Coverage for integrations / channels / media / vision.py: 45.0%

1""" 

2Vision Processor for image understanding. 

3 

4Supports multiple providers: openai, anthropic, google, local 

5""" 

6 

7import base64 

8import asyncio 

9from dataclasses import dataclass, field 

10from enum import Enum 

11from typing import Optional, List, Dict, Any, Union 

12from pathlib import Path 

13import logging 

14 

15logger = logging.getLogger(__name__) 

16 

17 

18class VisionProvider(Enum): 

19 """Supported vision providers.""" 

20 OPENAI = "openai" 

21 ANTHROPIC = "anthropic" 

22 GOOGLE = "google" 

23 LOCAL = "local" 

24 

25 

26@dataclass 

27class BoundingBox: 

28 """Bounding box for detected objects.""" 

29 x: float 

30 y: float 

31 width: float 

32 height: float 

33 confidence: float = 0.0 

34 

35 def to_dict(self) -> Dict[str, float]: 

36 return { 

37 "x": self.x, 

38 "y": self.y, 

39 "width": self.width, 

40 "height": self.height, 

41 "confidence": self.confidence 

42 } 

43 

44 

45@dataclass 

46class DetectedObject: 

47 """Detected object in an image.""" 

48 label: str 

49 confidence: float 

50 bounding_box: Optional[BoundingBox] = None 

51 attributes: Dict[str, Any] = field(default_factory=dict) 

52 

53 def to_dict(self) -> Dict[str, Any]: 

54 result = { 

55 "label": self.label, 

56 "confidence": self.confidence, 

57 "attributes": self.attributes 

58 } 

59 if self.bounding_box: 

60 result["bounding_box"] = self.bounding_box.to_dict() 

61 return result 

62 

63 

64@dataclass 

65class OCRResult: 

66 """OCR extraction result.""" 

67 text: str 

68 confidence: float 

69 language: Optional[str] = None 

70 regions: List[Dict[str, Any]] = field(default_factory=list) 

71 

72 def to_dict(self) -> Dict[str, Any]: 

73 return { 

74 "text": self.text, 

75 "confidence": self.confidence, 

76 "language": self.language, 

77 "regions": self.regions 

78 } 

79 

80 

81@dataclass 

82class ImageAnalysis: 

83 """Complete image analysis result.""" 

84 description: str 

85 objects: List[DetectedObject] = field(default_factory=list) 

86 text: Optional[OCRResult] = None 

87 tags: List[str] = field(default_factory=list) 

88 colors: List[str] = field(default_factory=list) 

89 is_safe: bool = True 

90 safety_categories: Dict[str, bool] = field(default_factory=dict) 

91 metadata: Dict[str, Any] = field(default_factory=dict) 

92 

93 def to_dict(self) -> Dict[str, Any]: 

94 return { 

95 "description": self.description, 

96 "objects": [obj.to_dict() for obj in self.objects], 

97 "text": self.text.to_dict() if self.text else None, 

98 "tags": self.tags, 

99 "colors": self.colors, 

100 "is_safe": self.is_safe, 

101 "safety_categories": self.safety_categories, 

102 "metadata": self.metadata 

103 } 

104 

105 

106class VisionProcessor: 

107 """ 

108 Vision processor for image understanding. 

109 

110 Supports multiple providers for image analysis, OCR, and object detection. 

111 """ 

112 

113 def __init__( 

114 self, 

115 provider: Union[VisionProvider, str] = VisionProvider.OPENAI, 

116 api_key: Optional[str] = None, 

117 model: Optional[str] = None, 

118 config: Optional[Dict[str, Any]] = None 

119 ): 

120 """ 

121 Initialize vision processor. 

122 

123 Args: 

124 provider: Vision provider to use 

125 api_key: API key for the provider 

126 model: Specific model to use 

127 config: Additional configuration options 

128 """ 

129 if isinstance(provider, str): 

130 provider = VisionProvider(provider.lower()) 

131 

132 self.provider = provider 

133 self.api_key = api_key 

134 self.config = config or {} 

135 

136 # Set default models per provider 

137 self.model = model or self._get_default_model() 

138 

139 # Initialize provider-specific client 

140 self._client = None 

141 self._initialized = False 

142 

143 def _get_default_model(self) -> str: 

144 """Get default model for provider.""" 

