def make_prediction(self):
if self.with_cuda:
self.model = self.model.cuda()
with torch.no_grad():
self.model.eval()
for _, batch, batch_2d in self.test_generator.next_epoch():
inputs_2d = torch.from_numpy(batch_2d.astype('float32'))
if self.with_cuda:
inputs_2d = inputs_2d.cuda()
predicted_3d_pos = self.model(inputs_2d)
if self.test_generator.augment_enabled():
predicted_3d_pos[1, :, :, 0] *= -1
predicted_3d_pos[1, :, self.joints_left +
self.joints_right] = predicted_3d_pos[
1, :,
self.joints_right + self.joints_left]
predicted_3d_pos = torch.mean(predicted_3d_pos,
dim=0,
keepdim=True)
predicted_3d_pos = predicted_3d_pos.squeeze(0).cpu().numpy()
rot = self.dataset.cameras()['detectron2'][0]['orientation']
predicted_3d_pos = camera_to_world(predicted_3d_pos, R=rot, t=0)
predicted_3d_pos[:, :, 2] -= np.min(predicted_3d_pos[:, :, 2])
self.prediction = predicted_3d_pos
def interface(model_pos, keypoints, W, H):
# input (N, 17, 2) return (N, 17, 3)
if not isinstance(keypoints, np.ndarray):
keypoints = np.array(keypoints)
from common.camera import normalize_screen_coordinates_new, camera_to_world, normalize_screen_coordinates
# keypoints = normalize_screen_coordinates_new(keypoints[..., :2], w=W, h=H)
keypoints = normalize_screen_coordinates(keypoints[..., :2],
w=1000,
h=1002)
input_keypoints = keypoints.copy()
# test_time_augmentation True
from common.generators import UnchunkedGenerator
gen = UnchunkedGenerator(None,
None, [input_keypoints],
pad=common.pad,
causal_shift=common.causal_shift,
augment=True,
kps_left=common.kps_left,
kps_right=common.kps_right,
joints_left=common.joints_left,
joints_right=common.joints_right)
prediction = evaluate(gen, model_pos, return_predictions=True)
prediction = camera_to_world(prediction, R=common.rot, t=0)
prediction[:, :, 2] -= np.min(prediction[:, :, 2])
return prediction
def gen_pose_frame(kpts, width, height, model_pos, pad, causal_shift=0):
# kpts: (M, T, N, 2)
norm_seqs = []
for kpt in kpts:
norm_kpt = normalize_screen_coordinates(kpt, w=width, h=height)
norm_seqs.append(norm_kpt)
gen = UnchunkedGenerator(None,
None,
norm_seqs,
pad=pad,
causal_shift=causal_shift,
augment=True,
kps_left=kps_left,
kps_right=kps_right,
joints_left=joints_left,
joints_right=joints_right)
prediction = evaluate(gen, model_pos)
prediction_to_world = []
for i in range(len(prediction)):
sub_prediction = prediction[i][0]
sub_prediction = camera_to_world(sub_prediction, R=rot, t=0)
sub_prediction[:, 2] -= np.amin(sub_prediction[:, 2])
prediction_to_world.append(sub_prediction)
return prediction_to_world
def predict(img_path):
# 1.检测关节点并显示
# 预处理输入图像和检测人体
x, img = data.transforms.presets.yolo.load_test(img_path, short=256)
# print("Shape of pre-processed image:", x.shape)
start = time.time()
# detect persons and bbox
class_ids, scores, bounding_boxes = detector(x)
# 2.预处理检测器的输出张量作为alpha_pose的输入
pose_input, upscale_bbox = detector_to_simple_pose(img, class_ids, scores, bounding_boxes)
global detector_time
detector_time += (time.time() - start)
print("detector cost time: {:.3f} seconds".format(time.time() - start))
prepare_end = time.time()
# 3.预测关节点
if pose_input is None:
return None, None
predicted_heatmap = pose_net(pose_input)
pred_coords, confidence = heatmap_to_coord(predicted_heatmap, upscale_bbox)
global predictor_2d_time
predictor_2d_time += (time.time() - prepare_end)
print("2d pose predictor cost time: {:.3f} seconds".format(time.time() - prepare_end))
# 4.显示2d姿态
# utils.viz.plot_keypoints(img, pred_coords, confidence, class_IDs, bounding_boxes, scores, box_thresh=0.5,
# keypoint_thresh=0.2)
# 5.坐标标准化
prepare_end = time.time()
kps = normalize_screen_coordinates(pred_coords.asnumpy(), w=img.shape[1], h=img.shape[0])
receptive_field = pose3d_predictor.receptive_field()
pad = (receptive_field - 1) // 2 # Padding on each side
causal_shift = 0
# 6.创建生成器作为3d预测器的输入
generator = UnchunkedGenerator(None, None, [kps], pad=pad, causal_shift=causal_shift, augment=False)
# 7.3d姿势估计和显示
prediction = predict_3d_pos(generator, pose3d_predictor)
global full_time, predictor_3d_time
predictor_3d_time += time.time() - prepare_end
full_time += time.time() - start
print("3d predictor time: {:.3f} seconds".format(time.time() - prepare_end))
rot = np.array([0.14070565, -0.15007018, -0.7552408, 0.62232804], dtype=np.float32)
prediction = camera_to_world(prediction, R=rot, t=0)
prediction[:, :, 2] -= np.min(prediction[:, :, 2])
return prediction, img
def predict_3d_joints(predictor, coords_2d, w, h):
# 坐标标准化
kps = normalize_screen_coordinates(coords_2d, w, h)
# print('kps.type: {}, kps.shape: {}'.format(type(kps), kps.shape))
# 2d keypoints生成器
receptive_field = predictor.receptive_field()
pad = (receptive_field - 1) // 2 # Padding on each side
causal_shift = 0
# 创建生成器作为3d预测器的输入
generator = UnchunkedGenerator(None, None, [kps], pad=pad, causal_shift=causal_shift, augment=False)
prediction = predict_3d_pos(generator, predictor)
prediction = camera_to_world(prediction, R=rot, t=0)
prediction[:, :, 2] -= np.min(prediction[:, :, 2])
return prediction
def gen_pose(kpts,
valid_frames,
width,
height,
model_pos,
pad,
causal_shift=0):
assert len(kpts.shape) == 4, 'The shape of kpts: {}'.format(kpts.shape)
assert kpts.shape[0] == len(valid_frames)
norm_seqs = []
for index, frames in enumerate(valid_frames):
seq_kps = kpts[index, frames]
norm_seq_kps = normalize_screen_coordinates(seq_kps, w=width, h=height)
norm_seqs.append(norm_seq_kps)
gen = UnchunkedGenerator(None,
None,
norm_seqs,
pad=pad,
causal_shift=causal_shift,
augment=True,
kps_left=kps_left,
kps_right=kps_right,
joints_left=joints_left,
joints_right=joints_right)
prediction = evaluate(gen, model_pos)
prediction_to_world = []
for i in range(len(prediction)):
