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import torch
import torch.nn.functional as F
from torch import Tensor
import numpy as np
import math

def resize_and_pad(image_tensor, output_size):
    """
    Resizes an image tensor to a square shape by scaling and padding.
    
    Args:
        image_tensor (torch.Tensor): Input image tensor of shape (H, W).
        output_size (int): The desired square output size.
        
    Returns:
        torch.Tensor: The resized and padded image tensor of shape (output_size, output_size).
    """
    original_h, original_w = image_tensor.shape
    
    # 1. Calculate the scale factor to fit the longest side to output_size
    scale = output_size / max(original_h, original_w)
    new_h, new_w = int(original_h * scale), int(original_w * scale)

    # Add batch and channel dimensions for F.interpolate
    image_tensor = image_tensor.unsqueeze(0).unsqueeze(0)
    
    # 2. Resize the image, preserving aspect ratio
    resized_image = F.interpolate(image_tensor, size=(new_h, new_w), mode='bilinear', align_corners=False)
    
    # 3. Calculate padding for the shorter side
    pad_h = output_size - new_h
    pad_w = output_size - new_w
    
    # Padding format is (left, right, top, bottom)
    padding = (pad_w // 2, pad_w - (pad_w // 2), pad_h // 2, pad_h - (pad_h // 2))
    
    # 4. Pad the image with a constant value (0 for black)
    padded_image = F.pad(resized_image, padding, "constant", 0)
    
    return padded_image.squeeze()


def resize(img, target_res=224, resize=True, to_pil=True, edge=False):
    original_width, original_height = img.size
    original_channels = len(img.getbands())
    if not edge:
        canvas = np.zeros([target_res, target_res, 3], dtype=np.uint8)
        if original_channels == 1:
            canvas = np.zeros([target_res, target_res], dtype=np.uint8)
        if original_height <= original_width:
            if resize:
                img = img.resize((target_res, int(np.around(target_res * original_height / original_width))), Image.Resampling.LANCZOS)
            width, height = img.size
            img = np.asarray(img)
            canvas[(width - height) // 2: (width + height) // 2] = img
        else:
            if resize:
                img = img.resize((int(np.around(target_res * original_width / original_height)), target_res), Image.Resampling.LANCZOS)
            width, height = img.size
            img = np.asarray(img)
            canvas[:, (height - width) // 2: (height + width) // 2] = img
    else:
        if original_height <= original_width:
            if resize:
                img = img.resize((target_res, int(np.around(target_res * original_height / original_width))), Image.Resampling.LANCZOS)
            width, height = img.size
            img = np.asarray(img)
            top_pad = (target_res - height) // 2
            bottom_pad = target_res - height - top_pad
            img = np.pad(img, pad_width=[(top_pad, bottom_pad), (0, 0), (0, 0)], mode='edge')
        else:
            if resize:
                img = img.resize((int(np.around(target_res * original_width / original_height)), target_res), Image.Resampling.LANCZOS)
            width, height = img.size
            img = np.asarray(img)
            left_pad = (target_res - width) // 2
            right_pad = target_res - width - left_pad
            img = np.pad(img, pad_width=[(0, 0), (left_pad, right_pad), (0, 0)], mode='edge')
        canvas = img
    if to_pil:
        canvas = Image.fromarray(canvas)
    return canvas

def scaled_shifted_sigmoid(
    x: Tensor,
    a: float = 1.0,   # vertical scale
    b: float = 1.0,   # slope (steepness)
    c: float = 0.0,   # horizontal shift (bias)
    d: float = 0.0,   # vertical shift (baseline)
) -> Tensor:
    """
    Compute a scaled-and-shifted sigmoid: y = a * sigmoid(b * x + c) + d.
    Parameters
    ----------
    x : torch.Tensor
        Input tensor.
    a : float, default 1.0
        Output scale (amplitude).
    b : float, default 1.0
        Input scale (controls slope).
    c : float, default 0.0
        Input shift (horizontal translation).
    d : float, default 0.0
        Output shift (vertical translation).
    Returns
    -------
    torch.Tensor
        Tensor with the same shape as `x` after applying the transformation.
    """
    return a * torch.sigmoid(b * x + c) + d


############
# for 2D to 3D correspondence with cropping

from scipy.ndimage import distance_transform_edt as edt
from scipy.ndimage import gaussian_filter
# from skimage import img_as_ubyte
from PIL import Image
from pathlib import Path
import numpy as np
from typing import Tuple

