import os
import numpy as np
import json
import cv2
import trimesh
from pts import PtsUtil

class DataLoadUtil:
    
    @staticmethod
    def get_path(root, scene_name, frame_idx):
        path = os.path.join(root, scene_name, f"{frame_idx}")
        return path
    
    @staticmethod
    def get_label_path(root, scene_name):
        path = os.path.join(root,scene_name, f"label.json")
        return path
    
    @staticmethod
    def get_sampled_model_points_path(root, scene_name):
        path = os.path.join(root,scene_name, f"sampled_model_points.txt")
        return path
    
    @staticmethod
    def get_scene_seq_length(root, scene_name):
        camera_params_path = os.path.join(root, scene_name, "camera_params")
        return len(os.listdir(camera_params_path))
    
    @staticmethod
    def load_downsampled_world_model_points(root, scene_name):
        model_path = DataLoadUtil.get_sampled_model_points_path(root, scene_name)
        model_points = np.loadtxt(model_path)
        return model_points

    @staticmethod
    def save_downsampled_world_model_points(root, scene_name, model_points):
        model_path = DataLoadUtil.get_sampled_model_points_path(root, scene_name)
        np.savetxt(model_path, model_points)
    
    @staticmethod
    def load_mesh_at(model_dir, object_name, world_object_pose):
        model_path = os.path.join(model_dir, object_name, "mesh.obj")
        mesh = trimesh.load(model_path)
        mesh.apply_transform(world_object_pose)
        return mesh
    
    @staticmethod
    def save_mesh_at(model_dir, output_dir, object_name, scene_name, world_object_pose):
        mesh = DataLoadUtil.load_mesh_at(model_dir, object_name, world_object_pose)
        model_path = os.path.join(output_dir, scene_name, "world_mesh.obj")
        mesh.export(model_path)

    @staticmethod
    def save_target_mesh_at_world_space(root, model_dir, scene_name):
        scene_info = DataLoadUtil.load_scene_info(root, scene_name)
        target_name = scene_info["target_name"]
        transformation = scene_info[target_name]
        location = transformation["location"]
        rotation_euler = transformation["rotation_euler"]
        pose_mat = trimesh.transformations.euler_matrix(*rotation_euler)
        pose_mat[:3, 3] = location

        mesh = DataLoadUtil.load_mesh_at(model_dir, target_name, pose_mat)
        mesh_dir = os.path.join(root, scene_name, "mesh")
        if not os.path.exists(mesh_dir):
            os.makedirs(mesh_dir)
        model_path = os.path.join(mesh_dir, "world_target_mesh.obj")
        mesh.export(model_path)
    
    @staticmethod
    def load_scene_info(root, scene_name):
        scene_info_path = os.path.join(root, scene_name, "scene_info.json")
        with open(scene_info_path, "r") as f:
            scene_info = json.load(f)
        return scene_info
    
    @staticmethod
    def load_target_object_pose(root, scene_name):
        scene_info = DataLoadUtil.load_scene_info(root, scene_name)
        target_name = scene_info["target_name"]
        transformation = scene_info[target_name]
        location = transformation["location"]
        rotation_euler = transformation["rotation_euler"]
        pose_mat = trimesh.transformations.euler_matrix(*rotation_euler)
        pose_mat[:3, 3] = location
        return pose_mat
    
    @staticmethod
    def load_depth(path, min_depth=0.01,max_depth=5.0,binocular=False):
        
        def load_depth_from_real_path(real_path, min_depth, max_depth):
            depth = cv2.imread(real_path, cv2.IMREAD_UNCHANGED)
            depth = depth.astype(np.float32) / 65535.0
            min_depth = min_depth
            max_depth = max_depth
            depth_meters = min_depth + (max_depth - min_depth) * depth
            return depth_meters
        
        if binocular:
            depth_path_L = os.path.join(os.path.dirname(path), "depth", os.path.basename(path) + "_L.png")
            depth_path_R = os.path.join(os.path.dirname(path), "depth", os.path.basename(path) + "_R.png")
            depth_meters_L = load_depth_from_real_path(depth_path_L, min_depth, max_depth)
            depth_meters_R = load_depth_from_real_path(depth_path_R, min_depth, max_depth)
            return depth_meters_L, depth_meters_R
        else:
            depth_path = os.path.join(os.path.dirname(path), "depth", os.path.basename(path) + ".png")
            depth_meters = load_depth_from_real_path(depth_path, min_depth, max_depth)
            return depth_meters
        
    @staticmethod
    def load_seg(path, binocular=False):
        if binocular:
            def clean_mask(mask_image):
                green = [0, 255, 0, 255]
                red = [255, 0, 0, 255]
                threshold = 2
                mask_image = np.where(np.abs(mask_image - green) <= threshold, green, mask_image)
                mask_image = np.where(np.abs(mask_image - red) <= threshold, red, mask_image)
                return mask_image
            mask_path_L = os.path.join(os.path.dirname(path), "mask", os.path.basename(path) + "_L.png")
            mask_image_L = clean_mask(cv2.imread(mask_path_L, cv2.IMREAD_UNCHANGED))
            mask_path_R = os.path.join(os.path.dirname(path), "mask", os.path.basename(path) + "_R.png")
            mask_image_R = clean_mask(cv2.imread(mask_path_R, cv2.IMREAD_UNCHANGED))
            return mask_image_L, mask_image_R
        else:
            mask_path = os.path.join(os.path.dirname(path), "mask", os.path.basename(path) + ".png")
            mask_image = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE)
            return mask_image
    
    @staticmethod
    def load_label(path):
        with open(path, 'r') as f:
            label_data = json.load(f)
        return label_data
    
