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🚀 add model check point

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This commit is contained in:
shuaiqing 2023-06-19 17:23:44 +08:00
parent e7800a1356
commit 11f13d6953
12 changed files with 793 additions and 22 deletions
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8
config/1v1p/fixhand.yml
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@ -26,7 +26,8 @@ args:
key_from_previous: [bbox]
args:
# ckpt: /nas/public/EasyMocapModels/hrnetv2_w18_coco_wholebody_hand_256x256-1c028db7_20210908.pth
ckpt: /nas/public/EasyMocapModels/hand/resnet_kp2d_clean.pt
ckpt: models/hand_resnet_kp2d_clean.pt
url: 1LTK7e9oAS6B3drmQyXwTZild6k87fEZa
mode: resnet
vis2d:
module: myeasymocap.io.vis.Vis2D
@ -42,13 +43,14 @@ args:
key_from_previous: [bbox]
key_keep: [meta, cameras, imgnames] # 将这些参数都保留到最后的输出中
args:
ckpt: models/manol_pca45_noflat.ckpt
ckpt: models/hand_manol_pca45_noflat.ckpt
url: '1KTi_oJ_udLRK3WZ3xyHzBUd6vKAApfT8'
# TODO: add visualize for Init MANO
at_final:
load_hand_model: # 载入身体模型
module: myeasymocap.io.model.MANOLoader
args:
cfg_path: config/model/mano.yml
cfg_path: config/model/manol.yml
model_path: models/manov1.2/MANO_LEFT.pkl #models/handmesh/data/MANO_RIGHT.pkl # load mano model
regressor_path: models/manov1.2/J_regressor_mano_LEFT.txt #models/handmesh/data/J_regressor_mano_RIGHT.txt
num_pca_comps: 45

9
config/1v1p/hand_detect_finetune.yml
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@ -26,7 +26,9 @@ args:
key_from_previous: [bbox]
args:
# ckpt: /nas/public/EasyMocapModels/hrnetv2_w18_coco_wholebody_hand_256x256-1c028db7_20210908.pth
ckpt: /nas/public/EasyMocapModels/hand/resnet_kp2d_clean.pt
# ckpt: /nas/public/EasyMocapModels/hand/resnet_kp2d_clean.pt
ckpt: models/hand_resnet_kp2d_clean.pt
url: 1LTK7e9oAS6B3drmQyXwTZild6k87fEZa
mode: resnet
vis2d:
module: myeasymocap.io.vis.Vis2D
@ -42,13 +44,14 @@ args:
key_from_previous: [bbox]
key_keep: [meta, cameras, imgnames] # 将这些参数都保留到最后的输出中
args:
ckpt: models/manol_pca45_noflat.ckpt
ckpt: models/hand_manol_pca45_noflat.ckpt
url: '1KTi_oJ_udLRK3WZ3xyHzBUd6vKAApfT8'
# TODO: add visualize for Init MANO
at_final:
load_hand_model: # 载入身体模型
module: myeasymocap.io.model.MANOLoader
args:
cfg_path: config/model/mano.yml
cfg_path: config/model/manol.yml
model_path: models/manov1.2/MANO_LEFT.pkl #models/handmesh/data/MANO_RIGHT.pkl # load mano model
regressor_path: models/manov1.2/J_regressor_mano_LEFT.txt #models/handmesh/data/J_regressor_mano_RIGHT.txt
num_pca_comps: 45

39
easymocap/mytools/camera_utils.py
View File

@ -33,11 +33,13 @@ class FileStorage(object):
self._write(' rows: {}'.format(value.shape[0]))
self._write(' cols: {}'.format(value.shape[1]))
self._write(' dt: d')
self._write(' data: [{}]'.format(', '.join(['{:.3f}'.format(i) for i in value.reshape(-1)])))
self._write(' data: [{}]'.format(', '.join(['{:.6f}'.format(i) for i in value.reshape(-1)])))
elif dt == 'list':
self._write('{}:'.format(key))
for elem in value:
self._write(' - "{}"'.format(elem))
elif dt == 'int':
self._write('{}: {}'.format(key, value))
def read(self, key, dt='mat'):
if dt == 'mat':
@ -52,6 +54,8 @@ class FileStorage(object):
if val != 'none':
results.append(val)
output = results
elif dt == 'int':
output = int(self.fs.getNode(key).real())
else:
raise NotImplementedError
return output