sub_prediction = prediction[i]
sub_prediction = camera_to_world(sub_prediction, R=rot, t=0)
# sub_prediction[:, :, 2] -= np.expand_dims(np.amin(sub_prediction[:, :, 2], axis=1), axis=1).repeat([17], axis=1)
# sub_prediction[:, :, 2] -= np.amin(sub_prediction[:, :, 2])
prediction_to_world.append(sub_prediction)
# prediction_to_world = np.asarray(prediction_to_world, dtype=np.float32)
return prediction_to_world
def gen_pose_frame_(kpts, width, height, model_pos, pad, causal_shift=0):
# input (N, 17, 2) return (N, 17, 3)
if not isinstance(kpts, np.ndarray):
kpts = np.array(kpts)
keypoints = normalize_screen_coordinates(kpts[..., :2], w=width, h=height)
input_keypoints = keypoints.copy()
# test_time_augmentation True
from common.generators import UnchunkedGenerator
gen = UnchunkedGenerator(None,
None, [input_keypoints],
pad=pad,
causal_shift=causal_shift,
augment=True,
kps_left=kps_left,
kps_right=kps_right,
joints_left=joints_left,
joints_right=joints_right)
prediction = evaluate(gen, model_pos)
prediction = camera_to_world(prediction[0], R=rot, t=0)
prediction[:, :, 2] -= np.min(prediction[:, :, 2])
return prediction
def video_pose(filepath,
ckpt_dir,
ckpt_name,
filter_widths,
show=False,
channels=1024,
save_file='output.mp4'):
# 加载3d姿势估计器
pose3d_predictor = get_pose3d_predictor(ckpt_dir,
ckpt_name,
filter_widths,
channels=channels)
receive_field = 1
for i in filter_widths:
receive_field *= i
# print(receive_field)
half = receive_field // 2
# 读取视频
cap = cv2.VideoCapture(filepath)
# 设置分辨率
cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 360)
# 帧率和帧数
fps = cap.get(cv2.CAP_PROP_FPS)
frame_count = cap.get(cv2.CAP_PROP_FRAME_COUNT)
print("Original FPS: {}, frame count: {}".format(fps, frame_count))
# pause = int(1000 / fps)
if show:
# 宽高
cv2.namedWindow('Video', 0)
cv2.resizeWindow('Video', 960, 540)
# 保存视频文件
print("Save the result to {}.".format(save_file))
wh = (1280, 720)
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
output_mp4 = cv2.VideoWriter(save_file, fourcc, fps, wh)
coords_2d_list = []
dicts = []
i = 0
# 因为设置了数据生成器的pad=0，因此需要获取前receive_field//2帧做准备
elapsed_time = 0
print("Preparing...")
while i < half:
ret_val, frame = cap.read()
if ret_val != 1:
print("Video is too short!")
output_mp4.release()
cap.release()
cv2.destroyAllWindows()
return
# noinspection PyBroadException
try:
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
except:
continue
# 生成2d关键点
current_frame = cap.get(cv2.CAP_PROP_POS_FRAMES)
joints_dict = detect_2d_joints(frame, current_frame)
dicts.append(joints_dict)
img, predict_coords = joints_dict['img'], joints_dict['coords']
normalized_coords = normalize_screen_coordinates(
predict_coords.asnumpy()[0], w=img.shape[1], h=img.shape[0])
coords_2d_list.append(normalized_coords)
i += 1
print("Starting to predict 3d pose...")
fps_time = time.time()
while True:
# 获取帧
i += 1
if i > receive_field and len(dicts) < 1:
break
ret_val, frame = cap.read()
if ret_val == 1:
try:
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
except:
continue
# 生成2d关键点
current_frame = cap.get(cv2.CAP_PROP_POS_FRAMES)
joints_dict = detect_2d_joints(frame, current_frame)
dicts.append(joints_dict)
img, predict_coords = joints_dict['img'], joints_dict['coords']
normalized_coords = normalize_screen_coordinates(
predict_coords.asnumpy()[0], w=img.shape[1], h=img.shape[0])
coords_2d_list.append(normalized_coords)
joints_dict = dicts[0]
if i > half + 1:
# 去除最左端的无用帧
coords_2d_list = coords_2d_list[1:]
dicts = dicts[1:]
if len(coords_2d_list) < receive_field:
if i < receive_field:
# 视频开头小于receive_field帧时，在左边进行pad操作
# print("kps_list length is {}, padding {} frames to left end.".format(len(kps_list), half))
while len(coords_2d_list) < receive_field:
coords_2d_list.insert(0, coords_2d_list[0])
elif len(coords_2d_list) > 0:
# 视频末尾不足receive_field帧时，在右边进行pad操作
# print("kps_list length is {}, padding 1 frames to right end.".format(len(kps_list)))
coords_2d_list.append(coords_2d_list[-1])
else:
break
# 构造2d关键点生成器
kps_2d = np.stack(coords_2d_list)
generator = joints_2d_generator(kps_2d, pose3d_predictor)
# print(generator.num_frames())
# 3d关键点预测
predictions = predict_3d_pos(generator, pose3d_predictor)
# print('predictions.shape: ', predictions.shape)
rot = np.array([0.14070565, -0.15007018, -0.7552408, 0.62232804],
dtype=np.float32)
predictions = camera_to_world(predictions, R=rot, t=0)
# We don't have the trajectory, but at least we can rebase the height
predictions[:, :, 2] -= np.min(predictions[:, :, 2])
coords_3d = predictions[0]
# print('predicted {} frame, elapsed time: {:.3f} seconds.'.format(predictions.shape[0], time.time() - fps_time))
interval = time.time() - fps_time
elapsed_time += interval
fps = 1.0 / interval
# 渲染图像
result_image = render_image(coords_3d=coords_3d,
skeleton=Skeleton(),
**joints_dict)
cv2.putText(result_image, "FPS: %.3f" % fps, (10, 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
result_image = cv2.cvtColor(result_image, cv2.COLOR_RGB2BGR)
if show:
# 实时显示
cv2.imshow('Video', result_image)
if cv2.waitKey(1) & 0xff == ord('q'):
break
# resize and write
to_write = cv2.resize(result_image, wh)
output_mp4.write(to_write)
fps_time = time.time()
output_mp4.release()
cap.release()
cv2.destroyAllWindows()
print("Average Fps: {:.3f}".format(frame_count / elapsed_time))
def reconstruction(args):
"""
Generate 3D poses from 2D keypoints detected from video, and visualize it
:param chk_file: The file path of model weight
:param kps_file: The file path of 2D keypoints
:param viz_output: The output path of animation
:param video_path: The input video path
:param kpts_format: The format of 2D keypoints, like MSCOCO, MPII, H36M, OpenPose. The default format is H36M
"""
print('Loading 2D keypoints ...')
