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import gradio as gr
import cv2
import numpy as np
import os
from PIL import Image
import torch
import torch.nn.functional as F
from torchvision.transforms import Compose
import tempfile
from gradio_imageslider import ImageSlider
from depth_anything.dpt import DepthAnything
from depth_anything.util.transform import Resize, NormalizeImage, PrepareForNet
css = """
#img-display-container {
max-height: 100vh;
}
#img-display-input {
max-height: 80vh;
}
#img-display-output {
max-height: 80vh;
}
"""
DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
model = DepthAnything.from_pretrained('LiheYoung/depth_anything_vitl14').to(DEVICE).eval()
title = "# Depth Anything"
description = """Official demo for **Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data**.
Please refer to our [paper](https://arxiv.org/abs/2401.10891), [project page](https://depth-anything.github.io), or [github](https://github.com/LiheYoung/Depth-Anything) for more details."""
transform = Compose([
Resize(
width=518,
height=518,
resize_target=False,
keep_aspect_ratio=True,
ensure_multiple_of=14,
resize_method='lower_bound',
image_interpolation_method=cv2.INTER_CUBIC,
),
NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
PrepareForNet(),
])
margin_width = 50
@torch.no_grad()
def predict_depth(model, image):
return model(image)
with gr.Blocks(css=css) as demo:
gr.Markdown(title)
gr.Markdown(description)
gr.Markdown("### Depth Prediction demo")
gr.Markdown("You can slide the output to compare the depth prediction with input image")
with gr.Tab("Image Depth Prediction"):
with gr.Row():
input_image = gr.Image(label="Input Image", type='numpy', elem_id='img-display-input')
depth_image_slider = ImageSlider(label="Depth Map with Slider View", elem_id='img-display-output', position=0.5)
raw_file = gr.File(label="16-bit raw depth (can be considered as disparity)")
submit = gr.Button("Submit")
def on_submit(image):
original_image = image.copy()
h, w = image.shape[:2]
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) / 255.0
image = transform({'image': image})['image']
image = torch.from_numpy(image).unsqueeze(0).to(DEVICE)
depth = predict_depth(model, image)
depth = F.interpolate(depth[None], (h, w), mode='bilinear', align_corners=False)[0, 0]
raw_depth = Image.fromarray(depth.cpu().numpy().astype('uint16'))
tmp = tempfile.NamedTemporaryFile(suffix='.png', delete=False)
raw_depth.save(tmp.name)
depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0
depth = depth.cpu().numpy().astype(np.uint8)
colored_depth = cv2.applyColorMap(depth, cv2.COLORMAP_INFERNO)[:, :, ::-1]
return [(original_image, colored_depth), tmp.name]
submit.click(on_submit, inputs=[input_image], outputs=[depth_image_slider, raw_file])
example_files = os.listdir('assets/examples')
example_files.sort()
example_files = [os.path.join('assets/examples', filename) for filename in example_files]
examples = gr.Examples(examples=example_files, inputs=[input_image], outputs=[depth_image_slider, raw_file], fn=on_submit, cache_examples=True)
with gr.Tab("Video Depth Prediction"):
with gr.Row():
input_video = gr.Video(label="Input Video")
submit = gr.Button("Submit")
processed_video = gr.Video(label="Processed Video")
def on_submit(filename):
raw_video = cv2.VideoCapture(filename)
frame_width, frame_height = int(raw_video.get(cv2.CAP_PROP_FRAME_WIDTH)), int(raw_video.get(cv2.CAP_PROP_FRAME_HEIGHT))
frame_rate = int(raw_video.get(cv2.CAP_PROP_FPS))
output_width = frame_width * 2 + margin_width
with tempfile.NamedTemporaryFile(delete=False, suffix='.mp4') as tmpfile:
output_path = tmpfile.name
out = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*"mp4v"), frame_rate, (output_width, frame_height))
while raw_video.isOpened():
ret, raw_frame = raw_video.read()
if not ret:
break
frame = cv2.cvtColor(raw_frame, cv2.COLOR_BGR2RGB) / 255.0
frame = transform({'image': frame})['image']
frame = torch.from_numpy(frame).unsqueeze(0).to(DEVICE)
depth = predict_depth(model, frame)
depth = F.interpolate(depth[None], (frame_height, frame_width), mode='bilinear', align_corners=False)[0, 0]
depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0
depth = depth.cpu().numpy().astype(np.uint8)
depth_color = cv2.applyColorMap(depth, cv2.COLORMAP_INFERNO)
split_region = np.ones((frame_height, margin_width, 3), dtype=np.uint8) * 255
combined_frame = cv2.hconcat([raw_frame, split_region, depth_color])
out.write(combined_frame)
raw_video.release()
out.release()
return output_path
submit.click(on_submit, inputs=[input_video], outputs=processed_video)
example_files = os.listdir('assets/examples_video')
example_files.sort()
example_files = [os.path.join('assets/examples_video', filename) for filename in example_files]
examples = gr.Examples(examples=example_files, inputs=[input_video], outputs=processed_video, fn=on_submit, cache_examples=True)
if __name__ == '__main__':
demo.queue().launch()
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