@ -114,6 +118,13 @@ def read_camera(intri_name, extri_name, cam_names=[]):
cams[cam] = {}
cams[cam]['K'] = intri.read('K_{}'.format( cam))
cams[cam]['invK'] = np.linalg.inv(cams[cam]['K'])
H = intri.read('H_{}'.format(cam), dt='int')
W = intri.read('W_{}'.format(cam), dt='int')
if H is None or W is None:
print('[camera] no H or W for {}'.format(cam))
H, W = -1, -1
cams[cam]['H'] = H
cams[cam]['W'] = W
Rvec = extri.read('R_{}'.format(cam))
Tvec = extri.read('T_{}'.format(cam))
assert Rvec is not None, cam
@ -129,6 +140,10 @@ def read_camera(intri_name, extri_name, cam_names=[]):
cams[cam]['P'] = P[cam]
cams[cam]['dist'] = intri.read('dist_{}'.format(cam))
if cams[cam]['dist'] is None:
cams[cam]['dist'] = intri.read('D_{}'.format(cam))
if cams[cam]['dist'] is None:
print('[camera] no dist for {}'.format(cam))
cams['basenames'] = cam_names
return cams
@ -155,6 +170,9 @@ def write_camera(camera, path):
key = key_.split('.')[0]
intri.write('K_{}'.format(key), val['K'])
intri.write('dist_{}'.format(key), val['dist'])
if 'H' in val.keys() and 'W' in val.keys():
intri.write('H_{}'.format(key), val['H'], dt='int')
intri.write('W_{}'.format(key), val['W'], dt='int')
if 'Rvec' not in val.keys():
val['Rvec'] = cv2.Rodrigues(val['R'])[0]
extri.write('R_{}'.format(key), val['Rvec'])
@ -174,7 +192,7 @@ def camera_from_img(img):
class Undistort:
distortMap = {}
@classmethod
def image(cls, frame, K, dist, sub=None):
def image(cls, frame, K, dist, sub=None, interp=cv2.INTER_NEAREST):
if sub is None:
return cv2.undistort(frame, K, dist, None)
else:
@ -183,7 +201,7 @@ class Undistort:
mapx, mapy = cv2.initUndistortRectifyMap(K, dist, None, K, (w,h), 5)
cls.distortMap[sub] = (mapx, mapy)
mapx, mapy = cls.distortMap[sub]
img = cv2.remap(frame, mapx, mapy, cv2.INTER_NEAREST)
img = cv2.remap(frame, mapx, mapy, interp)
return img
@staticmethod
@ -203,6 +221,21 @@ class Undistort:
bbox = np.array([kpts[0, 0], kpts[0, 1], kpts[1, 0], kpts[1, 1], bbox[4]])
return bbox
class Distort:
@staticmethod
def points(keypoints, K, dist):
pass
@staticmethod
def bbox(bbox, K, dist):
keypoints = np.array([[bbox[0], bbox[1]], [bbox[2], bbox[3]]], dtype=np.float32)
k3d = cv2.convertPointsToHomogeneous(keypoints)
k3d = (np.linalg.inv(K) @ k3d[:, 0].T).T[:, None]
k2d, _ = cv2.projectPoints(k3d, np.zeros((3,)), np.zeros((3,)), K, dist)
k2d = k2d[:, 0]
bbox = np.array([k2d[0,0], k2d[0,1], k2d[1, 0], k2d[1, 1], bbox[-1]])
return bbox
def unproj(kpts, invK):
homo = np.hstack([kpts[:, :2], np.ones_like(kpts[:, :1])])
homo = homo @ invK.T

35
easymocap/mytools/vis_base.py
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@ -2,14 +2,15 @@
@ Date: 2020-11-28 17:23:04
@ Author: Qing Shuai
@ LastEditors: Qing Shuai
@ LastEditTime: 2022-08-12 21:50:56
@ LastEditTime: 2022-10-27 15:13:56
@ FilePath: /EasyMocapPublic/easymocap/mytools/vis_base.py
'''
import cv2
import numpy as np
import json
def generate_colorbar(N = 20, cmap = 'jet', rand=True):
def generate_colorbar(N = 20, cmap = 'jet', rand=True,
ret_float=False, ret_array=False, ret_rgb=False):
bar = ((np.arange(N)/(N-1))*255).astype(np.uint8).reshape(-1, 1)
colorbar = cv2.applyColorMap(bar, cv2.COLORMAP_JET).squeeze()
if False:
@ -22,7 +23,12 @@ def generate_colorbar(N = 20, cmap = 'jet', rand=True):
rgb = colorbar[index, :]
else:
rgb = colorbar
rgb = rgb.tolist()
if ret_rgb:
rgb = rgb[:, ::-1]
if ret_float:
rgb = rgb/255.
