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import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation, writers
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
import subprocess as sp
from tools.color_edge import h36m_color_edge
def get_resolution(filename):
command = ['ffprobe', '-v', 'error', '-select_streams', 'v:0',
'-show_entries', 'stream=width,height', '-of', 'csv=p=0', filename]
with sp.Popen(command, stdout=sp.PIPE, bufsize=-1) as pipe:
for line in pipe.stdout:
w, h = line.decode().strip().split(',')
return int(w), int(h)
def get_fps(filename):
command = ['ffprobe', '-v', 'error', '-select_streams', 'v:0',
'-show_entries', 'stream=r_frame_rate', '-of', 'csv=p=0', filename]
with sp.Popen(command, stdout=sp.PIPE, bufsize=-1) as pipe:
for line in pipe.stdout:
a, b = line.decode().strip().split('/')
return int(a) / int(b)
def read_video(filename, skip=0, limit=-1):
w, h = get_resolution(filename)
command = ['ffmpeg',
'-i', filename,
'-f', 'image2pipe',
'-pix_fmt', 'rgb24',
'-vsync', '0',
'-vcodec', 'rawvideo', '-']
i = 0
with sp.Popen(command, stdout=sp.PIPE, bufsize=-1) as pipe:
while True:
data = pipe.stdout.read(w * h * 3)
if not data:
break
i += 1
if i > limit and limit != -1:
continue
if i > skip:
yield np.frombuffer(data, dtype='uint8').reshape((h, w, 3))
def downsample_tensor(X, factor):
length = X.shape[0] // factor * factor
return np.mean(X[:length].reshape(-1, factor, *X.shape[1:]), axis=1)
def render_animation(keypoints, keypoints_metadata, poses, skeleton, fps, bitrate, azim, output, viewport, limit=-1,
downsample=1, size=5, input_video_path=None, com_reconstrcution=False, input_video_skip=0):
"""
TODO
Render an animation. The supported output modes are:
-- 'interactive': display an interactive figure
(also works on notebooks if associated with %matplotlib inline)
-- 'html': render the animation as HTML5 video. Can be displayed in a notebook using HTML(...).
-- 'filename.mp4': render and export the animation as an h264 video (requires ffmpeg).
-- 'filename.gif': render and export the animation a gif file (requires imagemagick).
"""
plt.ioff()
num_person = keypoints.shape[1]
if num_person == 2 and com_reconstrcution:
fig = plt.figure(figsize=(size * (1 + len(poses)), size))
ax_in = fig.add_subplot(1, 2, 1)
else:
fig = plt.figure(figsize=(size * (1 + len(poses)), size))
ax_in = fig.add_subplot(1, 1 + len(poses), 1)
ax_in.get_xaxis().set_visible(False)
ax_in.get_yaxis().set_visible(False)
ax_in.set_axis_off()
# ax_in.set_title('Input')
ax_3d = []
lines_3d = []
radius = 1.7
if num_person == 2 and com_reconstrcution:
ax = fig.add_subplot(1, 2, 2, projection='3d')
ax.view_init(elev=15., azim=azim)
ax.set_xlim3d([-radius, radius])
ax.set_zlim3d([0, radius])
ax.set_ylim3d([-radius, radius])
ax.set_xticklabels([])
ax.set_yticklabels([])
ax.set_zticklabels([])
ax.dist = 7.5
ax_3d.append(ax)
lines_3d.append([])
poses = list(poses.values())
else:
for index, (title, data) in enumerate(poses.items()):
ax = fig.add_subplot(1, 1 + len(poses), index + 2, projection='3d')
ax.view_init(elev=15., azim=azim)
ax.set_xlim3d([-radius / 2, radius / 2])
ax.set_zlim3d([0, radius])
ax.set_ylim3d([-radius / 2, radius / 2])
ax.set_aspect('equal')
ax.set_xticklabels([])
ax.set_yticklabels([])
ax.set_zticklabels([])
ax.dist = 7.5
# ax.set_title(title) # , pad=35
ax_3d.append(ax)
lines_3d.append([])
poses = list(poses.values())
# Decode video
if input_video_path is None:
# Black background
all_frames = np.zeros((keypoints.shape[0], viewport[1], viewport[0]), dtype='uint8')
else:
# Load video using ffmpeg
all_frames = []
for f in read_video(input_video_path, skip=input_video_skip, limit=limit):
all_frames.append(f)
effective_length = min(keypoints.shape[0], len(all_frames))
