Spaces:
Runtime error
Runtime error
File size: 5,849 Bytes
9cfda96 |
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 |
import streamlit as st
import streamlit.components.v1 as components
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import models
from torchvision.transforms import ToTensor
import numpy as np
from PIL import Image
import math
from obj2html import obj2html
minDepth=10
maxDepth=1000
def my_DepthNorm(x, maxDepth):
return maxDepth / x
def vete(v, vt):
if v == vt:
return str(v)
return str(v)+"/"+str(vt)
def create_obj(img, objPath='model.obj', mtlPath='model.mtl', matName='colored', useMaterial=False):
w = img.shape[1]
h = img.shape[0]
FOV = math.pi/4
D = (img.shape[0]/2)/math.tan(FOV/2)
if max(objPath.find('\\'), objPath.find('/')) > -1:
os.makedirs(os.path.dirname(mtlPath), exist_ok=True)
with open(objPath, "w") as f:
if useMaterial:
f.write("mtllib " + mtlPath + "\n")
f.write("usemtl " + matName + "\n")
ids = np.zeros((img.shape[1], img.shape[0]), int)
vid = 1
all_x = []
all_y = []
all_z = []
for u in range(0, w):
for v in range(h-1, -1, -1):
d = img[v, u]
ids[u, v] = vid
if d == 0.0:
ids[u, v] = 0
vid += 1
x = u - w/2
y = v - h/2
z = -D
norm = 1 / math.sqrt(x*x + y*y + z*z)
t = d/(z*norm)
x = -t*x*norm
y = t*y*norm
z = -t*z*norm
f.write("v " + str(x) + " " + str(y) + " " + str(z) + "\n")
for u in range(0, img.shape[1]):
for v in range(0, img.shape[0]):
f.write("vt " + str(u/img.shape[1]) +
" " + str(v/img.shape[0]) + "\n")
for u in range(0, img.shape[1]-1):
for v in range(0, img.shape[0]-1):
v1 = ids[u, v]
v3 = ids[u+1, v]
v2 = ids[u, v+1]
v4 = ids[u+1, v+1]
if v1 == 0 or v2 == 0 or v3 == 0 or v4 == 0:
continue
f.write("f " + vete(v1, v1) + " " +
vete(v2, v2) + " " + vete(v3, v3) + "\n")
f.write("f " + vete(v3, v3) + " " +
vete(v2, v2) + " " + vete(v4, v4) + "\n")
class UpSample(nn.Sequential):
def __init__(self, skip_input, output_features):
super(UpSample, self).__init__()
self.convA = nn.Conv2d(skip_input, output_features, kernel_size=3, stride=1, padding=1)
self.leakyreluA = nn.LeakyReLU(0.2)
self.convB = nn.Conv2d(output_features, output_features, kernel_size=3, stride=1, padding=1)
self.leakyreluB = nn.LeakyReLU(0.2)
def forward(self, x, concat_with):
up_x = F.interpolate(x, size=[concat_with.size(2), concat_with.size(3)], mode='bilinear', align_corners=True)
return self.leakyreluB( self.convB( self.convA( torch.cat([up_x, concat_with], dim=1) ) ) )
class Decoder(nn.Module):
def __init__(self, num_features=1664, decoder_width = 1.0):
super(Decoder, self).__init__()
features = int(num_features * decoder_width)
self.conv2 = nn.Conv2d(num_features, features, kernel_size=1, stride=1, padding=0)
self.up1 = UpSample(skip_input=features//1 + 256, output_features=features//2)
self.up2 = UpSample(skip_input=features//2 + 128, output_features=features//4)
self.up3 = UpSample(skip_input=features//4 + 64, output_features=features//8)
self.up4 = UpSample(skip_input=features//8 + 64, output_features=features//16)
self.conv3 = nn.Conv2d(features//16, 1, kernel_size=3, stride=1, padding=1)
def forward(self, features):
x_block0, x_block1, x_block2, x_block3, x_block4 = features[3], features[4], features[6], features[8], features[12]
x_d0 = self.conv2(F.relu(x_block4))
x_d1 = self.up1(x_d0, x_block3)
x_d2 = self.up2(x_d1, x_block2)
x_d3 = self.up3(x_d2, x_block1)
x_d4 = self.up4(x_d3, x_block0)
return self.conv3(x_d4)
class Encoder(nn.Module):
def __init__(self):
super(Encoder, self).__init__()
self.original_model = models.densenet169( pretrained=False )
def forward(self, x):
features = [x]
for k, v in self.original_model.features._modules.items(): features.append( v(features[-1]) )
return features
class PTModel(nn.Module):
def __init__(self):
super(PTModel, self).__init__()
self.encoder = Encoder()
self.decoder = Decoder()
def forward(self, x):
return self.decoder( self.encoder(x) )
model = PTModel().float()
path = "https://github.com/nicolalandro/DenseDepth/releases/download/0.1/nyu.pth"
model.load_state_dict(torch.hub.load_state_dict_from_url(path, progress=True))
model.eval()
def predict(inp):
torch_image = ToTensor()(inp)
images = torch_image.unsqueeze(0)
with torch.no_grad():
predictions = model(images)
output = np.clip(my_DepthNorm(predictions.numpy(), maxDepth=maxDepth), minDepth, maxDepth) / maxDepth
depth = output[0,0,:,:]
img = Image.fromarray(np.uint8(depth*255))
create_obj(depth, 'model.obj')
html_string = obj2html('model.obj', html_elements_only=True)
return img, html_string
st.title("Monocular Depth Estimation")
uploader = st.file_uploader('Upload your portrait here',type=['jpg','jpeg','png'])
if uploader is not None:
pil_image = Image.open(uploader)
pil_depth, html_string = predict(pil_image)
col1, col2 = st.columns(2)
with col1:
st.image(pil_image)
with col2:
st.image(pil_depth)
components.html(html_string)
st.markdown(html_string, unsafe_allow_html=True)
|