if not ret_array:
rgb = rgb.tolist()
return rgb
# colors_bar_rgb = generate_colorbar(cmap='hsv')
@ -69,9 +75,11 @@ def get_rgb(index):
# elif index == 0:
# return (245, 150, 150)
col = list(colors_bar_rgb[index%len(colors_bar_rgb)])[::-1]
else:
elif isinstance(index, str):
col = colors_table.get(index, (1, 0, 0))
col = tuple([int(c*255) for c in col[::-1]])
else:
raise TypeError('index should be int or str')
return col
def get_rgb_01(index):
@ -150,14 +158,16 @@ def plot_keypoints(img, points, pid, config, vis_conf=False, use_limb_color=True
cv2.putText(img, '{:.1f}'.format(c), (int(x), int(y)),
cv2.FONT_HERSHEY_SIMPLEX, text_size, col, 2)
def plot_keypoints_auto(img, points, pid, vis_conf=False, use_limb_color=True, scale=1, lw=-1):
def plot_keypoints_auto(img, points, pid, vis_conf=False, use_limb_color=True, scale=1, lw=-1, config_name=None, lw_factor=1):
from ..dataset.config import CONFIG
config_name = {25: 'body25', 21: 'hand', 42:'handlr', 17: 'coco', 1:'points', 67:'bodyhand', 137: 'total', 79:'up'}[len(points)]
if config_name is None:
config_name = {25: 'body25', 15: 'body15', 21: 'hand', 42:'handlr', 17: 'coco', 1:'points', 67:'bodyhand', 137: 'total', 79:'up',
19:'ochuman'}[len(points)]
config = CONFIG[config_name]
if lw == -1:
lw = img.shape[0]//200
if config_name == 'hand':
lw = img.shape[0]//1000
lw = img.shape[0]//100
lw = max(lw, 1)
for ii, (i, j) in enumerate(config['kintree']):
if i >= len(points) or j >= len(points):
@ -169,9 +179,9 @@ def plot_keypoints_auto(img, points, pid, vis_conf=False, use_limb_color=True, s
col = get_rgb(config['colors'][ii])
else:
col = get_rgb(pid)
if pt1[0] < 0 or pt1[1] < 0 or pt1[0] > 10000 or pt1[1] > 10000:
if pt1[0] < -10000 or pt1[1] < -10000 or pt1[0] > 10000 or pt1[1] > 10000:
continue
if pt2[0] < 0 or pt2[1] < 0 or pt2[0] > 10000 or pt2[1] > 10000:
if pt2[0] < -10000 or pt2[1] < -10000 or pt2[0] > 10000 or pt2[1] > 10000:
continue
if pt1[-1] > 0.01 and pt2[-1] > 0.01:
image = cv2.line(
@ -191,12 +201,13 @@ def plot_keypoints_auto(img, points, pid, vis_conf=False, use_limb_color=True, s
if c > 0.01:
col = get_rgb(pid)
if len(points) == 1:
cv2.circle(img, (int(x+0.5), int(y+0.5)), lw*10, col, lw*2)
plot_cross(img, int(x+0.5), int(y+0.5), width=lw*5, col=col, lw=lw*2)
_lw = max(0, int(lw * lw_factor))
cv2.circle(img, (int(x+0.5), int(y+0.5)), _lw*2, col, lw*2)
plot_cross(img, int(x+0.5), int(y+0.5), width=_lw, col=col, lw=lw*2)
else:
cv2.circle(img, (int(x+0.5), int(y+0.5)), lw*2, col, -1)
if vis_conf:
cv2.putText(img, '{:.1f}'.format(c), (int(x), int(y)), cv2.FONT_HERSHEY_SIMPLEX, 1, col, 2)
cv2.putText(img, '{:.1f}'.format(c), (int(x), int(y)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, col, 2)
def plot_keypoints_total(img, annots, scale, pid_offset=0):
_lw = img.shape[0] // 150

9
myeasymocap/backbone/basetopdown.py
View File

@ -238,4 +238,11 @@ def get_preds_from_heatmaps(batch_heatmaps):
coords[n][p] += np.sign(diff) * .25
coords = coords.astype(np.float32) * 4
pred = np.dstack((coords, maxvals))
return pred
return pred
def gdown_models(ckpt, url):
print('Try to download model from {} to {}'.format(url, ckpt))
os.makedirs(os.path.dirname(ckpt), exist_ok=True)
cmd = 'gdown "{}" -O {}'.format(url, ckpt)
print('\n', cmd, '\n')
os.system(cmd)

0
myeasymocap/backbone/hand2d/__init__.py Normal file
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89
myeasymocap/backbone/hand2d/hand2d.py Normal file
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@ -0,0 +1,89 @@
import os
import cv2
import torch
import torch.nn as nn
import numpy as np
import math
# https://download.openmmlab.com/mmpose/hand/hrnetv2/hrnetv2_w18_rhd2d_256x256-95b20dd8_20210330.pth
# https://download.openmmlab.com/mmpose/hand/dark/hrnetv2_w18_onehand10k_256x256_dark-a2f80c64_20210330.pth
from .hrnet import PoseHighResolutionNet
from ..basetopdown import BaseTopDownModelCache, get_preds_from_heatmaps, gdown_models
