import numpy as np
import torch
from romatch.utils import *
from PIL import Image
from tqdm import tqdm
import torch.nn.functional as F
import romatch
import kornia.geometry.epipolar as kepi

# wrap cause pyposelib is still in dev
# will add in deps later
import poselib


class Mega1500PoseLibBenchmark:
    def __init__(self, data_root="data/megadepth", scene_names=None, num_ransac_iter=5, test_every=1) -> None:
        if scene_names is None:
            self.scene_names = [
                "0015_0.1_0.3.npz",
                "0015_0.3_0.5.npz",
                "0022_0.1_0.3.npz",
                "0022_0.3_0.5.npz",
                "0022_0.5_0.7.npz",
            ]
        else:
            self.scene_names = scene_names
        self.scenes = [np.load(f"{data_root}/{scene}", allow_pickle=True) for scene in self.scene_names]
        self.data_root = data_root
        self.num_ransac_iter = num_ransac_iter
        self.test_every = test_every

    def benchmark(self, model, model_name=None):
        with torch.no_grad():
            data_root = self.data_root
            tot_e_t, tot_e_R, tot_e_pose = [], [], []
            thresholds = [5, 10, 20]
            for scene_ind in range(len(self.scenes)):
                import os

                scene_name = os.path.splitext(self.scene_names[scene_ind])[0]
                scene = self.scenes[scene_ind]
                pairs = scene["pair_infos"]
                intrinsics = scene["intrinsics"]
                poses = scene["poses"]
                im_paths = scene["image_paths"]
                pair_inds = range(len(pairs))[:: self.test_every]
                for pairind in (pbar := tqdm(pair_inds, desc="Current AUC: ?")):
                    idx1, idx2 = pairs[pairind][0]
                    K1 = intrinsics[idx1].copy()
                    T1 = poses[idx1].copy()
                    R1, t1 = T1[:3, :3], T1[:3, 3]
                    K2 = intrinsics[idx2].copy()
                    T2 = poses[idx2].copy()
                    R2, t2 = T2[:3, :3], T2[:3, 3]
                    R, t = compute_relative_pose(R1, t1, R2, t2)
                    T1_to_2 = np.concatenate((R, t[:, None]), axis=-1)
                    im_A_path = f"{data_root}/{im_paths[idx1]}"
                    im_B_path = f"{data_root}/{im_paths[idx2]}"
                    dense_matches, dense_certainty = model.match(
                        im_A_path, im_B_path, K1.copy(), K2.copy(), T1_to_2.copy()
                    )
                    sparse_matches, _ = model.sample(dense_matches, dense_certainty, 5_000)

                    im_A = Image.open(im_A_path)
                    w1, h1 = im_A.size
                    im_B = Image.open(im_B_path)
                    w2, h2 = im_B.size
                    kpts1, kpts2 = model.to_pixel_coordinates(sparse_matches, h1, w1, h2, w2)
                    kpts1, kpts2 = kpts1.cpu().numpy(), kpts2.cpu().numpy()
                    for _ in range(self.num_ransac_iter):
                        shuffling = np.random.permutation(np.arange(len(kpts1)))
                        kpts1 = kpts1[shuffling]
                        kpts2 = kpts2[shuffling]
                        try:
                            threshold = 1
                            camera1 = {
                                "model": "PINHOLE",
                                "width": w1,
                                "height": h1,
                                "params": K1[[0, 1, 0, 1], [0, 1, 2, 2]],
                            }
                            camera2 = {
                                "model": "PINHOLE",
                                "width": w2,
                                "height": h2,
                                "params": K2[[0, 1, 0, 1], [0, 1, 2, 2]],
                            }
                            relpose, res = poselib.estimate_relative_pose(
                                kpts1,
                                kpts2,
                                camera1,
                                camera2,
                                ransac_opt={
                                    "max_reproj_error": 2 * threshold,
                                    "max_epipolar_error": threshold,
                                    "min_inliers": 8,
                                    "max_iterations": 10_000,
                                },
                            )
                            Rt_est = relpose.Rt
                            R_est, t_est = Rt_est[:3, :3], Rt_est[:3, 3:]
                            mask = np.array(res["inliers"]).astype(np.float32)
                            T1_to_2_est = np.concatenate((R_est, t_est), axis=-1)  #
                            e_t, e_R = compute_pose_error(T1_to_2_est, R, t)
                            e_pose = max(e_t, e_R)
                        except Exception as e:
                            print(repr(e))
                            e_t, e_R = 90, 90
                            e_pose = max(e_t, e_R)
                        tot_e_t.append(e_t)
                        tot_e_R.append(e_R)
                        tot_e_pose.append(e_pose)
                        pbar.set_description(f"Current AUC: {pose_auc(tot_e_pose, thresholds)}")
            tot_e_pose = np.array(tot_e_pose)
            auc = pose_auc(tot_e_pose, thresholds)
            acc_5 = (tot_e_pose < 5).mean()
            acc_10 = (tot_e_pose < 10).mean()
            acc_15 = (tot_e_pose < 15).mean()
            acc_20 = (tot_e_pose < 20).mean()
            map_5 = acc_5
            map_10 = np.mean([acc_5, acc_10])
            map_20 = np.mean([acc_5, acc_10, acc_15, acc_20])
            print(f"{model_name} auc: {auc}")
            return {
                "auc_5": auc[0],
                "auc_10": auc[1],
                "auc_20": auc[2],
                "map_5": map_5,
                "map_10": map_10,
                "map_20": map_20,
            }
