Full Code of ZheC/GTA-IM-Dataset for AI

Repository: ZheC/GTA-IM-Dataset
Branch: master
Commit: 31d7baaab027
Files: 11
Total size: 24.5 MB

Directory structure:
gitextract_uzga8gil/

├── .gitignore
├── LICENSE
├── README.md
├── demo/
│   ├── info_frames.npz
│   └── info_frames.pickle
├── environment.yml
├── gen_npz.py
├── gta_utils.py
├── vis_2d_pose_depth.py
├── vis_skeleton_pcd.py
└── vis_video.py

================================================
FILE CONTENTS
================================================

================================================
FILE: .gitignore
================================================
# Project specifics
2020*
GTA-IM*
data/

# Direnv stuffs
.direnv
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================================================
FILE: LICENSE
================================================
Copyright (c) 2020, Zhe Cao, Hang Gao, Qi-Zhi Cai
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================================================
FILE: README.md
================================================
# <b>GTA-IM Dataset</b> [[Website]](https://people.eecs.berkeley.edu/~zhecao/hmp/)

<div align=center>
<img src="assets/sample1.gif" width=32%>
<img src="assets/sample2.gif" width=32%>
<img src="assets/sample3.gif" width=32%>
</div>

<br>

**Long-term Human Motion Prediction with Scene Context, ECCV 2020 (Oral)** [PDF](https://arxiv.org/pdf/2007.03672.pdf)
<br>
[Zhe Cao](http://people.eecs.berkeley.edu/~zhecao/), [Hang Gao](http://people.eecs.berkeley.edu/~hangg/), [Karttikeya Mangalam](https://karttikeya.github.io/), [Qi-Zhi Cai](https://scholar.google.com/citations?user=oyh-YNwAAAAJ&hl=en), [Minh Vo](https://minhpvo.github.io/), [Jitendra Malik](https://people.eecs.berkeley.edu/~malik/). <br>

This repository maintains our GTA Indoor Motion dataset (GTA-IM) that emphasizes human-scene interactions in the indoor environments. We collect HD RGB-D image seuqences of 3D human motion from realistic game engine. The dataset has clean 3D human pose and camera pose annoations, and large diversity in human appearances, indoor environments, camera views, and human activities.

**Table of contents**<br>
1. [A demo for playing with our dataset.](#demo)<br>
2. [Instructions to request our full dataset.](#requesting-dataset)<br>
3. [Documentation on our dataset structure and contents.](#dataset-contents)<br>


## Demo

### (0) Getting Started
Clone this repository, and create local environment: `conda env create -f environment.yml`.

For your convinience, we provide a fragment of our data in `demo` directory. And in this section, you will be able to play with different parts of our data using maintained tool scripts.

### (1) 3D skeleton & point cloud
```bash
$ python vis_skeleton_pcd.py -h
usage: vis_skeleton_pcd.py [-h] [-pa PATH] [-f FRAME] [-fw FUSION_WINDOW]

# now visualize demo 3d skeleton and point cloud!
$ python vis_skeleton_pcd.py -pa demo -f 2720 -fw 80
```

You should be able to see a open3d viewer with our 3D skeleton and point cloud data, press 'h' in the viewer to see how to control the viewpoint:
<img src="assets/vis_skeleton_pcd.gif" width=100%>

Note that we use `open3d == 0.7.0`, the visualization code is not compatible with the newer version of open3d.

### (2) 2D skeleton & depth map
```bash
$ python vis_2d_pose_depth.py -h
usage: vis_2d_pose_depth.py [-h] [-pa PATH]

# now visualize 2d skeleton and depth map!
$ python vis_2d_pose_depth.py -pa demo
```

You should be able to find a created `demo/vis/` directory with `*_vis.jpg` that render to a movie strip like this:
<img src="assets/vis_2d_pose_depth.gif" width=80%>

### (3) RGB video
```bash
$ python vis_video.py -h
usage: vis_video.py [-h] [-pa PATH] [-s SCALE] [-fr FRAME_RATE]

# now visualize demo video!
$ python vis_video.py -pa demo -fr 15
```

You should be able to find a created `demo/vis/` directory with a `video.mp4`:

## Requesting Dataset

To obtain the Dataset, please send an email to [Zhe Cao](https://people.eecs.berkeley.edu/~zhecao/) (with the title "GTA-IM Dataset Download") stating:

- Your name, title and affilation
- Your intended use of the data
- The following statement:
    > With this email we declare that we will use the GTA-IM Dataset for non-commercial research purposes only. We also undertake to purchase a copy of Grand Theft Auto V. We will not redistribute the data in any form except in academic publications where necessary to present examples.

We will promptly reply with the download link.


## Dataset Contents

After you download data from our link and unzip, each sequence folder will contain the following files:

- `images`:
    - color images: `*.jpg`
    - depth images: `*.jpg`
    - instance masks: `*_id`.png

<br>

- `info_frames.pickle`: a pickle file contains camera information, 3d human poses (98 joints) in the global coordinate, weather condition, the character ID, and so on.

    ````python
    import pickle
    info = pickle.load(open(data_path + 'info_frames.pickle', 'rb'))
    print(info[0].keys())
    ````

<br>

- `info_frames.npz`: it contains five arrays. 21 joints out of 98 human joints are extraced to form the minimal skeleton. [Here](gen_npz.py) is how we generate it from raw captures.

