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Running
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Zero
File size: 3,394 Bytes
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# ------------------------------------------------------------------------------
# https://github.dev/HRNet/HigherHRNet-Human-Pose-Estimation
# Copyright (c) Microsoft
# Licensed under the MIT License.
# Written by Bin Xiao (leoxiaobin@gmail.com)
# Modified by Bowen Cheng (bcheng9@illinois.edu)
# ------------------------------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import logging
import torch
import torch.nn as nn
from pdb import set_trace as st
logger = logging.getLogger(__name__)
class HeatmapGenerator():
def __init__(self, heatmap_size, num_joints=68, sigma=2):
self.heatmap_size = heatmap_size
# self.image_size = image_size
self.num_joints = num_joints
if sigma < 0:
sigma = self.heatmap_size / 64
self.sigma = sigma
size = 6 * sigma + 3
x = np.arange(0, size, 1, float)
y = x[:, np.newaxis]
x0, y0 = 3 * sigma + 1, 3 * sigma + 1
self.g = np.exp(-((x - x0)**2 + (y - y0)**2) / (2 * sigma**2))
# def __call__(self, joints, image_size: np.ndarray):
def __call__(self, joints, image_size: int):
"""generate heatmap gt from joints
Args:
joints (np.ndarray): N,3
Returns:
hms: N,H,W
"""
hms = np.zeros((self.num_joints, self.heatmap_size, self.heatmap_size),
dtype=np.float32)
sigma = self.sigma
# feat_stride = image_size / [self.heatmap_size, self.heatmap_size]
feat_stride = image_size / self.heatmap_size
for idx, pt in enumerate(joints):
# for idx, pt in enumerate(p):
if pt[2] > 0:
# x = int(pt[0] / feat_stride[0] + 0.5)
# y = int(pt[1] / feat_stride[1] + 0.5) # normalize joints to heatmap size
x = int(pt[0] / feat_stride + 0.5)
y = int(pt[1] / feat_stride +
0.5) # normalize joints to heatmap size
if x < 0 or y < 0 or \
x >= self.heatmap_size or y >= self.heatmap_size:
continue
ul = int(np.round(x - 3 * sigma - 1)), int(
np.round(y - 3 * sigma - 1))
br = int(np.round(x + 3 * sigma + 2)), int(
np.round(y + 3 * sigma + 2))
c, d = max(0, -ul[0]), min(br[0], self.heatmap_size) - ul[0]
a, b = max(0, -ul[1]), min(br[1], self.heatmap_size) - ul[1]
cc, dd = max(0, ul[0]), min(br[0], self.heatmap_size)
aa, bb = max(0, ul[1]), min(br[1], self.heatmap_size)
hms[idx, aa:bb, cc:dd] = np.maximum(hms[idx, aa:bb, cc:dd],
self.g[a:b, c:d])
return hms
class HeatmapLoss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, pred, gt, mask=None):
# todo, add mask
assert pred.size() == gt.size()
loss = ((pred - gt)**2)
if mask is not None:
loss = loss * mask[:, None, :, :].expand_as(pred)
# loss = loss.mean(dim=3).mean(dim=2).mean(dim=1)
loss = loss.mean()
# loss = loss.mean(dim=3).mean(dim=2).sum(dim=1)
return loss
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