# ✨ New helper to find the object's bounding box from transparency
def get_bbox_from_alpha(image_path: Path) -> Tuple[int, int, int, int]:
    """Calculates a bounding box from the alpha channel of a PNG."""
    with Image.open(image_path).convert("RGBA") as img:
        alpha = np.array(img)[:, :, 3]
        non_transparent_pixels = np.argwhere(alpha > 0)
        y_min, x_min = non_transparent_pixels.min(axis=0)
        y_max, x_max = non_transparent_pixels.max(axis=0)
        return x_min, y_min, x_max, y_max

# ... (rest of your imports and functions)



#####################
# dataset utils loading functions
#####################
import os
import json
import numpy as np
import pandas as pd
import torch
from glob import glob
# from scipy.io import loadmat as read_mat
import scipy.io as sio


def read_mat(path, obj_name):
    r"""Reads specified objects from Matlab data file, (.mat)"""
    mat_contents = sio.loadmat(path)
    mat_obj = mat_contents[obj_name]

    return mat_obj

def process_kps_pascal(kps):
    # Step 1: Reshape the array to (20, 2) by adding nan values
    num_pad_rows = 20 - kps.shape[0]
    if num_pad_rows > 0:
        pad_values = np.full((num_pad_rows, 2), np.nan)
        kps = np.vstack((kps, pad_values))
        
    # Step 2: Reshape the array to (20, 3) 
    # Add an extra column: set to 1 if the row does not contain nan, 0 otherwise
    last_col = np.isnan(kps).any(axis=1)
    last_col = np.where(last_col, 0, 1)
    kps = np.column_stack((kps, last_col))

    # Step 3: Replace rows with nan values to all 0's
    mask = np.isnan(kps).any(axis=1)
    kps[mask] = 0

    return torch.tensor(kps).float()

def preprocess_kps_pad(kps, img_width, img_height, size):
    # Once an image has been pre-processed via border (or zero) padding,
    # the location of key points needs to be updated. This function applies
    # that pre-processing to the key points so they are correctly located
    # in the border-padded (or zero-padded) image.
    kps = kps.clone()
    scale = size / max(img_width, img_height)
    kps[:, [0, 1]] *= scale
    if img_height < img_width:
        new_h = int(np.around(size * img_height / img_width))
        offset_y = int((size - new_h) / 2)
        offset_x = 0
        kps[:, 1] += offset_y
    elif img_width < img_height:
        new_w = int(np.around(size * img_width / img_height))
        offset_x = int((size - new_w) / 2)
        offset_y = 0
        kps[:, 0] += offset_x
    else:
        offset_x = 0
        offset_y = 0
    kps *= kps[:, 2:3].clone()  # zero-out any non-visible key points
    return kps, offset_x, offset_y, scale


def load_pascal_data(path="data/PF-dataset-PASCAL", size=256, category='cat', split='test', subsample=None):
    
    def get_points(point_coords_list, idx):
        X = np.fromstring(point_coords_list.iloc[idx, 0], sep=";")
        Y = np.fromstring(point_coords_list.iloc[idx, 1], sep=";")
        Xpad = -np.ones(20)
        Xpad[: len(X)] = X
        Ypad = -np.ones(20)
        Ypad[: len(X)] = Y
        Zmask = np.zeros(20)
        Zmask[: len(X)] = 1
        point_coords = np.concatenate(
            (Xpad.reshape(1, 20), Ypad.reshape(1, 20), Zmask.reshape(1,20)), axis=0
        )
        # make arrays float tensor for subsequent processing
        point_coords = torch.Tensor(point_coords.astype(np.float32))
        return point_coords
    
    np.random.seed(42)
    files = []
    kps = []
    test_data = pd.read_csv(f'{path}/{split}_pairs_pf_pascal.csv')
    cls = ['aeroplane', 'bicycle', 'bird', 'boat', 'bottle',
                    'bus', 'car', 'cat', 'chair', 'cow',
                    'diningtable', 'dog', 'horse', 'motorbike', 'person',
                    'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor']
    cls_ids = test_data.iloc[:,2].values.astype("int") - 1
    cat_id = cls.index(category)
    subset_id = np.where(cls_ids == cat_id)[0]
    # logger.info(f'Number of Pairs for {category} = {len(subset_id)}')
    subset_pairs = test_data.iloc[subset_id,:]
    src_img_names = np.array(subset_pairs.iloc[:,0])
    trg_img_names = np.array(subset_pairs.iloc[:,1])
    # print(src_img_names.shape, trg_img_names.shape)
    if not split.startswith('train'):
        point_A_coords = subset_pairs.iloc[:,3:5]
        point_B_coords = subset_pairs.iloc[:,5:]
    # print(point_A_coords.shape, point_B_coords.shape)
    for i in range(len(src_img_names)):
        src_fn= f'{path}/../{src_img_names[i]}'
        trg_fn= f'{path}/../{trg_img_names[i]}'
        src_size=Image.open(src_fn).size
        trg_size=Image.open(trg_fn).size

