"""
Visual analysis tools for Percepta MCP server.
"""
import base64
import io
from typing import Dict, Any, Optional, List
from PIL import Image
import cv2
import numpy as np
import logging
from ..config import Settings
logger = logging.getLogger(__name__)
class VisualAnalysis:
"""Visual analysis and image processing tools."""
def __init__(self, settings: Settings):
self.settings = settings
async def analyze_image(self, image_data: str, analysis_type: str = "general", prompt: Optional[str] = None) -> Dict[str, Any]:
"""Analyze an image using AI models."""
try:
logger.info(f"Analyzing image with type: {analysis_type}")
# Decode base64 image
if image_data.startswith('data:'):
# Handle data URL format
_, data = image_data.split(',', 1)
image_bytes = base64.b64decode(data)
else:
# Handle raw base64
image_bytes = base64.b64decode(image_data)
# Open image to validate
image = Image.open(io.BytesIO(image_bytes))
width, height = image.size
# For now, return basic image info
# In a real implementation, this would use AI models for analysis
analysis_result: Dict[str, Any] = {
"width": width,
"height": height,
"format": image.format,
"mode": image.mode,
"analysis_type": analysis_type
}
if analysis_type == "text":
# Placeholder for OCR functionality
analysis_result["extracted_text"] = "OCR functionality would be implemented here"
elif analysis_type == "objects":
# Placeholder for object detection
analysis_result["detected_objects"] = ["Object detection would be implemented here"]
elif analysis_type == "general":
# Placeholder for general image analysis
analysis_result["description"] = "General image analysis would be implemented here"
if prompt:
analysis_result["prompt_response"] = f"Response to '{prompt}' would be implemented here"
return {
"success": True,
"analysis": analysis_result
}
except Exception as e:
logger.error(f"Image analysis error: {e}")
return {
"success": False,
"error": str(e)
}
async def extract_text(self, image_data: str, language: str = "eng") -> Dict[str, Any]:
"""Extract text from an image using OCR."""
try:
logger.info(f"Extracting text from image with language: {language}")
# Decode base64 image
if image_data.startswith('data:'):
_, data = image_data.split(',', 1)
image_bytes = base64.b64decode(data)
else:
image_bytes = base64.b64decode(image_data)
# Convert to OpenCV format
image_array = np.frombuffer(image_bytes, np.uint8)
image = cv2.imdecode(image_array, cv2.IMREAD_COLOR)
if image is None: # type: ignore
raise ValueError("Invalid image data")
# Placeholder for actual OCR implementation
# In a real implementation, this would use tesseract or similar
extracted_text = "OCR text extraction would be implemented here using tesseract-ocr or similar library"
# Get image dimensions
height, width = image.shape[:2]
return {
"success": True,
"text": extracted_text,
"language": language,
"image_info": {
"width": width,
"height": height
}
}
except Exception as e:
logger.error(f"Text extraction error: {e}")
return {
"success": False,
"error": str(e)
}
async def compare_images(self, image1_data: str, image2_data: str, method: str = "structural") -> Dict[str, Any]:
"""Compare two images and return similarity metrics."""