class TopDownAsMMPose(nn.Module):
def __init__(self, backbone, head):
super().__init__()
self.bacbone = backbone
self.head = head
def forward(self, x):
feat_list = self.bacbone(x)
size = feat_list[0].shape[-2:]
resized_inputs = [
nn.functional.interpolate(feat, size, mode='bilinear', align_corners=False) \
for feat in feat_list
]
resized_inputs = torch.cat(resized_inputs, 1)
out = self.head(resized_inputs)
pred = get_preds_from_heatmaps(out.detach().cpu().numpy())
return {'keypoints': pred}
class MyHand2D(BaseTopDownModelCache):
def __init__(self, ckpt, url=None, mode='hrnet'):
if mode == 'hrnet':
super().__init__(name='hand2d', bbox_scale=1.1, res_input=256)
backbone = PoseHighResolutionNet(inp_ch=3, out_ch=21, W=18, multi_scale_final=True, add_final_layer=False)
checkpoint = torch.load(ckpt, map_location='cpu')['state_dict']
self.load_checkpoint(backbone, checkpoint, prefix='backbone.', strict=True)
head = nn.Sequential(
nn.Conv2d(270, 270, kernel_size=1),
nn.BatchNorm2d(270),
nn.ReLU(inplace=True),
nn.Conv2d(270, 21, kernel_size=1)
)
self.load_checkpoint(head, checkpoint, prefix='keypoint_head.final_layer.', strict=True)
# self.model = nn.Sequential(backbone, head)
self.model = TopDownAsMMPose(backbone, head)
elif mode == 'resnet':
super().__init__(name='hand2d', bbox_scale=1.1, res_input=256, mean=[0., 0., 0.], std=[1., 1., 1.])
from .resnet import ResNet_Deconv
if not os.path.exists(ckpt) and url is not None:
gdown_models(ckpt, url)
assert os.path.exists(ckpt), f'{ckpt} not exists'
checkpoint = torch.load(ckpt, map_location='cpu')['state_dict']
model = ResNet_Deconv()
self.load_checkpoint(model, checkpoint, prefix='model.', strict=True)
self.model = model
self.model.eval()
self.device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
self.model.to(self.device)
def __call__(self, bbox, images, imgnames):
squeeze = False
if not isinstance(images, list):
images = [images]
imgnames = [imgnames]
bbox = [bbox]
squeeze = True
nViews = len(images)
kpts_all = []
for nv in range(nViews):
if bbox[nv].shape[0] == 0:
kpts_all.append(np.zeros((21, 3)))
continue
_bbox = bbox[nv]
if len(_bbox.shape) == 1:
_bbox = _bbox[None]
output = super().__call__(_bbox, images[nv], imgnames[nv])
kpts = output['params']['keypoints']
conf = kpts[..., -1:]
kpts = self.batch_affine_transform(kpts, output['params']['inv_trans'])
kpts = np.concatenate([kpts, conf], axis=-1)
if len(kpts.shape) == 3:
kpts = kpts[0]
kpts_all.append(kpts)
kpts_all = np.stack(kpts_all)
if squeeze:
kpts_all = kpts_all[0]
return {
'keypoints': kpts_all
}

161
myeasymocap/backbone/hand2d/resnet.py Normal file
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@ -0,0 +1,161 @@
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import os
import os.path as osp
import torch
import torch.nn as nn
from torchvision.models.resnet import BasicBlock, Bottleneck
from torchvision.models.resnet import model_urls
from ..basetopdown import get_preds_from_heatmaps
def make_conv_layers(feat_dims, kernel=3, stride=1, padding=1, bnrelu_final=True):
layers = []
for i in range(len(feat_dims)-1):
layers.append(
nn.Conv2d(
in_channels=feat_dims[i],
out_channels=feat_dims[i+1],
kernel_size=kernel,
stride=stride,
padding=padding
))
# Do not use BN and ReLU for final estimation
if i < len(feat_dims)-2 or (i == len(feat_dims)-2 and bnrelu_final):
layers.append(nn.BatchNorm2d(feat_dims[i+1]))
layers.append(nn.ReLU(inplace=True))
return nn.Sequential(*layers)
def make_deconv_layers(feat_dims, bnrelu_final=True):
layers = []
for i in range(len(feat_dims)-1):
layers.append(
nn.ConvTranspose2d(
in_channels=feat_dims[i],
out_channels=feat_dims[i+1],
kernel_size=4,
stride=2,
padding=1,
output_padding=0,
bias=False))
# Do not use BN and ReLU for final estimation
if i < len(feat_dims)-2 or (i == len(feat_dims)-2 and bnrelu_final):
layers.append(nn.BatchNorm2d(feat_dims[i+1]))
