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import gradio as gr
import cv2
import numpy as np
import os
import torch
import torch.nn.functional as F
from torchvision.transforms import Compose
import tempfile
import spaces
from depth_anything.dpt import DepthAnything
from depth_anything.util.transform import Resize, NormalizeImage, PrepareForNet
@spaces.GPU
@torch.no_grad()
def predict_depth(model, image):
return model(image)
def make_video(video_path, outdir='./vis_video_depth',encoder='vitl'):
# Define path for temporary processed frames
temp_frame_dir = tempfile.mkdtemp()
margin_width = 50
DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
depth_anything = DepthAnything.from_pretrained('LiheYoung/depth_anything_{}14'.format(encoder)).to(DEVICE).eval()
total_params = sum(param.numel() for param in depth_anything.parameters())
print('Total parameters: {:.2f}M'.format(total_params / 1e6))
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(),
])
if os.path.isfile(video_path):
if video_path.endswith('txt'):
with open(video_path, 'r') as f:
lines = f.read().splitlines()
else:
filenames = [video_path]
else:
filenames = os.listdir(video_path)
filenames = [os.path.join(video_path, filename) for filename in filenames if not filename.startswith('.')]
filenames.sort()
# os.makedirs(outdir, exist_ok=True)
for k, filename in enumerate(filenames):
print('Progress {:}/{:},'.format(k+1, len(filenames)), 'Processing', 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
filename = os.path.basename(filename)
# output_path = os.path.join(outdir, filename[:filename.rfind('.')] + '_video_depth.mp4')
with tempfile.NamedTemporaryFile(delete=False, suffix='.mp4') as tmpfile:
output_path = tmpfile.name
#out = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*"avc1"), frame_rate, (output_width, frame_height))
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
out = cv2.VideoWriter(output_path, fourcc, frame_rate, (output_width, frame_height))
# count=0
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(depth_anything, 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)
# frame_path = os.path.join(temp_frame_dir, f"frame_{count:05d}.png")
# cv2.imwrite(frame_path, combined_frame)
out.write(combined_frame)
# count += 1
raw_video.release()
out.release()
return output_path
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 Video Demo"
description = """Depth Anything on full video files.
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(),
])
@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("### Video Depth Prediction demo")
with gr.Row():
input_video = gr.Video(label="Input Video")
submit = gr.Button("Submit")
processed_video = gr.Video(label="Processed Video")
def on_submit(uploaded_video):
# Process the video and get the path of the output video
output_video_path = make_video(uploaded_video)
return output_video_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=False)
if __name__ == '__main__':
demo.queue().launch() |