def visualize_dataset(dataset_dir):
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D # NOQA
from chainercv.utils import read_image
from pose.visualizations import vis_point, vis_edge
dataset = GANeratedBaseDataset(dataset_dir, debug=False, mode="train")
idx = np.random.choice(len(dataset))
print(idx, len(dataset))
example = dataset.get_example(idx)
rgb_joint = example["rgb_joint"]
rgb_path = example["rgb_path"]
rgb = read_image(rgb_path)
fig = plt.figure(figsize=(5, 10))
ax2 = fig.add_subplot(211)
ax4 = fig.add_subplot(212, projection="3d")
color = [COLOR_MAP[k] for k in KEYPOINT_NAMES]
edge_color = [COLOR_MAP[s, t] for s, t in EDGES]
rgb_vu = example["rgb_camera"].zyx2vu(rgb_joint)
vis_point(rgb_vu, img=rgb, color=color, ax=ax2)
vis_edge(rgb_vu, indices=EDGES, color=edge_color, ax=ax2)
vis_point(rgb_joint, color=color, ax=ax4)
vis_edge(rgb_joint, indices=EDGES, color=edge_color, ax=ax4)
for ax in [ax4]:
ax.set_xlabel("x")
ax.set_ylabel("y")
ax.set_zlabel("z")
ax.view_init(-65, -90)
plt.show()
def visualize_dataset(dataset_dir):
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D # NOQA
from pose.visualizations import vis_point, vis_edge
dataset = MSRA15BaseDataset(dataset_dir, debug=True, mode="train")
example = dataset.get_example(128)
depth_joint = example["depth_joint"]
depth_path = example["depth_path"]
depth = dataset.read_depth(depth_path)
fig = plt.figure(figsize=(5, 10))
ax2 = fig.add_subplot(211)
ax4 = fig.add_subplot(212, projection="3d")
color = [COLOR_MAP[k] for k in KEYPOINT_NAMES]
edge_color = [COLOR_MAP[s, t] for s, t in EDGES]
depth_vu = DEPTH_CAMERA_INTR.zyx2vu(depth_joint)
vis_point(depth_vu, img=depth, color=color, ax=ax2)
vis_edge(depth_vu, indices=EDGES, color=edge_color, ax=ax2)
vis_point(depth_joint, color=color, ax=ax4)
vis_edge(depth_joint, indices=EDGES, color=edge_color, ax=ax4)
for ax in [ax4]:
ax.set_xlabel("x")
ax.set_ylabel("y")
ax.set_zlabel("z")
ax.view_init(-65, -90)
plt.show()
def visualize_dataset(dataset_dir):
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D # NOQA
from chainercv.utils import read_image
from pose.visualizations import vis_point, vis_edge
dataset = MultiViewBaseDataset(dataset_dir, debug=False)
print(len(dataset))
i = np.random.choice(len(dataset))
example = dataset.get_example(i)
rgb_path = example["rgb_path"]
rgb_joint = example["rgb_joint"]
print(rgb_path)
img = read_image(rgb_path)
fig = plt.figure(figsize=(8, 8))
ax1 = fig.add_subplot(211)
ax3 = fig.add_subplot(212, projection="3d")
rgb_vu = dataset.rgb_camera.zyx2vu(rgb_joint)
color = [COLOR_MAP[k] for k in STANDARD_KEYPOINT_NAMES]
edge_color = [COLOR_MAP[s, t] for s, t in EDGES]
vis_point(rgb_vu, img=img, color=color, ax=ax1)
vis_edge(rgb_vu, indices=EDGES, color=edge_color, ax=ax1)
vis_point(rgb_joint, color=color, ax=ax3)
vis_edge(rgb_joint, indices=EDGES, color=edge_color, ax=ax3)
for ax in [ax3]:
ax.set_xlabel("x")
ax.set_ylabel("y")
ax.set_zlabel("z")
ax.view_init(-65, -90)
plt.show()
