MaskClustering/dense_masks.py

import numpy as np
import os
import cv2
from pathlib import Path
import trimesh as tm
from sklearn.neighbors import KDTree
from tqdm import tqdm
from tqdm.contrib.concurrent import thread_map
from sklearn.cluster import DBSCAN


def load_scan(pcd_path):
    pcd_data = np.fromfile(pcd_path, dtype=np.float32).reshape(-1, 6)[:, :3]
    return pcd_data

def process_scene(data):
    scene_id, exp_name = data 
    pred_path = Path(f"data/prediction/scannet/baseline_scannet200/{scene_id}.npz")
    out_path = Path(f"data/prediction/scannet/{exp_name}/{scene_id}.npz")
    base_path = Path(f"/home/jovyan/users/lemeshko/scripts/gsam_result/yolo/{scene_id}")
    source_path = Path(f"/home/jovyan/users/kolodiazhnyi/data/scannet/posed_images/{scene_id}")
    scan_path = Path(f"/home/jovyan/users/bulat/workspace/3drec/Indoor/OKNO/data/scannet200/points/{scene_id}.bin")
    info_path = base_path / "infos.npy"

    # if out_path.exists():
    #     return
    vertices = load_scan(scan_path)
    kd = KDTree(vertices)
    max_dist = 0.05

    base_data = np.load(pred_path, allow_pickle=True)
    
    total_points_masks = base_data['pred_masks'].T
    
    for i, mask in enumerate(total_points_masks):
        mask = mask.astype(bool)
        points = vertices[mask]
        dists, inds = kd.query(points, k=5)
        dists = dists.flatten()
        inds = inds.flatten()

        dist_mask = dists < max_dist
        inds = inds[dist_mask]
        mask[inds] = True
        total_points_masks[i] = mask
        
    for i, mask in enumerate(total_points_masks):
        mask = mask.astype(bool)
        points = vertices[mask]
        db = DBSCAN(eps=0.3, min_samples=10)
        labels = db.fit_predict(points)
        
        # labels = db.labels_
        n_clusters = len(set(labels)) - (1 if -1 in labels else 0)
        biggest_cluster_ind = np.argmax(np.unique(labels, return_counts=True)[1])
        res_mask = (labels == biggest_cluster_ind) & (labels != -1)
        # print(f"{labels.shape} -> {res_mask.sum()}")
        new_mask = np.zeros_like(mask)
        new_mask[mask] = res_mask
        total_points_masks[i] = new_mask

    new_data = {
        k: v for k, v in base_data.items()
    }
    new_data['pred_masks'] = total_points_masks.T

    out_path.parent.mkdir(parents=True, exist_ok=True)
    # vs = []
    # cs = []
    # for i in range(new_data['pred_masks'].shape[1]):
    #     os.makedirs(f"pred_masks", exist_ok=True)
    #     v = vertices[new_data['pred_masks'][:, i]]
    #     c = np.random.rand(3)
    #     c = np.repeat(c[np.newaxis, :], len(v), axis=0)
    #     vs.append(v)
    #     cs.append(c)
    # tm.PointCloud(np.concatenate(vs, axis=0), colors=np.concatenate(cs, axis=0)).export(f"pred_masks/{scene_id}_mask.ply")
    
    print("uniques", np.unique(new_data['pred_masks'].sum(1)), [[k, v.shape] for k, v in new_data.items()])
    np.savez(out_path, **new_data)

    

if __name__ == "__main__":
    exp_name = "dense_masks_cluster_filtering"
    scenes = np.loadtxt("/home/jovyan/users/bulat/workspace/3drec/Indoor/MaskClustering/splits/scannet.txt", dtype=str)
    data = [(scene, exp_name) for scene in scenes]
    total_points_masks = thread_map(process_scene, data, chunksize=20)