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268d37c | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 | import gradio as gr
import open3d as o3d
import numpy as np
import time
import multiprocessing
import os
import tempfile
import trimesh
alignment_cache = {}
current_orientation = "+z"
def compute_alignment(files):
"""Align point clouds and cache results."""
ck = tuple(files)
if ck in alignment_cache: return alignment_cache[ck]
if len(files) < 2: return (None, None)
st = time.time()
print(f"Aligning {len(files)} point clouds using {multiprocessing.cpu_count()} CPU cores")
clouds, names = [], []
for p in files:
c = o3d.io.read_point_cloud(p)
if not c.has_points(): return (None, f"Failed to load point cloud from {os.path.basename(p)}")
clouds.append(c), names.append(os.path.basename(p))
ref = clouds[0]
trans = [np.eye(4)]
for i, src in enumerate(clouds[1:], 1):
vs = [0.5, 0.3, 0.1]
t = np.eye(4)
for v in vs:
s_down, r_down = src.voxel_down_sample(v), ref.voxel_down_sample(v)
rn = v * 2
s_down.estimate_normals(o3d.geometry.KDTreeSearchParamHybrid(radius=rn, max_nn=30))
r_down.estimate_normals(o3d.geometry.KDTreeSearchParamHybrid(radius=rn, max_nn=30))
d = v * 2
mi = 10 if i == 0 else 5
r = o3d.pipelines.registration.registration_icp(
s_down, r_down, d, t,
o3d.pipelines.registration.TransformationEstimationPointToPlane(),
o3d.pipelines.registration.ICPConvergenceCriteria(max_iteration=mi)
)
t = r.transformation
trans.append(t)
aligned = []
for c, t in zip(clouds, trans):
cc = o3d.geometry.PointCloud(c)
cc.transform(t)
aligned.append(cc)
ap = [np.asarray(x.points) for x in aligned]
if not ap: return (None, "Failed to process point clouds")
cat = np.vstack(ap)
mn, mx = np.min(cat, 0), np.max(cat, 0)
ctr = (mn + mx) / 2
scl = np.max(mx - mn)
combined = o3d.geometry.PointCloud()
for c in aligned: combined += c
tm = time.time() - st
msg = f"Successfully aligned {len(files)} point clouds in {tm:.2f} seconds."
data = {
'aligned_clouds': aligned, 'transformations': trans, 'clouds': clouds,
'filenames': names, 'global_center': ctr, 'global_scale': scl,
'combined_cloud': combined
}
alignment_cache[ck] = (data, msg)
return data, msg
def visualize_alignment(files, show_colors=False, show_outlines=True, point_density=10000,
orientation="+z", point_size=0.005, is_initial_view=False):
if not files or len(files) < 2: return (None, "Please upload at least 2 point cloud files (.ply)")
res, msg = compute_alignment(files)
if not res: return (None, msg)
clouds = res['aligned_clouds']
ctr, scl = res['global_center'], res['global_scale']
cols = [[1,0,0],[0,1,0],[0,0,1],[1,1,0],[1,0,1],[0,1,1],[1,0.5,0]]
tdir = tempfile.mkdtemp()
global current_orientation
current_orientation = orientation
normed, boxes = [], []
for i, c in enumerate(clouds):
cp = o3d.geometry.PointCloud(c)
if show_colors: cp.paint_uniform_color(cols[i % len(cols)])
pts = np.asarray(cp.points)
pts = (pts - ctr)/(scl/2)
n = o3d.geometry.PointCloud()
n.points = o3d.utility.Vector3dVector(pts)
if cp.has_colors(): n.colors = cp.colors
normed.append(n)
if show_outlines:
b = n.get_axis_aligned_bounding_box()
b.color = cols[i % len(cols)]
boxes.append(b)
comb = o3d.geometry.PointCloud()
for c in normed: comb += c
if point_density < len(comb.points):
bb = comb.get_axis_aligned_bounding_box()
vol = np.prod(bb.get_extent())
ppu = point_density / vol if vol > 0 else 1
vs = max(0.001, (1/ppu)**(1/3))
comb = comb.voxel_down_sample(vs)
vs, fs, vc = [], [], []
pts = np.asarray(comb.points)
if comb.has_colors(): pcols = np.asarray(comb.colors)
else: pcols = np.tile([0.8,0.8,0.8], (len(pts),1))
sph = trimesh.creation.icosphere(subdivisions=1, radius=point_size)
mxp = min(point_density, len(pts))
idxs = np.linspace(0, len(pts)-1, mxp, dtype=int)
for i, idx in enumerate(idxs):
p = pts[idx]
c = pcols[idx]
s = sph.copy()
s.apply_translation(p)
s.visual.vertex_colors = np.tile((c*255).astype(np.uint8), (len(s.vertices),1))
si = len(vs)
vs.extend(s.vertices)
fs.extend(s.faces + si)