    - `joints_2d`: 2d human poses on the HD image plane.
    - `joints_3d_cam`: 3d human poses in the current frame's camera coordinate
    - `joints_3d_world`: 3d human poses in the game/world coordinate
    - `world2cam_trans`: the world to camera transformation matrix for each frame
    - `intrinsics`: camera intrinsics

    <br>

    ````python
    import numpy as np
    info_npz = np.load(rec_idx+'info_frames.npz'); 
    print(info_npz.files)
    # 2d poses for frame 0
    print(npz['joints_2d'][0]) 
    ````


<br>

- `realtimeinfo.pickle`: a backup pickle file which contains all information from the data collection.

#### Joint Types

The human skeleton connection and joints index name:

```python
LIMBS = [
    (0, 1),  # head_center -> neck
    (1, 2),  # neck -> right_clavicle
    (2, 3),  # right_clavicle -> right_shoulder
    (3, 4),  # right_shoulder -> right_elbow
    (4, 5),  # right_elbow -> right_wrist
    (1, 6),  # neck -> left_clavicle
    (6, 7),  # left_clavicle -> left_shoulder
    (7, 8),  # left_shoulder -> left_elbow
    (8, 9),  # left_elbow -> left_wrist
    (1, 10),  # neck -> spine0
    (10, 11),  # spine0 -> spine1
    (11, 12),  # spine1 -> spine2
    (12, 13),  # spine2 -> spine3
    (13, 14),  # spine3 -> spine4
    (14, 15),  # spine4 -> right_hip
    (15, 16),  # right_hip -> right_knee
    (16, 17),  # right_knee -> right_ankle
    (14, 18),  # spine4 -> left_hip
    (18, 19),  # left_hip -> left_knee
    (19, 20)  # left_knee -> left_ankle
]
```

## Important Note

This dataset is for non-commercial research purpose only. Due to public interest, I decided to reimplement the data generation pipeline from scratch to collect the GTA-IM dataset again. I do not use Facebook resources to reproduce the data. 

## Citation

We believe in open research and we will be happy if you find this data useful.
If you use it, please consider citing our [work](https://people.eecs.berkeley.edu/~zhecao/hmp/preprint.pdf).

```latex
@incollection{caoHMP2020,
  author = {Zhe Cao and
    Hang Gao and
    Karttikeya Mangalam and
    Qizhi Cai and
    Minh Vo and
    Jitendra Malik},
  title = {Long-term human motion prediction with scene context},
  booktitle = ECCV,
  year = {2020},
  }
```

## Acknowledgement

Our data collection pipeline was built upon [this plugin](https://github.com/philkr/gamehook_gtav) and [this tool](https://github.com/fabbrimatteo/JTA-Mods).

## LICENSE
Our project is released under [CC-BY-NC 4.0](https://github.com/ZheC/GTA-IM-Dataset/tree/master/LICENSE).


================================================
FILE: demo/info_frames.pickle
================================================
[File too large to display: 24.5 MB]

================================================
FILE: environment.yml
================================================
name: gta-im
channels:
  - conda-forge
  - open3d-admin
dependencies:
  - python=3.6
  - tqdm
  - numpy
  - numba
  - pillow
  - matplotlib
  - opencv
  - open3d=0.7


================================================
FILE: gen_npz.py
================================================
"""
GTA-IM Dataset
"""

import glob
import os
import pickle

import numba
import numpy as np


@numba.jit(nopython=True, nogil=True)
def rot_axis(angle, axis):
    cg = np.cos(angle)
    sg = np.sin(angle)
    if axis == 0:  # X
        v = [0, 4, 5, 7, 8]
    elif axis == 1:  # Y
        v = [4, 0, 6, 2, 8]
    else:  # Z
        v = [8, 0, 1, 3, 4]
    RX = np.zeros(9, dtype=numba.float64)
    RX[v[0]] = 1.0
    RX[v[1]] = cg
    RX[v[2]] = -sg
    RX[v[3]] = sg
    RX[v[4]] = cg
    return RX.reshape(3, 3)


@numba.jit(nopython=True, nogil=True)
def rotate(vector, angle, inverse=False):
    """
    Rotation of x, y, z axis
    Forward rotate order: Z, Y, X
    Inverse rotate order: X^T, Y^T,Z^T
    Input:
        vector: vector in 3D coordinates
        angle: rotation along X, Y, Z (raw data from GTA)
    Output:
        out: rotated vector
    """
    gamma, beta, alpha = angle[0], angle[1], angle[2]

    # Rotation matrices around the X (gamma), Y (beta), and Z (alpha) axis
    RX = rot_axis(gamma, 0)
    RY = rot_axis(beta, 1)
    RZ = rot_axis(alpha, 2)