try:
logger.info(f"Comparing images using method: {method}")
# Decode both images
def decode_image(data: str) -> np.ndarray:
if data.startswith('data:'):
_, data = data.split(',', 1)
image_bytes = base64.b64decode(data)
else:
image_bytes = base64.b64decode(data)
image_array = np.frombuffer(image_bytes, np.uint8)
return cv2.imdecode(image_array, cv2.IMREAD_COLOR)
img1 = decode_image(image1_data)
img2 = decode_image(image2_data)
if img1 is None or img2 is None: # type: ignore
raise ValueError("Invalid image data")
# Resize images to same size for comparison
height = min(img1.shape[0], img2.shape[0])
width = min(img1.shape[1], img2.shape[1])
img1_resized = cv2.resize(img1, (width, height))
img2_resized = cv2.resize(img2, (width, height))
if method == "structural":
# Calculate structural similarity
# Convert to grayscale
gray1 = cv2.cvtColor(img1_resized, cv2.COLOR_BGR2GRAY)
gray2 = cv2.cvtColor(img2_resized, cv2.COLOR_BGR2GRAY)
# Calculate mean squared error as a simple similarity metric
mse = np.mean((gray1 - gray2) ** 2)
similarity = 1.0 / (1.0 + mse / 10000.0) # Normalize to 0-1 range
elif method == "histogram":
# Compare histograms
hist1 = cv2.calcHist([img1_resized], [0, 1, 2], None, [50, 50, 50], [0, 256, 0, 256, 0, 256])
hist2 = cv2.calcHist([img2_resized], [0, 1, 2], None, [50, 50, 50], [0, 256, 0, 256, 0, 256])
similarity = cv2.compareHist(hist1, hist2, cv2.HISTCMP_CORREL)
else:
# Default pixel-wise comparison
diff = cv2.absdiff(img1_resized, img2_resized)
similarity = 1.0 - (np.mean(diff) / 255.0)
return {
"success": True,
"similarity": float(similarity),
"method": method,
"image1_size": img1.shape[:2],
"image2_size": img2.shape[:2]
}
except Exception as e:
logger.error(f"Image comparison error: {e}")
return {
"success": False,
"error": str(e)
}
async def detect_objects(self, image_data: str, confidence_threshold: float = 0.5) -> Dict[str, Any]:
"""Detect objects in an image."""
try:
logger.info("Detecting objects in image")
# Decode image
if image_data.startswith('data:'):
_, data = image_data.split(',', 1)
image_bytes = base64.b64decode(data)
else:
image_bytes = base64.b64decode(image_data)
image_array = np.frombuffer(image_bytes, np.uint8)
image = cv2.imdecode(image_array, cv2.IMREAD_COLOR)
if image is None: # type: ignore
raise ValueError("Invalid image data")
# Placeholder for actual object detection
# In a real implementation, this would use YOLO, SSD, or similar
detected_objects: List[Dict[str, Any]] = [
{
"class": "placeholder",
"confidence": 0.9,
"bbox": [100, 100, 200, 200],
"description": "Object detection would be implemented here using YOLO or similar"
}
]
return {
"success": True,
"objects": detected_objects,
"confidence_threshold": confidence_threshold,
"image_size": image.shape[:2]
}
except Exception as e:
logger.error(f"Object detection error: {e}")
return {
"success": False,
"error": str(e)
}
async def enhance_image(self, image_data: str, enhancement_type: str = "auto") -> Dict[str, Any]:
"""Enhance an image with various filters and adjustments."""
try:
logger.info(f"Enhancing image with type: {enhancement_type}")
# Decode image
if image_data.startswith('data:'):
_, data = image_data.split(',', 1)
image_bytes = base64.b64decode(data)
else:
image_bytes = base64.b64decode(image_data)
image_array = np.frombuffer(image_bytes, np.uint8)
image = cv2.imdecode(image_array, cv2.IMREAD_COLOR)
if image is None: # type: ignore
raise ValueError("Invalid image data")
# Apply enhancement based on type
if enhancement_type == "sharpen":
kernel = np.array([[-1,-1,-1], [-1,9,-1], [-1,-1,-1]])
enhanced = cv2.filter2D(image, -1, kernel)
elif enhancement_type == "blur":
enhanced = cv2.GaussianBlur(image, (15, 15), 0)
elif enhancement_type == "brightness":
enhanced = cv2.convertScaleAbs(image, alpha=1.2, beta=30)
elif enhancement_type == "contrast":
enhanced = cv2.convertScaleAbs(image, alpha=1.5, beta=0)
else: # auto enhancement
# Apply automatic enhancement
lab = cv2.cvtColor(image, cv2.COLOR_BGR2LAB)
l, a, b = cv2.split(lab)
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
l = clahe.apply(l)
enhanced = cv2.merge([l, a, b])
enhanced = cv2.cvtColor(enhanced, cv2.COLOR_LAB2BGR)
# Encode enhanced image
_, buffer = cv2.imencode('.png', enhanced)
enhanced_b64 = base64.b64encode(buffer).decode('utf-8')
return {
"success": True,
"enhanced_image": enhanced_b64,
"enhancement_type": enhancement_type,
"mime_type": "image/png"
}
except Exception as e:
logger.error(f"Image enhancement error: {e}")
return {
"success": False,
"error": str(e)
}
