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"""
Object Detector Plugin
Detects objects in images using CLIP model.
"""
from typing import Dict, Any, List
from pathlib import Path
import numpy as np
from PIL import Image
from loguru import logger
from plugins.base import BasePlugin, PluginMetadata
class ObjectDetectorPlugin(BasePlugin):
"""
Detect objects in images using CLIP.
Uses zero-shot classification to identify objects
without requiring training data.
"""
def __init__(self):
"""Initialize ObjectDetectorPlugin."""
super().__init__()
self.model = None
self.processor = None
self.candidate_labels = [
"person", "people", "man", "woman", "child", "baby",
"dog", "cat", "bird", "animal",
"car", "vehicle", "bicycle", "motorcycle",
"building", "house", "tree", "plant", "flower",
"food", "plate", "cup", "bottle",
"computer", "phone", "keyboard", "screen",
"furniture", "chair", "table", "bed",
"nature", "landscape", "mountain", "ocean", "beach",
"sky", "cloud", "sunset", "sunrise",
"indoor", "outdoor", "room", "street",
]
@property
def metadata(self) -> PluginMetadata:
"""Return plugin metadata."""
return PluginMetadata(
name="object_detector",
version="0.1.0",
description="Detects objects using CLIP zero-shot classification",
author="AI Dev Collective",
requires=["transformers", "torch"],
category="detection",
priority=10,
)
def initialize(self) -> None:
"""Initialize the plugin and load CLIP model."""
try:
# Import here to avoid loading if plugin is not used
from transformers import CLIPProcessor, CLIPModel
import torch
logger.info("Loading CLIP model...")
model_name = "openai/clip-vit-base-patch32"
# Load model and processor
self.model = CLIPModel.from_pretrained(model_name)
self.processor = CLIPProcessor.from_pretrained(model_name)
# Set to eval mode
self.model.eval()
# Move to CPU (GPU support can be added later)
device = "cpu"
self.model.to(device)
self._initialized = True
logger.info(
f"CLIP model loaded successfully on {device}"
)
except Exception as e:
logger.error(f"Failed to initialize ObjectDetectorPlugin: {e}")
raise
def _detect_objects(
self,
image: Image.Image,
labels: List[str],
threshold: float = 0.3
) -> List[Dict[str, Any]]:
"""
Detect objects in image using CLIP.
Args:
image: PIL Image
labels: List of candidate labels
threshold: Confidence threshold
Returns:
List of detected objects
"""
import torch
# Prepare inputs
inputs = self.processor(
text=labels,
images=image,
return_tensors="pt",
padding=True
)
# Get predictions
with torch.no_grad():
outputs = self.model(**inputs)
logits_per_image = outputs.logits_per_image
probs = logits_per_image.softmax(dim=1)[0]
# Filter by threshold and sort
detected = []
for idx, (label, prob) in enumerate(zip(labels, probs)):
confidence = float(prob)
if confidence >= threshold:
detected.append({
"name": label,
"confidence": round(confidence, 4),
"index": idx,
})
# Sort by confidence
detected.sort(key=lambda x: x["confidence"], reverse=True)
return detected
def analyze(
self,
media: Any,
media_path: Path
) -> Dict[str, Any]:
"""
Detect objects in the image.
Args:
media: PIL Image or numpy array
media_path: Path to image file
Returns:
Dictionary with detected objects
"""
try:
# Check if initialized
if not self._initialized:
self.initialize()
# Validate input
if not self.validate_input(media):
return {"error": "Invalid input type"}
# Convert to PIL Image if numpy array
if isinstance(media, np.ndarray):
image = Image.fromarray(
(media * 255).astype(np.uint8) if media.max() <= 1
else media.astype(np.uint8)
)
else:
image = media
# Detect objects
objects = self._detect_objects(
image,
self.candidate_labels,
threshold=0.15
)
# Get top objects
top_objects = objects[:10]
# Categorize objects
categories = self._categorize_objects(top_objects)
result = {
"objects": top_objects,
"total_detected": len(objects),
"categories": categories,
"candidate_labels_count": len(self.candidate_labels),
"status": "success",
}
logger.debug(
f"Object detection complete: {len(top_objects)} objects found"
)
return result
except Exception as e:
logger.error(f"Object detection failed: {e}")
return {
"error": str(e),
"status": "failed"
}
def _categorize_objects(
self,
objects: List[Dict[str, Any]]
) -> Dict[str, List[str]]:
"""
Categorize detected objects.
Args:
objects: List of detected objects
Returns:
Dictionary of categories
"""
categories = {
"people": [],
"animals": [],
"vehicles": [],
"nature": [],
"objects": [],
"places": [],
}
for obj in objects:
name = obj["name"]
if name in ["person", "people", "man", "woman", "child", "baby"]:
categories["people"].append(name)
elif name in ["dog", "cat", "bird", "animal"]:
categories["animals"].append(name)
elif name in ["car", "vehicle", "bicycle", "motorcycle"]:
categories["vehicles"].append(name)
elif name in ["tree", "plant", "flower", "nature", "landscape",
"mountain", "ocean", "beach"]:
categories["nature"].append(name)
elif name in ["indoor", "outdoor", "room", "street", "building",
"house"]:
categories["places"].append(name)
else:
categories["objects"].append(name)
# Remove empty categories
categories = {k: v for k, v in categories.items() if v}
return categories
def cleanup(self) -> None:
"""Clean up model resources."""
if self.model is not None:
del self.model
self.model = None
if self.processor is not None:
del self.processor
self.processor = None
logger.info("ObjectDetectorPlugin cleanup complete")
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