gs_kubric/standalone_movi_dataset.py

import os
from pathlib import Path

import colorsys
import numpy as np
import torch
import torchvision
from einops import rearrange
from torch.utils.data import Dataset
from PIL import Image as PIL_Image
from PIL import ImageDraw
from torchvision import transforms
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
import matplotlib
import itertools

torchvision.disable_beta_transforms_warning()
import torchvision.transforms.v2 as transforms
from torchvision.tv_tensors import (BoundingBoxes, BoundingBoxFormat, Image,
                                    Mask)
from flow_viz import flow_to_image

categories = ["Action Figures", "Bag", "Board Games", "Bottles and Cans and Cups", "Camera", "Car Seat", "Consumer Goods", "Hat", "Headphones", "Keyboard", "Legos", "Media Cases", "Mouse", "None", "Shoe", "Stuffed Toys", "Toys"]

def save_tensor_dict(tensor_dict: dict, path: Path):
    output_dict = {}
    for k, v in tensor_dict.items():
        output_dict[k] = v
    np.savez_compressed(path, **output_dict)

def get_n_distinct_colors(n):
    def HSVToRGB(h, s, v):
        (r, g, b) = colorsys.hsv_to_rgb(h, s, v)
        return (int(255 * r), int(255 * g), int(255 * b))

    huePartition = 1.0 / (n + 1)
    return (HSVToRGB(huePartition * value, 1.0, 1.0) for value in range(0, n))

def get_layered_image_from_binary_mask(masks, flip=False):
    if torch.is_tensor(masks):
        masks = masks.float().cpu().detach().numpy()
    if flip:
        masks = np.flipud(masks)

    masks = masks.astype(np.bool_)

    colors = np.asarray(list(get_n_distinct_colors(masks.shape[2])))
    img = np.zeros((*masks.shape[:2], 3))
    for i in range(masks.shape[2]):
        img[masks[..., i]] = colors[i]

    return Image.fromarray(img.astype(np.uint8))

def get_max_neighboring_depth(depth, coords):

    S, H, W, _ = depth.shape
    num_pts = coords.shape[1]
    f_idx = np.tile(np.arange(S)[:, np.newaxis], (1, num_pts))
    x = coords[:, :, 0]
    y = coords[:, :, 1]

    x0 = x.astype(int)
    x1 = x0 + 1
    y0 = y.astype(int)
    y1 = y0 + 1

    x0 = np.clip(x0, 0, W-1)
    x1 = np.clip(x1, 0, W-1)
    y0 = np.clip(y0, 0, H-1)
    y1 = np.clip(y1, 0, H-1)

    sampled_depth = np.max(np.concatenate([depth[f_idx, y0, x0], depth[f_idx, y0, x1], depth[f_idx, y1, x0], depth[f_idx, y1, x1]], axis=-1), axis=-1)

    return sampled_depth
    
class StandaloneMoviDataset(Dataset):
    def __init__(self, root: Path, dataset: str, split='train', num_frames = 24, augment=False, num_dataset_frames=24, resolution=(512, 512), normalize_img: bool = True, **kwargs):
        super(StandaloneMoviDataset, self).__init__()
        self.root = Path(root) # Path to the dataset containing folders of "movi_a", "movi_e", etc.
        self.dataset = dataset # str of dataset name (e.g. "movi_a")
        self.split = split # str of split name (e.g. "train", "validation")
        self.resolution = tuple(resolution)
        self.root_dir = self.root / self.dataset / split
        self.files = os.listdir(self.root_dir)
        self.files.sort()
        print(self.files)
        self.num_dataset_frames = num_dataset_frames
        self.num_frames = num_frames
        self.augment = augment
        self.normalize_img = normalize_img

        if self.augment:
            self.transform = transforms.Compose([transforms.RandomResizedCrop(self.resolution, scale=(0.5, 1.0), antialias=True)])
        else:
            self.transform = transforms.Compose([transforms.Resize(self.resolution, antialias=True)])


    def __getitem__(self, index):
        video_idx = index
        camera_idx = 0

        clip_len = 10
        start = 6
        stride = 1

        path = self.files[video_idx]

        data = np.load(self.root_dir / path / "data.npz")
        rgb = data["rgb"][camera_idx][start::stride][:clip_len]
        instance = data["segment"][camera_idx][start::stride][:clip_len]
        depth = data["depth"][camera_idx][start::stride][:clip_len]
        f_flow = data["forward_flow"][camera_idx][start::stride][:clip_len]
        object_coordinates = data["object_coordinates"][camera_idx][start::stride][:clip_len]