keypoints, scores, _, _ = load_json(args.keypoints_file)
# Loading only one person's keypoints
if len(keypoints.shape) == 4:
keypoints = keypoints[0]
assert len(keypoints.shape) == 3
# Transform the keypoints format from different dataset (MSCOCO, MPII) to h36m format
if args.kpts_format == 'coco':
keypoints, valid_frames = coco_h36m(keypoints)
elif args.kpts_format == 'mpii':
keypoints, valid_frames = mpii_h36m(keypoints)
elif args.kpts_format == 'openpose':
# Convert 'Openpose' format to MSCOCO
order_coco = [i for i in range(17) if i != 1]
keypoints = keypoints[:order_coco]
keypoints, valid_frames = coco_h36m(keypoints)
else:
valid_frames = np.where(
np.sum(keypoints.reshape(-1, 34), axis=1) != 0)[0]
assert args.kpts_format == 'h36m'
# Get the width and height of video
cap = cv2.VideoCapture(args.video_path)
width = int(round(cap.get(cv2.CAP_PROP_FRAME_WIDTH)))
height = int(round(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)))
# normalize keypoints
input_keypoints = normalize_screen_coordinates(keypoints[..., :2],
w=width,
h=height)
if args.frames == 27:
filter_widths = [3, 3, 3]
channels = 128
elif args.frames == 81:
filter_widths = [3, 3, 3, 3]
channels = 64
else:
filter_widths = [3, 3, 3, 3, 3]
channels = 32
model_pos = SpatioTemporalModel(adj,
17,
2,
17,
filter_widths=filter_widths,
channels=channels,
dropout=0.05)
if torch.cuda.is_available():
model_pos = model_pos.cuda()
# load trained model
print('Loading checkpoint', args.weight)
chk_file = os.path.join('./checkpoint', args.weight)
checkpoint = torch.load(chk_file,
map_location=lambda storage, loc: storage)
model_pos.load_state_dict(checkpoint['model_pos'])
receptive_field = model_pos.receptive_field()
pad = (receptive_field - 1) // 2 # Padding on each side
causal_shift = 0
print('Reconstructing ...')
gen = UnchunkedGenerator(None,
None, [input_keypoints[valid_frames]],
pad=pad,
causal_shift=causal_shift,
augment=True,
kps_left=kps_left,
kps_right=kps_right,
joints_left=joints_left,
joints_right=joints_right)
prediction = evaluate(gen, model_pos, return_predictions=True)
prediction = camera_to_world(prediction, R=rot, t=0)
# We don't have the trajectory, but at least we can rebase the height
prediction[:, :, 2] -= np.min(prediction[:, :, 2])
prediction_new = np.zeros((*input_keypoints.shape[:-1], 3),
dtype=np.float32)
prediction_new[valid_frames] = prediction
print('Rendering ...')
anim_output = {'Reconstruction': prediction_new}
render_animation(keypoints,
keypoints_metadata,
anim_output,
h36m_skeleton,
25,
3000,
np.array(70., dtype=np.float32),
args.viz_output,
limit=-1,
downsample=1,
size=5,
input_video_path=args.video_path,
viewport=(width, height),
input_video_skip=0)
def main():
dataset_path = "./data/data_3d_h36m.npz" # 加载数据
from common.h36m_dataset import Human36mDataset
dataset = Human36mDataset(dataset_path)
dataset = read_3d_data(dataset)
cudnn.benchmark = True
device = torch.device("cpu")
from models.sem_gcn import SemGCN
from common.graph_utils import adj_mx_from_skeleton
p_dropout = None
adj = adj_mx_from_skeleton(dataset.skeleton())
model_pos = SemGCN(adj, 128, num_layers=4, p_dropout=p_dropout,
nodes_group=dataset.skeleton().joints_group()).to(device)
ckpt_path = "./checkpoint/pretrained/ckpt_semgcn_nonlocal_sh.pth.tar"
ckpt = torch.load(ckpt_path, map_location='cpu')
model_pos.load_state_dict(ckpt['state_dict'], False)
model_pos.eval()
# ============ 新增代码 ==============
# 从项目处理2d数据的代码中输出的一个人体数据
inputs_2d = [[483.0, 450], [503, 450], [503, 539], [496, 622], [469, 450], [462, 546], [469, 622], [483, 347],
[483, 326], [489, 264], [448, 347], [448, 408], [441, 463], [517, 347], [524, 408], [538, 463]]
# # openpose的测试样例识别结果
# inputs_2d = [[86.0, 137], [99, 128], [94, 127], [97, 110], [89, 105], [102, 129], [116, 116], [99, 110],
# [105, 93], [117, 69], [147, 63], [104, 93], [89, 69], [82, 38], [89, 139], [94, 140]]
inputs_2d = np.array(inputs_2d)
# inputs_2d[:, 1] = np.max(inputs_2d[:, 1]) - inputs_2d[:, 1] # 变成正的人体姿态，原始数据为倒立的
cam = dataset.cameras()['S1'][0] # 获取相机参数
inputs_2d[..., :2] = normalize_screen_coordinates(inputs_2d[..., :2], w=cam['res_w'], h=cam['res_h']) # 2d坐标处理
# 画出归一化屏幕坐标并且标记序号的二维关键点图像
print(inputs_2d) # 打印归一化后2d关键点坐标
d_x = inputs_2d[:, 0]
d_y = inputs_2d[:, 1]
plt.figure()
plt.scatter(d_x, d_y)
for i, txt in enumerate(np.arange(inputs_2d.shape[0])):
plt.annotate(txt, (d_x[i], d_y[i])) # 标号
# plt.show() # 显示2d关键点归一化后的图像
# 获取3d结果
inputs_2d = torch.tensor(inputs_2d, dtype=torch.float32) # 转换为张量
