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import os
from glob import glob
import numpy as np
import json
from matplotlib import pyplot as plt
import pandas as pd
def get_gts(clip):
'''
clip: abs path to the clip dir
'''
keypoints_files = sorted(glob(os.path.join(clip, 'keypoints_new/person_1')+'/*.json'))
upper_body_points = list(np.arange(0, 25))
poses = []
confs = []
neck_to_nose_len = []
mean_position = []
for kp_file in keypoints_files:
kp_load = json.load(open(kp_file, 'r'))['people'][0]
posepts = kp_load['pose_keypoints_2d']
lhandpts = kp_load['hand_left_keypoints_2d']
rhandpts = kp_load['hand_right_keypoints_2d']
facepts = kp_load['face_keypoints_2d']
neck = np.array(posepts).reshape(-1,3)[1]
nose = np.array(posepts).reshape(-1,3)[0]
x_offset = abs(neck[0]-nose[0])
y_offset = abs(neck[1]-nose[1])
neck_to_nose_len.append(y_offset)
mean_position.append([neck[0],neck[1]])
keypoints=np.array(posepts+lhandpts+rhandpts+facepts).reshape(-1,3)[:,:2]
upper_body = keypoints[upper_body_points, :]
hand_points = keypoints[25:, :]
keypoints = np.vstack([upper_body, hand_points])
poses.append(keypoints)
if len(neck_to_nose_len) > 0:
scale_factor = np.mean(neck_to_nose_len)
else:
raise ValueError(clip)
mean_position = np.mean(np.array(mean_position), axis=0)
unlocalized_poses = np.array(poses).copy()
localized_poses = []
for i in range(len(poses)):
keypoints = poses[i]
neck = keypoints[1].copy()
keypoints[:, 0] = (keypoints[:, 0] - neck[0]) / scale_factor
keypoints[:, 1] = (keypoints[:, 1] - neck[1]) / scale_factor
localized_poses.append(keypoints.reshape(-1))
localized_poses=np.array(localized_poses)
return unlocalized_poses, localized_poses, (scale_factor, mean_position)
def get_full_path(wav_name, speaker, split):
'''
get clip path from aud file
'''
wav_name = os.path.basename(wav_name)
wav_name = os.path.splitext(wav_name)[0]
clip_name, vid_name = wav_name[:10], wav_name[11:]
full_path = os.path.join('pose_dataset/videos/', speaker, 'clips', vid_name, 'images/half', split, clip_name)
assert os.path.isdir(full_path), full_path
return full_path
def smooth(res):
'''
res: (B, seq_len, pose_dim)
'''
window = [res[:, 7, :], res[:, 8, :], res[:, 9, :], res[:, 10, :], res[:, 11, :], res[:, 12, :]]
w_size=7
for i in range(10, res.shape[1]-3):
window.append(res[:, i+3, :])
if len(window) > w_size:
window = window[1:]
if (i%25) in [22, 23, 24, 0, 1, 2, 3]:
res[:, i, :] = np.mean(window, axis=1)
return res
def cvt25(pred_poses, gt_poses=None):
'''
gt_poses: (1, seq_len, 270), 135 *2
pred_poses: (B, seq_len, 108), 54 * 2
'''
if gt_poses is None:
gt_poses = np.zeros_like(pred_poses)
else:
gt_poses = gt_poses.repeat(pred_poses.shape[0], axis=0)
length = min(pred_poses.shape[1], gt_poses.shape[1])
pred_poses = pred_poses[:, :length, :]
gt_poses = gt_poses[:, :length, :]
gt_poses = gt_poses.reshape(gt_poses.shape[0], gt_poses.shape[1], -1, 2)
pred_poses = pred_poses.reshape(pred_poses.shape[0], pred_poses.shape[1], -1, 2)
gt_poses[:, :, [1, 2, 3, 4, 5, 6, 7], :] = pred_poses[:, :, 1:8, :]
gt_poses[:, :, 25:25+21+21, :] = pred_poses[:, :, 12:, :]
return gt_poses.reshape(gt_poses.shape[0], gt_poses.shape[1], -1)
def hand_points(seq):
'''
seq: (B, seq_len, 135*2)
hands only
'''
hand_idx = [1, 2, 3, 4,5 ,6,7] + list(range(25, 25+21+21))
seq = seq.reshape(seq.shape[0], seq.shape[1], -1, 2)
return seq[:, :, hand_idx, :].reshape(seq.shape[0], seq.shape[1], -1)
def valid_points(seq):
'''
hands with some head points
'''
valid_idx = [0, 1, 2, 3, 4,5 ,6,7, 8, 9, 10, 11] + list(range(25, 25+21+21))
seq = seq.reshape(seq.shape[0], seq.shape[1], -1, 2)
seq = seq[:, :, valid_idx, :].reshape(seq.shape[0], seq.shape[1], -1)
assert seq.shape[-1] == 108, seq.shape
return seq
def draw_cdf(seq, save_name='cdf.jpg', color='slatebule'):
plt.figure()
plt.hist(seq, bins=100, range=(0, 100), color=color)
plt.savefig(save_name)
def to_excel(seq, save_name='res.xlsx'):
'''
seq: (T)
'''
df = pd.DataFrame(seq)
writer = pd.ExcelWriter(save_name)
df.to_excel(writer, 'sheet1')
writer.save()
writer.close()
if __name__ == '__main__':
random_data = np.random.randint(0, 10, 100)
draw_cdf(random_data) |