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vis.py
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import os
import sys
import cv2
import glob
import copy
import torch
import argparse
import numpy as np
from tqdm import tqdm
from lib.preprocess import h36m_coco_format, revise_kpts
from lib.hrnet.gen_kpts import gen_video_kpts as hrnet_pose
from IPython import embed
import warnings
import matplotlib
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.gridspec as gridspec
plt.switch_backend('agg')
warnings.filterwarnings('ignore')
matplotlib.rcParams['pdf.fonttype'] = 42
matplotlib.rcParams['ps.fonttype'] = 42
sys.path.append(os.getcwd())
from common.utils import *
from common.camera import *
from model.mixste.hot_mixste import Model
def show2Dpose(kps, img):
colors = [(138, 201, 38),
(25, 130, 196),
(255, 202, 58)]
connections = [[0, 1], [1, 2], [2, 3], [0, 4], [4, 5],
[5, 6], [0, 7], [7, 8], [8, 9], [9, 10],
[8, 11], [11, 12], [12, 13], [8, 14], [14, 15], [15, 16]]
LR = [3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2]
thickness = 3
for j,c in enumerate(connections):
start = map(int, kps[c[0]])
end = map(int, kps[c[1]])
start = list(start)
end = list(end)
cv2.line(img, (start[0], start[1]), (end[0], end[1]), colors[LR[j]-1], thickness)
cv2.circle(img, (start[0], start[1]), thickness=-1, color=colors[LR[j]-1], radius=3)
cv2.circle(img, (end[0], end[1]), thickness=-1, color=colors[LR[j]-1], radius=3)
return img
def show3Dpose(vals, ax, fix_z):
ax.view_init(elev=15., azim=70)
colors = [(138/255, 201/255, 38/255),
(255/255, 202/255, 58/255),
(25/255, 130/255, 196/255)]
I = np.array( [0, 0, 1, 4, 2, 5, 0, 7, 8, 8, 14, 15, 11, 12, 8, 9])
J = np.array( [1, 4, 2, 5, 3, 6, 7, 8, 14, 11, 15, 16, 12, 13, 9, 10])
LR = [3, 3, 3, 3, 3, 3, 1, 1, 2, 2, 2, 2, 2, 2, 1, 1]
for i in np.arange( len(I) ):
x, y, z = [np.array( [vals[I[i], j], vals[J[i], j]] ) for j in range(3)]
ax.plot(x, y, z, lw=3, color = colors[LR[i]-1])
RADIUS = 0.72
xroot, yroot, zroot = vals[0,0], vals[0,1], vals[0,2]
ax.set_xlim3d([-RADIUS+xroot, RADIUS+xroot])
ax.set_ylim3d([-RADIUS+yroot, RADIUS+yroot])
if fix_z:
left_z = max(0.0, -RADIUS+zroot)
right_z = RADIUS+zroot
# ax.set_zlim3d([left_z, right_z])
ax.set_zlim3d([0, 1.5])
else:
ax.set_zlim3d([-RADIUS+zroot, RADIUS+zroot])
ax.set_aspect('equal') # works fine in matplotlib==2.2.2 or 3.7.1
white = (1.0, 1.0, 1.0, 0.0)
ax.xaxis.set_pane_color(white)
ax.yaxis.set_pane_color(white)
ax.zaxis.set_pane_color(white)
ax.tick_params('x', labelbottom = False)
ax.tick_params('y', labelleft = False)
ax.tick_params('z', labelleft = False)
def get_pose2D(video_path, output_dir):
cap = cv2.VideoCapture(video_path)
width = cap.get(cv2.CAP_PROP_FRAME_WIDTH)
height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
print('\nGenerating 2D pose...')
with torch.no_grad():
# the first frame of the video should be detected a person
keypoints, scores = hrnet_pose(video_path, det_dim=416, num_peroson=1, gen_output=True)
keypoints, scores, valid_frames = h36m_coco_format(keypoints, scores)
re_kpts = revise_kpts(keypoints, scores, valid_frames)
print('Generating 2D pose successfully!')