def visualize_crop(i):
example = dataset.get_example(i)
rgb_joint_zyx = example["rgb_joint"]
depth_joint_zyx = example["depth_joint"]
rgb = chainercv.utils.read_image(example["rgb_path"])
depth = dataset.read_depth(example["depth_path"])
depth_cropped, depth_vu_cropped, depth_camera_cropped, depth_crop_param = crop(
depth, depth_joint_zyx, dataset.depth_camera, return_param=True)
rgb_cropped, rgb_vu_cropped, rgb_camera_cropped, rgb_crop_param = crop(
rgb, rgb_joint_zyx, dataset.rgb_camera, return_param=True)
fig = plt.figure(figsize=(8, 8))
ax1 = fig.add_subplot(221)
ax2 = fig.add_subplot(222)
ax3 = fig.add_subplot(223, projection="3d")
ax4 = fig.add_subplot(224, projection="3d")
vis_image(depth_cropped, ax1)
vis_pose(depth_vu_cropped,
EDGES,
point_color=POINT_COLOR,
edge_color=EDGE_COLOR,
ax=ax1)
vis_image(rgb_cropped, ax2)
vis_pose(rgb_vu_cropped,
EDGES,
point_color=POINT_COLOR,
edge_color=EDGE_COLOR,
ax=ax2)
# plot 3D
# pull back depth map
uvd = convert_depth_to_uvd(depth_cropped)
u, v, d = uvd[:, ::10, ::10]
u = u.reshape(-1, 1)
v = v.reshape(-1, 1)
z = d.reshape(-1, 1)
vu = np.concatenate([v, u], axis=1)
zyx = depth_camera_cropped.vu2zyx(vu, z)
vis_point(zyx, ax=ax3)
zyx_com = depth_crop_param["zyx_com"]
vis_pose(depth_joint_zyx,
indices=EDGES,
edge_color=EDGE_COLOR,
point_color=POINT_COLOR,
ax=ax3)
vis_pose(rgb_joint_zyx,
indices=EDGES,
edge_color=EDGE_COLOR,
point_color=POINT_COLOR,
ax=ax4)
for ax in [ax3, ax4]:
ax.set_xlabel("x")
ax.set_ylabel("y")
ax.set_zlabel("z")
ax.view_init(-65, -90)
def visualize_dataset(dataset_dir):
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D # NOQA
from chainercv.utils import read_image
from pose.visualizations import vis_point, vis_edge
dataset = NYUBaseDataset(dataset_dir, debug=True, mode="train")
example = dataset.get_example(0)
camera_joint = example["rgb_joint"]
depth_joint = example["depth_joint"]
rgb_path = example["rgb_path"]
depth_path = example["depth_path"]
logger.info("> read {}".format(rgb_path))
img = read_image(rgb_path)
depth = dataset.read_depth(depth_path)
fig = plt.figure(figsize=(8, 8))
ax1 = fig.add_subplot(221)
ax2 = fig.add_subplot(222)
ax3 = fig.add_subplot(223, projection="3d")
ax4 = fig.add_subplot(224, projection="3d")
rgb_vu = RGB_CAMERA_INTR.zyx2vu(camera_joint)
color = [COLOR_MAP[k] for k in STANDARD_KEYPOINT_NAMES]
edge_color = [COLOR_MAP[s, t] for s, t in EDGES]
depth_vu = DEPTH_CAMERA_INTR.zyx2vu(depth_joint)
vis_point(rgb_vu, img=img, color=color, ax=ax1)
vis_edge(rgb_vu, indices=EDGES, color=edge_color, ax=ax1)
vis_point(depth_vu, img=depth, color=color, ax=ax2)
vis_edge(depth_vu, indices=EDGES, color=edge_color, ax=ax2)
vis_point(camera_joint, color=color, ax=ax3)
vis_edge(camera_joint, indices=EDGES, color=edge_color, ax=ax3)
vis_point(depth_joint, color=color, ax=ax4)
vis_edge(depth_joint, indices=EDGES, color=edge_color, ax=ax4)
for ax in [ax3, ax4]:
ax.set_xlabel("x")
ax.set_ylabel("y")
ax.set_zlabel("z")
ax.view_init(-65, -90)
plt.show()