        if not split.startswith('train'):
            point_coords_src = get_points(point_A_coords, i).transpose(1,0)
            point_coords_trg = get_points(point_B_coords, i).transpose(1,0)
        else:
            src_anns = os.path.join(path, 'Annotations', category,
                                    os.path.basename(src_fn))[:-4] + '.mat'
            trg_anns = os.path.join(path, 'Annotations', category,
                                    os.path.basename(trg_fn))[:-4] + '.mat'
            point_coords_src = process_kps_pascal(read_mat(src_anns, 'kps'))
            point_coords_trg = process_kps_pascal(read_mat(trg_anns, 'kps'))

        # print(src_size)
        source_kps, src_x, src_y, src_scale = preprocess_kps_pad(point_coords_src, src_size[0], src_size[1], size)
        target_kps, trg_x, trg_y, trg_scale = preprocess_kps_pad(point_coords_trg, trg_size[0], trg_size[1], size)
        kps.append(source_kps)
        kps.append(target_kps)
        files.append(src_fn)
        files.append(trg_fn)
    
    kps = torch.stack(kps)
    used_kps, = torch.where(kps[:, :, 2].any(dim=0))
    kps = kps[:, used_kps, :]
    # logger.info(f'Final number of used key points: {kps.size(1)}')
    return files, kps, None, used_kps


def load_spair_data(path="data/SPair-71k", size=256, category='cat', split='test', subsample=None):
    np.random.seed(42)
    pairs = sorted(glob(f'{path}/PairAnnotation/{split}/*:{category}.json'))
    if subsample is not None and subsample > 0:
        pairs = [pairs[ix] for ix in np.random.choice(len(pairs), subsample)]
    files = []
    thresholds = []
    kps = []
    category_anno = list(glob(f'{path}/ImageAnnotation/{category}/*.json'))[0]
    with open(category_anno) as f:
        num_kps = len(json.load(f)['kps'])
    for pair in pairs:
        source_kps = torch.zeros(num_kps, 3)
        target_kps = torch.zeros(num_kps, 3)
        with open(pair) as f:
            data = json.load(f)
        assert category == data["category"]
        source_fn = f'{path}/JPEGImages/{category}/{data["src_imname"]}'
        target_fn = f'{path}/JPEGImages/{category}/{data["trg_imname"]}'
        source_json_name = source_fn.replace('JPEGImages','ImageAnnotation').replace('jpg','json')
        target_json_name = target_fn.replace('JPEGImages','ImageAnnotation').replace('jpg','json')
        source_bbox = np.asarray(data["src_bndbox"])    # (x1, y1, x2, y2)
        target_bbox = np.asarray(data["trg_bndbox"])
        with open(source_json_name) as f:
            file = json.load(f)
            kpts_src = file['kps']
        with open(target_json_name) as f:
            file = json.load(f)
            kpts_trg = file['kps']

        source_size = data["src_imsize"][:2]  # (W, H)
        target_size = data["trg_imsize"][:2]  # (W, H)

        for i in range(30):
            point = kpts_src[str(i)]
            if point is None:
                source_kps[i, :3] = 0
            else:
                source_kps[i, :2] = torch.Tensor(point).float()  # set x and y
                source_kps[i, 2] = 1
        source_kps, src_x, src_y, src_scale = preprocess_kps_pad(source_kps, source_size[0], source_size[1], size)
        
        for i in range(30):
            point = kpts_trg[str(i)]
            if point is None:
                target_kps[i, :3] = 0
            else:
                target_kps[i, :2] = torch.Tensor(point).float()
                target_kps[i, 2] = 1
        # target_raw_kps = torch.cat([torch.tensor(data["trg_kps"], dtype=torch.float), torch.ones(kp_ixs.size(0), 1)], 1)
        # target_kps = blank_kps.scatter(dim=0, index=kp_ixs, src=target_raw_kps)
        target_kps, trg_x, trg_y, trg_scale = preprocess_kps_pad(target_kps, target_size[0], target_size[1], size)
        if split == 'test' or split == 'val':
            thresholds.append(max(target_bbox[3] - target_bbox[1], target_bbox[2] - target_bbox[0])*trg_scale)
        elif split == 'trn':
            thresholds.append(max(source_bbox[3] - source_bbox[1], source_bbox[2] - source_bbox[0])*src_scale)
            thresholds.append(max(target_bbox[3] - target_bbox[1], target_bbox[2] - target_bbox[0])*trg_scale)