outputs_3d = model_pos(inputs_2d).cpu() # 加载模型
outputs_3d[:, :, :] -= outputs_3d[:, :1, :] # Remove global offset / 移除全球偏移
predictions = [outputs_3d.detach().numpy()] # 预测结果
prediction = np.concatenate(predictions)[0] # 累加取第一个
# Invert camera transformation / 反相机的转换
prediction = camera_to_world(prediction, R=cam['orientation'], t=0) # R和t的参数设置影响不大，有多种写法和选取的相机参数有关，有些S没有t等等问题
prediction[:, 2] -= np.min(prediction[:, 2]) # 向上偏移min(prediction[:, 2]),作用是把坐标变为正数
print('prediction')
print(prediction) # 打印画图的3d坐标
plt.figure()
ax = plt.subplot(111, projection='3d') # 创建一个三维的绘图工程
o_x = prediction[:, 0]
o_y = prediction[:, 1]
o_z = prediction[:, 2]
print(o_x)
print(o_y)
print(o_z)
ax.scatter(o_x, o_y, o_z)
temp = o_x
x = [temp[9], temp[8], temp[7], temp[10], temp[11], temp[12]]
temp = o_y
y = [temp[9], temp[8], temp[7], temp[10], temp[11], temp[12]]
temp = o_z
z = [temp[9], temp[8], temp[7], temp[10], temp[11], temp[12]]
ax.plot(x, y, z)
temp = o_x
x = [temp[7], temp[0], temp[4], temp[5], temp[6]]
temp = o_y
y = [temp[7], temp[0], temp[4], temp[5], temp[6]]
temp = o_z
z = [temp[7], temp[0], temp[4], temp[5], temp[6]]
ax.plot(x, y, z)
temp = o_x
x = [temp[0], temp[1], temp[2], temp[3]]
temp = o_y
y = [temp[0], temp[1], temp[2], temp[3]]
temp = o_z
z = [temp[0], temp[1], temp[2], temp[3]]
ax.plot(x, y, z)
temp = o_x
x = [temp[7], temp[13], temp[14], temp[15]]
temp = o_y
y = [temp[7], temp[13], temp[14], temp[15]]
temp = o_z
z = [temp[7], temp[13], temp[14], temp[15]]
ax.plot(x, y, z)
# temp = o_x
# x = [temp[0], temp[14]]
# temp = o_y
# y = [temp[0], temp[14]]
# temp = o_z
# z = [temp[0], temp[14]]
# ax.plot(y, x, z)
#
# temp = o_x
# x = [temp[0], temp[15]]
# temp = o_y
# y = [temp[0], temp[15]]
# temp = o_z
# z = [temp[0], temp[15]]
# ax.plot(y, x, z)
# 改变坐标比例的代码，该代码的效果是z坐标轴是其他坐标的两倍
from matplotlib.pyplot import MultipleLocatort
major_locator = MultipleLocator(0.5)
ax.xaxis.set_major_locator(major_locator)
ax.yaxis.set_major_locator(major_locator)
ax.zaxis.set_major_locator(major_locator)
ax.get_proj = lambda: np.dot(Axes3D.get_proj(ax), np.diag([0.5, 0.5, 1, 1]))
plt.show()
action_list.append(action)
print(action_list)
print(len(action_list))
for action in action_list:
position_list = []
for idx,carema_id in enumerate(id_order):
f = args.from_source + '/' + subject + '/MyPoseFeatures/D3_Positions_mono/'+action+'.'+carema_id+'.cdf.mat'
# if subject == 'S11' and action == 'Directions':
# continue # Discard corrupted video
# Use consistent naming convention
hf = loadmat(f)
positions = hf['data'][0, 0].reshape(-1, 32, 3)
positions /= 1000 # Meters instead of millimeters
positions_universal = camera_to_world(positions,R=np.array(camera_info[idx]['orientation']),t=np.array(camera_info[idx]['translation'])/1000)
position_list.append(positions_universal.astype('float32'))
canonical_name = action.replace('TakingPhoto', 'Photo') \
.replace('WalkingDog', 'WalkDog')
# if action == 'Directions 1':
# print('checking...')
# print(position_list[0]-position_list[1])
# print(position_list[1]-position_list[2])
# print(position_list[2]-position_list[3])
output[subject][canonical_name] = sum(position_list)/4
if action == 'Directions 1':
print(output[subject][canonical_name])
print(output[subject][canonical_name]-position_list[0])
print('Saving...')
#np.savez_compressed(output_filename, positions_3d=output)
def draw_3Dimg_c3dpo(pos, image, display=None, kpt2D=None, shape=None):
from mpl_toolkits.mplot3d import Axes3D # projection 3D 必须要这个
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
fig = plt.figure(figsize=(12, 6))
canvas = FigureCanvas(fig)
# 2D
fig.add_subplot(131)
if isinstance(kpt2D, np.ndarray):
plt.imshow(draw_2Dimg(image, kpt2D))
else:
plt.imshow(image)
# c3dpo 矫正
# if shape is not None:
# index_list = [0, 5, 7, 9, 6, 8, 10, 11, 13, 15, 12, 14, 16]
# pos[index_list] = shape
# 3D
ax = fig.add_subplot(132, projection='3d')
radius = 1.7
ax.view_init(elev=15., azim=70.)
ax.set_xlim3d([-radius / 2, radius / 2])
ax.set_zlim3d([0, radius])
ax.set_ylim3d([-radius / 2, radius / 2])
ax.set_aspect('equal')
# 坐标轴刻度
# ax.set_xticklabels([])
# ax.set_yticklabels([])
# ax.set_zticklabels([])
# ax.dist = 7.5
parents = common.skeleton_parents
joints_right = common.joints_right
for j, j_parent in enumerate(parents):
if j_parent == -1:
continue
col = 'red' if j in joints_right else 'black'
# 画图3D
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)
# c3dpo
bx = fig.add_subplot(133, projection='3d')
bx.view_init(elev=15., azim=15.)
