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Configuration error
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import numpy as np
import torch
import cv2
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import os
import matplotlib.patches as patches
from sklearn.manifold import TSNE
import open3d as o3d
kintree = {
'kintree': [[1, 0], [2, 1], [3, 2], [4, 3], [5, 1], [6, 5], [7, 6], [8, 1],
[9, 8], [10, 9], [11, 10], [12, 8], [13, 12], [14, 13],
[15, 0], [16, 0], [17, 15], [18, 16], [19, 14], [20, 19],
[21, 14], [22, 11], [23, 22], [24, 11]],
'color': [
'k', 'r', 'r', 'r', 'b', 'b', 'b', 'k', 'r', 'r', 'r', 'b', 'b', 'b',
'y', 'y', 'y', 'y', 'b', 'b', 'b', 'r', 'r', 'r'
]
}
def plotSkel3D(pts,
config=kintree,
ax=None,
phi=0,
theta=0,
max_range=1,
linewidth=4,
color=None):
multi = False
if torch.is_tensor(pts):
if len(pts.shape) == 3:
print(">>> Visualize multiperson ...")
multi = True
if pts.shape[1] != 3:
pts = pts.transpose(1, 2)
elif len(pts.shape) == 2:
if pts.shape[0] != 3:
pts = pts.transpose(0, 1)
else:
raise RuntimeError('The dimension of the points is wrong!')
pts = pts.detach().cpu().numpy()
else:
if pts.shape[0] != 3:
pts = pts.T
# pts : bn, 3, NumOfPoints or (3, N)
if ax is None:
print('>>> create figure ...')
fig = plt.figure(figsize=[5, 5])
ax = fig.add_subplot(111, projection='3d')
for idx, (i, j) in enumerate(config['kintree']):
if multi:
for b in range(pts.shape[0]):
ax.plot([pts[b][0][i], pts[b][0][j]],
[pts[b][1][i], pts[b][1][j]],
[pts[b][2][i], pts[b][2][j]],
lw=linewidth,
color=config['color'][idx] if color is None else color,
alpha=1)
else:
ax.plot([pts[0][i], pts[0][j]], [pts[1][i], pts[1][j]],
[pts[2][i], pts[2][j]],
lw=linewidth,
color=config['color'][idx],
alpha=1)
if multi:
for b in range(pts.shape[0]):
ax.scatter(pts[b][0], pts[b][1], pts[b][2], color='r', alpha=1)
else:
ax.scatter(pts[0], pts[1], pts[2], color='r', alpha=1, s=0.5)
ax.view_init(phi, theta)
ax.set_xlim(-max_range, max_range)
ax.set_ylim(-max_range, max_range)
ax.set_zlim(-0.05, 2)
# ax.axis('equal')
plt.xlabel('x')
plt.ylabel('y')
# plt.zlabel('z')
return ax
def plotSkel2D(pts,
config=kintree,
ax=None,
linewidth=2,
alpha=1,
max_range=1,
imgshape=None,
thres=0.1):
if len(pts.shape) == 2:
pts = pts[None, :, :] #(nP, nJ, 2/3)
elif len(pts.shape) == 3:
pass
else:
raise RuntimeError('The dimension of the points is wrong!')
