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import gradio as gr
import torch
from models.pretrained_decv2 import enc_dec_model
from models.densenet_v2 import Densenet
from models.unet_resnet18 import ResNet18UNet
from models.unet_resnet50 import UNetWithResnet50Encoder
import numpy as np
import cv2
# kb cropping
def cropping(img):
h_im, w_im = img.shape[:2]
margin_top = int(h_im - 352)
margin_left = int((w_im - 1216) / 2)
img = img[margin_top: margin_top + 352,
margin_left: margin_left + 1216]
return img
DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
print(DEVICE)
CWD = "."
CKPT_FILE_NAMES = {
'Indoor':{
'Resnet_enc':'resnet_nyu_best.ckpt',
'Unet':'resnet18_unet_epoch_08_model_kitti_and_nyu.ckpt',
'Densenet_enc':'densenet_epoch_15_model.ckpt'
},
'Outdoor':{
'Resnet_enc':'resnet_encdecmodel_epoch_05_model_nyu_and_kitti.ckpt',
'Unet':'resnet50_unet_epoch_02_model_nyuandkitti.ckpt',
'Densenet_enc':'densenet_nyu_then_kitti_epoch_10_model.ckpt'
}
}
MODEL_CLASSES = {
'Indoor': {
'Resnet_enc':enc_dec_model,
'Unet':ResNet18UNet,
'Densenet_enc':Densenet
},
'Outdoor': {
'Resnet_enc':enc_dec_model,
'Unet':UNetWithResnet50Encoder,
'Densenet_enc':Densenet
},
}
def load_model(ckpt, model, optimizer=None):
ckpt_dict = torch.load(ckpt, map_location='cpu')
# keep backward compatibility
if 'model' not in ckpt_dict and 'optimizer' not in ckpt_dict:
state_dict = ckpt_dict
else:
state_dict = ckpt_dict['model']
weights = {}
for key, value in state_dict.items():
if key.startswith('module.'):
weights[key[len('module.'):]] = value
else:
weights[key] = value
model.load_state_dict(weights)
if optimizer is not None:
optimizer_state = ckpt_dict['optimizer']
optimizer.load_state_dict(optimizer_state)
def predict(location, model_name, img):
ckpt_dir = f"{CWD}/ckpt/{CKPT_FILE_NAMES[location][model_name]}"
if location == 'nyu':
max_depth = 10
else:
max_depth = 80
model = MODEL_CLASSES[location][model_name](max_depth).to(DEVICE)
load_model(ckpt_dir,model)
# print(img.shape)
# assert False
if img.shape == (375,1242,3):
img = cropping(img)
img = torch.tensor(img).permute(2, 0, 1).float().to(DEVICE)
input_RGB = img.unsqueeze(0)
print(input_RGB.shape)
with torch.no_grad():
pred = model(input_RGB)
pred_d = pred['pred_d']
pred_d_numpy = pred_d.squeeze().cpu().numpy()
# pred_d_numpy = (pred_d_numpy - pred_d_numpy.mean())/pred_d_numpy.std()
pred_d_numpy = np.clip((pred_d_numpy / pred_d_numpy[15:,:].max()) * 255, 0,255)
# pred_d_numpy = (pred_d_numpy / pred_d_numpy.max()) * 255
pred_d_numpy = pred_d_numpy.astype(np.uint8)
pred_d_color = cv2.applyColorMap(pred_d_numpy, cv2.COLORMAP_RAINBOW)
pred_d_color = cv2.cvtColor(pred_d_color, cv2.COLOR_BGR2RGB)
# del model
return pred_d_color
with gr.Blocks() as demo:
gr.Markdown("# Monocular Depth Estimation")
with gr.Row():
location = gr.Radio(choices=['Indoor', 'Outdoor'],value='Indoor', label = "Select Location Type")
model_name = gr.Radio(['Unet', 'Resnet_enc', 'Densenet_enc'],value="Densenet_enc" ,label="Select model")
with gr.Row():
with gr.Column():
input_image = gr.Image(label = "Input Image for Depth Estimation")
with gr.Column():
output_depth_map = gr.Image(label = "Depth prediction Heatmap")
with gr.Row():
predict_btn = gr.Button("Generate Depthmap")
predict_btn.click(fn=predict, inputs=[location, model_name, input_image], outputs=output_depth_map)
with gr.Row():
gr.Examples(['./demo_data/Bathroom.jpg', './demo_data/Bedroom.jpg', './demo_data/Bookstore.jpg', './demo_data/Classroom.jpg', './demo_data/Computerlab.jpg', './demo_data/kitti_1.png'], inputs=input_image)
demo.launch() |