# Simple inference script for wildlife ensemble detector
import torch
import cv2
import numpy as np
from detectron2 import model_zoo
from detectron2.config import get_cfg
from detectron2.engine import DefaultPredictor

def load_ensemble_models(inception_path, resnet_path, class_names):
    """Load both ensemble models for inference"""
    
    # You'll need to copy the model_architecture.py content here
    # and register the backbones before loading
    
    # Setup configs (simplified)
    cfg_inception = get_cfg()
    cfg_inception.merge_from_file(model_zoo.get_config_file("COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml"))
    cfg_inception.MODEL.BACKBONE.NAME = "InceptionBackboneWrapper"
    cfg_inception.MODEL.ROI_HEADS.NUM_CLASSES = len(class_names)
    cfg_inception.MODEL.WEIGHTS = inception_path
    cfg_inception.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5
    
    cfg_resnet = get_cfg()
    cfg_resnet.merge_from_file(model_zoo.get_config_file("COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml"))
    cfg_resnet.MODEL.BACKBONE.NAME = "ResNetBackboneWrapper"
    cfg_resnet.MODEL.ROI_HEADS.NUM_CLASSES = len(class_names)
    cfg_resnet.MODEL.WEIGHTS = resnet_path
    cfg_resnet.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5
    
    predictor_inception = DefaultPredictor(cfg_inception)
    predictor_resnet = DefaultPredictor(cfg_resnet)
    
    return predictor_inception, predictor_resnet

def predict_ensemble(image_path, predictor_inception, predictor_resnet, class_names):
    """Run ensemble inference on an image"""
    
    img = cv2.imread(image_path)
    
    # Get predictions from both models
    outputs_inc = predictor_inception(img)
    outputs_res = predictor_resnet(img)
    
    # Combine predictions (simplified)
    # Add your ensemble logic here
    
    return outputs_inc, outputs_res