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import os.path as osp
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import torch
from dot.utils.io import create_folder
def to_rgb(tensor, mode, tracks=None, is_video=False, to_torch=True, reshape_as_video=False):
if isinstance(tensor, list):
tensor = torch.stack(tensor)
tensor = tensor.cpu().numpy()
if is_video:
batch_size, time_steps = tensor.shape[:2]
if mode == "flow":
height, width = tensor.shape[-3: -1]
tensor = np.reshape(tensor, (-1, height, width, 2))
tensor = flow_to_rgb(tensor)
elif mode == "mask":
height, width = tensor.shape[-2:]
tensor = np.reshape(tensor, (-1, 1, height, width))
tensor = np.repeat(tensor, 3, axis=1)
else:
height, width = tensor.shape[-2:]
tensor = np.reshape(tensor, (-1, 3, height, width))
if tracks is not None:
samples = tracks.size(-2)
tracks = tracks.cpu().numpy()
tracks = np.reshape(tracks, (-1, samples, 3))
traj, occ = tracks[..., :2], 1 - tracks[..., 2]
if is_video:
tensor = np.reshape(tensor, (-1, time_steps, 3, height, width))
traj = np.reshape(traj, (-1, time_steps, samples, 2))
occ = np.reshape(occ, (-1, time_steps, samples))
new_tensor = []
for t in range(time_steps):
pos_t = traj[:, t]
occ_t = occ[:, t]
new_tensor.append(plot_tracks(tensor[:, t], pos_t, occ_t, tracks=traj[:, :t + 1]))
tensor = np.stack(new_tensor, axis=1)
else:
tensor = plot_tracks(tensor, traj, occ)
if is_video and reshape_as_video:
tensor = np.reshape(tensor, (batch_size, time_steps, 3, height, width))
else:
tensor = np.reshape(tensor, (-1, 3, height, width))
if to_torch:
tensor = torch.from_numpy(tensor)
return tensor
def flow_to_rgb(flow, transparent=False):
flow = np.copy(flow)
H, W = flow.shape[-3: -1]
mul = 20.
scaling = mul / (H ** 2 + W ** 2) ** 0.5
direction = (np.arctan2(flow[..., 0], flow[..., 1]) + np.pi) / (2 * np.pi)
norm = np.linalg.norm(flow, axis=-1)
magnitude = np.clip(norm * scaling, 0., 1.)
saturation = np.ones_like(direction)
if transparent:
hsv = np.stack([direction, saturation, np.ones_like(magnitude)], axis=-1)
else:
hsv = np.stack([direction, saturation, magnitude], axis=-1)
rgb = matplotlib.colors.hsv_to_rgb(hsv)
rgb = np.moveaxis(rgb, -1, -3)
if transparent:
return np.concatenate([rgb, np.expand_dims(magnitude, axis=-3)], axis=-3)
return rgb
def plot_tracks(rgb, points, occluded, tracks=None, trackgroup=None):
"""Plot tracks with matplotlib.
Adapted from: https://github.com/google-research/kubric/blob/main/challenges/point_tracking/dataset.py"""
rgb = rgb.transpose(0, 2, 3, 1)
_, height, width, _ = rgb.shape
points = points.transpose(1, 0, 2).copy() # clone, otherwise it updates points array
# points[..., 0] *= (width - 1)
# points[..., 1] *= (height - 1)
if tracks is not None:
tracks = tracks.copy()
# tracks[..., 0] *= (width - 1)
# tracks[..., 1] *= (height - 1)
if occluded is not None:
occluded = occluded.transpose(1, 0)
disp = []
cmap = plt.cm.hsv
z_list = np.arange(points.shape[0]) if trackgroup is None else np.array(trackgroup)
# random permutation of the colors so nearby points in the list can get different colors
np.random.seed(0)
z_list = np.random.permutation(np.max(z_list) + 1)[z_list]
colors = cmap(z_list / (np.max(z_list) + 1))
figure_dpi = 64
for i in range(rgb.shape[0]):
fig = plt.figure(
figsize=(width / figure_dpi, height / figure_dpi),
dpi=figure_dpi,
frameon=False,
facecolor='w')
ax = fig.add_subplot()
ax.axis('off')
ax.imshow(rgb[i])
valid = points[:, i, 0] > 0
valid = np.logical_and(valid, points[:, i, 0] < rgb.shape[2] - 1)
valid = np.logical_and(valid, points[:, i, 1] > 0)
valid = np.logical_and(valid, points[:, i, 1] < rgb.shape[1] - 1)
if occluded is not None:
colalpha = np.concatenate([colors[:, :-1], 1 - occluded[:, i:i + 1]], axis=1)
else:
colalpha = colors[:, :-1]
