lib/pymafx/utils/demo_utils.py

# -*- coding: utf-8 -*-

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# holder of all proprietary rights on this computer program.
# You can only use this computer program if you have closed
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# program from someone who is authorized to grant you that right.
# Any use of the computer program without a valid license is prohibited and
# liable to prosecution.
#
# Copyright©2019 Max-Planck-Gesellschaft zur Förderung
# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute
# for Intelligent Systems. All rights reserved.
#
# Contact: ps-license@tuebingen.mpg.de

import json
import os
import os.path as osp
import subprocess
import time
# from pytube import YouTube
from collections import OrderedDict

import cv2
import numpy as np
import torch
from datasets.data_utils.img_utils import get_single_image_crop_demo
from utils.geometry import rotation_matrix_to_angle_axis
from utils.smooth_bbox import get_all_bbox_params, get_smooth_bbox_params


def preprocess_video(video, joints2d, bboxes, frames, scale=1.0, crop_size=224):
    """
    Read video, do normalize and crop it according to the bounding box.
    If there are bounding box annotations, use them to crop the image.
    If no bounding box is specified but openpose detections are available, use them to get the bounding box.

    :param video (ndarray): input video
    :param joints2d (ndarray, NxJx3): openpose detections
    :param bboxes (ndarray, Nx5): bbox detections
    :param scale (float): bbox crop scaling factor
    :param crop_size (int): crop width and height
    :return: cropped video, cropped and normalized video, modified bboxes, modified joints2d
    """

    if joints2d is not None:
        bboxes, time_pt1, time_pt2 = get_all_bbox_params(joints2d, vis_thresh=0.3)
        bboxes[:, 2:] = 150. / bboxes[:, 2:]
        bboxes = np.stack([bboxes[:, 0], bboxes[:, 1], bboxes[:, 2], bboxes[:, 2]]).T

        video = video[time_pt1:time_pt2]
        joints2d = joints2d[time_pt1:time_pt2]
        frames = frames[time_pt1:time_pt2]

    shape = video.shape

    temp_video = np.zeros((shape[0], crop_size, crop_size, shape[-1]))
    norm_video = torch.zeros(shape[0], shape[-1], crop_size, crop_size)

    for idx in range(video.shape[0]):

        img = video[idx]
        bbox = bboxes[idx]

        j2d = joints2d[idx] if joints2d is not None else None

        norm_img, raw_img, kp_2d = get_single_image_crop_demo(
            img, bbox, kp_2d=j2d, scale=scale, crop_size=crop_size
        )

        if joints2d is not None:
            joints2d[idx] = kp_2d

        temp_video[idx] = raw_img
        norm_video[idx] = norm_img

    temp_video = temp_video.astype(np.uint8)

    return temp_video, norm_video, bboxes, joints2d, frames


def download_youtube_clip(url, download_folder):
    return YouTube(url).streams.first().download(output_path=download_folder)


def smplify_runner(
    pred_rotmat,
    pred_betas,
    pred_cam,
    j2d,
    device,
    batch_size,
    lr=1.0,
    opt_steps=1,
    use_lbfgs=True,
    pose2aa=True
):
    smplify = TemporalSMPLify(
        step_size=lr,
        batch_size=batch_size,
        num_iters=opt_steps,
        focal_length=5000.,
        use_lbfgs=use_lbfgs,
        device=device,
    # max_iter=10,
    )
    # Convert predicted rotation matrices to axis-angle
    if pose2aa:
        pred_pose = rotation_matrix_to_angle_axis(pred_rotmat.detach()).reshape(batch_size, -1)
    else:
        pred_pose = pred_rotmat

    # Calculate camera parameters for smplify
    pred_cam_t = torch.stack([
        pred_cam[:, 1], pred_cam[:, 2], 2 * 5000 / (224 * pred_cam[:, 0] + 1e-9)
    ],
                             dim=-1)

    gt_keypoints_2d_orig = j2d
    # Before running compute reprojection error of the network
    opt_joint_loss = smplify.get_fitting_loss(
        pred_pose.detach(), pred_betas.detach(), pred_cam_t.detach(),
        0.5 * 224 * torch.ones(batch_size, 2, device=device), gt_keypoints_2d_orig
    ).mean(dim=-1)

    best_prediction_id = torch.argmin(opt_joint_loss).item()
    pred_betas = pred_betas[best_prediction_id].unsqueeze(0)
    # pred_betas = pred_betas[best_prediction_id:best_prediction_id+2] # .unsqueeze(0)
    # top5_best_idxs = torch.topk(opt_joint_loss, 5, largest=False)[1]
    # breakpoint()

    start = time.time()
    # Run SMPLify optimization initialized from the network prediction
    # new_opt_vertices, new_opt_joints, \
    # new_opt_pose, new_opt_betas, \
    # new_opt_cam_t, \
    output, new_opt_joint_loss = smplify(
        pred_pose.detach(),
        pred_betas.detach(),
        pred_cam_t.detach(),
        0.5 * 224 * torch.ones(batch_size, 2, device=device),
        gt_keypoints_2d_orig,
    )
    new_opt_joint_loss = new_opt_joint_loss.mean(dim=-1)
    # smplify_time = time.time() - start
    # print(f'Smplify time: {smplify_time}')
    # Will update the dictionary for the examples where the new loss is less than the current one
    update = (new_opt_joint_loss < opt_joint_loss)