        kps.append(source_kps)
        kps.append(target_kps)
        files.append(source_fn)
        files.append(target_fn)
    kps = torch.stack(kps)
    used_kps, = torch.where(kps[:, :, 2].any(dim=0))
    kps = kps[:, used_kps, :]

    return files, kps, thresholds, used_kps


def load_specific_pascal_pair(
    source_image_id: str,
    target_image_id: str,
    path: str = "data/PF-dataset-PASCAL",
    size: int = 256,
    split: str = 'test'
):
    """
    Loads and processes a specific pair of source and target images from the PASCAL dataset.
    Args:
        source_image_id: The identifier of the source image (e.g., '2011_001407').
        target_image_id: The identifier of the target image (e.g., '2010_004184').
        path: The base path to the PF-PASCAL dataset directory.
        size: The target size for preprocessing images.
        split: The dataset split to use ('test', 'train', etc.).
    Returns:
        A tuple containing:
        - files (list): A list with the full paths to the source and target images.
        - kps (torch.Tensor): A tensor of processed keypoints for the image pair.
        - None: A placeholder to match the original function's return format.
        - used_kps_indices (torch.Tensor): A tensor of indices for keypoints present in either image.
    """
    
    def get_points_from_strings(x_str: str, y_str: str) -> torch.Tensor:
        """Parses coordinate strings, pads them, and returns a tensor."""
        X = np.fromstring(x_str, sep=";")
        Y = np.fromstring(y_str, sep=";")
        
        # Pad arrays to a fixed size of 20 (as in the original function)
        Xpad = -np.ones(20)
        Xpad[:len(X)] = X
        Ypad = -np.ones(20)
        Ypad[:len(Y)] = Y
        
        # Create a mask for valid keypoints
        Zmask = np.zeros(20)
        Zmask[:len(X)] = 1
        
        point_coords = np.stack((Xpad, Ypad, Zmask), axis=0)
        return torch.from_numpy(point_coords.astype(np.float32))

    # Construct the path to the CSV file and load it
    csv_path = os.path.join(path, f'{split}_pairs_pf_pascal.csv')
    try:
        pairs_df = pd.read_csv(csv_path)
    except FileNotFoundError:
        print(f"Error: CSV file not found at '{csv_path}'")
        return None, None, None, None

    # Find the specific row matching the source and target image IDs
    pair_row = pairs_df[
        pairs_df['source_image'].str.contains(source_image_id) &
        pairs_df['target_image'].str.contains(target_image_id)
    ]

    if pair_row.empty:
        print(f"Error: Pair for source '{source_image_id}' and target '{target_image_id}' not found.")
        return None, None, None, None
    
    # Select the first match
    pair_data = pair_row.iloc[0]

    # Get full image paths and dimensions
    src_fn = os.path.join(path, '..', pair_data['source_image'])
    trg_fn = os.path.join(path, '..', pair_data['target_image'])
    
    try:
        src_size = Image.open(src_fn).size
        trg_size = Image.open(trg_fn).size
    except FileNotFoundError as e:
        print(f"Error: Image file not found: {e.filename}")
        return None, None, None, None

    # Process keypoints based on the split type
    if not split.startswith('train'):
        point_coords_src = get_points_from_strings(pair_data['XA'], pair_data['YA']).T
        point_coords_trg = get_points_from_strings(pair_data['XB'], pair_data['YB']).T
    else:
        # This logic for the 'train' split is preserved from the original function
        cls_list = ['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 
                    'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 
                    'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor']
        category = cls_list[pair_data['class'] - 1]
        
        src_anns_path = os.path.join(path, 'Annotations', category, os.path.basename(src_fn).replace('.jpg', '.mat'))
        trg_anns_path = os.path.join(path, 'Annotations', category, os.path.basename(trg_fn).replace('.jpg', '.mat'))
        
        point_coords_src = process_kps_pascal(read_mat(src_anns_path, 'kps'))
        point_coords_trg = process_kps_pascal(read_mat(trg_anns_path, 'kps'))

    # Preprocess keypoints (e.g., padding and scaling)
    source_kps, _, _, _ = preprocess_kps_pad(point_coords_src, src_size[0], src_size[1], size)
    target_kps, _, _, _ = preprocess_kps_pad(point_coords_trg, trg_size[0], trg_size[1], size)

