demo_oculus_unified.py

#!/usr/bin/env python3
"""
Oculus 0.2 Unified Demo

Demonstrates all features of the unified Oculus model:
- Text mode (captioning, VQA)
- Point mode (counting objects)
- Box mode (detection with bounding boxes)
- Polygon mode (segmentation)
- Optional reasoning with thinking traces
- Focus system for fine-grained perception
"""

import os
import sys
import requests
from pathlib import Path
from io import BytesIO

from PIL import Image
import torch

# Add parent to path
sys.path.insert(0, str(Path(__file__).parent))

from oculus_unified_model import OculusForConditionalGeneration, OculusConfig


def download_image(url: str) -> Image.Image:
    """Download image from URL."""
    headers = {'User-Agent': 'Mozilla/5.0'}
    response = requests.get(url, headers=headers, timeout=10)
    response.raise_for_status()
    return Image.open(BytesIO(response.content)).convert('RGB')


def print_header(title: str):
    print("\n" + "=" * 70)
    print(f"🔮 {title}")
    print("=" * 70)


def print_section(title: str):
    print(f"\n{'─' * 70}")
    print(f"   {title}")
    print(f"{'─' * 70}")


def demo():
    print_header("OCULUS 0.2 UNIFIED MODEL DEMO")
    
    # ================================================================
    # Load Model
    # ================================================================
    print("\n[1] Loading Oculus Model...")
    
    # Check if we have trained weights
    weights_path = Path(__file__).parent / "checkpoints" / "oculus_coco" / "final"
    
    if weights_path.exists():
        print(f"    Found trained weights at: {weights_path}")
        model = OculusForConditionalGeneration.from_pretrained(weights_path)
    else:
        print("    Using default configuration")
        config = OculusConfig(
            reasoning_enabled=True,
            enable_focus=True,
        )
        model = OculusForConditionalGeneration(config)
    
    print("    ✓ Model loaded!")
    
    # ================================================================
    # Test Images
    # ================================================================
    test_images = [
        {
            "name": "Cat on Couch",
            "url": "https://upload.wikimedia.org/wikipedia/commons/thumb/3/3a/Cat03.jpg/1200px-Cat03.jpg"
        },
        {
            "name": "Golden Gate Bridge",
            "url": "https://upload.wikimedia.org/wikipedia/commons/thumb/0/0c/GoldenGateBridge-001.jpg/1200px-GoldenGateBridge-001.jpg"
        },
    ]
    
    for test in test_images:
        print_header(f"Testing: {test['name']}")
        
        try:
            print("\n[Downloading image...]")
            image = download_image(test["url"])
            print(f"    Image size: {image.size}")
            
            # ========================================================
            # Mode 1: TEXT (Captioning)
            # ========================================================
            print_section("📝 TEXT MODE - Captioning")
            
            output = model.generate(
                image=image,
                prompt="Describe this image in detail",
                mode="text",
                think=False
            )
            
            print(f"    Caption: \"{output.text}\"")
            
            # ========================================================
            # Mode 2: TEXT with Reasoning
            # ========================================================
            print_section("🧠 TEXT MODE - With Reasoning")
            
            output = model.generate(
                image=image,
                prompt="What is the main subject of this image?",
                mode="text",
                think=True  # Enable thinking traces
            )
            
            if output.thinking_trace:
                print(f"    💭 Thinking: {output.thinking_trace[:200]}...")
            print(f"    Answer: \"{output.text}\"")
            
            # ========================================================
            # Mode 3: TEXT (VQA)
            # ========================================================
            print_section("❓ TEXT MODE - VQA")
            
            questions = [
                "What colors are visible in this image?",
                "Is this indoors or outdoors?",
            ]
            
            for q in questions:
                output = model.generate(
                    image=image,
                    prompt=q,
                    mode="text"
                )
                print(f"    Q: {q}")
                print(f"    A: {output.text}")
            
            # ========================================================
            # Mode 4: POINT (Counting)
            # ========================================================
            print_section("📍 POINT MODE - Object Counting")
            
            output = model.generate(
                image=image,
                prompt="Find objects",
                mode="point"
            )
            
            print(f"    Detected {len(output.points)} points")
            for i, (pt, label, conf) in enumerate(zip(
                output.points[:5],
                output.labels[:5],
                output.confidences[:5]
            )):
                print(f"      Point {i+1}: {pt} (class={label}, conf={conf:.2f})")
            
            # ========================================================
            # Mode 5: BOX (Detection)
            # ========================================================
            print_section("📦 BOX MODE - Object Detection")
            
            output = model.generate(
                image=image,
                prompt="Detect all objects",
                mode="box"
            )
            
            print(f"    Detected {len(output.boxes)} boxes")
            for i, (box, label, conf) in enumerate(zip(
                output.boxes[:5],
                output.labels[:5],
                output.confidences[:5]
            )):
                print(f"      Box {i+1}: {[f'{b:.2f}' for b in box]} (class={label}, conf={conf:.2f})")
            
            # ========================================================
            # Mode 6: POLYGON (Segmentation)
            # ========================================================
            print_section("🔷 POLYGON MODE - Segmentation")
            
            output = model.generate(
                image=image,
                prompt="Segment the scene",
                mode="polygon"
            )
            
            print(f"    Segmentation mask shape: {output.mask.shape if output.mask is not None else 'N/A'}")
            print(f"    Detected {len(output.polygons)} regions")
            for i, (poly, label) in enumerate(zip(
                output.polygons[:3],
                output.labels[:3]
            )):
                print(f"      Region {i+1}: class={label}, vertices={len(poly)}")
            
            print("\n    ✅ All modes successful!")
            
        except Exception as e:
            print(f"\n    ❌ Error: {e}")
            import traceback
            traceback.print_exc()
    
    # ================================================================
    # Summary
    # ================================================================
    print_header("DEMO COMPLETE")
    
    print("""
    Oculus 0.2 supports:
    
    📝 TEXT MODE
       - Image captioning
       - Visual question answering
       - With optional reasoning traces
    
    📍 POINT MODE
       - Object counting
       - Point localization
    
    📦 BOX MODE
       - Object detection
       - Bounding box prediction
    
    🔷 POLYGON MODE
       - Semantic segmentation
       - Instance segmentation
    
    🧠 REASONING
       - Optional thinking traces
       - Multi-step reasoning
    
    🔍 FOCUS SYSTEM
       - Zoom & crop for fine-grained perception
       - Automatic region detection
    
    Usage:
    ```python
    from oculus_unified_model import OculusForConditionalGeneration
    
    model = OculusForConditionalGeneration.from_pretrained("./checkpoints/oculus_coco/final")
    
    # Caption
    output = model.generate(image, mode="text", prompt="Describe this")
    
    # VQA with reasoning
    output = model.generate(image, mode="text", prompt="What color is it?", think=True)
    
    # Detection
    output = model.generate(image, mode="box", prompt="Find cars")
    
    # Segmentation
    output = model.generate(image, mode="polygon")
    ```
    """)


if __name__ == "__main__":
    demo()