tasks/image.py

from fastapi import APIRouter
from datetime import datetime
from datasets import load_dataset
import numpy as np
from sklearn.metrics import accuracy_score, precision_score, recall_score
import random
import os

from .utils.evaluation import ImageEvaluationRequest
from .utils.emissions import tracker, clean_emissions_data, get_space_info

from dotenv import load_dotenv
load_dotenv()

# Dependencies for inference
import logging
from pathlib import Path
from ultralytics import YOLO
from torch import device
from torch.cuda import is_available

router = APIRouter()

DESCRIPTION = "Frugal Object Detector for forest fires"
ROUTE = "/image"

def parse_boxes(annotation_string):
    """Parse multiple boxes from a single annotation string.
    Each box has 5 values: class_id, x_center, y_center, width, height"""
    values = [float(x) for x in annotation_string.strip().split()]
    boxes = []
    # Each box has 5 values
    for i in range(0, len(values), 5):
        if i + 5 <= len(values):
            # Skip class_id (first value) and take the next 4 values
            box = values[i+1:i+5]
            boxes.append(box)
    return boxes

def compute_iou(box1, box2):
    """Compute Intersection over Union (IoU) between two YOLO format boxes."""
    # Convert YOLO format (x_center, y_center, width, height) to corners
    def yolo_to_corners(box):
        x_center, y_center, width, height = box
        x1 = x_center - width/2
        y1 = y_center - height/2
        x2 = x_center + width/2
        y2 = y_center + height/2
        return np.array([x1, y1, x2, y2])
    
    box1_corners = yolo_to_corners(box1)
    box2_corners = yolo_to_corners(box2)
    
    # Calculate intersection
    x1 = max(box1_corners[0], box2_corners[0])
    y1 = max(box1_corners[1], box2_corners[1])
    x2 = min(box1_corners[2], box2_corners[2])
    y2 = min(box1_corners[3], box2_corners[3])
    
    intersection = max(0, x2 - x1) * max(0, y2 - y1)
    
    # Calculate union
    box1_area = (box1_corners[2] - box1_corners[0]) * (box1_corners[3] - box1_corners[1])
    box2_area = (box2_corners[2] - box2_corners[0]) * (box2_corners[3] - box2_corners[1])
    union = box1_area + box2_area - intersection
    
    return intersection / (union + 1e-6)

def compute_max_iou(true_boxes, pred_box):
    """Compute maximum IoU between a predicted box and all true boxes"""
    max_iou = 0
    for true_box in true_boxes:
        iou = compute_iou(true_box, pred_box)
        max_iou = max(max_iou, iou)
    return max_iou

@router.post(ROUTE, tags=["Image Task"],
             description=DESCRIPTION)
async def evaluate_image(request: ImageEvaluationRequest):
    """
    Evaluate image classification and object detection for forest fire smoke.
    
    Current Model: Yolo11 nano
    
    Metrics:
    - Classification accuracy: Whether an image contains smoke or not
    - Object Detection accuracy: IoU (Intersection over Union) for smoke bounding boxes
    """
    # Get space info
    username, space_url = get_space_info()
    
    # Load and prepare the dataset
    dataset = load_dataset(request.dataset_name, token=os.getenv("HF_TOKEN"))
    
    # Split dataset
    train_test = dataset["train"].train_test_split(test_size=request.test_size, seed=request.test_seed)
    test_dataset = train_test["test"]
    
    # Start tracking emissions
    tracker.start()
    tracker.start_task("inference")
    
    #--------------------------------------------------------------------------------------------
    # YOUR MODEL INFERENCE CODE HERE
    # Update the code below to replace the random baseline with your model inference
    #--------------------------------------------------------------------------------------------  

    THRESHOLD = 0.18
    IMGSIZE = 1280

    # Load model
    model_path = Path("tasks", "models")
    model_name = "best_gpu_fp16.pt"
    logging.info(f"Loading model {model_name}")
    model = YOLO(Path(model_path, model_name), task="detect")
    device_name = device("cuda" if is_available() else "cpu")
    model.to(device_name)
    
    # Preprocessing the annotations before the loop to avoid repeated parsing
    annotations = [example.get("annotations", "").strip() for example in test_dataset]
    true_labels = [int(len(ann) > 0) for ann in annotations]

    # Initialize lists
    predictions = []
    true_boxes_list = []
    pred_boxes = []

    logging.info(f"Inference start on device: {device_name}")
    for i, example in enumerate(test_dataset):
        has_smoke = true_labels[i]
        annotation = annotations[i]

        # Make prediction
        results = model.predict(example["image"], device=device_name, conf=THRESHOLD, verbose=False, imgsz=IMGSIZE)[0]
        pred_has_smoke = len(results) > 0
        predictions.append(int(pred_has_smoke))

        # If there's a true box, parse it and add box prediction
        if has_smoke:
            # Parse all true boxes from the annotation
            image_true_boxes = parse_boxes(annotation)
            true_boxes_list.append(image_true_boxes)

            # Append bounding box for the prediction
            if results.boxes.cls.numel() != 0:
                pred_boxes.append(results.boxes[0].xywhn.tolist()[0])
            else:
                pred_boxes.append([0, 0, 0, 0])
    
    #--------------------------------------------------------------------------------------------
    # YOUR MODEL INFERENCE STOPS HERE
    #--------------------------------------------------------------------------------------------   
    
    # Stop tracking emissions
    emissions_data = tracker.stop_task()
    
    # Calculate classification metrics
    classification_accuracy = accuracy_score(true_labels, predictions)
    classification_precision = precision_score(true_labels, predictions)
    classification_recall = recall_score(true_labels, predictions)
    
    # Calculate mean IoU for object detection (only for images with smoke)
    # For each image, we compute the max IoU between the predicted box and all true boxes
    ious = []
    for true_boxes, pred_box in zip(true_boxes_list, pred_boxes):
        max_iou = compute_max_iou(true_boxes, pred_box)
        ious.append(max_iou)
    
    mean_iou = float(np.mean(ious)) if ious else 0.0
    
    # Prepare results dictionary
    results = {
        "username": username,
        "space_url": space_url,
        "submission_timestamp": datetime.now().isoformat(),
        "model_description": DESCRIPTION,
        "classification_accuracy": float(classification_accuracy),
        "classification_precision": float(classification_precision),
        "classification_recall": float(classification_recall),
        "mean_iou": mean_iou,
        "energy_consumed_wh": emissions_data.energy_consumed * 1000,
        "emissions_gco2eq": emissions_data.emissions * 1000,
        "emissions_data": clean_emissions_data(emissions_data),
        "api_route": ROUTE,
        "dataset_config": {
            "dataset_name": request.dataset_name,
            "test_size": request.test_size,
            "test_seed": request.test_seed
        }
    }
    
    return results