    # Stack keypoints and find the indices of keypoints present in at least one image
    kps = torch.stack([source_kps, target_kps])
    used_kps_indices, = torch.where(kps[:, :, 2].any(dim=0))
    
    # Filter the keypoints tensor to include only the used keypoints
    kps_final = kps[:, used_kps_indices, :]

    return [src_fn, trg_fn], kps_final, None, used_kps_indices

import matplotlib.pyplot as plt
def load_img_and_kps(idx, files, kps, img_size=224, edge=False, load_masked=False):
    if load_masked:
        img_rgba = Image.open(files[idx].replace('JPEGImages', 'JPEGImages_bgd_rmv').replace('.jpg', '_bgd_rmv.png')).convert('RGBA')
        
        # img_rgba = Image.open(path_image).convert("RGBA")

        # 2. create a white background and composite
        img = Image.new("RGB", img_rgba.size, (0, 0, 0))  # choose any colour here
        img.paste(img_rgba, mask=img_rgba.split()[3])               # mask = alpha channel
        plt.imsave("img2_masked_before_resize.png", np.array(img))
        # print(np.array(img).shape)
    else:
        img = Image.open(files[idx]).convert('RGB')
    img = resize(img, img_size, resize=True, to_pil=True, edge=edge)
    if load_masked:
        plt.imsave("img2_masked_after_resize.png", np.array(img))
    img_kps = kps[idx]

    return img, img_kps


import os
import json
from glob import glob
import numpy as np
import torch

# NOTE: The helper function preprocess_kps_pad(kps, width, height, size) 
# is assumed to be defined elsewhere, as in your original code.

def load_specific_spair_pair(
    source_image_name: str,
    target_image_name: str,
    category: str,
    path: str = "data/SPair-71k",
    size: int = 256,
    split: str = 'test',
    unfiltered: bool = False
    
):
    """
    Loads and processes a specific pair of images from the SPair-71k dataset.
    Args:
        source_image_name (str): Filename of the source image (e.g., '2008_002719.jpg').
        target_image_name (str): Filename of the target image (e.g., '2008_004100.jpg').
        category (str): The object category (e.g., 'aeroplane').
        path (str): The base path to the SPair-71k dataset directory.
        size (int): The target size for preprocessing images.
        split (str): The dataset split to use ('test', 'trn', 'val').
    Returns:
        A tuple containing:
        - files (list): Full paths to the source and target images.
        - kps (torch.Tensor): Processed keypoints for the pair.
        - thresholds (list): Bounding-box based thresholds for the pair.
        - used_kps_indices (torch.Tensor): Indices of keypoints present in either image.
    """
    
    # Helper to create a keypoint tensor from the annotation dictionary
    def _get_kps_tensor(kps_dict, num_kps):
        kps_tensor = torch.zeros(num_kps, 3)
        for i in range(num_kps):
            point = kps_dict.get(str(i)) # Use .get() for safety
            if point is not None:
                kps_tensor[i, :2] = torch.tensor(point, dtype=torch.float)
                kps_tensor[i, 2] = 1.0 # Mark as visible
        return kps_tensor

    # --- 1. Find the correct pair annotation file ---
    pair_annotation_path = os.path.join(path, 'PairAnnotation', split)
    candidate_files = glob(os.path.join(pair_annotation_path, f'*:{category}.json'))
    
    pair_data = None
    for file_path in candidate_files:
        with open(file_path) as f:
            data = json.load(f)
            if data['src_imname'] == source_image_name and data['trg_imname'] == target_image_name:
                pair_data = data
                break
    
    if pair_data is None:
        print(f"Error: Pair for '{source_image_name}' and '{target_image_name}' not found.")
        return None, None, None, None

    # --- 2. Process the found pair ---
    source_fn = os.path.join(path, 'JPEGImages', category, pair_data['src_imname'])
    target_fn = os.path.join(path, 'JPEGImages', category, pair_data['trg_imname'])
    files = [source_fn, target_fn]

    # Get total number of keypoints for the category
    try:
        category_anno_path = glob(os.path.join(path, 'ImageAnnotation', category, '*.json'))[0]
        with open(category_anno_path) as f:
            num_kps = len(json.load(f)['kps'])
    except IndexError:
        print(f"Error: No image annotations found for category '{category}'.")
        return None, None, None, None

    # Get keypoints from individual image annotation files
    source_json_path = source_fn.replace('JPEGImages', 'ImageAnnotation').replace('.jpg', '.json')
    target_json_path = target_fn.replace('JPEGImages', 'ImageAnnotation').replace('.jpg', '.json')

    with open(source_json_path) as f:
        kpts_src_dict = json.load(f)['kps']
    with open(target_json_path) as f:
        kpts_trg_dict = json.load(f)['kps']
        
    source_kps_raw = _get_kps_tensor(kpts_src_dict, num_kps)
    target_kps_raw = _get_kps_tensor(kpts_trg_dict, num_kps)