vc.extend(s.visual.vertex_colors)
cm = trimesh.Trimesh(vertices=vs, faces=fs, vertex_colors=vc)
if show_outlines:
box_edges = [(0,1),(1,2),(2,3),(3,0),(4,5),(5,6),(6,7),(7,4),
(0,4),(1,5),(2,6),(3,7)]
for i,b in enumerate(boxes):
mb, xb = b.min_bound, b.max_bound
bv = [[mb[0],mb[1],mb[2]],[xb[0],mb[1],mb[2]],[xb[0],xb[1],mb[2]],[mb[0],xb[1],mb[2]],
[mb[0],mb[1],xb[2]],[xb[0],mb[1],xb[2]],[xb[0],xb[1],xb[2]],[mb[0],xb[1],xb[2]]]
bc = cols[i % len(cols)]
r = point_size*0.8
for s0,s1 in box_edges:
p0, p1 = bv[s0], bv[s1]
d = np.array(p1) - np.array(p0)
ln = np.linalg.norm(d)
if ln<1e-6: continue
d /= ln
za = np.array([0,0,1])
if abs(np.dot(d,za))>0.999: ra = np.array([1,0,0])
else:
ra = np.cross(za,d)
ra /= np.linalg.norm(ra)
ang = np.arccos(np.dot(za,d))
rot = trimesh.transformations.rotation_matrix(ang,ra)
trn = trimesh.transformations.translation_matrix(p0)
cyl = trimesh.creation.cylinder(radius=r,height=ln,sections=8)
cyl.apply_translation([0,0,ln/2])
cyl.apply_transform(rot)
cyl.apply_transform(trn)
cyl.visual.face_colors = (np.array(bc)*255).astype(np.uint8)
cm = trimesh.util.concatenate([cm,cyl])
sc = trimesh.Scene(cm)
sc.add_geometry(trimesh.creation.axis(origin_size=0.01, axis_radius=0.0025))
if orientation == "+y":
sc.apply_transform(trimesh.transformations.rotation_matrix(-np.pi/2,[1,0,0]))
elif orientation == "-y":
sc.apply_transform(trimesh.transformations.rotation_matrix(np.pi/2,[1,0,0]))
elif orientation == "-z":
sc.apply_transform(trimesh.transformations.rotation_matrix(np.pi,[1,0,0]))
fo = os.path.join(tdir,"aligned_scene.glb")
sc.export(fo)
return (fo, msg)
def process_upload(files, sc, so, pd, o, ps):
d, m = compute_alignment(files)
if d is None: return (None, m)
return visualize_alignment(files, sc, so, pd, o, ps, True)
def update_visualization_only(files, sc, so, pd, o, ps):
if not files or len(files)<2: return None
if tuple(files) not in alignment_cache: return None
mo, _ = visualize_alignment(files, sc, so, pd, o, ps, False)
return mo
with gr.Blocks(theme=gr.themes.Base()) as app:
gr.Markdown("# Point Cloud Alignment Tool - 3D ICP")
with gr.Row():
with gr.Column(scale=1):
file_input = gr.File(file_count="multiple", file_types=[".ply"], label="Upload Point Cloud Files (.ply)", type="filepath")
with gr.Row():
show_colors = gr.Checkbox(label="Show Colored Models", value=False)
show_outlines = gr.Checkbox(label="Show Bounding Box Outlines", value=True)
point_density = gr.Slider(1000, 500000, 10000, step=1000, label="Point Density (fewer points = faster rendering)")
orientation = gr.Dropdown(["+z","-z","+y","-y"], value="+z", label="Model Orientation (up direction)")
point_size = gr.Slider(0.001, 0.05, 0.005, step=0.001, label="Point Size")
submit_btn = gr.Button("Align Point Clouds", variant="primary")
with gr.Column(scale=2):
output_model = gr.Model3D(label="Aligned Point Clouds", clear_color=[0.1,0.1,0.1,1.0])
output_text = gr.Textbox(label="Status")
submit_btn.click(process_upload,
[file_input, show_colors, show_outlines, point_density, orientation, point_size],
[output_model, output_text])
show_colors.change(update_visualization_only, [file_input, show_colors, show_outlines, point_density, orientation, point_size], [output_model])
show_outlines.change(update_visualization_only, [file_input, show_colors, show_outlines, point_density, orientation, point_size], [output_model])
point_density.change(update_visualization_only, [file_input, show_colors, show_outlines, point_density, orientation, point_size], [output_model])
orientation.change(update_visualization_only, [file_input, show_colors, show_outlines, point_density, orientation, point_size], [output_model])
point_size.change(update_visualization_only, [file_input, show_colors, show_outlines, point_density, orientation, point_size], [output_model])
if __name__ == "__main__":
print(f"Using {multiprocessing.cpu_count()} CPU cores for parallel processing")
app.launch(share=True)
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