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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)
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