layers.append(nn.ReLU(inplace=True))
return nn.Sequential(*layers)
class ResNetBackbone(nn.Module):
def __init__(self, resnet_type):
resnet_spec = {18: (BasicBlock, [2, 2, 2, 2], [64, 64, 128, 256, 512], 'resnet18'),
34: (BasicBlock, [3, 4, 6, 3], [64, 64, 128, 256, 512], 'resnet34'),
50: (Bottleneck, [3, 4, 6, 3], [64, 256, 512, 1024, 2048], 'resnet50'),
101: (Bottleneck, [3, 4, 23, 3], [64, 256, 512, 1024, 2048], 'resnet101'),
152: (Bottleneck, [3, 8, 36, 3], [64, 256, 512, 1024, 2048], 'resnet152')}
block, layers, channels, name = resnet_spec[resnet_type]
self.name = name
self.inplanes = 64
super(ResNetBackbone, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
bias=False) # RGB
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
for m in self.modules():
if isinstance(m, nn.Conv2d):
# nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
nn.init.normal_(m.weight, mean=0, std=0.001)
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
def _make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
return x
def init_weights(self):
org_resnet = torch.utils.model_zoo.load_url(model_urls[self.name])
# drop orginal resnet fc layer, add 'None' in case of no fc layer, that will raise error
org_resnet.pop('fc.weight', None)
org_resnet.pop('fc.bias', None)
self.load_state_dict(org_resnet)
print("Initialize resnet from model zoo")
class ResNet_Deconv(nn.Module):
def __init__(self):
super().__init__()
self.hm2d_size = 64
self.resnet = ResNetBackbone(50)
self.deconv = make_deconv_layers([2048, 256, 256, 256])
self.conv_hm2d = make_conv_layers([256, 21],kernel=1,stride=1,padding=0,bnrelu_final=False)
self.resnet.init_weights()
self.deconv.apply(self.init_weights)
self.conv_hm2d.apply(self.init_weights)
@staticmethod
def init_weights(m):
if type(m) == nn.ConvTranspose2d:
nn.init.normal_(m.weight,std=0.001)
elif type(m) == nn.Conv2d:
nn.init.normal_(m.weight,std=0.001)
nn.init.constant_(m.bias, 0)
elif type(m) == nn.BatchNorm2d:
nn.init.constant_(m.weight,1)
nn.init.constant_(m.bias,0)
elif type(m) == nn.Linear:
nn.init.normal_(m.weight,std=0.01)
nn.init.constant_(m.bias,0)
def forward(self, img):
x_feat = self.resnet(img)
x_feat = self.deconv(x_feat)
x_hm2d = self.conv_hm2d(x_feat)
pred = get_preds_from_heatmaps(x_hm2d.detach().cpu().numpy())
return {
'keypoints': pred
}

0
myeasymocap/backbone/hmr/__init__.py Normal file
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35
myeasymocap/backbone/hmr/hmr.py Normal file
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@ -0,0 +1,35 @@
import os
import numpy as np
import torch
from ..basetopdown import BaseTopDownModelCache, gdown_models
import pickle
from .models import hmr
class MyHMR(BaseTopDownModelCache):
def __init__(self, ckpt, url=None):
super().__init__('handhmr', bbox_scale=1., res_input=224)
self.model = hmr()
self.model.eval()
if not os.path.exists(ckpt) and url is not None:
gdown_models(ckpt, url)
assert os.path.exists(ckpt), f'{ckpt} not exists'
checkpoint = torch.load(ckpt)
self.device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
state_dict = checkpoint['state_dict']
prefix = 'model.'
self.load_checkpoint(self.model, state_dict, prefix, strict=True)
self.model.to(self.device)
def __call__(self, bbox, images, imgnames):
output = super().__call__(bbox, images, imgnames)
Rh = output['params']['poses'][:3].copy()
poses = output['params']['poses'][3:]
Th = np.zeros_like(Rh)
Th[2] = 1.
output['params'] = {
'Rh': Rh,
'Th': Th,
'poses': poses,
'shapes': output['params']['shapes'],
}
return output

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'''
Date: 2021-10-25 11:51:37 am
Author: dihuangdh
Descriptions:
-----
LastEditTime: 2021-10-25 1:50:40 pm
LastEditors: dihuangdh
'''
import torch
from torchvision.transforms import Normalize
import numpy as np
import cv2
from .models import hmr
class constants:
FOCAL_LENGTH = 5000.