    # print(f"Source keypoints raw: {source_kps_raw.shape}, Target keypoints raw: {target_kps_raw.shape}")

    # Preprocess keypoints (padding, scaling, etc.)
    w_src, h_src = pair_data["src_imsize"][:2]
    w_trg, h_trg = pair_data["trg_imsize"][:2]
    
    source_kps, src_x, src_y, src_scale = preprocess_kps_pad(source_kps_raw, w_src, h_src, size)
    target_kps, trg_x, trg_y, trg_scale = preprocess_kps_pad(target_kps_raw, w_trg, h_trg, size)
    
    # Calculate thresholds from bounding boxes
    source_bbox = np.asarray(pair_data["src_bndbox"])
    target_bbox = np.asarray(pair_data["trg_bndbox"])
    thresholds = []
    if split == 'test' or split == 'val':
        thresholds.append(max(target_bbox[3] - target_bbox[1], target_bbox[2] - target_bbox[0]) * trg_scale)
    elif split == 'trn':
        thresholds.append(max(source_bbox[3] - source_bbox[1], source_bbox[2] - source_bbox[0]) * src_scale)
        thresholds.append(max(target_bbox[3] - target_bbox[1], target_bbox[2] - target_bbox[0]) * trg_scale)

    # --- 3. Format output ---
    kps = torch.stack([source_kps, target_kps])
    used_kps_indices, = torch.where(kps[:, :, 2].any(dim=0))
    kps_final = kps[:, used_kps_indices, :]

    if unfiltered:
        return files, kps, thresholds, used_kps_indices
    else:
        return files, kps_final, thresholds, used_kps_indices




######################################
# original loading function
######################################

def load_spair_data(path="data/SPair-71k", size=256, category='cat', split='test', subsample=None):
    np.random.seed(42)
    pairs = sorted(glob(f'{path}/PairAnnotation/{split}/*:{category}.json'))
    if subsample is not None and subsample > 0:
        pairs = [pairs[ix] for ix in np.random.choice(len(pairs), subsample)]
    files = []
    thresholds = []
    kps = []
    category_anno = list(glob(f'{path}/ImageAnnotation/{category}/*.json'))[0]
    with open(category_anno) as f:
        num_kps = len(json.load(f)['kps'])
    for pair in pairs:
        source_kps = torch.zeros(num_kps, 3)
        target_kps = torch.zeros(num_kps, 3)
        with open(pair) as f:
            data = json.load(f)
        assert category == data["category"]
        source_fn = f'{path}/JPEGImages/{category}/{data["src_imname"]}'
        target_fn = f'{path}/JPEGImages/{category}/{data["trg_imname"]}'
        source_json_name = source_fn.replace('JPEGImages','ImageAnnotation').replace('jpg','json')
        target_json_name = target_fn.replace('JPEGImages','ImageAnnotation').replace('jpg','json')
        source_bbox = np.asarray(data["src_bndbox"])    # (x1, y1, x2, y2)
        target_bbox = np.asarray(data["trg_bndbox"])
        with open(source_json_name) as f:
            file = json.load(f)
            kpts_src = file['kps']
        with open(target_json_name) as f:
            file = json.load(f)
            kpts_trg = file['kps']

        source_size = data["src_imsize"][:2]  # (W, H)
        target_size = data["trg_imsize"][:2]  # (W, H)

        for i in range(30):
            point = kpts_src[str(i)]
            if point is None:
                source_kps[i, :3] = 0
            else:
                source_kps[i, :2] = torch.Tensor(point).float()  # set x and y
                source_kps[i, 2] = 1
        source_kps, src_x, src_y, src_scale = preprocess_kps_pad(source_kps, source_size[0], source_size[1], size)
        
        for i in range(30):
            point = kpts_trg[str(i)]
            if point is None:
                target_kps[i, :3] = 0
            else:
                target_kps[i, :2] = torch.Tensor(point).float()
                target_kps[i, 2] = 1
        # target_raw_kps = torch.cat([torch.tensor(data["trg_kps"], dtype=torch.float), torch.ones(kp_ixs.size(0), 1)], 1)
        # target_kps = blank_kps.scatter(dim=0, index=kp_ixs, src=target_raw_kps)
        target_kps, trg_x, trg_y, trg_scale = preprocess_kps_pad(target_kps, target_size[0], target_size[1], size)
        if split == 'test' or split == 'val':
            thresholds.append(max(target_bbox[3] - target_bbox[1], target_bbox[2] - target_bbox[0])*trg_scale)
        elif split == 'trn':
            thresholds.append(max(source_bbox[3] - source_bbox[1], source_bbox[2] - source_bbox[0])*src_scale)
            thresholds.append(max(target_bbox[3] - target_bbox[1], target_bbox[2] - target_bbox[0])*trg_scale)