    # Composed rotation matrix with (RX, RY, RZ)
    if inverse:
        return np.dot(np.dot(np.dot(RX.T, RY.T), RZ.T), vector)
    else:
        return np.dot(np.dot(np.dot(RZ, RY), RX), vector)


def angle2rot(rotation, inverse=False):
    return rotate(np.eye(3), rotation, inverse=inverse)


class Pose:
    def __init__(self, position, rotation):
        # relative position to the 1st frame: (X, Y, Z)
        # relative rotation to the previous frame: (r_x, r_y, r_z)
        self.position = position
        self.rotation = angle2rot(rotation)
        magic_rot = angle2rot(np.array([np.pi / 2, 0, 0]), inverse=True)
        self.rotation = self.rotation.dot(magic_rot)


def get_focal_length(cam_near_clip, cam_field_of_view):
    near_clip_height = (
        2 * cam_near_clip * np.tan(cam_field_of_view / 2.0 * (np.pi / 180.0))
    )

    # camera focal length
    return 1080.0 / near_clip_height * cam_near_clip


def get_cam_extr(cam_pos, cam_rot):
    cam_pos = np.array(cam_pos)
    cam_rot = np.array(cam_rot)

    pose = Pose(cam_pos, cam_rot / 180.0 * np.pi)
    cam_extr = np.eye(4)
    cam_extr[:3, :3] = pose.rotation
    cam_extr[:3, -1] = pose.position

    return cam_extr


if __name__ == '__main__':
    rec_inds = glob.glob('2020*')
    for data_path in rec_inds:
        if '.zip' in data_path:
            continue
        print(data_path)
        data_path += '/'
        info_path = data_path + 'realtimeinfo.gz'
        info = pickle.load(open(info_path, 'rb'))['frames']

        new_info = []
        joints_2d_seq = []
        joints_3d_cam_seq = []
        joints_3d_world_seq = []
        world2cam_trans = []
        intrinsics = []
        count = 0
        for i in range(len(info)):
            infot = info[i]
            # Change the image names
            prefix = data_path + str(infot['time'])
            if os.path.exists(prefix + '_final.jpg') and os.path.exists(
                prefix + '_depth.png'
            ):
                os.rename(
                    prefix + '_final.jpg',
                    data_path + '{:05d}'.format(count) + '.jpg',
                )
                os.rename(
                    prefix + '_depth.png',
                    data_path + '{:05d}'.format(count) + '.png',
                )
                os.rename(
                    prefix + '_id.png',
                    data_path + '{:05d}'.format(count) + '_id.png',
                )
                count = count + 1

                # 3d keypoints
                keypoint = [
                    infot['head'],
                    infot['neck'],
                    infot['right_clavicle'],
                    infot['right_shoulder'],
                    infot['right_elbow'],
                    infot['right_wrist'],
                    infot['left_clavicle'],
                    infot['left_shoulder'],
                    infot['left_elbow'],
                    infot['left_wrist'],
                    infot['spine0'],
                    infot['spine1'],
                    infot['spine2'],
                    infot['spine3'],
                    infot['spine4'],
                    infot['right_hip'],
                    infot['right_knee'],
                    infot['right_ankle'],
                    infot['left_hip'],
                    infot['left_knee'],
                    infot['left_ankle'],
                ]

                # camera parameters
                cam_near_clip = infot['cam_near_clip']
                cam_field_of_view = infot['cam_field_of_view']
                focal_length = get_focal_length(
                    cam_near_clip, cam_field_of_view
                )
                intrinsic = np.asarray(
                    [
                        [focal_length, 0, 960.0],
                        [0, focal_length, 540.0],
                        [0, 0, 1],
                    ]
                )
                cam_extr_ref = get_cam_extr(infot['cam_pos'], infot['cam_rot'])

                joints = np.asarray(keypoint)
                jn = joints.shape[0]

                joints_world = np.concatenate(
                    [joints, np.ones((jn, 1))], axis=-1
                )
                joints_cam = joints_world.dot(np.linalg.inv(cam_extr_ref.T))[
                    :, :3
                ]
                joints_2d = np.matmul(intrinsic, joints_cam.T)
                joints_2d = (
                    joints_2d[0] / joints_2d[2],
                    joints_2d[1] / joints_2d[2],
                )
                gta_pose_2d = np.asarray(joints_2d).T.reshape(jn, 2)
                joints_cam = joints_cam.reshape(jn, 3)

                joints_2d_seq.append(np.asarray(joints_2d).T)
                joints_3d_cam_seq.append(joints_cam)
                joints_3d_world_seq.append(joints)
                world2cam_trans.append(np.linalg.inv(cam_extr_ref.T))
                intrinsics.append(intrinsic)

                new_info.append(infot)

        np.savez(
            data_path + 'info_frames.npz',
            joints_2d=np.asarray(joints_2d_seq),
            joints_3d_cam=np.asarray(joints_3d_cam_seq),
            joints_3d_world=np.asarray(joints_3d_world_seq),
            world2cam_trans=np.asarray(world2cam_trans),
            intrinsics=np.asarray(intrinsics),
        )

        fn = open(data_path + 'info_frames.pickle', 'wb')
        pickle.dump(new_info, fn)


