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import gradio as gr
import numpy as np
import torch
from torchvision.transforms import Compose
import cv2
from dpt.models import DPTDepthModel, DPTSegmentationModel
from dpt.transforms import Resize, NormalizeImage, PrepareForNet
import os
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print("device: %s" % device)
default_models = {
"dpt_hybrid": "weights/dpt_hybrid-midas-501f0c75.pt",
"segment_hybrid": "weights/dpt_hybrid-ade20k-53898607.pt"
}
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
depth_model = DPTDepthModel(
path=default_models["dpt_hybrid"],
backbone="vitb_rn50_384",
non_negative=True,
enable_attention_hooks=False,
)
depth_model.eval()
depth_model.to(device)
seg_model = DPTSegmentationModel(
150,
path=default_models["segment_hybrid"],
backbone="vitb_rn50_384",
)
seg_model.eval()
seg_model.to(device)
# Transform
net_w = net_h = 384
normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
transform = Compose(
[
Resize(
net_w,
net_h,
resize_target=None,
keep_aspect_ratio=True,
ensure_multiple_of=32,
resize_method="minimal",
image_interpolation_method=cv2.INTER_CUBIC,
),
normalization,
PrepareForNet(),
]
)
def write_depth(depth, bits=1, absolute_depth=False):
"""Write depth map to pfm and png file.
Args:
path (str): filepath without extension
depth (array): depth
"""
# write_pfm(path + ".pfm", depth.astype(np.float32))
if absolute_depth:
out = depth
else:
depth_min = depth.min()
depth_max = depth.max()
max_val = (2 ** (8 * bits)) - 1
if depth_max - depth_min > np.finfo("float").eps:
out = max_val * (depth - depth_min) / (depth_max - depth_min)
else:
out = np.zeros(depth.shape, dtype=depth.dtype)
if bits == 1:
return out.astype("uint8")
elif bits == 2:
return out.astype("uint16")
def DPT(image):
img_input = transform({"image": image})["image"]
# compute
with torch.no_grad():
sample = torch.from_numpy(img_input).to(device).unsqueeze(0)
prediction = depth_model.forward(sample)
prediction = (
torch.nn.functional.interpolate(
prediction.unsqueeze(1),
size=image.shape[:2],
mode="bicubic",
align_corners=False,
)
.squeeze()
.cpu()
.numpy()
)
depth_img = write_depth(prediction, bits=2)
return depth_img
def Segment(image):
img_input = transform({"image": image})["image"]
# compute
with torch.no_grad():
sample = torch.from_numpy(img_input).to(device).unsqueeze(0)
# if optimize == True and device == torch.device("cuda"):
# sample = sample.to(memory_format=torch.channels_last)
# sample = sample.half()
out = seg_model.forward(sample)
prediction = torch.nn.functional.interpolate(
out, size=image.shape[:2], mode="bicubic", align_corners=False
)
prediction = torch.argmax(prediction, dim=1) + 1
prediction = prediction.squeeze().cpu().numpy()
return prediction
title = " AISeed AI Application Demo "
description = "# A Demo of Deep Learning for Depth Estimation"
example_list = [["examples/" + example] for example in os.listdir("examples")]
with gr.Blocks() as demo:
demo.title = title
gr.Markdown(description)
with gr.Row():
with gr.Column():
im_2 = gr.Image(label="Depth Image")
im_3 = gr.Image(label="Segment Image")
with gr.Column():
im = gr.Image(label="Input Image")
btn1 = gr.Button(value="Depth Estimator")
btn1.click(DPT, inputs=[im], outputs=[im_2])
btn2 = gr.Button(value="Segment")
btn2.click(Segment, inputs=[im], outputs=[im_3])
gr.Examples(examples=example_list,
inputs=[im],
outputs=[im_2])
if __name__ == "__main__":
demo.launch() |