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Depth-Estimation.py
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Depth-Estimation.py
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import torch
import sys
from torch.autograd import Variable
import numpy as np
from options.train_options import TrainOptions
opt = TrainOptions().parse() # set CUDA_VISIBLE_DEVICES before import torch
from data.data_loader import CreateDataLoader
from models.models import create_model
from skimage import io
from skimage.transform import resize
img_path = 'demo.jpg'
model = create_model(opt)
input_height = 384
input_width = 512
def test_simple(model):
total_loss =0
toal_count = 0
model.switch_to_eval()
img = np.float32(io.imread(img_path))/255.0
img = resize(img, (input_height, input_width), order = 1)
input_img = torch.from_numpy( np.transpose(img, (2,0,1)) ).contiguous().float()
input_img = input_img.unsqueeze(0)
input_images = Variable(input_img.cuda() )
pred_log_depth = model.netG.forward(input_images)
pred_log_depth = torch.squeeze(pred_log_depth)
pred_depth = torch.exp(pred_log_depth)
# visualize prediction using inverse depth, so that we don't need sky segmentation (if you want to use RGB map for visualization, \
# you have to run semantic segmentation to mask the sky first since the depth of sky is random from CNN)
pred_inv_depth = 1/pred_depth
pred_inv_depth = pred_inv_depth.data.cpu().numpy()
# you might also use percentile for better visualization
pred_inv_depth = pred_inv_depth/np.amax(pred_inv_depth)
io.imsave('demo.png', pred_inv_depth)
# print(pred_inv_depth.shape)
sys.exit()
test_simple(model)