all_frames = all_frames[:effective_length]
keypoints = keypoints[input_video_skip:] # todo remove
for idx in range(len(poses)):
poses[idx] = poses[idx][input_video_skip:]
if fps is None:
fps = get_fps(input_video_path)
if downsample > 1:
keypoints = downsample_tensor(keypoints, downsample)
all_frames = downsample_tensor(np.array(all_frames), downsample).astype('uint8')
for idx in range(len(poses)):
poses[idx] = downsample_tensor(poses[idx], downsample)
fps /= downsample
initialized = False
image = None
lines = []
points = None
if limit < 1:
limit = len(all_frames)
else:
limit = min(limit, len(all_frames))
parents = skeleton.parents()
index = [i for i in np.arange(17)]
def update_video(i):
nonlocal initialized, image, lines, points
joints_right_2d = keypoints_metadata['keypoints_symmetry'][1]
if num_person == 2:
joints_right_2d_two = []
joints_right_2d_two += joints_right_2d
joints_right_2d_second = [i + 17 for i in joints_right_2d]
joints_right_2d_two += joints_right_2d_second
colors_2d = np.full(34, 'black')
colors_2d[joints_right_2d_two] = 'red'
else:
colors_2d = np.full(17, 'black')
colors_2d[joints_right_2d] = 'red'
if not initialized:
image = ax_in.imshow(all_frames[i], aspect='equal')
for j, j_parent in zip(index, parents):
if j_parent == -1:
continue
if len(parents) == 17 and keypoints_metadata['layout_name'] != 'coco':
for m in range(num_person):
# Draw skeleton only if keypoints match (otherwise we don't have the parents definition)
lines.append(ax_in.plot([keypoints[i, m, j, 0], keypoints[i, m, j_parent, 0]],
[keypoints[i, m, j, 1], keypoints[i, m, j_parent, 1]],
color='pink'))
# Apply different colors for each joint
col = h36m_color_edge(j)
if com_reconstrcution:
for pose in poses:
pos = pose[i]
lines_3d[0].append(ax_3d[0].plot([pos[j, 0], pos[j_parent, 0]],
[pos[j, 1], pos[j_parent, 1]],
[pos[j, 2], pos[j_parent, 2]], zdir='z', c=col, linewidth=3))
else:
for n, ax in enumerate(ax_3d):
pos = poses[n][i]
lines_3d[n].append(ax.plot([pos[j, 0], pos[j_parent, 0]],
[pos[j, 1], pos[j_parent, 1]],
[pos[j, 2], pos[j_parent, 2]], zdir='z', c=col, linewidth=3))
points = ax_in.scatter(*keypoints[i].reshape(17*num_person, 2).T, 10, color=colors_2d, edgecolors='white', zorder=10)
initialized = True
else:
image.set_data(all_frames[i])
for j, j_parent in zip(index, parents):
if j_parent == -1:
continue
if len(parents) == 17 and keypoints_metadata['layout_name'] != 'coco':
for m in range(num_person):
lines[j + 16*m - 1][0].set_data([keypoints[i, m, j, 0], keypoints[i, m, j_parent, 0]],
[keypoints[i, m, j, 1], keypoints[i, m, j_parent, 1]])
if com_reconstrcution:
for k, pose in enumerate(poses):
pos = pose[i]
lines_3d[0][j + k*16 - 1][0].set_xdata([pos[j, 0], pos[j_parent, 0]])
lines_3d[0][j + k*16 - 1][0].set_ydata([pos[j, 1], pos[j_parent, 1]])
lines_3d[0][j + k*16 - 1][0].set_3d_properties([pos[j, 2], pos[j_parent, 2]], zdir='z')
else:
for n, ax in enumerate(ax_3d):
pos = poses[n][i]
lines_3d[n][j - 1][0].set_xdata([pos[j, 0], pos[j_parent, 0]])
lines_3d[n][j - 1][0].set_ydata([pos[j, 1], pos[j_parent, 1]])
lines_3d[n][j - 1][0].set_3d_properties([pos[j, 2], pos[j_parent, 2]], zdir='z')
points.set_offsets(keypoints[i].reshape(17*num_person, 2))
print('{}/{} '.format(i, limit), end='\r')
fig.tight_layout()
anim = FuncAnimation(fig, update_video, frames=np.arange(0, limit), interval=1000 / fps, repeat=False)
if output.endswith('.mp4'):
Writer = writers['ffmpeg']
writer = Writer(fps=fps, metadata={}, bitrate=bitrate)
anim.save(output, writer=writer)
elif output.endswith('.gif'):
anim.save(output, dpi=80, writer='imagemagick')
else:
raise ValueError('Unsupported output format (only .mp4 and .gif are supported)')
plt.close()
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