================================================
FILE: gta_utils.py
================================================
"""
GTA-IM Dataset
"""

import cv2
import numpy as np

LIMBS = [
    (0, 1),  # head_center -> neck
    (1, 2),  # neck -> right_clavicle
    (2, 3),  # right_clavicle -> right_shoulder
    (3, 4),  # right_shoulder -> right_elbow
    (4, 5),  # right_elbow -> right_wrist
    (1, 6),  # neck -> left_clavicle
    (6, 7),  # left_clavicle -> left_shoulder
    (7, 8),  # left_shoulder -> left_elbow
    (8, 9),  # left_elbow -> left_wrist
    (1, 10),  # neck -> spine0
    (10, 11),  # spine0 -> spine1
    (11, 12),  # spine1 -> spine2
    (12, 13),  # spine2 -> spine3
    (13, 14),  # spine3 -> spine4
    (14, 15),  # spine4 -> right_hip
    (15, 16),  # right_hip -> right_knee
    (16, 17),  # right_knee -> right_ankle
    (14, 18),  # spine4 -> left_hip
    (18, 19),  # left_hip -> left_knee
    (19, 20),  # left_knee -> left_ankle
]


####################
# camera utils.
def get_focal_length(cam_near_clip, cam_field_of_view):
    near_clip_height = (
        2 * cam_near_clip * np.tan(cam_field_of_view / 2.0 * (np.pi / 180.0))
    )

    # camera focal length
    return 1080.0 / near_clip_height * cam_near_clip


def get_2d_from_3d(
    vertex,
    cam_coords,
    cam_rotation,
    cam_near_clip,
    cam_field_of_view,
    WIDTH=1920,
    HEIGHT=1080,
):
    WORLD_NORTH = np.array([0.0, 1.0, 0.0], 'double')
    WORLD_UP = np.array([0.0, 0.0, 1.0], 'double')
    WORLD_EAST = np.array([1.0, 0.0, 0.0], 'double')
    theta = (np.pi / 180.0) * cam_rotation
    cam_dir = rotate(WORLD_NORTH, theta)
    clip_plane_center = cam_coords + cam_near_clip * cam_dir
    camera_center = -cam_near_clip * cam_dir
    near_clip_height = (
        2 * cam_near_clip * np.tan(cam_field_of_view / 2.0 * (np.pi / 180.0))
    )
    near_clip_width = near_clip_height * WIDTH / HEIGHT

    cam_up = rotate(WORLD_UP, theta)
    cam_east = rotate(WORLD_EAST, theta)
    near_clip_to_target = vertex - clip_plane_center

    camera_to_target = near_clip_to_target - camera_center

    camera_to_target_unit_vector = camera_to_target * (
        1.0 / np.linalg.norm(camera_to_target)
    )

    view_plane_dist = cam_near_clip / cam_dir.dot(camera_to_target_unit_vector)

    new_origin = (
        clip_plane_center
        + (near_clip_height / 2.0) * cam_up
        - (near_clip_width / 2.0) * cam_east
    )

    view_plane_point = (
        view_plane_dist * camera_to_target_unit_vector
    ) + camera_center
    view_plane_point = (view_plane_point + clip_plane_center) - new_origin
    viewPlaneX = view_plane_point.dot(cam_east)
    viewPlaneZ = view_plane_point.dot(cam_up)
    screenX = viewPlaneX / near_clip_width
    screenY = -viewPlaneZ / near_clip_height

    # screenX and screenY between (0, 1)
    ret = np.array([screenX, screenY], 'double')
    return ret


def screen_x_to_view_plane(x, cam_near_clip, cam_field_of_view):
    # x in (0, 1)
    near_clip_height = (
        2 * cam_near_clip * np.tan(cam_field_of_view / 2.0 * (np.pi / 180.0))
    )
    near_clip_width = near_clip_height * 1920.0 / 1080.0

    viewPlaneX = x * near_clip_width

    return viewPlaneX


def generate_id_map(map_path):
    id_map = cv2.imread(map_path, -1)
    h, w, _ = id_map.shape
    id_map = np.concatenate(
        (id_map, np.zeros((h, w, 1), dtype=np.uint8)), axis=2
    )
    id_map.dtype = np.uint32
    return id_map


def get_depth(
    vertex, cam_coords, cam_rotation, cam_near_clip, cam_field_of_view
):
    WORLD_NORTH = np.array([0.0, 1.0, 0.0], 'double')
    theta = (np.pi / 180.0) * cam_rotation
    cam_dir = rotate(WORLD_NORTH, theta)
    clip_plane_center = cam_coords + cam_near_clip * cam_dir
    camera_center = -cam_near_clip * cam_dir

    near_clip_to_target = vertex - clip_plane_center

    camera_to_target = near_clip_to_target - camera_center
    camera_to_target_unit_vector = camera_to_target * (
        1.0 / np.linalg.norm(camera_to_target)
    )

    depth = np.linalg.norm(camera_to_target) * cam_dir.dot(
        camera_to_target_unit_vector
    )
    depth = depth - cam_near_clip

    return depth


def get_kitti_format_camera_coords(
    vertex, cam_coords, cam_rotation, cam_near_clip
):
    cam_dir, cam_up, cam_east = get_cam_dir_vecs(cam_rotation)

    clip_plane_center = cam_coords + cam_near_clip * cam_dir

    camera_center = -cam_near_clip * cam_dir

    near_clip_to_target = vertex - clip_plane_center

    camera_to_target = near_clip_to_target - camera_center
    camera_to_target_unit_vector = camera_to_target * (
        1.0 / np.linalg.norm(camera_to_target)
    )