145 defaults = { 

146 VisionProvider.OPENAI: "gpt-4o", 

147 VisionProvider.ANTHROPIC: "claude-3-5-sonnet-20241022", 

148 VisionProvider.GOOGLE: "gemini-1.5-pro", 

149 VisionProvider.LOCAL: "llava" 

150 } 

151 return defaults.get(self.provider, "default") 

152 

153 async def _ensure_initialized(self): 

154 """Ensure provider client is initialized.""" 

155 if self._initialized: 

156 return 

157 

158 if self.provider == VisionProvider.OPENAI: 

159 # Would initialize OpenAI client 

160 pass 

161 elif self.provider == VisionProvider.ANTHROPIC: 

162 # Would initialize Anthropic client 

163 pass 

164 elif self.provider == VisionProvider.GOOGLE: 

165 # Would initialize Google client 

166 pass 

167 elif self.provider == VisionProvider.LOCAL: 

168 # Would initialize local model 

169 pass 

170 

171 self._initialized = True 

172 

173 def _encode_image(self, image_source: Union[str, bytes, Path]) -> str: 

174 """Encode image to base64.""" 

175 if isinstance(image_source, bytes): 

176 return base64.b64encode(image_source).decode('utf-8') 

177 

178 if isinstance(image_source, (str, Path)): 

179 path = Path(image_source) 

180 if path.exists(): 

181 with open(path, 'rb') as f: 

182 return base64.b64encode(f.read()).decode('utf-8') 

183 # Assume it's a URL or already base64 

184 if isinstance(image_source, str): 

185 if image_source.startswith(('http://', 'https://')): 

186 return image_source # Return URL as-is 

187 return image_source # Assume already base64 

188 

189 raise ValueError(f"Cannot encode image from: {type(image_source)}") 

190 

191 async def analyze_image( 

192 self, 

193 image: Union[str, bytes, Path], 

194 prompt: Optional[str] = None, 

195 include_objects: bool = True, 

196 include_text: bool = True, 

197 include_safety: bool = True 

198 ) -> ImageAnalysis: 

199 """ 

200 Perform comprehensive image analysis. 

201 

202 Args: 

203 image: Image path, URL, or bytes 

204 prompt: Optional custom analysis prompt 

205 include_objects: Whether to detect objects 

206 include_text: Whether to extract text (OCR) 

207 include_safety: Whether to check content safety 

208 

209 Returns: 

210 ImageAnalysis with all requested analysis results 

211 """ 

212 await self._ensure_initialized() 

213 

214 encoded = self._encode_image(image) 

215 

216 # Simulated analysis for now - would call actual provider 

217 analysis = ImageAnalysis( 

218 description="An image was analyzed", 

219 tags=["image"], 

220 colors=["unknown"], 

221 metadata={"provider": self.provider.value, "model": self.model} 

222 ) 

223 

224 if include_objects: 

225 objects = await self.detect_objects(image) 

226 analysis.objects = objects 

227 

228 if include_text: 

229 ocr = await self.extract_text(image) 

230 analysis.text = ocr 

231 

232 if include_safety: 

233 analysis.is_safe = True 

234 analysis.safety_categories = { 

235 "adult": False, 

236 "violence": False, 

237 "hate": False 

238 } 

239 

240 return analysis 

241 

242 async def extract_text( 

243 self, 

244 image: Union[str, bytes, Path], 

245 language_hint: Optional[str] = None 

246 ) -> OCRResult: 

247 """ 

248 Extract text from image using OCR. 

249 

250 Args: 

251 image: Image path, URL, or bytes 

252 language_hint: Expected language for better accuracy 

253 

254 Returns: 

255 OCRResult with extracted text and confidence 

256 """ 

257 await self._ensure_initialized() 

258 

259 encoded = self._encode_image(image) 

260 

261 # Simulated OCR - would call actual provider 

262 # Different providers have different OCR capabilities 

263 if self.provider == VisionProvider.GOOGLE: 

264 # Google Vision API has dedicated OCR 

265 pass 

266 elif self.provider == VisionProvider.OPENAI: 

267 # GPT-4V can extract text 

268 pass 

269 elif self.provider == VisionProvider.ANTHROPIC: 

270 # Claude can extract text 

271 pass 

272 elif self.provider == VisionProvider.LOCAL: 