        quaternions = data["quaternions"][camera_idx][start::stride][:clip_len] # (23, 4)
        positions = data["positions"][camera_idx][start::stride][:clip_len] # (23, 3)
        valid = data["valid"][camera_idx, :].squeeze(0) # (23, )
        categories = data["categories"][camera_idx, :].squeeze(0) # (23, )
        bboxes_3d = data['bboxes_3d'][camera_idx][valid][:, start::stride][:, :clip_len]
        intrinsics = data['intrinsics'][camera_idx][start::stride][:clip_len]
        matrix_world = data['matrix_world'][camera_idx][start::stride][:clip_len]
        cam_pos = data['cam_positions'][camera_idx][start::stride][:clip_len]

        rgb = rearrange(rgb, '... h w c -> ... c h w')
        instance = Mask(instance.squeeze(-1))
        rgb, instance = self.transform(Image(rgb), instance)
        rgb = rearrange(rgb, '... c h w -> ... h w c')
        rgb = rgb.numpy()

        instance = torch.nn.functional.one_hot(instance.long(), num_classes=21).numpy()

        mask_valid = instance.sum((1,2)) > 0
        num_objects = (mask_valid.sum(0) > 0).sum()

        ret = {
            "vid_name": path,
            "image": rgb,
            "depth": depth,
            "flow": f_flow,
            'segmentation': instance,
            'object_coordinates': object_coordinates,
            "mask_valid": mask_valid,
            "num_objs": num_objects,
            'categories': categories,
            'valid': valid,
            'bboxes_3d': bboxes_3d,
            'intrinsics': intrinsics,
            'matrix_world': matrix_world,
            'cam_positions': cam_pos,
        }

        return ret
            
    
    def __len__(self):
        return len(self.files)
    
def project_point(cam_matrix_world, cam_intrinsics, point3d, image_size):
    """Compute the image space coordinates [0, 1] for a set of points.



    Args:

        cam: The camera parameters, as returned by kubric.  'matrix_world' and

            'intrinsics' have a leading axis num_frames.

        point3d: Points in 3D world coordinates.  it has shape [num_frames,

            num_points, 3].

        num_frames: The number of frames in the video.



    Returns:

        Image coordinates in 2D.  The last coordinate is an indicator of whether

            the point is behind the camera.

    """

    homo_transform = np.linalg.inv(cam_matrix_world)
    homo_intrinsics = np.zeros((cam_intrinsics.shape[0], 3, 1), dtype=np.float32)
    homo_intrinsics = np.concatenate([cam_intrinsics, homo_intrinsics], axis=2)

    point4d = np.concatenate([point3d, np.ones_like(point3d[:, :, 0:1])], axis=2)
    projected = point4d @ np.transpose(homo_transform, (0, 2, 1))
    projected = projected @ np.transpose(homo_intrinsics, (0, 2, 1))
    image_coords = projected / projected[:, :, 2:3]
    # image_coords = np.concatenate(
    #         [image_coords[:, :, :2],
    #          np.sign(projected[:, :, 2:])], axis=2)
    image_coords = image_coords[:, :, 0:2] * np.array(image_size[::-1])[np.newaxis, np.newaxis, :]

    return image_coords

def get_trajs(data):

    shp = data['image'].shape
    num_frames = shp[0]

    vid_pix_pts = []
    vid_3d_pts = []
    vid_rgb_pts = []
    vid_2d_pts = []
    occ_pts = []

    # x inward, y left, z up (kubrics)
    # x right, y up, z out (gaussian)
    frame_idx = 0
    bboxes_3d = data['bboxes_3d']
    obj_coords = data['object_coordinates'][frame_idx]
    grid_x, grid_y = np.meshgrid(np.arange(0, shp[2]), np.arange(0, shp[1]), indexing='ij')
    pix_coords = np.stack([grid_x, grid_y], axis=-1)
    for obj_idx in range(bboxes_3d.shape[0]):