bx.set_xlim3d([-radius / 2, radius / 2])
bx.set_zlim3d([0, radius])
bx.set_ylim3d([-radius / 2, radius / 2])
bx.set_aspect('equal')
from common.camera import camera_to_world
shape = camera_to_world(shape, R=common.c3dpo_rot, t=0)
shape[:, 2] -= np.min(shape[:, 2])
order_pair = ((1, 2), (2, 3), (4, 5), (5, 6), (0, 1), (0, 4), (0, 7),
(7, 8), (8, 9), (11, 12), (12, 13), (14, 15), (15, 16),
(11, 8), (14, 8))
# scale
# shape *= 0.7
for j, j_parent in order_pair:
bx.plot([shape[j, 0], shape[j_parent, 0]],
[shape[j, 1], shape[j_parent, 1]],
[shape[j, 2], shape[j_parent, 2]],
zdir='z',
c='red')
# bx.scatter(shape[:, 0], shape[:, 1], shape[:, 2])
width, height = fig.get_size_inches() * fig.get_dpi()
canvas.draw() # draw the canvas, cache the renderer
image = np.fromstring(canvas.tostring_rgb(),
dtype='uint8').reshape(int(height), int(width), 3)
if display:
cv2.imshow('im', image)
cv2.waitKey(1)
return image
def predict(img_path):
# 1.预处理输入图像和检测人体
x, img = data.transforms.presets.yolo.load_test(img_path, short=256)
# detector.summary(x)
# print("x.shape:", x.shape)
start = time.time()
# detect persons and bbox,
class_ids, scores, bounding_boxes = detector(x) # shape: [sample_idx, class_idx, instance]
# print("bounding_boxes.shape", bounding_boxes.shape, "bounding_boxes[0, 0]:", bounding_boxes[0, 0])
# 2.预处理检测器的输出张量作为mobile_pose的输入
pose_input, upscale_bbox = detector_to_mobile_pose(img, class_ids, scores, bounding_boxes)
print("detector cost time: {:.3f} seconds".format(time.time() - start))
global detector_time
detector_time += (time.time() - start)
if pose_input is None:
return None, None
# 4.2d关节点预测
# pose_net.summary(pose_input)
start_time = time.time()
predicted_heatmap = pose_net(pose_input)
pred_coords, confidence = heatmap_to_coord(predicted_heatmap, upscale_bbox)
# print("type(pre_coords): {}, shape(pre_coords): {}".format(type(pred_coords), pred_coords.shape))
# print("pred_coords: {}".format(pred_coords))
global predictor_2d_time
predictor_2d_time += (time.time() - start_time)
print("2d pose predictor cost time: {:.3f} seconds".format(time.time() - start_time))
# 5.显示2d姿态
# ax = utils.viz.plot_keypoints(img, pred_coords, confidence, class_IDs, bounding_boxes, scores, box_thresh=0.5,
# keypoint_thresh=0.2)
# print(pred_coords)
# 6.坐标标准化
start_time = time.time()
kps = normalize_screen_coordinates(pred_coords.asnumpy(), w=img.shape[1], h=img.shape[0])
# print('kps.type: {}, kps.shape: {}'.format(type(kps), kps.shape))
# 7.2d keypoints生成器
receptive_field = pose3d_predictor.receptive_field()
pad = (receptive_field - 1) // 2 # Padding on each side
causal_shift = 0
# 创建生成器作为3d预测器的输入
generator = UnchunkedGenerator(None, None, [kps], pad=pad, causal_shift=causal_shift, augment=False)
# 8.3d姿势估计和显示
prediction = predict_3d_pos(generator, pose3d_predictor)
global predictor_3d_time, full_time
predictor_3d_time += (time.time() - start_time)
full_time += (time.time() - start)
print("3d pose predictor cost time: {:.3f} seconds".format(time.time() - start_time))
# print("prediction.shape: ", prediction.shape)
rot = np.array([0.14070565, -0.15007018, -0.7552408, 0.62232804], dtype=np.float32)
prediction = camera_to_world(prediction, R=rot, t=0)
# We don't have the trajectory, but at least we can rebase the height
prediction[:, :, 2] -= np.min(prediction[:, :, 2])
elapsed = time.time() - start
print("Total elapsed time of predicting image file {}: {:.3f} seconds".format(img_path, elapsed))
return prediction, img
def videpose_infer(args):
from common.camera import normalize_screen_coordinates, camera_to_world, image_coordinates
from common.generators import UnchunkedGenerator
from common.model import TemporalModel
from common.utils import Timer, evaluate, add_path
from videopose import get_detector_2d, ckpt_time, metadata, time0
import gene_npz
gene_npz.args.outputpath = str(args.viz_output / "alpha_pose_kunkun_cut")
print(gene_npz.args)
# detector_2d = get_detector_2d(args.detector_2d)
detector_2d = gene_npz.generate_kpts(args.detector_2d)
assert detector_2d, 'detector_2d should be in ({alpha, hr, open}_pose)'
# 2D kpts loads or generate
if not args.input_npz:
video_name = args.viz_video
keypoints = detector_2d(video_name)
else:
npz = np.load(args.input_npz)
keypoints = npz['kpts'] # (N, 17, 2)
keypoints_symmetry = metadata['keypoints_symmetry']
kps_left, kps_right = list(
keypoints_symmetry[0]), list(keypoints_symmetry[1])
joints_left, joints_right = list(
[4, 5, 6, 11, 12, 13]), list([1, 2, 3, 14, 15, 16])
# normlization keypoints Suppose using the camera parameter
keypoints = normalize_screen_coordinates(
keypoints[..., :2], w=1000, h=1002)
model_pos = TemporalModel(17, 2, 17, filter_widths=[3, 3, 3, 3, 3], causal=args.causal, dropout=args.dropout, channels=args.channels,
dense=args.dense)
if torch.cuda.is_available():
model_pos = model_pos.cuda()
ckpt, time1 = ckpt_time(time0)
print('-------------- load data spends {:.2f} seconds'.format(ckpt))
# load trained model
chk_filename = os.path.join(
args.checkpoint, args.resume if args.resume else args.evaluate)
print('Loading checkpoint', chk_filename)
checkpoint = torch.load(
chk_filename, map_location=lambda storage, loc: storage) # 把loc映射到storage
model_pos.load_state_dict(checkpoint['model_pos'])
ckpt, time2 = ckpt_time(time1)
print('-------------- load 3D model spends {:.2f} seconds'.format(ckpt))
# Receptive field: 243 frames for args.arc [3, 3, 3, 3, 3]
receptive_field = model_pos.receptive_field()
pad = (receptive_field - 1) // 2 # Padding on each side
causal_shift = 0
print('Rendering...')