    z = np.linalg.norm(camera_to_target) * cam_dir.dot(
        camera_to_target_unit_vector
    )
    y = -np.linalg.norm(camera_to_target) * cam_up.dot(
        camera_to_target_unit_vector
    )
    x = np.linalg.norm(camera_to_target) * cam_east.dot(
        camera_to_target_unit_vector
    )

    return np.array([x, y, z])


def get_cam_dir_vecs(cam_rotation):
    WORLD_NORTH = np.array([0.0, 1.0, 0.0], 'double')
    WORLD_UP = np.array([0.0, 0.0, 1.0], 'double')
    WORLD_EAST = np.array([1.0, 0.0, 0.0], 'double')
    theta = (np.pi / 180.0) * cam_rotation
    cam_dir = rotate(WORLD_NORTH, theta)
    cam_up = rotate(WORLD_UP, theta)
    cam_east = rotate(WORLD_EAST, theta)

    return cam_dir, cam_up, cam_east


def is_before_clip_plane(
    vertex,
    cam_coords,
    cam_rotation,
    cam_near_clip,
    cam_field_of_view,
    WIDTH=1920,
    HEIGHT=2080,
):
    WORLD_NORTH = np.array([0.0, 1.0, 0.0], 'double')
    theta = (np.pi / 180.0) * cam_rotation
    cam_dir = rotate(WORLD_NORTH, theta)
    clip_plane_center = cam_coords + cam_near_clip * cam_dir
    camera_center = -cam_near_clip * cam_dir

    near_clip_to_target = vertex - clip_plane_center

    camera_to_target = near_clip_to_target - camera_center

    camera_to_target_unit_vector = camera_to_target * (
        1.0 / np.linalg.norm(camera_to_target)
    )

    if cam_dir.dot(camera_to_target_unit_vector) > 0:
        return True
    else:
        return False


def get_clip_center_and_dir(cam_coords, cam_rotation, cam_near_clip):
    WORLD_NORTH = np.array([0.0, 1.0, 0.0], 'double')
    theta = (np.pi / 180.0) * cam_rotation
    cam_dir = rotate(WORLD_NORTH, theta)
    clip_plane_center = cam_coords + cam_near_clip * cam_dir
    return clip_plane_center, cam_dir


def rotate(a, t):
    d = np.zeros(3, 'double')
    d[0] = np.cos(t[2]) * (
        np.cos(t[1]) * a[0]
        + np.sin(t[1]) * (np.sin(t[0]) * a[1] + np.cos(t[0]) * a[2])
    ) - (np.sin(t[2]) * (np.cos(t[0]) * a[1] - np.sin(t[0]) * a[2]))
    d[1] = np.sin(t[2]) * (
        np.cos(t[1]) * a[0]
        + np.sin(t[1]) * (np.sin(t[0]) * a[1] + np.cos(t[0]) * a[2])
    ) + (np.cos(t[2]) * (np.cos(t[0]) * a[1] - np.sin(t[0]) * a[2]))
    d[2] = -np.sin(t[1]) * a[0] + np.cos(t[1]) * (
        np.sin(t[0]) * a[1] + np.cos(t[0]) * a[2]
    )
    return d


def get_intersect_point(center_pt, cam_dir, vertex1, vertex2):
    c1 = center_pt[0]
    c2 = center_pt[1]
    c3 = center_pt[2]
    a1 = cam_dir[0]
    a2 = cam_dir[1]
    a3 = cam_dir[2]
    x1 = vertex1[0]
    y1 = vertex1[1]
    z1 = vertex1[2]
    x2 = vertex2[0]
    y2 = vertex2[1]
    z2 = vertex2[2]

    k_up = a1 * (x1 - c1) + a2 * (y1 - c2) + a3 * (z1 - c3)
    k_down = a1 * (x1 - x2) + a2 * (y1 - y2) + a3 * (z1 - z2)
    k = k_up / k_down
    inter_point = (1 - k) * vertex1 + k * vertex2

    return inter_point


####################
# dataset utils.
def is_inside(x, y):
    return x >= 0 and x <= 1 and y >= 0 and y <= 1


def get_cut_edge(x1, y1, x2, y2):
    # (x1, y1) inside while (x2, y2) outside
    dx = x2 - x1
    dy = y2 - y1
    ratio_pool = []
    if x2 < 0:
        ratio = (x1 - 0) / (x1 - x2)
        ratio_pool.append(ratio)
    if x2 > 1:
        ratio = (1 - x1) / (x2 - x1)
        ratio_pool.append(ratio)
    if y2 < 0:
        ratio = (y1 - 0) / (y1 - y2)
        ratio_pool.append(ratio)
    if y2 > 1:
        ratio = (1 - y1) / (y2 - y1)
        ratio_pool.append(ratio)
    actual_ratio = min(ratio_pool)
    return x1 + actual_ratio * dx, y1 + actual_ratio * dy


def get_min_max_x_y_from_line(x1, y1, x2, y2):
    if is_inside(x1, y1) and is_inside(x2, y2):
        return min(x1, x2), max(x1, x2), min(y1, y2), max(y1, y2)
    if (not is_inside(x1, y1)) and (not is_inside(x2, y2)):
        return None, None, None, None
    if is_inside(x1, y1) and not is_inside(x2, y2):
        x2, y2 = get_cut_edge(x1, y1, x2, y2)
        return min(x1, x2), max(x1, x2), min(y1, y2), max(y1, y2)
    if is_inside(x2, y2) and not is_inside(x1, y1):
        x1, y1 = get_cut_edge(x2, y2, x1, y1)
        return min(x1, x2), max(x1, x2), min(y1, y2), max(y1, y2)


def get_angle_in_2pi(unit_vec):
    theta = np.arccos(unit_vec[0])
    if unit_vec[1] > 0:
        return theta
    else:
        return 2 * np.pi - theta