        kps.append(source_kps)
        kps.append(target_kps)
        files.append(source_fn)
        files.append(target_fn)
    kps = torch.stack(kps)
    used_kps, = torch.where(kps[:, :, 2].any(dim=0))
    kps = kps[:, used_kps, :]

    return files, kps, thresholds, used_kps


def load_pascal_data(path="data/PF-dataset-PASCAL", size=256, category='cat', split='test', subsample=None):
    
    def get_points(point_coords_list, idx):
        X = np.fromstring(point_coords_list.iloc[idx, 0], sep=";")
        Y = np.fromstring(point_coords_list.iloc[idx, 1], sep=";")
        Xpad = -np.ones(20)
        Xpad[: len(X)] = X
        Ypad = -np.ones(20)
        Ypad[: len(X)] = Y
        Zmask = np.zeros(20)
        Zmask[: len(X)] = 1
        point_coords = np.concatenate(
            (Xpad.reshape(1, 20), Ypad.reshape(1, 20), Zmask.reshape(1,20)), axis=0
        )
        # make arrays float tensor for subsequent processing
        point_coords = torch.Tensor(point_coords.astype(np.float32))
        return point_coords
    
    np.random.seed(42)
    files = []
    kps = []
    test_data = pd.read_csv(f'{path}/{split}_pairs_pf_pascal.csv')
    cls = ['aeroplane', 'bicycle', 'bird', 'boat', 'bottle',
                    'bus', 'car', 'cat', 'chair', 'cow',
                    'diningtable', 'dog', 'horse', 'motorbike', 'person',
                    'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor']
    cls_ids = test_data.iloc[:,2].values.astype("int") - 1
    cat_id = cls.index(category)
    subset_id = np.where(cls_ids == cat_id)[0]
    # logger.info(f'Number of Pairs for {category} = {len(subset_id)}')
    subset_pairs = test_data.iloc[subset_id,:]
    src_img_names = np.array(subset_pairs.iloc[:,0])
    trg_img_names = np.array(subset_pairs.iloc[:,1])
    # print(src_img_names.shape, trg_img_names.shape)
    if not split.startswith('train'):
        point_A_coords = subset_pairs.iloc[:,3:5]
        point_B_coords = subset_pairs.iloc[:,5:]
    # print(point_A_coords.shape, point_B_coords.shape)
    for i in range(len(src_img_names)):
        src_fn= f'{path}/../{src_img_names[i]}'
        trg_fn= f'{path}/../{trg_img_names[i]}'
        src_size=Image.open(src_fn).size
        trg_size=Image.open(trg_fn).size

        if not split.startswith('train'):
            point_coords_src = get_points(point_A_coords, i).transpose(1,0)
            point_coords_trg = get_points(point_B_coords, i).transpose(1,0)
        else:
            src_anns = os.path.join(path, 'Annotations', category,
                                    os.path.basename(src_fn))[:-4] + '.mat'
            trg_anns = os.path.join(path, 'Annotations', category,
                                    os.path.basename(trg_fn))[:-4] + '.mat'
            point_coords_src = process_kps_pascal(read_mat(src_anns, 'kps'))
            point_coords_trg = process_kps_pascal(read_mat(trg_anns, 'kps'))

        # print(src_size)
        source_kps, src_x, src_y, src_scale = preprocess_kps_pad(point_coords_src, src_size[0], src_size[1], size)
        target_kps, trg_x, trg_y, trg_scale = preprocess_kps_pad(point_coords_trg, trg_size[0], trg_size[1], size)
        kps.append(source_kps)
        kps.append(target_kps)
        files.append(src_fn)
        files.append(trg_fn)
    
    kps = torch.stack(kps)
    used_kps, = torch.where(kps[:, :, 2].any(dim=0))
    kps = kps[:, used_kps, :]
    # logger.info(f'Final number of used key points: {kps.size(1)}')
    return files, kps, None, used_kps


