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59630ba | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 | import torch
import cv2
import lietorch
import droid_backends
import time
import argparse
import numpy as np
import open3d as o3d
from lietorch import SE3
import geom.projective_ops as pops
CAM_POINTS = np.array([
[ 0, 0, 0],
[-1, -1, 1.5],
[ 1, -1, 1.5],
[ 1, 1, 1.5],
[-1, 1, 1.5],
[-0.5, 1, 1.5],
[ 0.5, 1, 1.5],
[ 0, 1.2, 1.5]])
CAM_LINES = np.array([
[1,2], [2,3], [3,4], [4,1], [1,0], [0,2], [3,0], [0,4], [5,7], [7,6]])
def white_balance(img):
# from https://stackoverflow.com/questions/46390779/automatic-white-balancing-with-grayworld-assumption
result = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
avg_a = np.average(result[:, :, 1])
avg_b = np.average(result[:, :, 2])
result[:, :, 1] = result[:, :, 1] - ((avg_a - 128) * (result[:, :, 0] / 255.0) * 1.1)
result[:, :, 2] = result[:, :, 2] - ((avg_b - 128) * (result[:, :, 0] / 255.0) * 1.1)
result = cv2.cvtColor(result, cv2.COLOR_LAB2BGR)
return result
def create_camera_actor(g, scale=0.05):
""" build open3d camera polydata """
camera_actor = o3d.geometry.LineSet(
points=o3d.utility.Vector3dVector(scale * CAM_POINTS),
lines=o3d.utility.Vector2iVector(CAM_LINES))
color = (g * 1.0, 0.5 * (1-g), 0.9 * (1-g))
camera_actor.paint_uniform_color(color)
return camera_actor
def create_point_actor(points, colors):
""" open3d point cloud from numpy array """
point_cloud = o3d.geometry.PointCloud()
point_cloud.points = o3d.utility.Vector3dVector(points)
point_cloud.colors = o3d.utility.Vector3dVector(colors)
return point_cloud
def droid_visualization(video, device="cuda:0"):
""" DROID visualization frontend """
torch.cuda.set_device(device)
droid_visualization.video = video
droid_visualization.cameras = {}
droid_visualization.points = {}
droid_visualization.warmup = 8
droid_visualization.scale = 1.0
droid_visualization.ix = 0
droid_visualization.filter_thresh = 0.005
def increase_filter(vis):
droid_visualization.filter_thresh *= 2
with droid_visualization.video.get_lock():
droid_visualization.video.dirty[:droid_visualization.video.counter.value] = True
def decrease_filter(vis):
droid_visualization.filter_thresh *= 0.5
with droid_visualization.video.get_lock():
droid_visualization.video.dirty[:droid_visualization.video.counter.value] = True
def animation_callback(vis):
cam = vis.get_view_control().convert_to_pinhole_camera_parameters()
with torch.no_grad():
with video.get_lock():
t = video.counter.value
dirty_index, = torch.where(video.dirty.clone())
dirty_index = dirty_index
if len(dirty_index) == 0:
return
video.dirty[dirty_index] = False
# convert poses to 4x4 matrix
poses = torch.index_select(video.poses, 0, dirty_index)
disps = torch.index_select(video.disps, 0, dirty_index)
Ps = SE3(poses).inv().matrix().cpu().numpy()
images = torch.index_select(video.images, 0, dirty_index)
images = images.cpu()[:,[2,1,0],3::8,3::8].permute(0,2,3,1) / 255.0
points = droid_backends.iproj(SE3(poses).inv().data, disps, video.intrinsics[0]).cpu()
thresh = droid_visualization.filter_thresh * torch.ones_like(disps.mean(dim=[1,2]))
count = droid_backends.depth_filter(
video.poses, video.disps, video.intrinsics[0], dirty_index, thresh)
count = count.cpu()
disps = disps.cpu()
masks = ((count >= 2) & (disps > .5*disps.mean(dim=[1,2], keepdim=True)))
for i in range(len(dirty_index)):
pose = Ps[i]
ix = dirty_index[i].item()
if ix in droid_visualization.cameras:
vis.remove_geometry(droid_visualization.cameras[ix])
del droid_visualization.cameras[ix]
if ix in droid_visualization.points:
vis.remove_geometry(droid_visualization.points[ix])
del droid_visualization.points[ix]
### add camera actor ###
cam_actor = create_camera_actor(True)
cam_actor.transform(pose)
vis.add_geometry(cam_actor)
droid_visualization.cameras[ix] = cam_actor
mask = masks[i].reshape(-1)
pts = points[i].reshape(-1, 3)[mask].cpu().numpy()
clr = images[i].reshape(-1, 3)[mask].cpu().numpy()
## add point actor ###
point_actor = create_point_actor(pts, clr)
vis.add_geometry(point_actor)
droid_visualization.points[ix] = point_actor
# hack to allow interacting with vizualization during inference
if len(droid_visualization.cameras) >= droid_visualization.warmup:
cam = vis.get_view_control().convert_from_pinhole_camera_parameters(cam)
droid_visualization.ix += 1
vis.poll_events()
vis.update_renderer()
### create Open3D visualization ###
vis = o3d.visualization.VisualizerWithKeyCallback()
vis.register_animation_callback(animation_callback)
vis.register_key_callback(ord("S"), increase_filter)
vis.register_key_callback(ord("A"), decrease_filter)
vis.create_window(height=540, width=960)
vis.get_render_option().load_from_json("misc/renderoption.json")
vis.run()
vis.destroy_window()
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