IMG_RES = 224
# Mean and standard deviation for normalizing input image
IMG_NORM_MEAN = [0.485, 0.456, 0.406]
IMG_NORM_STD = [0.229, 0.224, 0.225]
def get_transform(center, scale, res, rot=0):
"""Generate transformation matrix."""
h = 200 * scale
t = np.zeros((3, 3))
t[0, 0] = float(res[1]) / h
t[1, 1] = float(res[0]) / h
t[0, 2] = res[1] * (-float(center[0]) / h + .5)
t[1, 2] = res[0] * (-float(center[1]) / h + .5)
t[2, 2] = 1
if not rot == 0:
rot = -rot # To match direction of rotation from cropping
rot_mat = np.zeros((3,3))
rot_rad = rot * np.pi / 180
sn,cs = np.sin(rot_rad), np.cos(rot_rad)
rot_mat[0,:2] = [cs, -sn]
rot_mat[1,:2] = [sn, cs]
rot_mat[2,2] = 1
# Need to rotate around center
t_mat = np.eye(3)
t_mat[0,2] = -res[1]/2
t_mat[1,2] = -res[0]/2
t_inv = t_mat.copy()
t_inv[:2,2] *= -1
t = np.dot(t_inv,np.dot(rot_mat,np.dot(t_mat,t)))
return t
def transform(pt, center, scale, res, invert=0, rot=0):
"""Transform pixel location to different reference."""
t = get_transform(center, scale, res, rot=rot)
if invert:
t = np.linalg.inv(t)
new_pt = np.array([pt[0]-1, pt[1]-1, 1.]).T
new_pt = np.dot(t, new_pt)
return new_pt[:2].astype(int)+1
def crop(img, center, scale, res, rot=0, bias=0):
"""Crop image according to the supplied bounding box."""
# Upper left point
ul = np.array(transform([1, 1], center, scale, res, invert=1))-1
# Bottom right point
br = np.array(transform([res[0]+1,
res[1]+1], center, scale, res, invert=1))-1
# Padding so that when rotated proper amount of context is included
pad = int(np.linalg.norm(br - ul) / 2 - float(br[1] - ul[1]) / 2)
if not rot == 0:
ul -= pad
br += pad
new_shape = [br[1] - ul[1], br[0] - ul[0]]
if len(img.shape) > 2:
new_shape += [img.shape[2]]
new_img = np.zeros(new_shape) + bias
# Range to fill new array
new_x = max(0, -ul[0]), min(br[0], len(img[0])) - ul[0]
new_y = max(0, -ul[1]), min(br[1], len(img)) - ul[1]
# Range to sample from original image
old_x = max(0, ul[0]), min(len(img[0]), br[0])
old_y = max(0, ul[1]), min(len(img), br[1])
new_img[new_y[0]:new_y[1], new_x[0]:new_x[1]] = img[old_y[0]:old_y[1],
old_x[0]:old_x[1]]
if not rot == 0:
# Remove padding
new_img = scipy.misc.imrotate(new_img, rot)
new_img = new_img[pad:-pad, pad:-pad]
new_img = cv2.resize(new_img, (res[0], res[1]))
return new_img
def process_image(img, bbox, input_res=224):
"""Read image, do preprocessing and possibly crop it according to the bounding box.
If there are bounding box annotations, use them to crop the image.
If no bounding box is specified but openpose detections are available, use them to get the bounding box.
"""
img = img[:, :, ::-1].copy()
normalize_img = Normalize(mean=constants.IMG_NORM_MEAN, std=constants.IMG_NORM_STD)
l, t, r, b = bbox[:4]
center = [(l+r)/2, (t+b)/2]
width = max(r-l, b-t)
scale = width/200.0
img = crop(img, center, scale, (input_res, input_res))
img = img.astype(np.float32) / 255.