####################
# math utils.
def vec_cos(a, b):
    prod = a.dot(b)
    prod = prod * 1.0 / np.linalg.norm(a) / np.linalg.norm(b)
    return prod


def compute_bbox_ratio(bbox2, bbox):
    # bbox2 is inside bbox
    s = (bbox[2] - bbox[0]) * (bbox[3] - bbox[1])
    s2 = (bbox2[2] - bbox2[0]) * (bbox2[3] - bbox2[1])
    return s2 * 1.0 / s


def compute_iou(boxA, boxB):
    if (
        boxA[0] > boxB[2]
        or boxB[0] > boxA[2]
        or boxA[1] > boxB[3]
        or boxB[1] > boxA[3]
    ):
        return 0
    # determine the (x, y)-coordinates of the intersection rectangle
    xA = max(boxA[0], boxB[0])
    yA = max(boxA[1], boxB[1])
    xB = min(boxA[2], boxB[2])
    yB = min(boxA[3], boxB[3])

    # compute the area of intersection rectangle
    interArea = (xB - xA + 1) * (yB - yA + 1)

    # compute the area of both the prediction and ground-truth
    # rectangles
    boxAArea = (boxA[2] - boxA[0] + 1) * (boxA[3] - boxA[1] + 1)
    boxBArea = (boxB[2] - boxB[0] + 1) * (boxB[3] - boxB[1] + 1)

    # compute the intersection over union by taking the intersection
    # area and dividing it by the sum of prediction + ground-truth
    # areas - the interesection area
    iou = interArea / float(boxAArea + boxBArea - interArea)

    # return the intersection over union value
    return iou


def project2dline(
    p1,
    p2,
    cam_coords,
    cam_rotation,
    cam_near_clip=0.15,
    cam_field_of_view=50.0,
    WIDTH=1920,
    HEIGHT=2080,
):
    before1 = is_before_clip_plane(
        p1, cam_coords, cam_rotation, cam_near_clip, cam_field_of_view
    )
    before2 = is_before_clip_plane(
        p2, cam_coords, cam_rotation, cam_near_clip, cam_field_of_view
    )
    if not (before1 or before2):
        return None
    if before1 and before2:
        cp1 = get_2d_from_3d(
            p1,
            cam_coords,
            cam_rotation,
            cam_near_clip,
            cam_field_of_view,
            WIDTH,
            HEIGHT,
        )
        cp2 = get_2d_from_3d(
            p2,
            cam_coords,
            cam_rotation,
            cam_near_clip,
            cam_field_of_view,
            WIDTH,
            HEIGHT,
        )
        x1 = int(cp1[0] * WIDTH)
        x2 = int(cp2[0] * WIDTH)
        y1 = int(cp1[1] * HEIGHT)
        y2 = int(cp2[1] * HEIGHT)
        return [[x1, y1], [x2, y2]]
    center_pt, cam_dir = get_clip_center_and_dir(
        cam_coords, cam_rotation, cam_near_clip
    )
    if before1 and not before2:
        inter2 = get_intersect_point(center_pt, cam_dir, p1, p2)
        cp1 = get_2d_from_3d(
            p1,
            cam_coords,
            cam_rotation,
            cam_near_clip,
            cam_field_of_view,
            WIDTH,
            HEIGHT,
        )
        cp2 = get_2d_from_3d(
            inter2,
            cam_coords,
            cam_rotation,
            cam_near_clip,
            cam_field_of_view,
            WIDTH,
            HEIGHT,
        )
        x1 = int(cp1[0] * WIDTH)
        x2 = int(cp2[0] * WIDTH)
        y1 = int(cp1[1] * HEIGHT)
        y2 = int(cp2[1] * HEIGHT)
        return [[x1, y1], [x2, y2]]
    if before2 and not before1:
        inter1 = get_intersect_point(center_pt, cam_dir, p1, p2)
        cp2 = get_2d_from_3d(
            p2,
            cam_coords,
            cam_rotation,
            cam_near_clip,
            cam_field_of_view,
            WIDTH,
            HEIGHT,
        )
        cp1 = get_2d_from_3d(
            inter1,
            cam_coords,
            cam_rotation,
            cam_near_clip,
            cam_field_of_view,
            WIDTH,
            HEIGHT,
        )
        x1 = int(cp1[0] * WIDTH)
        x2 = int(cp2[0] * WIDTH)
        y1 = int(cp1[1] * HEIGHT)
        y2 = int(cp2[1] * HEIGHT)
        return [[x1, y1], [x2, y2]]