output_dir += 'input_2D/'
os.makedirs(output_dir, exist_ok=True)
output_npz = output_dir + 'input_keypoints_2d.npz'
np.savez_compressed(output_npz, reconstruction=keypoints)
def img2video(video_path, output_dir):
cap = cv2.VideoCapture(video_path)
fps = int(cap.get(cv2.CAP_PROP_FPS)) + 5
fourcc = cv2.VideoWriter_fourcc(*"mp4v")
names = sorted(glob.glob(os.path.join(output_dir + 'pose/', '*.png')))
img = cv2.imread(names[0])
size = (img.shape[1], img.shape[0])
videoWrite = cv2.VideoWriter(output_dir + video_name + '.mp4', fourcc, fps, size)
for name in names:
img = cv2.imread(name)
videoWrite.write(img)
videoWrite.release()
def showimage(ax, img):
ax.set_xticks([])
ax.set_yticks([])
plt.axis('off')
ax.imshow(img)
def get_pose3D(video_path, output_dir, fix_z):
args, _ = argparse.ArgumentParser().parse_known_args()
args.layers, args.channel, args.d_hid, args.frames = 8, 512, 1024, 243
args.token_num, args.layer_index = 81, 3
args.pad = (args.frames - 1) // 2
args.previous_dir = 'checkpoint/pretrained/hot_mixste'
args.n_joints, args.out_joints = 17, 17
## Reload
model = Model(args).cuda()
model_dict = model.state_dict()
# Put the pretrained model in 'checkpoint/pretrained/hot_mixste'
model_path = sorted(glob.glob(os.path.join(args.previous_dir, '*.pth')))[0]
pre_dict = torch.load(model_path)
model_dict = model.state_dict()
state_dict = {k: v for k, v in pre_dict.items() if k in model_dict.keys()}
model_dict.update(state_dict)
model.load_state_dict(model_dict)
model.eval()
## input
keypoints = np.load(output_dir + 'input_2D/input_keypoints_2d.npz', allow_pickle=True)['reconstruction']
cap = cv2.VideoCapture(video_path)
video_length = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
n_chunks = video_length // args.frames + 1
offset = (n_chunks * args.frames - video_length) // 2
ret, img = cap.read()
img_size = img.shape
## 3D
print('\nGenerating 3D pose...')
frame_sum = 0
for i in tqdm(range(n_chunks)):
## input frames
start_index = i*args.frames - offset
end_index = (i+1)*args.frames - offset
low_index = max(start_index, 0)
high_index = min(end_index, video_length)
pad_left = low_index - start_index
pad_right = end_index - high_index
if pad_left != 0 or pad_right != 0:
input_2D_no = np.pad(keypoints[0][low_index:high_index], ((pad_left, pad_right), (0, 0), (0, 0)), 'edge')
else:
input_2D_no = keypoints[0][low_index:high_index]
joints_left = [4, 5, 6, 11, 12, 13]
joints_right = [1, 2, 3, 14, 15, 16]
input_2D = normalize_screen_coordinates(input_2D_no, w=img_size[1], h=img_size[0])
input_2D_aug = copy.deepcopy(input_2D)
input_2D_aug[ :, :, 0] *= -1
input_2D_aug[ :, joints_left + joints_right] = input_2D_aug[ :, joints_right + joints_left]
input_2D = np.concatenate((np.expand_dims(input_2D, axis=0), np.expand_dims(input_2D_aug, axis=0)), 0)
input_2D = input_2D[np.newaxis, :, :, :, :]
input_2D = torch.from_numpy(input_2D.astype('float32')).cuda()
N = input_2D.size(0)
## estimation
with torch.no_grad():
output_3D_non_flip = model(input_2D[:, 0])
output_3D_flip = model(input_2D[:, 1])
output_3D_flip[:, :, :, 0] *= -1
output_3D_flip[:, :, joints_left + joints_right, :] = output_3D_flip[:, :, joints_right + joints_left, :]
output_3D = (output_3D_non_flip + output_3D_flip) / 2
if pad_left != 0 and pad_right != 0:
output_3D = output_3D[:, pad_left:-pad_right]
input_2D_no = input_2D_no[pad_left:-pad_right]
elif pad_left != 0:
output_3D = output_3D[:, pad_left:]