img = torch.from_numpy(img).permute(2,0,1)
norm_img = normalize_img(img.clone())[None]
return img, norm_img
def solve_translation(X, x, K):
A = np.zeros((2*X.shape[0], 3))
b = np.zeros((2*X.shape[0], 1))
fx, fy = K[0, 0], K[1, 1]
cx, cy = K[0, 2], K[1, 2]
for nj in range(X.shape[0]):
A[2*nj, 0] = 1
A[2*nj + 1, 1] = 1
A[2*nj, 2] = -(x[nj, 0] - cx)/fx
A[2*nj+1, 2] = -(x[nj, 1] - cy)/fy
b[2*nj, 0] = X[nj, 2]*(x[nj, 0] - cx)/fx - X[nj, 0]
b[2*nj+1, 0] = X[nj, 2]*(x[nj, 1] - cy)/fy - X[nj, 1]
A[2*nj:2*nj+2, :] *= x[nj, 2]
b[2*nj:2*nj+2, :] *= x[nj, 2]
trans = np.linalg.inv(A.T @ A) @ A.T @ b
return trans.T[0]
def estimate_translation_np(S, joints_2d, joints_conf, K):
"""Find camera translation that brings 3D joints S closest to 2D the corresponding joints_2d.
Input:
S: (25, 3) 3D joint locations
joints: (25, 3) 2D joint locations and confidence
Returns:
(3,) camera translation vector
"""
num_joints = S.shape[0]
# focal length
f = np.array([K[0, 0], K[1, 1]])
# optical center
center = np.array([K[0, 2], K[1, 2]])
# transformations
Z = np.reshape(np.tile(S[:,2],(2,1)).T,-1)
XY = np.reshape(S[:,0:2],-1)
O = np.tile(center,num_joints)
F = np.tile(f,num_joints)
weight2 = np.reshape(np.tile(np.sqrt(joints_conf),(2,1)).T,-1)
# least squares
Q = np.array([F*np.tile(np.array([1,0]),num_joints), F*np.tile(np.array([0,1]),num_joints), O-np.reshape(joints_2d,-1)]).T
c = (np.reshape(joints_2d,-1)-O)*Z - F*XY
# weighted least squares
W = np.diagflat(weight2)
Q = np.dot(W,Q)
c = np.dot(W,c)
# square matrix
A = np.dot(Q.T,Q)
b = np.dot(Q.T,c)
# solution
trans = np.linalg.solve(A, b)
return trans
class HMR:
def __init__(self, checkpoint, device) -> None:
model = hmr().to(device)
checkpoint = torch.load(checkpoint)
state_dict = checkpoint['state_dict']
# update state_dict, remove 'model.'
for key in list(state_dict.keys()):
state_dict[key[6:]] = state_dict.pop(key)
model.load_state_dict(state_dict, strict=False)
# Load SMPL model
model.eval()
self.model = model
self.device = device
def forward(self, img, bbox, use_rh_th=True):
# Preprocess input image and generate predictions
img, norm_img = process_image(img, bbox, input_res=constants.IMG_RES)
with torch.no_grad():
pred_rotmat, pred_betas, pred_camera = self.model(norm_img.to(self.device))
results = {
'shapes': pred_betas.detach().cpu().numpy()
}
results['poses'] = pred_rotmat.detach().cpu().numpy()
if use_rh_th:
body_params = {
'poses': results['poses'],
'shapes': results['shapes'],
'Rh': results['poses'][:, :3].copy(),
'Th': np.zeros((1, 3)),
}
body_params['Th'][0, 2] = 5
body_params['poses'][:, :3] = 0
results = body_params
return results
def __call__(self, body_model, img, bbox, kpts, camera, ret_vertices=True):
body_params = self.forward(img.copy(), bbox)
body_params = body_model.check_params(body_params)
# only use body joints to estimation translation
nJoints = 21
keypoints3d = body_model(return_verts=False, return_tensor=False, **body_params)[0]
trans = solve_translation(keypoints3d[:nJoints], kpts[:nJoints], camera['K'])
body_params['Th'] += trans[None, :]
if body_params['Th'][0, 2] < 0:
body_params['Th'] = -body_params['Th']
Rhold = cv2.Rodrigues(body_params['Rh'])[0]
rotx = cv2.Rodrigues(np.pi*np.array([1., 0, 0]))[0]
Rhold = rotx @ Rhold
body_params['Rh'] = cv2.Rodrigues(Rhold)[0].reshape(1, 3)
# convert to world coordinate
Rhold = cv2.Rodrigues(body_params['Rh'])[0]
Thold = body_params['Th']
Rh = camera['R'].T @ Rhold
Th = (camera['R'].T @ (Thold.T - camera['T'])).T
body_params['Th'] = Th
body_params['Rh'] = cv2.Rodrigues(Rh)[0].reshape(1, 3)
keypoints3d = body_model(return_verts=False, return_tensor=False, **body_params)[0]
results = {'body_params': body_params, 'keypoints3d': keypoints3d}
if ret_vertices:
vertices = body_model(return_verts=True, return_tensor=False, **body_params)[0]
results['vertices'] = vertices
return results
def init_with_hmr(body_model, spin_model, img, bbox, kpts, camera):