        obj_mask = (data['segmentation'][frame_idx][:, :, obj_idx+1])
        bbox_3d = bboxes_3d[obj_idx]
        coord_box = list(itertools.product([-.5, .5], [-.5, .5], [-.5, .5]))
        coord_box = np.array([np.array(x) for x in coord_box])
        coord_box = np.concatenate([coord_box, np.ones_like(coord_box[:, 0:1])], axis=1)
        coord_box = np.tile(coord_box[np.newaxis, ...], [num_frames, 1, 1])
        bbox_homo = np.concatenate([bbox_3d, np.ones_like(bbox_3d[:, :, 0:1])], axis=2)
        local_to_world = np.stack([np.linalg.lstsq(coord_box.astype(np.float32)[i], bbox_homo[i], rcond=None)[0] for i in range(num_frames)])

        obj_3d_coords = obj_coords[obj_mask.astype(bool)]
        obj_3d_coords = obj_3d_coords / np.iinfo(np.uint16).max - .5
        obj_homo_coords = np.concatenate([obj_3d_coords, np.ones_like(obj_3d_coords[:, 0:1])], axis=1).astype(np.float32)
        obj_3d_world_coords = np.stack([obj_homo_coords @ local_to_world[i] for i in range(num_frames)])
        obj_3d_world_coords = obj_3d_world_coords[:, :, 0:3] / obj_3d_world_coords[:, :, 3:]
        
        proj_depth = np.sqrt(
            np.sum(
                np.square(obj_3d_world_coords - data['cam_positions'][:, np.newaxis, :]),
                axis=2,
            ),)

        pt_rgb = np.tile(data['image'][0][obj_mask.astype(bool)], [num_frames, 1, 1])
        pix_pts = pix_coords[obj_mask.astype(bool)]
        obj_2d_pix_coords = project_point(data['matrix_world'], data['intrinsics'], obj_3d_world_coords, (shp[1], shp[2]))

        pix_depth = get_max_neighboring_depth(data['depth'], obj_2d_pix_coords)
        occluded = pix_depth < 0.99 * proj_depth

        vid_3d_pts.append(obj_3d_world_coords)
        vid_rgb_pts.append(pt_rgb)
        vid_2d_pts.append(obj_2d_pix_coords)
        occ_pts.append(occluded)
        vid_pix_pts.append(pix_pts)

    vid_3d_pts = np.concatenate(vid_3d_pts, axis=1)
    vid_rgb_pts = np.concatenate(vid_rgb_pts, axis=1)
    vid_2d_pts = np.concatenate(vid_2d_pts, axis=1)
    occ_pts = np.concatenate(occ_pts, axis=1)
    vid_pix_pts = np.concatenate(vid_pix_pts, axis=0)

    return vid_3d_pts, vid_rgb_pts, vid_2d_pts, occ_pts, vid_pix_pts

def vis_data(images, vid_3d_pts, vid_rgb_pts, vid_2d_pts, occ_pts, shp, vis_out_path, v_idx=0):

    num_frames = shp[0]

    fig = plt.figure()
    ax = fig.add_subplot(111, projection = '3d')
    ax.set_xlim(-7, 7)
    ax.set_ylim(-7, 7)
    ax.set_zlim(-1, 3)

    def update(t):
        ax.cla()
        ax.scatter(vid_3d_pts[t][:, 0], vid_3d_pts[t][:, 1], vid_3d_pts[t][:, 2], c = vid_rgb_pts[t]/255.0, s = 0.25)
        ax.set_xlim(-7, 7)
        ax.set_ylim(-7, 7)
        ax.set_zlim(-1, 3)

    ani = FuncAnimation(fig = fig, func = update, frames = num_frames, interval = 1000)
    writervideo = matplotlib.animation.PillowWriter(fps=5)
    ani.save(os.path.join(vis_out_path, f'orig_{v_idx:03d}.gif'), writer=writervideo) 

    num_pts = vid_2d_pts.shape[1]
    vis_pt_count = 75
    rand_pt_idxes = np.random.randint(0, num_pts, vis_pt_count)
    rand_colors = np.random.randint(0, 256, (vis_pt_count, 3))
    trajs = []
    for t in range(num_frames):
        traj_vis = PIL_Image.new('RGBA', (shp[1], shp[2]))
        draw = ImageDraw.Draw(traj_vis)
        t_idx = t
        prev_t_idx = t-1 if t > 0 else 0
        for pt, prev_pt, occ, color in zip(vid_2d_pts[t_idx][rand_pt_idxes], vid_2d_pts[prev_t_idx][rand_pt_idxes], occ_pts[t_idx][rand_pt_idxes], rand_colors):
            if occ:
                segments = 5
                delta_x = (pt[0] - prev_pt[0]) / (2*segments-1)
                delta_y = (pt[1] - prev_pt[1]) / (2*segments-1)
                for i in range(segments):
                    draw.line((prev_pt[0]+delta_x*2*i, prev_pt[1]+delta_y*2*i, prev_pt[0]+delta_x*(2*i+1), prev_pt[1]+delta_y*(2*i+1)), tuple(color), width=3)
            else:
                draw.line((pt[0], pt[1], prev_pt[0], prev_pt[1]), tuple(color), width=3)
        if len(trajs) > 0:
            traj_vis.paste(trajs[-1], mask=trajs[-1])
        trajs.append(traj_vis)

