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import gradio as gr
import subprocess as sp
from skimage.measure import marching_cubes
import nibabel as nib
from nibabel.processing import resample_to_output
import numpy as np
import random
def nifti_to_glb(path):
# load NIFTI into numpy array
image = nib.load(path)
resampled = resample_to_output(image, [1, 1, 1], order=1)
data = resampled.get_fdata().astype("uint8")
# extract surface
verts, faces, normals, values = marching_cubes(data, 0)
faces += 1
with open('prediction.obj', 'w') as thefile:
for item in verts:
thefile.write("v {0} {1} {2}\n".format(item[0],item[1],item[2]))
for item in normals:
thefile.write("vn {0} {1} {2}\n".format(item[0],item[1],item[2]))
for item in faces:
thefile.write("f {0}//{0} {1}//{1} {2}//{2}\n".format(item[0],item[1],item[2]))
def run_model(input_path):
from livermask.utils.run import run_analysis
run_analysis(cpu=True, extension='.nii', path=input_path, output='prediction', verbose=True, vessels=False, name="/home/user/app/model.h5", mp_enabled=False)
def load_mesh(mesh_file_name):
path = mesh_file_name.name
run_model(path)
nifti_to_glb("prediction-livermask.nii")
return "./prediction.obj"
def setup_gallery(data_path, pred_path):
image = nib.load(data_path)
resampled = resample_to_output(image, [1, 1, 1], order=1)
data = resampled.get_fdata().astype("uint8")
image = nib.load(pred_path)
resampled = resample_to_output(image, [1, 1, 1], order=0)
pred = resampled.get_fdata().astype("uint8")
def load_ct_to_numpy(data_path):
if type(data_path) != str:
data_path = data_path.name
image = nib.load(data_path)
data = image.get_fdata()
data = np.rot90(data, k=1, axes=(0, 1))
data[data < -150] = -150
data[data > 250] = 250
data = data - np.amin(data)
data = data / np.amax(data) * 255
data = data.astype("uint8")
print(data.shape)
return [data[..., i] for i in range(data.shape[-1])]
def upload_file(file):
return file.name
#def select_section(evt: gr.SelectData):
# return section_labels[evt.index]
if __name__ == "__main__":
print("Launching demo...")
with gr.Blocks() as demo:
"""
with gr.Blocks() as demo:
with gr.Row():
text1 = gr.Textbox(label="t1")
slider2 = gr.Textbox(label="slide")
drop3 = gr.Dropdown(["a", "b", "c"], label="d3")
with gr.Row():
with gr.Column(scale=1, min_width=600):
text1 = gr.Textbox(label="prompt 1")
text2 = gr.Textbox(label="prompt 2")
inbtw = gr.Button("Between")
text4 = gr.Textbox(label="prompt 1")
text5 = gr.Textbox(label="prompt 2")
with gr.Column(scale=2, min_width=600):
img1 = gr.Image("images/cheetah.jpg")
btn = gr.Button("Go").style(full_width=True)
greeter_1 = gr.Interface(lambda name: f"Hello {name}!", inputs="textbox", outputs=gr.Textbox(label="Greeter 1"))
greeter_2 = gr.Interface(lambda name: f"Greetings {name}!", inputs="textbox", outputs=gr.Textbox(label="Greeter 2"))
demo = gr.Parallel(greeter_1, greeter_2)
volume_renderer = gr.Interface(
fn=load_mesh,
inputs=gr.UploadButton(label="Click to Upload a File", file_types=[".nii", ".nii.nz"], file_count="single"),
outputs=gr.Model3D(clear_color=[0.0, 0.0, 0.0, 0.0], label="3D Model"),
title="livermask: Automatic Liver Parenchyma segmentation in CT",
description="Using pretrained deep learning model trained on the LiTS17 dataset",
)
"""
with gr.Row():
# file_output = gr.File()
upload_button = gr.UploadButton(label="Click to Upload a File", file_types=[".nii", ".nii.nz"], file_count="single")
# upload_button.upload(upload_file, upload_button, file_output)
#select_btn = gr.Button("Run analysis")
#select_btn.click(fn=upload_file, inputs=upload_button, outputs=output, api_name="Analysis")
#upload_button.click(section, [img_input, num_boxes, num_segments], img_output)
#print("file output:", file_output)
images = load_ct_to_numpy("./test-volume.nii")
def variable_outputs(k):
k = int(k) - 1
out = [gr.AnnotatedImage.update(visible=False)] * len(images)
out[k] = gr.AnnotatedImage.update(visible=True)
return out
def section(img, num_segments):
sections = []
for b in range(num_segments):
x = random.randint(0, img.shape[1])
y = random.randint(0, img.shape[0])
r = random.randint(0, min(x, y, img.shape[1] - x, img.shape[0] - y))
mask = np.zeros(img.shape[:2])
for i in range(img.shape[0]):
for j in range(img.shape[1]):
dist_square = (i - y) ** 2 + (j - x) ** 2
if dist_square < r**2:
mask[i, j] = round((r**2 - dist_square) / r**2 * 4) / 4
sections.append((mask, "parenchyma"))
return (img, sections)
with gr.Row():
s = gr.Slider(1, len(images), value=1, step=1, label="Which 2D slice to show")
with gr.Row():
with gr.Box():
images_boxes = []
for i, image in enumerate(images):
visibility = True if i == 1 else False # only first slide visible - change slide through slider
t = gr.AnnotatedImage(value=section(image, 1), visible=visibility).style(color_map={"parenchyma": "#ffae00"}, width=image.shape[1])
images_boxes.append(t)
s.change(variable_outputs, s, images_boxes)
#upload_button.upload(upload_file, upload_button, file_output)
#section_btn.click(section, [images[40], num_boxes, num_segments], img_output)
#ct_images.upload(section, [images[40], num_boxes, num_segments], img_output)
#demo = gr.Interface(
# fn=load_ct_to_numpy,
# inputs=gr.UploadButton(label="Click to Upload a File", file_types=[".nii", ".nii.nz"], file_count="single"),
# outputs=gr.Gallery(label="CT slices").style(columns=[4], rows=[4], object_fit="contain", height="auto"),
# title="livermask: Automatic Liver Parenchyma segmentation in CT",
# description="Using pretrained deep learning model trained on the LiTS17 dataset",
#)
# sharing app publicly -> share=True: https://gradio.app/sharing-your-app/
# inference times > 60 seconds -> need queue(): https://github.com/tloen/alpaca-lora/issues/60#issuecomment-1510006062
demo.queue().launch(server_name="0.0.0.0", server_port=7860, share=True)
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