####################
# io utils.
def read_depthmap(name, cam_near_clip, cam_far_clip):
    depth = cv2.imread(name)
    depth = np.concatenate(
        (depth, np.zeros_like(depth[:, :, 0:1], dtype=np.uint8)), axis=2
    )
    depth.dtype = np.uint32
    depth = 0.05 * 1000 / depth.astype('float')
    depth = (
        cam_near_clip
        * cam_far_clip
        / (cam_near_clip + depth * (cam_far_clip - cam_near_clip))
    )
    return depth


================================================
FILE: vis_2d_pose_depth.py
================================================
"""
GTA-IM Dataset
"""

import argparse
import os
import pickle

import cv2
import matplotlib.pyplot as plt
import numpy as np

from gta_utils import LIMBS, read_depthmap


def single_vis(args):
    joints_2d = np.load(args.path + '/info_frames.npz')['joints_2d']
    info = pickle.load(open(args.path + '/info_frames.pickle', 'rb'))
    if not os.path.exists(args.outpath):
        os.mkdir(args.outpath)
    for idx in range(30, len(info)):
        if os.path.exists(
            os.path.join(args.path, '{:05d}'.format(idx) + '.jpg')
        ):
            keypoint = joints_2d[idx]

            # root
            root_pos = (int(keypoint[14, 0]), int(keypoint[14, 1]))
            # color image
            frame = cv2.imread(
                os.path.join(args.path, '{:05d}'.format(idx) + '.jpg')
            )
            frame = cv2.circle(frame, tuple(root_pos), 10, (0, 0, 255), 20)
            # depth map
            infot = info[idx]
            cam_near_clip = infot['cam_near_clip']
            if 'cam_far_clip' in infot.keys():
                cam_far_clip = infot['cam_far_clip']
            else:
                cam_far_clip = 800.    
            fname = os.path.join(args.path, '{:05d}'.format(idx) + '.png')
            depthmap = read_depthmap(fname, cam_near_clip, cam_far_clip)

            # plot joints
            for i0, i1 in LIMBS:
                p1 = (int(keypoint[i0, 0]), int(keypoint[i0, 1]))
                p2 = (int(keypoint[i1, 0]), int(keypoint[i1, 1]))
                frame = cv2.line(frame, tuple(p1), tuple(p2), (0, 255, 0), 20)


            fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(16, 9), sharey=True)
            ax1.imshow(frame[:, :, ::-1])
            ax1.axis('off')
            # visaulize the disparity
            ax2.imshow(100.0 / depthmap[:, :, 0], cmap='plasma')
            ax2.axis('off')
            # tight figure
            plt.subplots_adjust(top = 1, bottom = 0, right = 1, left = 0, 
            hspace = 0, wspace = 0)
            plt.margins(0,0)
            plt.gca().xaxis.set_major_locator(plt.NullLocator())
            plt.gca().yaxis.set_major_locator(plt.NullLocator())
            plt.savefig(
                os.path.join(args.outpath, str(idx) + '_vis.jpg'), 
                bbox_inches='tight',
                pad_inches=0)
            plt.close()


if __name__ == '__main__':
    parser = argparse.ArgumentParser(description=None)
    parser.add_argument('-pa', '--path', default='2020-06-10-09-27-04/')
    args = parser.parse_args()
    args.outpath = args.path + '/vis/'
    single_vis(args)


================================================
FILE: vis_skeleton_pcd.py
================================================
"""
GTA-IM Dataset
"""

import argparse
import os
import pickle
import sys

import cv2
import numpy as np
import open3d as o3d
from open3d import (LineSet, PinholeCameraIntrinsic, Vector2iVector,
                    Vector3dVector, draw_geometries)

from gta_utils import LIMBS, read_depthmap

sys.path.append('./')


def create_skeleton_viz_data(nskeletons, njoints):
    lines = []
    colors = []
    for i in range(nskeletons):
        cur_lines = np.asarray(LIMBS)
        cur_lines += i * njoints
        lines.append(cur_lines)

        single_color = np.zeros([njoints, 3])
        single_color[:] = [0.0, float(i) / nskeletons, 1.0]
        colors.append(single_color[1:])

    lines = np.concatenate(lines, axis=0)
    colors = np.asarray(colors).reshape(-1, 3)
    return lines, colors


def vis_skeleton_pcd(rec_idx, f_id, fusion_window=20):
    info = pickle.load(open(rec_idx + '/info_frames.pickle', 'rb'))
    info_npz = np.load(rec_idx + '/info_frames.npz')

    pcd = o3d.geometry.PointCloud()
    global_pcd = o3d.geometry.PointCloud()
    # use nearby RGBD frames to create the environment point cloud
    for i in range(f_id - fusion_window // 2, f_id + fusion_window // 2, 10):
        fname = rec_idx + '/' + '{:05d}'.format(i) + '.png'
        if os.path.exists(fname):
            infot = info[i]
            cam_near_clip = infot['cam_near_clip']
            if 'cam_far_clip' in infot.keys():
                cam_far_clip = infot['cam_far_clip']
            else:
                cam_far_clip = 800. 
            depth = read_depthmap(fname, cam_near_clip, cam_far_clip)
            # delete points that are more than 20 meters away
            depth[depth > 20.0] = 0