    @staticmethod
    def load_rgb(path):
        rgb_path = os.path.join(os.path.dirname(path), "rgb", os.path.basename(path) + ".png")
        rgb_image = cv2.imread(rgb_path, cv2.IMREAD_COLOR)
        return rgb_image
    
    @staticmethod
    def cam_pose_transformation(cam_pose_before):
        offset = np.asarray([
            [1, 0, 0, 0],
            [0, -1, 0, 0],
            [0, 0, -1, 0],
            [0, 0, 0, 1]])  
        cam_pose_after =  cam_pose_before @ offset
        return cam_pose_after
    
    @staticmethod
    def load_cam_info(path, binocular=False):
        camera_params_path = os.path.join(os.path.dirname(path), "camera_params", os.path.basename(path) + ".json")
        with open(camera_params_path, 'r') as f:
            label_data = json.load(f)
        cam_to_world = np.asarray(label_data["extrinsic"])
        cam_to_world = DataLoadUtil.cam_pose_transformation(cam_to_world)
        cam_intrinsic = np.asarray(label_data["intrinsic"])
        cam_info = {
            "cam_to_world": cam_to_world,
            "cam_intrinsic": cam_intrinsic,
            "far_plane": label_data["far_plane"],
            "near_plane": label_data["near_plane"]
        }
        if binocular:
            cam_to_world_R = np.asarray(label_data["extrinsic_R"])
            cam_to_world_R = DataLoadUtil.cam_pose_transformation(cam_to_world_R)
            cam_info["cam_to_world_R"] = cam_to_world_R
        return cam_info
    
    @staticmethod
    def get_target_point_cloud(depth, cam_intrinsic, cam_extrinsic, mask, target_mask_label=(0,255,0,255)):
        h, w = depth.shape
        i, j = np.meshgrid(np.arange(w), np.arange(h), indexing='xy')
        
        z = depth
        x = (i - cam_intrinsic[0, 2]) * z / cam_intrinsic[0, 0]
        y = (j - cam_intrinsic[1, 2]) * z / cam_intrinsic[1, 1]
        
        points_camera = np.stack((x, y, z), axis=-1).reshape(-1, 3)
        mask = mask.reshape(-1,4)

        target_mask = (mask == target_mask_label).all(axis=-1)  
        
        target_points_camera = points_camera[target_mask]
        target_points_camera_aug = np.concatenate([target_points_camera, np.ones((target_points_camera.shape[0], 1))], axis=-1)
        
        target_points_world = np.dot(cam_extrinsic, target_points_camera_aug.T).T[:, :3]
        return {
            "points_world": target_points_world,
            "points_camera": target_points_camera
        }
        
    @staticmethod
    def get_target_point_cloud_world_from_path(path, binocular=False, random_downsample_N=65536, voxel_size = 0.005, target_mask_label=(0,255,0,255)):
        cam_info = DataLoadUtil.load_cam_info(path, binocular=binocular)
        if binocular:
            depth_L, depth_R = DataLoadUtil.load_depth(path, cam_info['near_plane'], cam_info['far_plane'], binocular=True)
            mask_L, mask_R = DataLoadUtil.load_seg(path, binocular=True)
            point_cloud_L = DataLoadUtil.get_target_point_cloud(depth_L, cam_info['cam_intrinsic'], cam_info['cam_to_world'], mask_L, target_mask_label)['points_world']
            point_cloud_R = DataLoadUtil.get_target_point_cloud(depth_R, cam_info['cam_intrinsic'], cam_info['cam_to_world_R'], mask_R, target_mask_label)['points_world']
            point_cloud_L = PtsUtil.random_downsample_point_cloud(point_cloud_L, random_downsample_N)
            point_cloud_R = PtsUtil.random_downsample_point_cloud(point_cloud_R, random_downsample_N)
            overlap_points = DataLoadUtil.get_overlapping_points(point_cloud_L, point_cloud_R, voxel_size)
            return overlap_points
        else:
            depth = DataLoadUtil.load_depth(path, cam_info['near_plane'], cam_info['far_plane'])
            mask = DataLoadUtil.load_seg(path)
            point_cloud = DataLoadUtil.get_target_point_cloud(depth, cam_info['cam_intrinsic'], cam_info['cam_to_world'], mask)['points_world']
            return point_cloud
            
    
    @staticmethod
    def voxelize_points(points, voxel_size):
 
        voxel_indices = np.floor(points / voxel_size).astype(np.int32)
        unique_voxels = np.unique(voxel_indices, axis=0, return_inverse=True)
        return unique_voxels
    
    @staticmethod
    def get_overlapping_points(point_cloud_L, point_cloud_R, voxel_size=0.005):
        voxels_L, indices_L = DataLoadUtil.voxelize_points(point_cloud_L, voxel_size)
        voxels_R, _ = DataLoadUtil.voxelize_points(point_cloud_R, voxel_size)

        voxel_indices_L = voxels_L.view([('', voxels_L.dtype)]*3)
        voxel_indices_R = voxels_R.view([('', voxels_R.dtype)]*3)
        overlapping_voxels = np.intersect1d(voxel_indices_L, voxel_indices_R)
        mask_L = np.isin(indices_L, np.where(np.isin(voxel_indices_L, overlapping_voxels))[0])
        overlapping_points = point_cloud_L[mask_L]
        return overlapping_points
    
    @staticmethod
    def load_points_normals(root, scene_name):
        points_path = os.path.join(root, scene_name, "points_and_normals.txt")
        points_normals = np.loadtxt(points_path)
        return points_normals