body_params = spin_model.forward(img.copy(), bbox)
body_params = body_model.check_params(body_params)
# only use body joints to estimation translation
nJoints = 15
keypoints3d = body_model(return_verts=False, return_tensor=False, **body_params)[0]
trans = estimate_translation_np(keypoints3d[:nJoints], kpts[:nJoints, :2], kpts[:nJoints, 2], camera['K'])
body_params['Th'] += trans[None, :]
# convert to world coordinate
Rhold = cv2.Rodrigues(body_params['Rh'])[0]
Thold = body_params['Th']
Rh = camera['R'].T @ Rhold
Th = (camera['R'].T @ (Thold.T - camera['T'])).T
body_params['Th'] = Th
body_params['Rh'] = cv2.Rodrigues(Rh)[0].reshape(1, 3)
vertices = body_model(return_verts=True, return_tensor=False, **body_params)[0]
keypoints3d = body_model(return_verts=False, return_tensor=False, **body_params)[0]
results = {'body_params': body_params, 'vertices': vertices, 'keypoints3d': keypoints3d}
return results
class
if __name__ == '__main__':
pass

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'''
Date: 2021-10-25 11:51:29 am
Author: dihuangdh
Descriptions:
-----
LastEditTime: 2021-10-25 11:51:58 am
LastEditors: dihuangdh
'''
import torch
import torch.nn as nn
import torchvision.models.resnet as resnet
import numpy as np
import math
class Bottleneck(nn.Module):
""" Redefinition of Bottleneck residual block
Adapted from the official PyTorch implementation
"""
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * 4)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class HMR(nn.Module):
""" SMPL Iterative Regressor with ResNet50 backbone
"""
def __init__(self, block, layers):
self.inplanes = 64
super(HMR, self).__init__()
npose = 3 + 45
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
self.avgpool = nn.AvgPool2d(7, stride=1)
self.fc1 = nn.Linear(512 * block.expansion + npose + 13, 1024)
self.drop1 = nn.Dropout()
self.fc2 = nn.Linear(1024, 1024)
self.drop2 = nn.Dropout()
self.decpose = nn.Linear(1024, npose)
self.decshape = nn.Linear(1024, 10)
self.deccam = nn.Linear(1024, 3)
nn.init.xavier_uniform_(self.decpose.weight, gain=0.01)
nn.init.xavier_uniform_(self.decshape.weight, gain=0.01)
nn.init.xavier_uniform_(self.deccam.weight, gain=0.01)
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
init_pose = torch.zeros(npose).unsqueeze(0)
init_shape = torch.zeros(10).unsqueeze(0)
init_cam = torch.zeros(3).unsqueeze(0)
self.register_buffer('init_pose', init_pose)
self.register_buffer('init_shape', init_shape)
self.register_buffer('init_cam', init_cam)
def _make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def forward(self, x, init_pose=None, init_shape=None, init_cam=None, n_iter=3):
batch_size = x.shape[0]
if init_pose is None:
init_pose = self.init_pose.expand(batch_size, -1)
if init_shape is None:
init_shape = self.init_shape.expand(batch_size, -1)
if init_cam is None:
init_cam = self.init_cam.expand(batch_size, -1)
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x1 = self.layer1(x)
x2 = self.layer2(x1)
x3 = self.layer3(x2)
x4 = self.layer4(x3)
xf = self.avgpool(x4)
xf = xf.view(xf.size(0), -1)
pred_pose = init_pose
pred_shape = init_shape
pred_cam = init_cam
for i in range(n_iter):
xc = torch.cat([xf, pred_pose, pred_shape, pred_cam],1)
xc = self.fc1(xc)
xc = self.drop1(xc)
xc = self.fc2(xc)
xc = self.drop2(xc)
pred_pose = self.decpose(xc) + pred_pose
pred_shape = self.decshape(xc) + pred_shape
pred_cam = self.deccam(xc) + pred_cam
# pred_rotmat = rot6d_to_rotmat(pred_pose).view(batch_size, 24, 3, 3)
return {
'poses': pred_pose,
'shapes': pred_shape,
'cam': pred_cam
}
def hmr(pretrained=True, **kwargs):
""" Constructs an HMR model with ResNet50 backbone.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = HMR(Bottleneck, [3, 4, 6, 3], **kwargs)
if pretrained:
resnet_imagenet = resnet.resnet50(pretrained=True)
model.load_state_dict(resnet_imagenet.state_dict(),strict=False)
return model