input_keypoints = keypoints.copy()
gen = UnchunkedGenerator(None, None, [input_keypoints],
pad=pad, causal_shift=causal_shift, augment=args.test_time_augmentation,
kps_left=kps_left, kps_right=kps_right, joints_left=joints_left, joints_right=joints_right)
prediction = evaluate(gen, model_pos, return_predictions=True)
# save 3D joint points
np.save(args.viz_output / "test_3d_output.npy",
prediction, allow_pickle=True)
rot = np.array([0.14070565, -0.15007018, -0.7552408,
0.62232804], dtype=np.float32)
prediction = camera_to_world(prediction, R=rot, t=0)
# We don't have the trajectory, but at least we can rebase the height
prediction[:, :, 2] -= np.min(prediction[:, :, 2])
anim_output = {'Reconstruction': prediction}
input_keypoints = image_coordinates(
input_keypoints[..., :2], w=1000, h=1002)
ckpt, time3 = ckpt_time(time2)
print(
'-------------- generate reconstruction 3D data spends {:.2f} seconds'.format(ckpt))
ckpt, time4 = ckpt_time(time3)
print('total spend {:2f} second'.format(ckpt))
def main(args):
print('==> Using settings {}'.format(args))
convm = torch.zeros(3, 17, 17, dtype=torch.float)
print('==> Loading dataset...')
dataset_path = path.join('data', 'data_3d_' + args.dataset + '.npz')
if args.dataset == 'h36m':
from common.h36m_dataset import Human36mDataset
dataset = Human36mDataset(dataset_path)
else:
raise KeyError('Invalid dataset')
print('==> Preparing data...')
dataset = read_3d_data(dataset)
print('==> Loading 2D detections...')
keypoints = create_2d_data(
path.join('data',
'data_2d_' + args.dataset + '_' + args.keypoints + '.npz'),
dataset)
cudnn.benchmark = True
device = torch.device("cuda")
# Create model
print("==> Creating model...")
if args.architecture == 'linear':
from models.linear_model import LinearModel, init_weights
num_joints = dataset.skeleton().num_joints()
model_pos = LinearModel(num_joints * 2,
(num_joints - 1) * 3).to(device)
model_pos.apply(init_weights)
elif args.architecture == 'gcn':
from models.sem_gcn import SemGCN
from common.graph_utils import adj_mx_from_skeleton
p_dropout = (None if args.dropout == 0.0 else args.dropout)
adj = adj_mx_from_skeleton(dataset.skeleton())
model_pos = SemGCN(convm,
adj,
args.hid_dim,
num_layers=args.num_layers,
p_dropout=p_dropout,
nodes_group=dataset.skeleton().joints_group()
if args.non_local else None).to(device)
else:
raise KeyError('Invalid model architecture')
print("==> Total parameters: {:.2f}M".format(
sum(p.numel() for p in model_pos.parameters()) / 1000000.0))
# Resume from a checkpoint
ckpt_path = args.evaluate
if path.isfile(ckpt_path):
print("==> Loading checkpoint '{}'".format(ckpt_path))
ckpt = torch.load(ckpt_path)
start_epoch = ckpt['epoch']
error_best = ckpt['error']
model_pos.load_state_dict(ckpt['state_dict'])
print("==> Loaded checkpoint (Epoch: {} | Error: {})".format(
start_epoch, error_best))
else:
raise RuntimeError("==> No checkpoint found at '{}'".format(ckpt_path))
print('==> Rendering...')
poses_2d = keypoints[args.viz_subject][args.viz_action]
out_poses_2d = poses_2d[args.viz_camera]
out_actions = [args.viz_camera] * out_poses_2d.shape[0]
poses_3d = dataset[args.viz_subject][args.viz_action]['positions_3d']
assert len(poses_3d) == len(poses_2d), 'Camera count mismatch'
out_poses_3d = poses_3d[args.viz_camera]
ground_truth = dataset[args.viz_subject][args.viz_action]['positions_3d'][
args.viz_camera].copy()
input_keypoints = out_poses_2d.copy()
render_loader = DataLoader(PoseGenerator([out_poses_3d], [out_poses_2d],
[out_actions]),
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
pin_memory=True)
prediction = evaluate(render_loader, model_pos, device,
args.architecture)[0]
# Invert camera transformation
cam = dataset.cameras()[args.viz_subject][args.viz_camera]
prediction = camera_to_world(prediction, R=cam['orientation'], t=0)
prediction[:, :, 2] -= np.min(prediction[:, :, 2])
ground_truth = camera_to_world(ground_truth, R=cam['orientation'], t=0)
ground_truth[:, :, 2] -= np.min(ground_truth[:, :, 2])
anim_output = {'Regression': prediction, 'Ground truth': ground_truth}
input_keypoints = image_coordinates(input_keypoints[..., :2],
w=cam['res_w'],
h=cam['res_h'])
render_animation(input_keypoints,
anim_output,
dataset.skeleton(),
dataset.fps(),
args.viz_bitrate,
cam['azimuth'],
args.viz_output,
limit=args.viz_limit,
downsample=args.viz_downsample,
size=args.viz_size,
input_video_path=args.viz_video,
viewport=(cam['res_w'], cam['res_h']),
input_video_skip=args.viz_skip)
def analyze_frame(h, frame):
boxes, keypoints = infer.inference_on_frame(h['predictor'], frame)
# step 4: prepare data.
# take 2d keypoints, that's it
# first element is empty array, second is our actual frame data, a 3d numpy array with first dimension 1, second and third being the 17 joints of 3 doubles each.
kp = keypoints[1][0][:2, :].T # extract (x, y) just like in prepare_data_2d_custom code
# what to do if kp is NaN or missing data or something?
# I guess just ignore it
# they do this at the end of step4. but we keep it simple, and take the data from step2 directly into a variable.
# output[canonical_name]['custom'] = [data[0]['keypoints'].astype('float32')]
#output_custom_canonical_bullshit = kp.astype('float32')
# this is what happens at the end of step4. which is a file that is loaded in the beginning of step 5.
# np.savez_compressed(os.path.join(args.dataoutputdir, output_prefix_2d + args.output), positions_2d=output, metadata=metadata)
