Spaces:
Runtime error
Runtime error
File size: 5,266 Bytes
2ddc005 1d37acd 2ddc005 1d37acd 2ddc005 1d37acd e2dc8d7 1d37acd 2ddc005 b9afc96 2ddc005 be529c0 2ddc005 1d37acd 7b9ea01 2ddc005 1d37acd 2ddc005 3c74d2a a336e88 1d37acd b9afc96 9ce1914 1d37acd |
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 |
from tqdm.auto import trange
from PIL import Image
import gradio as gr
import numpy as np
import pyrender
import trimesh
import scipy
import torch
import cv2
import os
class MidasDepth(object):
def __init__(self, model_type="DPT_Large", device=torch.device("cuda" if torch.cuda.is_available() else "cpu")):
self.device = device
self.midas = torch.hub.load("intel-isl/MiDaS", model_type).to(self.device).eval().requires_grad_(False)
self.transform = torch.hub.load("intel-isl/MiDaS", "transforms").dpt_transform
def get_depth(self, image):
if not isinstance(image, np.ndarray):
image = np.asarray(image)
if (image > 1).any():
image = image.astype("float64") / 255.
with torch.inference_mode():
batch = self.transform(image[..., :3]).to(self.device)
prediction = self.midas(batch)
prediction = torch.nn.functional.interpolate(
prediction.unsqueeze(1),
size=image.shape[:2],
mode="bicubic",
align_corners=False,
).squeeze()
return prediction.detach().cpu().numpy()
def process_depth(dep):
depth = dep.copy()
depth -= depth.min()
depth /= depth.max()
depth = 1 / np.clip(depth, 0.2, 1)
blurred = cv2.medianBlur(depth, 5) # 9 not available because it requires 8-bit
maxd = cv2.dilate(blurred, np.ones((3, 3)))
mind = cv2.erode(blurred, np.ones((3, 3)))
edges = maxd - mind
threshold = .05 # Better to have false positives
pick_edges = edges > threshold
return depth, pick_edges
def make_mesh(pic, depth, pick_edges):
faces = []
im = np.asarray(pic)
grid = np.mgrid[0:im.shape[0], 0:im.shape[1]].transpose(1, 2, 0
).reshape(-1, 2)[..., ::-1]
flat_grid = grid[:, 1] * im.shape[1] + grid[:, 0]
positions = np.concatenate(((grid - np.array(im.shape[:-1])[np.newaxis, :]
/ 2) / im.shape[1] * 2,
depth.flatten()[flat_grid][..., np.newaxis]),
axis=-1)
positions[:, :-1] *= positions[:, -1:]
positions[:, 1] *= -1
colors = im.reshape(-1, 3)[flat_grid]
c = lambda x, y: y * im.shape[1] + x
for y in trange(im.shape[0]):
for x in range(im.shape[1]):
if pick_edges[y, x]:
continue
if x > 0 and y > 0:
faces.append([c(x, y), c(x, y - 1), c(x - 1, y)])
if x < im.shape[1] - 1 and y < im.shape[0] - 1:
faces.append([c(x, y), c(x, y + 1), c(x + 1, y)])
face_colors = np.asarray([colors[i[0]] for i in faces])
tri_mesh = trimesh.Trimesh(vertices=positions * np.array([1.0, 1.0, -1.0]),
faces=faces,
face_colors=np.concatenate((face_colors,
face_colors[..., -1:]
* 0 + 255),
axis=-1).reshape(-1, 4),
smooth=False,
)
return tri_mesh
def args_to_mat(tx, ty, tz, rx, ry, rz):
mat = np.eye(4)
mat[:3, :3] = scipy.spatial.transform.Rotation.from_euler("XYZ", (rx, ry, rz)).as_matrix()
mat[:3, 3] = tx, ty, tz
return mat
def render(mesh, mat):
mesh = pyrender.mesh.Mesh.from_trimesh(mesh, smooth=False)
scene = pyrender.Scene(ambient_light=np.array([1.0, 1.0, 1.0]))
camera = pyrender.PerspectiveCamera(yfov=np.pi / 2, aspectRatio=1.0)
scene.add(camera, pose=mat)
scene.add(mesh)
r = pyrender.OffscreenRenderer(1024, 1024)
rgb, d = r.render(scene, pyrender.constants.RenderFlags.FLAT)
mask = d == 0
rgb = rgb.copy()
rgb[mask] = 0
res = Image.fromarray(np.concatenate((rgb,
((mask[..., np.newaxis]) == 0)
.astype(np.uint8) * 255), axis=-1))
return res
def main():
os.environ["PYOPENGL_PLATFORM"] = "egl" # "osmesa"
midas = MidasDepth()
def fn(pic, *args):
depth, pick_edges = process_depth(midas.get_depth(pic))
mesh = make_mesh(pic, depth, pick_edges)
frame = render(mesh, args_to_mat(*args))
return np.asarray(frame), (255 / np.asarray(depth)).astype(np.uint8), None
interface = gr.Interface(fn=fn, inputs=[
gr.inputs.Image(label="src", type="numpy"),
gr.inputs.Number(label="tx", default=0.0),
gr.inputs.Number(label="ty", default=0.0),
gr.inputs.Number(label="tz", default=0.0),
gr.inputs.Number(label="rx", default=0.0),
gr.inputs.Number(label="ry", default=0.0),
gr.inputs.Number(label="rz", default=0.0)
], outputs=[
gr.outputs.Image(type="numpy", label="result"),
gr.outputs.Image(type="numpy", label="depth"),
gr.outputs.Video(label="interpolated")
], title="DALL·E 6D", description="Lift DALL·E 2 (or any other model) into 3D!")
gr.TabbedInterface([interface], ["Warp 3D images"]).launch()
if __name__ == '__main__':
main()
|