import torch
import torchvision.transforms as T
from torchvision.models.detection import maskrcnn_resnet50_fpn
from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
import uuid
import os
import cv2
import json 


input_images_dir = 'data/input_images/'
segmented_objects_dir = 'data/segmented_objects/'
os.makedirs(input_images_dir, exist_ok=True)
os.makedirs(segmented_objects_dir, exist_ok=True)

#Loading the model

def load_model():
    model = maskrcnn_resnet50_fpn(pretrained=True)
    # Using a different backbone
    #model = maskrcnn_resnet50_fpn(pretrained=False, pretrained_backbone=False, backbone_name='resnext50_32x4d')
    model.eval()  
    """
    We have set this to evaluation mode, 
    because we have loaded a pretrained model 
    so we must deactivate dropout layers and other 
    training-specific behaviors.
    """
    return model

model = load_model() #model initialization


def transform_image(image):
    transform = T.Compose([
        T.Resize((256, 256)),  # Resize to match model input
        T.ToTensor(),          # Convert to torch tensor
        T.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))  # Normalize
    ])
    return transform(image).unsqueeze(0) # Add batch dimension to get [1,C,H,W] #C is channels, RGB has 3, greyscale has 1
 

# # Test image transformation
# image_path = "D:\multiobject.jpeg"  # Replace with the path to your image
# image_tensor = transform_image(image_path)

def run_inference(model,image_tensor):
    with torch.no_grad():
        outputs = model(image_tensor)
    return outputs

def extract_object(image, mask):
    img_np = np.array(image)
    
    # Resize mask to match image dimensions
    mask_resized = cv2.resize(mask, (img_np.shape[1], img_np.shape[0]), interpolation=cv2.INTER_NEAREST)
    
    # Create an empty image with the same dimensions as the original image
    object_img = np.zeros_like(img_np)

    # Apply the mask to the image
    for c in range(3):  # Assuming image has 3 channels (RGB)
        object_img[:, :, c] = img_np[:, :, c] * mask_resized
    
    return Image.fromarray(object_img)

# def extract_object(image, mask):
#     object_img = Image.fromarray((np.array(image) * mask[:, :, None]).astype(np.uint8))
#     return object_img

# Save the input image
def save_input_image(image, master_id):
    input_image_path = os.path.join(input_images_dir, f'{master_id}.png')
    image.save(input_image_path)
    return input_image_path

# Save the extracted objects and their metadata
def save_objects_and_metadata(extracted_objects, master_id):
    object_metadata = []
    
    for i, obj_img in enumerate(extracted_objects):
        object_id = str(uuid.uuid4())
        object_image_path = os.path.join(segmented_objects_dir, f'{object_id}.png')
        obj_img.save(object_image_path)
        
        metadata = {
            'object_id': object_id,
            'master_id': master_id,
            'object_image_path': object_image_path
        }
        object_metadata.append(metadata)
    
    metadata_file = os.path.join(segmented_objects_dir, f'{master_id}_metadata.json')
    with open(metadata_file, 'w') as f:
        json.dump(object_metadata, f, indent=4)
    
    return object_metadata