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app.py
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app.py
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import os
import tempfile
import cv2
import gradio as gr
import numpy as np
import pycuda.driver as cuda # GPU-CPU
import tensorrt as trt
from depth_anything.util.transform import NormalizeImage, PrepareForNet, Resize
from gradio_imageslider import ImageSlider
from PIL import Image
from torchvision.transforms import Compose
import pycuda.autoinit
import time
engine_path = '/home/lwq/DepthAM/depth-anything-tensorrt-build/depth_anything_vitl14.engine'
css = """
#img-display-container {
max-height: 100vh;
}
#img-display-input {
max-height: 80vh;
}
#img-display-output {
max-height: 80vh;
}
"""
title = "# Depth Anything"
description = """TensorRT API demo for **Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data**.
Please refer to [paper](https://arxiv.org/abs/2401.10891), [project page](https://depth-anything.github.io), [github](https://github.com/LiheYoung/Depth-Anything), [github for tensorrt](https://github.com/spacewalk01/depth-anything-tensorrt) for more details."""
transform = Compose([
Resize(
width=518,
height=518,
resize_target=False,
keep_aspect_ratio=False,
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(),
])
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.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):
# Create a CUDA context
ctx = cuda.Device(0).make_context()
try:
original_image = image.copy()
h, w = image.shape[:2]
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) / 255.0
image = transform({'image': image})['image']
image = image[None]
# Create logger and load the TensorRT engine
logger = trt.Logger(trt.Logger.WARNING)
with open(engine_path, 'rb') as f, trt.Runtime(logger) as runtime:
engine = runtime.deserialize_cuda_engine(f.read())
with engine.create_execution_context() as context:
input_shape = context.get_tensor_shape('input')
output_shape = context.get_tensor_shape('output')
h_input = cuda.pagelocked_empty(trt.volume(input_shape), dtype=np.float32)
h_output = cuda.pagelocked_empty(trt.volume(output_shape), dtype=np.float32)
d_input = cuda.mem_alloc(h_input.nbytes)
d_output = cuda.mem_alloc(h_output.nbytes)
stream = cuda.Stream()
# Copy the input image to the pagelocked memory
np.copyto(h_input, image.ravel())
# Copy the input to the GPU, execute the inference, and copy the output back to the CPU
cuda.memcpy_htod_async(d_input, h_input, stream)
context.execute_async_v2(bindings=[int(d_input), int(d_output)], stream_handle=stream.handle)
cuda.memcpy_dtoh_async(h_output, d_output, stream)
stream.synchronize()
depth = h_output
depth = np.reshape(depth, (518, 518))
depth = cv2.resize(depth, (w, h))
raw_depth = Image.fromarray(depth.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.astype(np.uint8)
colored_depth = cv2.applyColorMap(depth, cv2.COLORMAP_INFERNO)[:, :, ::-1]
finally:
ctx.pop() # Ensure the context is popped at the end of the function
return [(original_image, colored_depth), tmp.name] # colored_depth
submit.click(on_submit, inputs=[input_image], outputs=[depth_image_slider, raw_file])
example_files = os.listdir('examples')
example_files.sort()
example_files = [os.path.join('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=False)
if __name__ == '__main__':
demo.queue().launch()