def load_eval_data(args, path, category, split):
    # if args.EVAL_DATASET == 'ap10k':
    #     files, kps, thresholds, used_kps = load_ap10k_data(path, args.ANNO_SIZE, category, split, args.TEST_SAMPLE)
    print(f"Loading evaluation data for dataset: {args.EVAL_DATASET}, category: {category}, split: {split}, test sample: {args.TEST_SAMPLE}")
    if args.EVAL_DATASET == 'pascal':
        files, kps, thresholds, used_kps = load_pascal_data(path, args.ANNO_SIZE, category, split, args.TEST_SAMPLE)
    elif args.EVAL_DATASET == 'spair':
        files, kps, thresholds, used_kps = load_spair_data(path, args.ANNO_SIZE, category, split, args.TEST_SAMPLE)

    return files, kps, thresholds, used_kps


###### plot helper
from PIL import Image, ImageDraw, ImageFont

def draw_bbox_point_grid(
    image,
    bbox=None,
    point=None,
    box_color=(0, 255, 0),
    pt_color=(255, 0, 0),
    width=5,
    draw_grid=False,
    step=50,                # pixels between grid lines
    grid_color=(255, 255, 255),
    grid_width=1,
    add_text=True,
    dilation=28
):
    """Draw bbox, point, and optional grid on a PIL image.
    Args
    ----
    image (PIL.Image): target image (modified in place if not copied).
    bbox  (list | tuple): [x1, y1, x2, y2] or None.
    point (tuple): (x, y) or None.
    color (tuple): RGB for bbox / point.
    width (int): line width for bbox.
    draw_grid (bool): enable/disable grid.
    step (int): grid spacing in pixels.
    grid_color (tuple): RGB for grid.
    grid_width (int): line width for grid.
    """
    draw = ImageDraw.Draw(image)

    if dilation > 0 and bbox is not None:
        # Dilation logic: expand bbox by dilation pixels
        x1, y1, x2, y2 = bbox
        bbox = (x1 - dilation, y1 - dilation, x2 + dilation, y2 + dilation)

    # ── draw grid ───────────────────────────────────────────
    if draw_grid and step > 0:
        w, h = image.size
        # vertical lines
        for x in range(0, w, step):
            draw.line([(x, 0), (x, h)], fill=grid_color, width=grid_width)
        # horizontal lines
        for y in range(0, h, step):
            draw.line([(0, y), (w, y)], fill=grid_color, width=grid_width)

    # ── draw bbox ──────────────────────────────────────────
    if bbox is not None:
        draw.rectangle(bbox, outline=box_color, width=width)

    # ── draw point ─────────────────────────────────────────
    if point is not None:
        radius = 20
        x, y = point
        draw.ellipse(
            (x - radius, y - radius, x + radius, y + radius),
            fill=pt_color
        )
        # add a white text at the center of the point
        # add a white text at the center of the point
        if add_text:
            text = "Ref"
            # Try to use a better font, or fall back to the default if not found
            # try:
            font = ImageFont.truetype("DejaVuSans.ttf", size=26)
            # except IOError:
            #     print('test')
            #     font = ImageFont.load_default()

            # Get text bounding box for centering
            print(font)
            bbox_text = draw.textbbox((0, 0), text, font=font)
            text_width = bbox_text[2] - bbox_text[0]
            text_height = bbox_text[3] - bbox_text[1]

            text_x = x - text_width // 2
            text_y = y - text_height // 2
            draw.text((text_x, text_y), text, font=font, fill=(255, 255, 255), text_anchor = "mm")


    return image


def square_bbox_to_multiple(bbox, multiple=14):
    """
    Expand a rectangle to the smallest *square* box whose side length
    
    1. is at least the longer side of `bbox`, and  
    2. is the nearest *upper* multiple of `multiple`.
    
    The square is centred on the original box.
    
    Parameters
    ----------
    bbox : list | tuple
        [x1, y1, x2, y2] (top-left / bottom-right in pixels)
    multiple : int, optional
        Grid size to snap the side length to (default: 14).
    
    Returns
    -------
    list
        [new_x1, new_y1, new_x2, new_y2] square bounding box.
    """
    x1, y1, x2, y2 = map(float, bbox)
    w, h = x2 - x1, y2 - y1
    
    # 1) choose the longer side, then round **up** to nearest multiple
    side = math.ceil(max(w, h) / multiple) * multiple
    
    # 2) centre the square on the original box
    cx, cy = (x1 + x2) / 2, (y1 + y2) / 2
    half = side / 2
    
    new_x1 = int(round(cx - half))
    new_y1 = int(round(cy - half))
    new_x2 = new_x1 + side
    new_y2 = new_y1 + side
    
    return [new_x1, new_y1, new_x2, new_y2]