# this is the bullshit they do in the original script.
# confusingly, keypoints is actually just data, until it is set to keypoints[positions_2d]
# keypoints = np.load('data/data_2d_' + args.dataset + '_' + args.keypoints + '.npz', allow_pickle=True)
# step 5: ..... all the other shit
# starting to copy stuff over from run.py
# extract dataset from the init dictionary
dataset = h['dataset']
keypoints_metadata = h['keypoints_metadata']
keypoints_symmetry = h['keypoints_symmetry']
kps_left = h['kps_left']
kps_right = h['kps_right']
joints_left = h['joints_left']
joints_right = h['joints_right']
# normalize
for i in range(len(kp)):
koord = kp[i]
kp[i] = normalize_screen_coordinates(koord, h['frame_metadata']['w'], h['frame_metadata']['h'])
#for kps in enumerate(keypoints):
# kps[..., :2] = normalize_screen_coordinates(kps[..., :2], frame_metadata['w'], frame_metadata['h'])
# this is taken from the args.architecture and run.py and just hardcoded, skipping a lot of nonsense
filter_widths = [int(x) for x in "3,3,3,3,3".split(',')]
skeleton_num_joints = dataset.skeleton().num_joints()
#skeleton_num_joints = 17
causal = True
dropout = 0.25
channels = 1024
dense = False
model_pos_train = TemporalModelOptimized1f(kp.shape[-2], kp.shape[-1], skeleton_num_joints,
filter_widths=filter_widths, causal=causal, dropout=dropout,
channels=channels)
model_pos = TemporalModel(kp.shape[-2], kp.shape[-1], skeleton_num_joints,
filter_widths=filter_widths, causal=causal, dropout=dropout,
channels=channels, dense=dense)
receptive_field = model_pos.receptive_field()
print('INFO: Receptive field: {} frames'.format(receptive_field))
pad = (receptive_field - 1) // 2 # Padding on each side
#if args.causal:
# print('INFO: Using causal convolutions')
# causal_shift = pad
#else:
# causal_shift = 0
causal_shift = pad
model_params = 0
for parameter in model_pos.parameters():
model_params += parameter.numel()
print('INFO: Trainable parameter count:', model_params)
if torch.cuda.is_available():
model_pos = model_pos.cuda()
model_pos_train = model_pos_train.cuda()
#if args.resume or args.evaluate:
if True:
chk_filename = "checkpoint/pretrained_h36m_detectron_coco.bin"
print('Loading checkpoint', chk_filename)
checkpoint = torch.load(chk_filename, map_location=lambda storage, loc: storage)
print('This model was trained for {} epochs'.format(checkpoint['epoch']))
model_pos_train.load_state_dict(checkpoint['model_pos'])
model_pos.load_state_dict(checkpoint['model_pos'])
# false in our particular case... we might benefit from getting rid of model_traj,
# unless it's super fast then we should just keep it in case we ever upgrade
if 'model_traj' in checkpoint:
# Load trajectory model if it contained in the checkpoint (e.g. for inference in the wild)
model_traj = TemporalModel(kp.shape[-2], kp.shape[-1], 1,
filter_widths=filter_widths, causal=causal, dropout=dropout, channels=channels,
dense=dense)
if torch.cuda.is_available():
model_traj = model_traj.cuda()
model_traj.load_state_dict(checkpoint['model_traj'])
else:
model_traj = None
test_generator = UnchunkedGenerator(None, None, kp,
pad=pad, causal_shift=causal_shift, augment=False,
kps_left=kps_left, kps_right=kps_right,
joints_left=joints_left, joints_right=joints_right)
print('INFO: Testing on {} frames'.format(test_generator.num_frames()))
# Evaluate
def evaluate(eval_generator, action=None, return_predictions=False, use_trajectory_model=False):
epoch_loss_3d_pos = 0
epoch_loss_3d_pos_procrustes = 0
epoch_loss_3d_pos_scale = 0
epoch_loss_3d_vel = 0
with torch.no_grad():
if not use_trajectory_model:
model_pos.eval()
else:
model_traj.eval()
N = 0
for _, batch, batch_2d in eval_generator.next_epoch():
inputs_2d = torch.from_numpy(batch_2d.astype('float32'))
if torch.cuda.is_available():
inputs_2d = inputs_2d.cuda()
# Positional model
if not use_trajectory_model:
predicted_3d_pos = model_pos(inputs_2d)
else:
predicted_3d_pos = model_traj(inputs_2d)
# Test-time augmentation (if enabled)
if eval_generator.augment_enabled():
# Undo flipping and take average with non-flipped version
predicted_3d_pos[1, :, :, 0] *= -1
if not use_trajectory_model:
predicted_3d_pos[1, :, joints_left + joints_right] = predicted_3d_pos[1, :, joints_right + joints_left]
predicted_3d_pos = torch.mean(predicted_3d_pos, dim=0, keepdim=True)
if return_predictions:
return predicted_3d_pos.squeeze(0).cpu().numpy()
inputs_3d = torch.from_numpy(batch.astype('float32'))
if torch.cuda.is_available():
inputs_3d = inputs_3d.cuda()
inputs_3d[:, :, 0] = 0
if eval_generator.augment_enabled():
inputs_3d = inputs_3d[:1]
error = mpjpe(predicted_3d_pos, inputs_3d)
epoch_loss_3d_pos_scale += inputs_3d.shape[0]*inputs_3d.shape[1] * n_mpjpe(predicted_3d_pos, inputs_3d).item()
epoch_loss_3d_pos += inputs_3d.shape[0]*inputs_3d.shape[1] * error.item()
N += inputs_3d.shape[0] * inputs_3d.shape[1]
inputs = inputs_3d.cpu().numpy().reshape(-1, inputs_3d.shape[-2], inputs_3d.shape[-1])
predicted_3d_pos = predicted_3d_pos.cpu().numpy().reshape(-1, inputs_3d.shape[-2], inputs_3d.shape[-1])
epoch_loss_3d_pos_procrustes += inputs_3d.shape[0]*inputs_3d.shape[1] * p_mpjpe(predicted_3d_pos, inputs)
# Compute velocity error
epoch_loss_3d_vel += inputs_3d.shape[0]*inputs_3d.shape[1] * mean_velocity_error(predicted_3d_pos, inputs)
if action is None:
print('----------')
else:
print('----'+action+'----')
e1 = (epoch_loss_3d_pos / N)*1000
e2 = (epoch_loss_3d_pos_procrustes / N)*1000
e3 = (epoch_loss_3d_pos_scale / N)*1000
ev = (epoch_loss_3d_vel / N)*1000
print('Test time augmentation:', eval_generator.augment_enabled())
print('Protocol #1 Error (MPJPE):', e1, 'mm')
print('Protocol #2 Error (P-MPJPE):', e2, 'mm')
print('Protocol #3 Error (N-MPJPE):', e3, 'mm')
print('Velocity Error (MPJVE):', ev, 'mm')
print('----------')
return e1, e2, e3, ev
image_keypoints2d = kp
gen = UnchunkedGenerator(None, None, [[image_keypoints2d]],
pad=pad, causal_shift=causal_shift, augment=False,
kps_left=kps_left, kps_right=kps_right, joints_left=joints_left, joints_right=joints_right)
prediction = evaluate(gen, return_predictions=True)