if torch.is_tensor(pts):
pts = pts.detach().cpu().numpy()
if pts.shape[2] == 3 or pts.shape[2] == 2:
pts = pts.transpose((0, 2, 1))
# pts : bn, 2/3, NumOfPoints or (2/3, N)
if ax is None:
fig = plt.figure(figsize=[5, 5])
ax = fig.add_subplot(111)
if 'color' in config.keys():
colors = config['color']
else:
colors = ['b' for _ in range(len(config['kintree']))]
def inrange(imgshape, pts):
if pts[0] < 5 or \
pts[0] > imgshape[1] - 5 or \
pts[1] < 5 or \
pts[1] > imgshape[0] - 5:
return False
else:
return True
for nP in range(pts.shape[0]):
for idx, (i, j) in enumerate(config['kintree']):
if pts.shape[1] == 3: # with confidence
if np.min(pts[nP][2][[i, j]]) < thres:
continue
lw = linewidth * 2 * np.min(pts[nP][2][[i, j]])
else:
lw = linewidth
if imgshape is not None:
if inrange(imgshape, pts[nP, :, i]) and \
inrange(imgshape, pts[nP, :, j]):
pass
else:
continue
ax.plot([pts[nP][0][i], pts[nP][0][j]],
[pts[nP][1][i], pts[nP][1][j]],
lw=lw,
color=colors[idx],
alpha=1)
# if pts.shape[1] > 2:
# ax.scatter(pts[nP][0], pts[nP][1], s=10*(pts[nP][2]-thres), c='r')
if False:
ax.axis('equal')
plt.xlabel('x')
plt.ylabel('y')
else:
ax.axis('off')
return ax
def draw_skeleton(img, kpts2d):
cv_img = img.copy()
for kp in kpts2d:
if kp.shape[-1] == 2 or (kp.shape[-1] == 3 and kp[-1] > 0):
cv_img = cv2.circle(cv_img, tuple(kp[:2].astype(int)), 10,
(255, 0, 0))
return cv_img
def vis_frame(data_root, im_data, camera):
from external.SMPL_CPP.build.python import pysmplceres
from .smpl_renderer import Renderer
imgs = [
cv2.imread(os.path.join(data_root, im_path))
for im_path in im_data['ims']
]
imgs = [cv2.resize(img, (1024, 1024)) for img in imgs]
Ks = np.array(camera['K'])
Rs = np.array(camera['R'])
Ts = np.array(camera['T']).transpose(0, 2, 1) / 1000
faces = np.loadtxt('data/smpl/faces.txt').astype(np.int32)
render = Renderer(height=1024, width=1024, faces=faces)
vertices = pysmplceres.getVertices(im_data['smpl_result'])
imgsrender = render.render_multiview(vertices[0], Ks, Rs, Ts, imgs)
for img in imgsrender:
plt.imshow(img[..., ::-1])
plt.show()
def vis_skeleton_frame(data_root, im_data, camera):
from external.SMPL_CPP.build.python import pysmplceres
from .smpl_renderer import Renderer
imgs = [
cv2.imread(os.path.join(data_root, im_path))
for im_path in im_data['ims']
]
imgs = [cv2.resize(img, (1024, 1024)) for img in imgs]
kpts2d = np.array(im_data['kpts2d'])
for img, kpts in zip(imgs, kpts2d):
_, ax = plt.subplots(1, 1)
ax.imshow(img[..., ::-1])
plotSkel2D(kpts, ax=ax)
plt.show()
def vis_bbox(img, corners_2d, coord):
_, ax = plt.subplots(1)
ax.imshow(img)
ax.add_patch(
patches.Polygon(xy=corners_2d[[0, 1, 3, 2, 0, 4, 6, 2]],
fill=False,
linewidth=1,
edgecolor='g'))
ax.add_patch(
patches.Polygon(xy=corners_2d[[5, 4, 6, 7, 5, 1, 3, 7]],
fill=False,
linewidth=1,
edgecolor='g'))
ax.plot(coord[:, 1], coord[:, 0], '.')
plt.show()
def tsne_colors(data):
"""
N x D np.array data
"""
tsne = TSNE(n_components=1,
verbose=1,
perplexity=40,
n_iter=300,
random_state=0)
tsne_results = tsne.fit_transform(data)
tsne_results = np.squeeze(tsne_results)
tsne_min = np.min(tsne_results)
tsne_max = np.max(tsne_results)
tsne_results = (tsne_results - tsne_min) / (tsne_max - tsne_min)
colors = plt.cm.Spectral(tsne_results)[:, :3]
return colors
def get_colored_pc(pts, rgb):
pc = o3d.geometry.PointCloud()
pc.points = o3d.utility.Vector3dVector(pts)
colors = np.zeros_like(pts)
colors += rgb
pc.colors = o3d.utility.Vector3dVector(colors)
return pc
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