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import numpy as np
import torch
import matplotlib.pyplot as plt
import matplotlib as mpl
import os
import cv2
from PIL import Image
from .visualizer import Visualizer
movable_imap = {
0: 'one_hand',
1: 'two_hands',
2: 'fixture',
-100: 'n/a',
}
rigid_imap = {
1: 'yes',
0: 'no',
2: 'bad',
-100: 'n/a',
}
kinematic_imap = {
0: 'freeform',
1: 'rotation',
2: 'translation',
-100: 'n/a'
}
action_imap = {
0: 'free',
1: 'pull',
2: 'push',
-100: 'n/a',
}
def draw_properties(
output_path,
movable,
rigid,
kinematic,
action,
):
fig = plt.figure()
vis_phy = np.ones((80, 60, 3), dtype=np.uint8) * 255
plt.imshow(vis_phy)
fig.axes[0].add_patch(
mpl.patches.FancyBboxPatch(
(10, 10),
40,
60,
ec=np.array([79, 129, 189]) / 255.0,
boxstyle=mpl.patches.BoxStyle("Round", pad=5),
fill=False,
linewidth=5,
)
)
fontsize = 20
if rigid_imap[rigid] == 'yes':
rigid_text = 'Rigid: Yes'
else:
rigid_text = 'Rigid: No'
plt.text(30, 20, rigid_text, fontsize=fontsize, horizontalalignment='center')
if movable_imap[movable] == 'one_hand':
movable_text = "Move: 1 hand"
elif movable_imap[movable] == 'two_hands':
movable_text = "Move: 2 hands"
else:
movable_text = 'Move: Fixture'
plt.text(30, 35, movable_text, fontsize=fontsize, horizontalalignment='center')
if kinematic_imap[kinematic] == 'rotation':
kinematic_text = 'Motion: Rot'
elif kinematic_imap[kinematic] == 'translation':
kinematic_text = 'Motion: Trans'
else:
kinematic_text = 'Motion: Free'
plt.text(30, 50, kinematic_text, fontsize=fontsize, horizontalalignment='center')
if action_imap[action] == 'pull':
action_text = 'Action: Pull'
elif action_imap[action] == 'push':
action_text = 'Action: Push'
else:
action_text = 'Action: Free'
plt.text(30, 65, action_text, fontsize=fontsize, horizontalalignment='center')
plt.axis('off')
plt.savefig(output_path, bbox_inches='tight', pad_inches=0)
plt.close(fig)
def draw_localization(rgb, output_path, bbox, mask, axis, colors=None, alpha=0.5):
if colors is None:
# colors = np.array([[246, 95, 16], ]) / 255.0 # orange
#colors = np.array([[227, 13, 255], ]) / 255.0 # purple
colors = np.array([[178, 13, 255], ]) / 255.0 # purple
pred_entry = {
'keypoint': None,
'bbox': bbox,
'mask': mask,
'affordance': None,
'move': None,
'rigid': None,
'kinematic': None,
'pull_or_push': None,
'axis': axis,
}
instances = [pred_entry]
vis = Visualizer(rgb)
vis.overlay_instances(instances, assigned_colors=colors, alpha=alpha)
vis.output.save(output_path)
def draw_affordance(rgb, output_path, affordance, alpha=0.5):
# normalize affordance
aff_min = affordance.min()
aff_max = affordance.max()
affordance = (affordance - aff_min) / (aff_max - aff_min)
# convert rgb to gray scale for better visualization
rgb_gray = rgb.copy()
# rgb_gray[:, :, 0] = rgb.mean(axis=2)
# rgb_gray[:, :, 1] = rgb.mean(axis=2)
# rgb_gray[:, :, 2] = rgb.mean(axis=2)
heatmap_img = cv2.applyColorMap((affordance * 255.0).astype(np.uint8), cv2.COLORMAP_HOT)[:, :, ::-1]
vis = cv2.addWeighted(heatmap_img, alpha, rgb_gray, 1-alpha, 0)
return Image.fromarray(vis) #.save(output_path) |