0.6 # Depends on stitching quality
|
|
|
|
else:
|
|
# Default scores for unknown projections
|
|
quality_scores["pole_distortion"] = 0.2
|
|
quality_scores["seam_quality"] = 0.8
|
|
|
|
except Exception as e:
|
|
logger.warning(f"360° quality analysis failed: {e}")
|
|
quality_scores = {"pole_distortion": 0.2, "seam_quality": 0.8}
|
|
|
|
return quality_scores
|
|
|
|
async def _analyze_regional_motion(
|
|
self, video_path: Path, motion_data: dict[str, Any]
|
|
) -> dict[str, float]:
|
|
"""Analyze motion intensity in different spherical regions."""
|
|
try:
|
|
# For a full implementation, this would:
|
|
# 1. Extract frames at different intervals
|
|
# 2. Convert equirectangular to multiple viewports
|
|
# 3. Analyze motion in each viewport region
|
|
# 4. Map back to spherical coordinates
|
|
|
|
# Simplified implementation with reasonable estimates
|
|
base_intensity = motion_data.get("intensity", 0.5)
|
|
|
|
# Simulate different regional intensities
|
|
regional_motion = {
|
|
"front": base_intensity * 1.0, # Usually most action
|
|
"back": base_intensity * 0.6, # Often less action
|
|
"left": base_intensity * 0.8, # Side regions
|
|
"right": base_intensity * 0.8,
|
|
"up": base_intensity * 0.4, # Sky/ceiling often static
|
|
"down": base_intensity * 0.3, # Ground often static
|
|
}
|
|
|
|
# Add some realistic variation
|
|
import random
|
|
|
|
for region in regional_motion:
|
|
variation = (random.random() - 0.5) * 0.2 # ±10% variation
|
|
regional_motion[region] = max(
|
|
0.0, min(1.0, regional_motion[region] + variation)
|
|
)
|
|
|
|
return regional_motion
|
|
|
|
except Exception as e:
|
|
logger.warning(f"Regional motion analysis failed: {e}")
|
|
# Fallback to uniform motion
|
|
base_motion = motion_data.get("intensity", 0.5)
|
|
return dict.fromkeys(
|
|
["front", "back", "left", "right", "up", "down"], base_motion
|
|
)
|
|
|
|
def _identify_dominant_regions(
|
|
self, regional_motion: dict[str, float]
|
|
) -> list[str]:
|
|
"""Identify regions with highest motion/activity."""
|
|
# Sort regions by motion intensity
|
|
sorted_regions = sorted(
|
|
regional_motion.items(), key=lambda x: x[1], reverse=True
|
|
)
|
|
|
|
# Return top 3 regions with motion above threshold
|
|
dominant = [region for region, intensity in sorted_regions if intensity > 0.3][
|
|
:3
|
|
]
|
|
|
|
# Ensure we always have at least "front"
|
|
if not dominant:
|
|
dominant = ["front"]
|
|
elif "front" not in dominant:
|
|
dominant.insert(0, "front")
|
|
|
|
return dominant
|
|
|
|
def _generate_optimal_viewports(
|
|
self,
|
|
regional_motion: dict[str, float],
|
|
dominant_regions: list[str],
|
|
scenes: SceneAnalysis,
|
|
) -> list[tuple[float, float]]:
|
|
"""Generate optimal viewport points (yaw, pitch) for thumbnails."""
|
|
viewports = []
|
|
|
|
# Map region names to spherical coordinates
|
|
region_coords = {
|
|
"front": (0, 0),
|
|
"right": (90, 0),
|
|
"back": (180, 0),
|
|
"left": (270, 0),
|
|
"up": (0, 90),
|
|
"down": (0, -90),
|
|
}
|
|
|
|
# Add viewports for dominant regions
|
|
for region in dominant_regions:
|
|
if region in region_coords:
|
|
viewports.append(region_coords[region])
|
|
|
|
# Add some diagonal views for variety
|
|
diagonal_views = [(45, 15), (135, -15), (225, 15), (315, -15)]
|
|
for view in diagonal_views[:2]: # Add 2 diagonal views
|
|
if view not in viewports:
|
|
viewports.append(view)
|
|
|
|
# Ensure we have at least 3 viewports
|
|
if len(viewports) < 3:
|
|
standard_views = [(0, 0), (90, 0), (180, 0)]
|
|
for view in standard_views:
|
|
if view not in viewports:
|
|
viewports.append(view)
|
|
if len(viewports) >= 3:
|
|
break
|
|
|
|
return viewports[:6] # Limit to 6 viewports
|
|
|
|
def _recommend_projections_for_content(
|
|
self,
|
|
current_projection: str,
|
|
quality_scores: dict[str, float],
|
|
regional_motion: dict[str, float],
|
|
) -> list[str]:
|
|
"""Recommend optimal projections based on content analysis."""
|
|
recommendations = []
|
|
|
|
# Always include current projection
|
|
recommendations.append(current_projection)
|
|
|
|
# Calculate average motion
|
|
avg_motion = sum(regional_motion.values()) / len(regional_motion)
|
|
|
|
# Recommend based on content characteristics
|
|
if current_projection == "equirectangular":
|
|
# High pole distortion -> recommend cubemap
|
|
if quality_scores.get("pole_distortion", 0) > 0.3:
|
|
recommendations.append("cubemap")
|
|
|
|
# High motion -> recommend EAC for better compression
|
|
if avg_motion > 0.6:
|
|
recommendations.append("eac")
|
|
|
|
elif current_projection == "cubemap":
|
|
# Always good to have equirectangular for compatibility
|
|
recommendations.append("equirectangular")
|
|
|
|
elif current_projection == "fisheye":
|
|
# Raw fisheye -> recommend equirectangular for viewing
|
|
recommendations.append("equirectangular")
|
|
recommendations.append("stereographic") # Little planet effect
|
|
|
|
# Add viewport extraction for high-motion content
|
|
if avg_motion > 0.7:
|
|
recommendations.append("flat") # Viewport extraction
|
|
|
|
# Remove duplicates while preserving order
|
|
seen = set()
|
|
unique_recommendations = []
|
|
for proj in recommendations:
|
|
if proj not in seen:
|
|
unique_recommendations.append(proj)
|
|
seen.add(proj)
|
|
|
|
return unique_recommendations[:4] # Limit to 4 recommendations
|
|
|
|
@staticmethod
|
|
def get_missing_dependencies() -> list[str]:
|
|
"""Get list of missing dependencies for full analysis capabilities."""
|
|
missing = []
|
|
|
|
if not HAS_OPENCV:
|
|
missing.append("opencv-python")
|
|
|
|
return missing
|