semantic-segmentation/SemanticModel/.ipynb_checkpoints/utilities-checkpoint.py

import os
import cv2
import shutil
import imageio
import numpy as np
from glob import glob
from pathlib import Path
from typing import List, Tuple, Optional

def validate_dimensions(width: int, height: int, stride: int = 32) -> Tuple[int, int]:
    if height % stride != 0 or width % stride != 0:
        new_height = ((height // stride + 1) * stride 
                     if height % stride != 0 else height)
        new_width = ((width // stride + 1) * stride 
                    if width % stride != 0 else width)
        print(f'Adjusted dimensions to: {new_height}H x {new_width}W')
    return width, height

def calc_image_size(image: np.ndarray, target_size: int) -> Tuple[int, int]:
    height, width = image.shape[:2]
    aspect_ratio = width / height
    
    if aspect_ratio >= 1:
        new_width = target_size
        new_height = int(target_size / aspect_ratio)
    else:
        new_height = target_size
        new_width = int(target_size * aspect_ratio)
        
    return validate_dimensions(new_width, new_height)

def convert_coordinates(transform: np.ndarray, x: float, y: float) -> Tuple[float, float]:
    transformed = transform @ np.array([x, y, 1])
    return transformed[0], transformed[1]

def list_images(directory: str, mask_format: bool = False) -> List[str]:
    extensions = ['*.png', '*.PNG'] if mask_format else [
        '*.jpg', '*.jpeg', '*.png', '*.tif', '*.tiff',
        '*.JPG', '*.JPEG', '*.PNG', '*.TIF', '*.TIFF'
    ]
    
    image_paths = []
    for ext in extensions:
        image_paths.extend(glob(os.path.join(directory, ext)))
    
    return sorted(list(set(image_paths)))

def prepare_dataset_split(root_dir: str, train_ratio: float = 0.7, 
                         generate_empty_masks: bool = False) -> None:
    image_dir = os.path.join(root_dir, 'Images')
    mask_dir = os.path.join(root_dir, 'Masks')
    
    if not all(os.path.exists(d) for d in [image_dir, mask_dir]):
        raise Exception("Required 'Images' and 'Masks' directories not found")
    
    image_paths = np.array(list_images(image_dir))
    mask_paths = np.array(list_images(mask_dir, mask_format=True))
    
    if generate_empty_masks:
        temp_dir = os.path.join(mask_dir, 'temp')
        create_empty_masks(image_dir, outdir=temp_dir)
        
        for mask_path in list_images(temp_dir, mask_format=True):
            target_path = os.path.join(mask_dir, os.path.basename(mask_path))
            if not os.path.exists(target_path):
                shutil.move(mask_path, target_path)
        
        shutil.rmtree(temp_dir)
        mask_paths = np.array(list_images(mask_dir, mask_format=True))
    
    if len(image_paths) != len(mask_paths):
        raise Exception(f"Unmatched images ({len(image_paths)}) and masks ({len(mask_paths)})")
    
    train_ratio = float(train_ratio)
    if not (0 < train_ratio <= 1):
        raise ValueError(f"Invalid train ratio: {train_ratio}")
    
    train_size = int(np.floor(train_ratio * len(image_paths)))
    indices = np.random.permutation(len(image_paths))
    
    splits = {
        'train': {'indices': indices[:train_size]},
        'val': {'indices': indices[train_size:]} if train_ratio < 1 else None
    }
    
    for split_name, split_data in splits.items():
        if split_data is None:
            continue
            
        split_dir = os.path.join(root_dir, split_name)
        for subdir in ['Images', 'Masks']:
            subdir_path = os.path.join(split_dir, subdir)
            os.makedirs(subdir_path, exist_ok=True)
            
            sources = image_paths if subdir == 'Images' else mask_paths
            for idx in split_data['indices']:
                source = sources[idx]
                destination = os.path.join(subdir_path, os.path.basename(source))
                shutil.copyfile(source, destination)
        
        print(f"Created {split_name} split with {len(split_data['indices'])} samples")

def create_empty_masks(image_dir: str, pixel_value: int = 0, 
                      outdir: Optional[str] = None) -> str:
    outdir = outdir or os.path.join(image_dir, 'Masks')
    os.makedirs(outdir, exist_ok=True)
    
    image_paths = list_images(image_dir)
    print(f"Generating {len(image_paths)} empty masks...")
    
    for image_path in image_paths:
        image = imageio.imread(image_path)
        mask = np.full((image.shape[0], image.shape[1]), pixel_value, dtype='uint8')
        
        output_path = os.path.join(outdir, 
                                 f"{Path(image_path).stem}.png")
        imageio.imwrite(output_path, mask)
    
    return outdir