    new_opt_vertices = output['verts']
    new_opt_cam_t = output['theta'][:, :3]
    new_opt_pose = output['theta'][:, 3:75]
    new_opt_betas = output['theta'][:, 75:]
    new_opt_joints3d = output['kp_3d']

    return_val = [
        update,
        new_opt_vertices.cpu(),
        new_opt_cam_t.cpu(),
        new_opt_pose.cpu(),
        new_opt_betas.cpu(),
        new_opt_joints3d.cpu(),
        new_opt_joint_loss,
        opt_joint_loss,
    ]

    return return_val


def trim_videos(filename, start_time, end_time, output_filename):
    command = [
        'ffmpeg', '-i',
        '"%s"' % filename, '-ss',
        str(start_time), '-t',
        str(end_time - start_time), '-c:v', 'libx264', '-c:a', 'copy', '-threads', '1', '-loglevel',
        'panic',
        '"%s"' % output_filename
    ]
    # command = ' '.join(command)
    subprocess.call(command)


def video_to_images(vid_file, img_folder=None, return_info=False):
    if img_folder is None:
        img_folder = osp.join(osp.expanduser('~'), 'tmp', osp.basename(vid_file).replace('.', '_'))
        # img_folder = osp.join('/tmp', osp.basename(vid_file).replace('.', '_'))

    print(img_folder)
    os.makedirs(img_folder, exist_ok=True)

    command = ['ffmpeg', '-i', vid_file, '-f', 'image2', '-v', 'error', f'{img_folder}/%06d.png']
    print(f'Running \"{" ".join(command)}\"')

    try:
        subprocess.call(command)
    except:
        subprocess.call(f'{" ".join(command)}', shell=True)

    print(f'Images saved to \"{img_folder}\"')

    img_shape = cv2.imread(osp.join(img_folder, '000001.png')).shape

    if return_info:
        return img_folder, len(os.listdir(img_folder)), img_shape
    else:
        return img_folder


def download_url(url, outdir):
    print(f'Downloading files from {url}')
    cmd = ['wget', '-c', url, '-P', outdir]
    subprocess.call(cmd)


def download_ckpt(outdir='data/vibe_data', use_3dpw=False):
    os.makedirs(outdir, exist_ok=True)

    if use_3dpw:
        ckpt_file = 'data/vibe_data/vibe_model_w_3dpw.pth.tar'
        url = 'https://www.dropbox.com/s/41ozgqorcp095ja/vibe_model_w_3dpw.pth.tar'
        if not os.path.isfile(ckpt_file):
            download_url(url=url, outdir=outdir)
    else:
        ckpt_file = 'data/vibe_data/vibe_model_wo_3dpw.pth.tar'
        url = 'https://www.dropbox.com/s/amj2p8bmf6g56k6/vibe_model_wo_3dpw.pth.tar'
        if not os.path.isfile(ckpt_file):
            download_url(url=url, outdir=outdir)

    return ckpt_file


def images_to_video(img_folder, output_vid_file):
    os.makedirs(img_folder, exist_ok=True)

    command = [
        'ffmpeg',
        '-y',
        '-threads',
        '16',
        '-i',
        f'{img_folder}/%06d.png',
        '-profile:v',
        'baseline',
        '-level',
        '3.0',
        '-c:v',
        'libx264',
        '-pix_fmt',
        'yuv420p',
        '-an',
        '-v',
        'error',
        output_vid_file,
    ]

    print(f'Running \"{" ".join(command)}\"')
    try:
        subprocess.call(command)
    except:
        subprocess.call(f'{" ".join(command)}', shell=True)


def convert_crop_cam_to_orig_img(cam, bbox, img_width, img_height):
    '''
    Convert predicted camera from cropped image coordinates
    to original image coordinates
    :param cam (ndarray, shape=(3,)): weak perspective camera in cropped img coordinates
    :param bbox (ndarray, shape=(4,)): bbox coordinates (c_x, c_y, h)
    :param img_width (int): original image width
    :param img_height (int): original image height
    :return:
    '''
    cx, cy, h = bbox[:, 0], bbox[:, 1], bbox[:, 2]
    hw, hh = img_width / 2., img_height / 2.
    sx = cam[:, 0] * (1. / (img_width / h))
    sy = cam[:, 0] * (1. / (img_height / h))
    tx = ((cx - hw) / hw / sx) + cam[:, 1]
    ty = ((cy - hh) / hh / sy) + cam[:, 2]
    orig_cam = np.stack([sx, sy, tx, ty]).T
    return orig_cam


def prepare_rendering_results(results_dict, nframes):
    frame_results = [{} for _ in range(nframes)]
    for person_id, person_data in results_dict.items():
        for idx, frame_id in enumerate(person_data['frame_ids']):
            frame_results[frame_id][person_id] = {
                'verts': person_data['verts'][idx],
                'smplx_verts':
                person_data['smplx_verts'][idx] if 'smplx_verts' in person_data else None,
                'cam': person_data['orig_cam'][idx],
                'cam_t': person_data['orig_cam_t'][idx] if 'orig_cam_t' in person_data else None,
            # 'cam': person_data['pred_cam'][idx],
            }

    # naive depth ordering based on the scale of the weak perspective camera
    for frame_id, frame_data in enumerate(frame_results):
        # sort based on y-scale of the cam in original image coords
        sort_idx = np.argsort([v['cam'][1] for k, v in frame_data.items()])
        frame_results[frame_id] = OrderedDict({
            list(frame_data.keys())[i]: frame_data[list(frame_data.keys())[i]]
            for i in sort_idx
        })

    return frame_results