# Note: matplotlib uses pixel coordinates, not raster.
ax.scatter(
points[valid, i, 0] - 0.5,
points[valid, i, 1] - 0.5,
s=3,
c=colalpha[valid],
)
if tracks is not None:
for j in range(tracks.shape[2]):
track_color = colors[j] # Use a different color for each track
x = tracks[i, :, j, 0]
y = tracks[i, :, j, 1]
valid_track = x > 0
valid_track = np.logical_and(valid_track, x < rgb.shape[2] - 1)
valid_track = np.logical_and(valid_track, y > 0)
valid_track = np.logical_and(valid_track, y < rgb.shape[1] - 1)
ax.plot(x[valid_track] - 0.5, y[valid_track] - 0.5, color=track_color, marker=None)
if occluded is not None:
occ2 = occluded[:, i:i + 1]
colalpha = np.concatenate([colors[:, :-1], occ2], axis=1)
ax.scatter(
points[valid, i, 0],
points[valid, i, 1],
s=20,
facecolors='none',
edgecolors=colalpha[valid],
)
plt.subplots_adjust(top=1, bottom=0, right=1, left=0, hspace=0, wspace=0)
plt.margins(0, 0)
fig.canvas.draw()
width, height = fig.get_size_inches() * fig.get_dpi()
img = np.frombuffer(
fig.canvas.tostring_rgb(),
dtype='uint8').reshape(int(height), int(width), 3)
disp.append(np.copy(img))
plt.close("all")
return np.stack(disp, axis=0).astype(float).transpose(0, 3, 1, 2) / 255 # TODO : inconsistent
def plot_points(src_frame, tgt_frame, src_points, tgt_points, save_path, max_points=256):
_, H, W = src_frame.shape
src_frame = src_frame.permute(1, 2, 0).cpu().numpy()
tgt_frame = tgt_frame.permute(1, 2, 0).cpu().numpy()
src_points = src_points.cpu().numpy()
tgt_points = tgt_points.cpu().numpy()
src_pos, src_alpha = src_points[..., :2], src_points[..., 2]
tgt_pos, tgt_alpha = tgt_points[..., :2], tgt_points[..., 2]
src_pos = np.stack([src_pos[..., 0] * (W - 1), src_pos[..., 1] * (H - 1)], axis=-1)
tgt_pos = np.stack([tgt_pos[..., 0] * (W - 1), tgt_pos[..., 1] * (H - 1)], axis=-1)
plt.figure()
ax = plt.gca()
P = 10
plt.imshow(np.concatenate((src_frame, np.ones_like(src_frame[:, :P]), tgt_frame), axis=1))
indices = np.random.choice(len(src_pos), size=min(max_points, len(src_pos)), replace=False)
for i in indices:
if src_alpha[i] == 1:
ax.scatter(src_pos[i, 0], src_pos[i, 1], s=5, c="black", marker='x')
else:
ax.scatter(src_pos[i, 0], src_pos[i, 1], s=5, linewidths=1.5, c="black", marker='o')
ax.scatter(src_pos[i, 0], src_pos[i, 1], s=2.5, c="white", marker='o')
if tgt_alpha[i] == 1:
ax.scatter(W + P + tgt_pos[i, 0], tgt_pos[i, 1], s=5, c="black", marker='x')
else:
ax.scatter(W + P + tgt_pos[i, 0], tgt_pos[i, 1], s=5, linewidths=1.5, c="black", marker='o')
ax.scatter(W + P + tgt_pos[i, 0], tgt_pos[i, 1], s=2.5, c="white", marker='o')
plt.plot([src_pos[i, 0], W + P + tgt_pos[i, 0]], [src_pos[i, 1], tgt_pos[i, 1]], linewidth=0.5, c="black")
# Save
ax.axis('off')
plt.tight_layout()
plt.subplots_adjust(wspace=0)
create_folder(osp.dirname(save_path))
plt.savefig(save_path, bbox_inches='tight', pad_inches=0)
plt.close() |