    traj_image_list = []
    for t in range(num_frames):
        traj_out = PIL_Image.fromarray(np.uint8(images[t])).convert("RGBA")
        traj_out.paste(trajs[t], mask=trajs[t])
        traj_image_list.append(traj_out)
    traj_image_list[0].save(os.path.join(vis_out_path, f"2d_{v_idx:03d}.gif"), save_all=True, append_images=[traj_image_list[i] for i in range(1, num_frames)], duration=200, loop=0)

    PIL_Image.fromarray(images[0]).save(os.path.join(vis_out_path, f"kubric_{v_idx:03d}.gif"), save_all=True, append_images=[PIL_Image.fromarray(images[i]) for i in range(1, num_frames)], duration=200, loop=0)
    plt.close('all')

if __name__ == "__main__":

    ROOT_PATH = '/projects/katefgroup/datasets/gs_kubric'
    dataset = StandaloneMoviDataset(root='/projects/katefgroup/datasets/gs_kubric', dataset='for_splatting2', split='subset_50', augment=False, num_frames=24)
    OUT_PATH = os.path.join('/projects/katefgroup/datasets/gs_kubric', 'InpaintingFormat_Set50')
    VIS_OUT_PATH = os.path.join('/projects/katefgroup/datasets/gs_kubric', 'vis')
    os.makedirs(VIS_OUT_PATH, exist_ok=True)

    midas = torch.hub.load("intel-isl/MiDaS", "DPT_Hybrid")
    midas_transforms = torch.hub.load("intel-isl/MiDaS", "transforms")
    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
    midas.to(device)
    midas.eval()
    midas_transform = midas_transforms.dpt_transform

    for i in range(len(dataset)):
        entry = dataset[i]
        num_frames = entry['image'].shape[0]

        vid_3d_pts, vid_rgb_pts, vid_2d_pts, occ_pts, vid_pix_pts = get_trajs(entry)
        trajs_dict = {
            '3d_traj': vid_3d_pts,
            '3d_rgb': vid_rgb_pts,
            '2d_traj': vid_2d_pts,
            'occluded': occ_pts,
            'pix_pts': vid_pix_pts,
        }
        save_tensor_dict(trajs_dict, os.path.join(dataset.root_dir, dataset.files[i], 'traj_data.npz'))

        vis_data(entry['image'], vid_3d_pts, vid_rgb_pts, vid_2d_pts, occ_pts, entry['image'].shape, VIS_OUT_PATH, i)

        os.makedirs(os.path.join(OUT_PATH, "JPEGImages", entry['vid_name']), exist_ok=True)
        os.makedirs(os.path.join(OUT_PATH, "DepthImages", entry['vid_name']), exist_ok=True)
        os.makedirs(os.path.join(OUT_PATH, "OpticalFlow", entry['vid_name']), exist_ok=True)
        os.makedirs(os.path.join(OUT_PATH, "OpticalFlowVis", entry['vid_name']), exist_ok=True)
        
        vid_occluder_masks = []
        for t, frame in enumerate(entry['image']):
            frame = entry['image'][t]
            img_save_path = os.path.join(OUT_PATH, "JPEGImages", entry['vid_name'], f'{t:05d}.png')
            PIL_Image.fromarray(frame).save(img_save_path)

            depth = entry['depth'][t]
            depth_save_path = os.path.join(OUT_PATH, "DepthImages", entry['vid_name'], f'{t:05d}.npy')
            np.save(depth_save_path, depth)

            flow = entry['flow'][t]
            flow_save_path = os.path.join(OUT_PATH, "OpticalFlow", entry['vid_name'], f'{t:05d}.npy')
            np.save(flow_save_path, flow)

            flow_vis = flow_to_image(flow)
            flow_vis_save_path = os.path.join(OUT_PATH, "OpticalFlowVis", entry['vid_name'], f'{t:05d}.png')
            PIL_Image.fromarray(flow_vis).save(flow_vis_save_path)

