mirror of
https://github.com/gosticks/body-pose-animation.git
synced 2025-10-16 11:45:42 +00:00
93 lines
3.0 KiB
Python
93 lines
3.0 KiB
Python
import pickle
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from model import SMPLyModel
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from renderer import DefaultRenderer
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import cv2
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from tqdm import tqdm
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import numpy as np
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from scipy import interpolate
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def make_video(images, video_name: str, fps=5, ext: str = "mp4"):
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images = np.array(images)
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width = images.shape[2]
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height = images.shape[1]
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fourcc = 0
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if ext == "mp4":
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fourcc = cv2.VideoWriter_fourcc(*'MP4V')
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video_name = video_name + "." + ext
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video = cv2.VideoWriter(
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video_name, fourcc, fps, (width, height), True)
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print("creating video with size", width, height)
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for idx in tqdm(range(len(images))):
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img = images[idx]
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im_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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video.write(im_rgb)
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video.release()
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def video_from_pkl(filename, video_name, config, ext: str = "mp4"):
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with open(filename, "rb") as fp:
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model_outs = pickle.load(fp)
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save_to_video(model_outs, video_name, config)
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def save_to_video(poses, video_name, config, fps=30, interpolated=False):
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r = DefaultRenderer(
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offscreen=True
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)
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r.start()
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model_anim = SMPLyModel.model_from_conf(config)
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frames = []
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for body_pose, cam_trans in tqdm(poses):
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r.render_model_with_tfs(model_anim, body_pose, keep_pose=True,
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render_joints=False, transforms=cam_trans, interpolated=interpolated)
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frames.append(r.get_snapshot())
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make_video(frames, video_name, fps)
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def interpolate_poses(poses, num_intermediate=5):
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"""
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Interpolate vertices and cameras between pairs of frames by adding intermediate results
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:param poses: optimized poses
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:param num_intermediate: amount of intermediate results to insert between each pair of frames
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:return: interpolated poses, list of tuples (body_pose, camera_pose)
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"""
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new_poses = []
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for i in range(len(poses) - 1):
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if len(poses) < 2:
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return poses
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else:
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# Shape of one matrix of vertices = torch.Size([1, 10475, 3])
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pose_1 = poses[i][0].vertices.detach().cpu().numpy()
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pose_2 = poses[i + 1][0].vertices.detach().cpu().numpy()
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poses_pair = np.concatenate((pose_1, pose_2), axis=0)
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camera_1 = np.expand_dims(poses[i][1], axis=0)
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camera_2 = np.expand_dims(poses[i + 1][1], axis=0)
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camera_pair = np.concatenate((camera_1, camera_2), axis=0)
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x = np.arange(poses_pair.shape[0])
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f1 = interpolate.interp1d(x, poses_pair, axis=0)
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f2 = interpolate.interp1d(x, camera_pair, axis=0)
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evenly_spaced_points = np.linspace(x[0], x[-1], (poses_pair.shape[0] - 1) * (num_intermediate + 1) + 1)
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new_frames = f1(evenly_spaced_points)
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new_cameras = f2(evenly_spaced_points)
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arr = [(new_frames[i], new_cameras[i]) for i in range(new_frames.shape[0])]
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if 0 < i < len(poses) - 1:
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arr.pop(0) # remove first frame that was already added in the last interpolation
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new_poses += arr
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return new_poses
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