# here is the data format
# public enum VideoPose3dJointOrder
# {
# HIP = 0,
# R_HIP = 1,
# R_KNEE = 2,
# R_FOOT = 3,
# L_HIP = 4,
# L_KNEE = 5,
# L_FOOT = 6,
# SPINE = 7,
# THORAX = 8,
# NOSE = 9,
# HEAD = 10,
# L_SHOULDER = 11,
# L_ELBOW = 12,
# L_WRIST = 13,
# R_SHOULDER = 14,
# R_ELBOW = 15,
# R_WRIST = 16
# }
# this bugs out. dunno what the hell they were trying to do.
# anyway we can fix it by just getting width/height some other way.
# Invert camera transformation
cam = dataset.cameras()
width = cam['frame'][0]['res_w']
height = cam['frame'][0]['res_h']
image_keypoints2d = image_coordinates(image_keypoints2d[..., :2], w=width, h=height)
viz_camera = 0
# If the ground truth is not available, take the camera extrinsic params from a random subject.
# They are almost the same, and anyway, we only need this for visualization purposes.
for subject in dataset.cameras():
if 'orientation' in dataset.cameras()[subject][viz_camera]:
rot = dataset.cameras()[subject][viz_camera]['orientation']
break
prediction = camera_to_world(prediction, R=rot, t=0)
# We don't have the trajectory, but at least we can rebase the height
prediction[:, :, 2] -= np.min(prediction[:, :, 2])
# because algo was meant for a list of frames, we take the first frame (our only frame)
prediction3d = prediction[0]
return prediction3d, image_keypoints2d
# do we want to visualize? this code used to write to json and create a video for visualization
#if args.viz_output is not None:
if True:
anim_output = {'Reconstruction': prediction}
# format the data in the same format as mediapipe, so we can load it in unity with the same script
# we need a list (frames) of lists of 3d landmarks.
unity_landmarks = prediction.tolist()
# how to send data? or display it?
# maybe draw it on the webcam feed....?!?!?!
#with open(args.output_json, "w") as json_file:
# json.dump(unity_landmarks, json_file)
#if args.rendervideo == "yes":
# from common.visualization import render_animation
# render_animation(input_keypoints, keypoints_metadata, anim_output,
# dataset.skeleton(), dataset.fps(), args.viz_bitrate, cam['azimuth'], args.viz_output,
# limit=args.viz_limit, downsample=args.viz_downsample, size=args.viz_size,
# input_video_path=args.viz_video, viewport=(cam['res_w'], cam['res_h']),
# input_video_skip=args.viz_skip)
we_re_done_here = 1
# Predictions are in camera space
np.save(args.viz_export, prediction)
if args.viz_output is not None:
# Invert camera transformation
cam = dataset.cameras()[args.viz_subject][args.viz_camera]
# If the ground truth is not available, take the camera extrinsic params from a random subject.
# They are almost the same, and anyway, we only need this for visualization purposes.
rot = None
for subject in dataset.cameras():
if 'orientation' in dataset.cameras()[subject][args.viz_camera]:
rot = dataset.cameras()[subject][
args.viz_camera]['orientation']
break
prediction = camera_to_world(prediction, R=rot, t=0)
# We don't have the trajectory, but at least we can rebase the height
prediction[:, :, 2] -= np.min(prediction[:, :, 2])
anim_output = {'Reconstruction': prediction}
input_keypoints = image_coordinates(input_keypoints[..., :2],
w=cam['res_w'],
h=cam['res_h'])
# print('w, h:', cam['res_w'], cam['res_h'])
from common.visualization import render_animation
print("rot:", rot)
print("cam['azimuth']:", cam['azimuth'])
render_animation(input_keypoints,
keypoints_metadata,
def draw_3Dimg_adjust(pos, image, display=None, kpt2D=None, shapes=None):
from mpl_toolkits.mplot3d import Axes3D # projection 3D 必须要这个
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
fig = plt.figure(figsize=(12, 6))
canvas = FigureCanvas(fig)
# 2D
fig.add_subplot(131)
if isinstance(kpt2D, np.ndarray):
plt.imshow(draw_2Dimg(image, kpt2D))
else:
plt.imshow(image)
# nrsfm 矫正
# if shapes is not None:
# index_list = [0, 5, 7, 9, 6, 8, 10, 11, 13, 15, 12, 14, 16]
# pos[index_list] = shapes[-1]
# 3D
ax = fig.add_subplot(132, projection='3d')
radius = 1.7
ax.view_init(elev=15., azim=70.)
ax.set_xlim3d([-radius / 2, radius / 2])
ax.set_zlim3d([0, radius])
ax.set_ylim3d([-radius / 2, radius / 2])
ax.set_aspect('equal')
# 坐标轴刻度
# ax.set_xticklabels([])
# ax.set_yticklabels([])
# ax.set_zticklabels([])
# ax.dist = 7.5
parents = common.skeleton_parents
joints_right = common.joints_right
for j, j_parent in enumerate(parents):
if j_parent == -1:
continue
col = 'red' if j in joints_right else 'black'
# 画图3D
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)
# nrsfm
bx = fig.add_subplot(133, projection='3d')
bx.view_init(elev=15., azim=70.)
bx.set_xlim3d([-radius / 2, radius / 2])
bx.set_zlim3d([0, radius])
bx.set_ylim3d([-radius / 2, radius / 2])
bx.set_aspect('equal')
# to_world
from common.camera import camera_to_world
shapes = np.array(shapes)
shapes = shapes[:, :, [0, 2, 1]]
shapes = camera_to_world(shapes, R=common.rot, t=0)
shapes[:, :, 2] -= np.min(shapes[:, :, 2])
one_shape = shapes[-1]
# one_shape = one_shape[:, [0, 2, 1]]
for j, j_parent in common.my_connections:
bx.plot([one_shape[j, 0], one_shape[j_parent, 0]],
[one_shape[j, 1], one_shape[j_parent, 1]],
[one_shape[j, 2], one_shape[j_parent, 2]],
zdir='z',
c='black')
width, height = fig.get_size_inches() * fig.get_dpi()
canvas.draw() # draw the canvas, cache the renderer
image = np.fromstring(canvas.tostring_rgb(),
dtype='uint8').reshape(int(height), int(width), 3)
if display:
cv2.imshow('im', image)
cv2.waitKey(1)
return image