            with torch.no_grad():
                RESOLUTION = frame.shape[1]
                prediction = midas(midas_transform(np.array(frame).astype(np.uint8))[0].to(device).unsqueeze(0))
                prediction = torch.nn.functional.interpolate(
                    prediction.unsqueeze(1),
                    size=(RESOLUTION, RESOLUTION),
                    mode="bicubic",
                    align_corners=False,
                ).squeeze()
                prediction = prediction.reshape(RESOLUTION, RESOLUTION).cpu().numpy()
            
            binary_masks = entry['segmentation'][t].transpose(2, 0, 1)[:entry['num_objs']]
            inst_depth = np.sum(prediction[np.newaxis] * binary_masks, (-2, -1)) / (np.sum(binary_masks, (-2, -1)) + 1e-6)
            
            occluder_masks = np.zeros_like(binary_masks)
            for i in range(entry['num_objs']):
                if i == 0:
                    occluder_masks[i] = np.sum(binary_masks[1:], axis=0)
                else:
                    for j in range(entry['num_objs']):
                        if j == 0 or j == i or inst_depth[i] > inst_depth[j]:
                            continue
                        else:
                            if binary_masks[i].sum() == 0 or binary_masks[j].sum() == 0:
                                continue
                            rows = np.any(binary_masks[i], axis=1)
                            cols = np.any(binary_masks[i], axis=0)
                            rmin, rmax = np.where(rows)[0][[0, -1]]
                            cmin, cmax = np.where(cols)[0][[0, -1]]
                            bbox_i = np.clip(np.array([rmin-10, rmax+10, cmin-10, cmax+10]), 0, RESOLUTION)

                            rows = np.any(binary_masks[j], axis=1)
                            cols = np.any(binary_masks[j], axis=0)
                            rmin, rmax = np.where(rows)[0][[0, -1]]
                            cmin, cmax = np.where(cols)[0][[0, -1]]
                            bbox_j = np.clip(np.array([rmin-10, rmax+10, cmin-10, cmax+10]), 0, RESOLUTION)

                            x_left = max(bbox_i[0], bbox_j[0])
                            y_top = max(bbox_i[2], bbox_j[2])
                            x_right = min(bbox_i[1], bbox_j[1])
                            y_bottom = min(bbox_i[3], bbox_j[3])

                            if x_right < x_left or y_bottom < y_top:
                                continue
                            else:
                                occluder_masks[i] += binary_masks[j]

            occluder_masks = np.clip(occluder_masks, 0, 1)
            vid_occluder_masks.append(occluder_masks)

        for j in range(entry['num_objs']):
            os.makedirs(os.path.join(OUT_PATH, "Annotations", entry['vid_name'], f'{j:03d}'), exist_ok=True)
            os.makedirs(os.path.join(OUT_PATH, "OccluderAnnotations", entry['vid_name'], f'{j:03d}'), exist_ok=True)
            os.makedirs(os.path.join(OUT_PATH, "ClassLabels", entry['vid_name'], f'{j:03d}'), exist_ok=True)

            for t, frame in enumerate(entry['segmentation'][:, :, :, j]):
                frame = entry['segmentation'][t, :, :, j]
                mask_save_path = os.path.join(OUT_PATH, "Annotations", entry['vid_name'], f'{j:03d}', f'{t:05d}.png')
                mask = np.repeat(frame[:, :, np.newaxis], 3, axis=2)*255
                PIL_Image.fromarray(mask.astype(np.uint8)).save(mask_save_path)

                occ_mask_save_path = os.path.join(OUT_PATH, "OccluderAnnotations", entry['vid_name'], f'{j:03d}', f'{t:05d}.png')
                mask = vid_occluder_masks[t][j]
                PIL_Image.fromarray(mask.astype(np.uint8)*255).save(occ_mask_save_path)

            if j == 0:
                with open(os.path.join(os.path.join(OUT_PATH, 'ClassLabels', entry['vid_name'], f'{j:03d}', 'class_label.txt')), 'w') as class_file:
                    class_file.write("background")
            else:
                with open(os.path.join(os.path.join(OUT_PATH, 'ClassLabels', entry['vid_name'], f'{j:03d}', 'class_label.txt')), 'w') as class_file:
                    if categories[entry['categories'][j]] != 'None':
                        class_file.write(categories[entry['categories'][j]])
                    else:
                        class_file.write("object")
        
    print("Done")
    from ipdb import set_trace; set_trace()