            # obtain the human mask
            p = info_npz['joints_2d'][i, 0]
            fname = rec_idx + '/' + '{:05d}'.format(i) + '_id.png'
            id_map = cv2.imread(fname, cv2.IMREAD_ANYDEPTH)
            human_id = id_map[
                np.clip(int(p[1]), 0, 1079), np.clip(int(p[0]), 0, 1919)
            ]

            mask = id_map == human_id
            kernel = np.ones((3, 3), np.uint8)
            mask_dilation = cv2.dilate(
                mask.astype(np.uint8), kernel, iterations=1
            )
            depth = depth * (1 - mask_dilation[..., None])
            depth = o3d.geometry.Image(depth.astype(np.float32))
            # cv2.imshow('tt', mask.astype(np.uint8)*255)
            # cv2.waitKey(0)

            fname = rec_idx + '/' + '{:05d}'.format(i) + '.jpg'
            color_raw = o3d.io.read_image(fname)

            focal_length = info_npz['intrinsics'][f_id, 0, 0]
            rgbd_image = o3d.geometry.create_rgbd_image_from_color_and_depth(
                color_raw,
                depth,
                depth_scale=1.0,
                depth_trunc=15.0,
                convert_rgb_to_intensity=False,
            )
            pcd = o3d.geometry.create_point_cloud_from_rgbd_image(
                rgbd_image,
                o3d.camera.PinholeCameraIntrinsic(
                    PinholeCameraIntrinsic(
                        1920, 1080, focal_length, focal_length, 960.0, 540.0
                    )
                ),
            )
            depth_pts = np.asarray(pcd.points)

            depth_pts_aug = np.hstack(
                [depth_pts, np.ones([depth_pts.shape[0], 1])]
            )
            cam_extr_ref = np.linalg.inv(info_npz['world2cam_trans'][i])
            depth_pts = depth_pts_aug.dot(cam_extr_ref)[:, :3]
            pcd.points = Vector3dVector(depth_pts)

            global_pcd.points.extend(pcd.points)
            global_pcd.colors.extend(pcd.colors)

    # read gt pose in world coordinate, visualize nearby frame as well
    joints = info_npz['joints_3d_world'][(f_id - 30) : (f_id + 30) : 10]
    tl, jn, _ = joints.shape
    joints = joints.reshape(-1, 3)

    # create skeletons in open3d
    nskeletons = tl
    lines, colors = create_skeleton_viz_data(nskeletons, jn)
    line_set = LineSet()
    line_set.points = Vector3dVector(joints)
    line_set.lines = Vector2iVector(lines)
    line_set.colors = Vector3dVector(colors)

    vis_list = [global_pcd, line_set]
    for j in range(joints.shape[0]):
        # spine joints
        if j % jn == 11 or j % jn == 12 or j % jn == 13:
            continue
        transformation = np.identity(4)
        transformation[:3, 3] = joints[j]
        # head joint
        if j % jn == 0:
            r = 0.07
        else:
            r = 0.03

        sphere = o3d.geometry.create_mesh_sphere(radius=r)
        sphere.paint_uniform_color([0.0, float(j // jn) / nskeletons, 1.0])
        vis_list.append(sphere.transform(transformation))

    draw_geometries(vis_list)


if __name__ == '__main__':
    parser = argparse.ArgumentParser(description=None)
    parser.add_argument('-pa', '--path', default='2020-06-10-21-47-45')
    parser.add_argument(
        '-f', '--frame', default=180, type=int, help='frame to visualize'
    )
    parser.add_argument(
        '-fw',
        '--fusion-window',
        default=20,
        type=int,
        help='timesteps of RGB frames for fusing',
    )
    args = parser.parse_args()

    vis_skeleton_pcd(args.path + '/', args.frame, args.fusion_window)


================================================
FILE: vis_video.py
================================================
"""
GTA-IM Dataset
"""

import argparse
import glob
import os
import os.path as osp
import sys

import cv2
from tqdm import tqdm

if __name__ == '__main__':
    parser = argparse.ArgumentParser(description=None)
    parser.add_argument('-pa', '--path', default='2020-06-10-21-47-45')
    parser.add_argument('-s', '--scale', default=4, type=int, help='down scale')
    parser.add_argument(
        '-fr', '--frame_rate', default=5, type=int, help='frame_rate'
    )
    args = parser.parse_args()
    args.outpath = args.path + '/vis/'
    if not osp.exists(args.outpath):
        os.mkdir(args.outpath)

    ims = sorted(glob.glob(args.path + '/*.jpg'))
    if osp.exists(osp.join(args.outpath, 'video.mp4')):
        sys.exit()

    img_array = []
    for filename in tqdm(ims, desc='frame'):
        img = cv2.imread(filename)
        height, width, layers = img.shape
        size = (width // args.scale, height // args.scale)
        img = cv2.resize(img, size, interpolation=cv2.INTER_LINEAR)
        img_array.append(img)

    out = cv2.VideoWriter(
        osp.join(args.outpath, 'video.mp4'),
        cv2.VideoWriter_fourcc(*'mp4v'),
        args.frame_rate,
        size,
    )
    for i in range(len(img_array)):
        out.write(img_array[i])
    out.release()