273 # Would use tesseract or similar 

274 pass 

275 

276 return OCRResult( 

277 text="", 

278 confidence=0.0, 

279 language=language_hint 

280 ) 

281 

282 async def describe( 

283 self, 

284 image: Union[str, bytes, Path], 

285 detail_level: str = "medium", 

286 max_tokens: int = 300 

287 ) -> str: 

288 """ 

289 Generate a description of the image. 

290 

291 Args: 

292 image: Image path, URL, or bytes 

293 detail_level: Level of detail (low, medium, high) 

294 max_tokens: Maximum tokens in response 

295 

296 Returns: 

297 Text description of the image 

298 """ 

299 await self._ensure_initialized() 

300 

301 encoded = self._encode_image(image) 

302 

303 detail_prompts = { 

304 "low": "Briefly describe this image in one sentence.", 

305 "medium": "Describe this image, including main subjects and setting.", 

306 "high": "Provide a detailed description of this image, including all visible elements, colors, composition, and any text visible." 

307 } 

308 

309 prompt = detail_prompts.get(detail_level, detail_prompts["medium"]) 

310 

311 # Would call actual provider here 

312 return f"Image description (provider: {self.provider.value})" 

313 

314 async def detect_objects( 

315 self, 

316 image: Union[str, bytes, Path], 

317 confidence_threshold: float = 0.5, 

318 max_objects: int = 20 

319 ) -> List[DetectedObject]: 

320 """ 

321 Detect objects in an image. 

322 

323 Args: 

324 image: Image path, URL, or bytes 

325 confidence_threshold: Minimum confidence for detection 

326 max_objects: Maximum number of objects to return 

327 

328 Returns: 

329 List of detected objects with labels and bounding boxes 

330 """ 

331 await self._ensure_initialized() 

332 

333 encoded = self._encode_image(image) 

334 

335 # Simulated object detection - would call actual provider 

336 # Some providers (Google, local YOLO) return bounding boxes 

337 # LLM providers (OpenAI, Anthropic) return object lists without boxes 

338 

339 return [] 

340 

341 async def compare_images( 

342 self, 

343 image1: Union[str, bytes, Path], 

344 image2: Union[str, bytes, Path], 

345 aspects: Optional[List[str]] = None 

346 ) -> Dict[str, Any]: 

347 """ 

348 Compare two images. 

349 

350 Args: 

351 image1: First image 

352 image2: Second image 

353 aspects: Specific aspects to compare (e.g., ["style", "content", "colors"]) 

354 

355 Returns: 

356 Comparison results 

357 """ 

358 await self._ensure_initialized() 

359 

360 aspects = aspects or ["overall", "content", "style"] 

361 

362 return { 

363 "similarity": 0.0, 

364 "differences": [], 

365 "aspects": {aspect: {"similarity": 0.0} for aspect in aspects} 

366 } 

367 

368 async def check_safety( 

369 self, 

370 image: Union[str, bytes, Path] 

371 ) -> Dict[str, Any]: 

372 """ 

373 Check image for safety/content moderation. 

374 

375 Args: 

376 image: Image to check 

377 

378 Returns: 

379 Safety check results 

380 """ 

381 await self._ensure_initialized() 

382 

383 return { 

384 "is_safe": True, 

385 "categories": { 

386 "adult": {"detected": False, "confidence": 0.0}, 

387 "violence": {"detected": False, "confidence": 0.0}, 

388 "hate": {"detected": False, "confidence": 0.0}, 

389 "self_harm": {"detected": False, "confidence": 0.0} 

390 } 

391 } 

392 

393 def get_supported_formats(self) -> List[str]: 

394 """Get list of supported image formats.""" 

395 return ["jpeg", "jpg", "png", "gif", "webp", "bmp"] 

396 

397 def get_max_image_size(self) -> int: 

398 """Get maximum supported image size in bytes.""" 

399 limits = { 

400 VisionProvider.OPENAI: 20 * 1024 * 1024, # 20MB 

401 VisionProvider.ANTHROPIC: 10 * 1024 * 1024, # 10MB (approximate) 

402 VisionProvider.GOOGLE: 20 * 1024 * 1024, # 20MB 

403 VisionProvider.LOCAL: 50 * 1024 * 1024 # 50MB (depends on local setup) 

404 } 

405 return limits.get(self.provider, 10 * 1024 * 1024)