input_2D_no = input_2D_no[pad_left:]
elif pad_right != 0:
output_3D = output_3D[:, :-pad_right]
input_2D_no = input_2D_no[:-pad_right]
output_3D[:, :, 0, :] = 0
post_out = output_3D[0].cpu().detach().numpy()
if i == 0:
output_3d_all = post_out
else:
output_3d_all = np.concatenate([output_3d_all, post_out], axis = 0)
## h36m_cameras_extrinsic_params in common/camera.py
# https://github.com/facebookresearch/VideoPose3D/blob/main/common/custom_dataset.py#L23
rot = [0.1407056450843811, -0.1500701755285263, -0.755240797996521, 0.6223280429840088]
rot = np.array(rot, dtype='float32')
post_out = camera_to_world(post_out, R=rot, t=0)
post_out[:, :, 2] -= np.min(post_out[:, 2])
## 2D
for j in range(low_index, high_index):
jj = j - frame_sum
if i == 0 and j == 0:
pass
else:
ret, img = cap.read()
img_size = img.shape
image = show2Dpose(input_2D_no[jj], copy.deepcopy(img))
output_dir_2D = output_dir +'pose2D/'
os.makedirs(output_dir_2D, exist_ok=True)
cv2.imwrite(output_dir_2D + str(('%04d'% j)) + '_2D.png', image)
## 3D
fig = plt.figure(figsize=(9.6, 5.4))
gs = gridspec.GridSpec(1, 1)
gs.update(wspace=-0.00, hspace=0.05)
ax = plt.subplot(gs[0], projection='3d')
show3Dpose(post_out[jj], ax, fix_z)
output_dir_3D = output_dir +'pose3D/'
os.makedirs(output_dir_3D, exist_ok=True)
plt.savefig(output_dir_3D + str(('%04d'% j)) + '_3D.png', dpi=200, format='png', bbox_inches = 'tight')
frame_sum = high_index
## save 3D keypoints
os.makedirs(output_dir + 'output_3D/', exist_ok=True)
output_npz = output_dir + 'output_3D/' + 'output_keypoints_3d.npz'
np.savez_compressed(output_npz, reconstruction=output_3d_all)
print('Generating 3D pose successfully!')
## all
image_dir = 'results/'
image_2d_dir = sorted(glob.glob(os.path.join(output_dir_2D, '*.png')))
image_3d_dir = sorted(glob.glob(os.path.join(output_dir_3D, '*.png')))
print('\nGenerating demo...')
for i in tqdm(range(len(image_2d_dir))):
image_2d = plt.imread(image_2d_dir[i])
image_3d = plt.imread(image_3d_dir[i])
## crop
edge = (image_2d.shape[1] - image_2d.shape[0]) // 2 - 1
# image_2d = image_2d[:, edge:image_2d.shape[1] - edge]
edge_1 = 10
image_2d = image_2d[edge_1:image_2d.shape[0] - edge_1, edge + edge_1:image_2d.shape[1] - edge - edge_1]
edge = 130
image_3d = image_3d[edge:image_3d.shape[0] - edge, edge:image_3d.shape[1] - edge]
## show
font_size = 12
fig = plt.figure(figsize=(9.6, 5.4))
ax = plt.subplot(121)
showimage(ax, image_2d)
ax.set_title("Input", fontsize = font_size)
ax = plt.subplot(122)
showimage(ax, image_3d)
ax.set_title("Reconstruction", fontsize = font_size)
## save
output_dir_pose = output_dir +'pose/'
os.makedirs(output_dir_pose, exist_ok=True)
plt.savefig(output_dir_pose + str(('%04d'% i)) + '_pose.png', dpi=200, bbox_inches = 'tight')
plt.close()
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--video', type=str, default='sample_video.mp4', help='input video')
parser.add_argument('--gpu', type=str, default='0', help='input video')
parser.add_argument('--fix_z', action='store_true', help='fix z axis')
args = parser.parse_args()
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
video_path = './demo/video/' + args.video
video_name = video_path.split('/')[-1].split('.')[0]
output_dir = './demo/output/' + video_name + '/'
get_pose2D(video_path, output_dir)
get_pose3D(video_path, output_dir, args.fix_z)
img2video(video_path, output_dir)
print('Generating demo successfully!')