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zlzheimer-diagnostic-system.py
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import pywebio
import nibabel
import io
from matplotlib import pyplot as plt
import PIL.Image
import torch
import random
def generate_random_str(target_length=32):
random_str = ''
base_str = 'ABCDEFGHIGKLMNOPQRSTUVWXYZabcdefghigklmnopqrstuvwxyz0123456789'
length = len(base_str) - 1
for i in range(target_length):
random_str += base_str[random.randint(0, length)]
return random_str
def zlzheimer_diagnostic_system(is_demo=False):
"""
基于 PyWebIO 和 PyTorch 的阿尔兹海默智能诊断系统
"""
def show_img(img):
"""
接收二进制图像，用于展示图像
"""
pywebio.output.popup("加载中", [
pywebio.output.put_loading(),
])
pywebio.output.popup("上传的图像",[
pywebio.output.put_image(img),
])
from pyecharts.charts import Bar
def draw_chart(x_label,data):
"""
输入 x 轴标签和数据列表，返回图表的 HTML 文本
"""
chart = (
Bar()
.add_xaxis(x_label) # 设置 x 轴标签列表
.add_yaxis("output_value", data) # 传入数据列表和标签
.set_global_opts(title_opts={"text": "模型输出", "subtext": ""},) # text 的值为图表标题
)
return chart.render_notebook() # 返回图表对象的 html 文本
while 1:
pywebio.output.clear()
pywebio.output.put_markdown("# 基于 PyWebIO 和 PyTorch 的阿尔兹海默智能诊断系统")
pywebio.output.put_warning('识别结果仅供参考')
pywebio.output.put_row([
pywebio.output.put_button('使用示例图像',
onclick=lambda: zlzheimer_diagnostic_system(is_demo=True), disabled=is_demo),
pywebio.output.put_link(name="项目github仓库", url="https://github.com/bytesc/Image-Recognition-system"),
pywebio.output.put_link(name="演示数据集", url="https://github.com/bytesc/lqdata"),
])
train_img = PIL.Image.open("./img/net_graph.png")
brain_img = PIL.Image.open("./img/brain_demo1.png")
pywebio.output.put_row(
[pywebio.output.put_collapse("", [pywebio.output.put_image(train_img)], open=True),
pywebio.output.put_collapse("", [pywebio.output.put_image(brain_img),
pywebio.output.put_link(name=" 参考文献", url="https://github.com/moboehle/Pytorch-LRP"),], open=True),
]
)
# input_img = pywebio.input.file_upload(label="上传图片", accept=[".jpg", ".png", ".jpeg"])
nii_path = "./demodata/demo.nii"
if not is_demo:
pywebio.input.actions("", [{'label': "上传图像", 'value': "", 'color': 'success', }])
input_img = pywebio.input.file_upload(label="上传图像", accept=[".nii"], required=True)
pywebio.output.popup("加载中", [
pywebio.output.put_loading(),
])
input_img = io.BytesIO(input_img['content'])
nii_path = "./uploaded_img/" + generate_random_str() + ".nii"
with open(nii_path, 'wb') as file:
file.write(input_img.getvalue()) # 保存到本地
if is_demo:
pywebio.output.popup("加载中", [
pywebio.output.put_loading(),
])
is_demo = False
img = nibabel.load(nii_path)
img = img.get_fdata()
print(img.shape)
# (166, 256, 256, 1)
torch.no_grad()
test_model = torch.load("./myModel_109.pth", map_location=torch.device('cpu'))
test_model.eval()
processed_img = torch.from_numpy(img)
processed_img = processed_img.squeeze()
processed_img = processed_img.reshape(1, -1, 256, 256)
processed_img = processed_img[:, 0:160, :, :].float()
processed_img = processed_img.reshape((1, 1, -1, 256, 256))
output = None
with torch.no_grad():
output = test_model(processed_img)
ans_y = output.squeeze().tolist()
print(ans_y)
del test_model,processed_img
from datasets import LABEL_LIST
if min(ans_y) < 0:
m = min(ans_y)
for i in range(len(ans_y)):
ans_y[i] -= 1.2 * m
chart_html = draw_chart(LABEL_LIST, ans_y)
pywebio.output.put_html(chart_html)
ans = LABEL_LIST[output.argmax(1).item()]
if ans == 'AD':
ans += '（阿尔茨海默病）'
elif ans == 'CN':
ans += '（认知正常）'
elif ans == 'MCI':
ans += '（轻度认知障碍）'
elif ans == 'EMCI':
ans += '（早期轻度认知障碍）'
elif ans == 'LMCI':
ans += '（晚期轻度认知障碍）'
show_result = [pywebio.output.put_markdown("诊断为：\n # " + ans),
pywebio.output.put_warning('结果仅供参考'),]
pywebio.output.popup(title='AI识别结果', content=show_result)
while 1:
act = pywebio.input.actions(' ', ['查看图像', '上传新图像'], )
if act == '上传新图像':
pywebio.output.clear()
break
dim = pywebio.input.radio(label='查看视角', options=[('X',0,True), ('Y',1,False), ('Z',2,False)],
required=True, inline=True)
max_index = 0
print(dim)
if dim == 0:
max_index = img.shape[0]
if dim == 1:
max_index = img.shape[1]
if dim == 2:
max_index = img.shape[2]
index = pywebio.input.slider("查看层数", max_value=max_index - 1, step=1, value=100)
if img.ndim == 4:
if dim == 0:
plt.imshow(img[index+1, :, :, :], cmap='gray')
if dim == 1:
plt.imshow(img[:, index+1, :, :], cmap='gray')
if dim == 2:
plt.imshow(img[:, :, index+1, :], cmap='gray')
if img.ndim == 3:
if dim == 0:
plt.imshow(img[index, :, :], cmap='gray')
elif dim == 1:
plt.imshow(img[:, index, :], cmap='gray')
elif dim == 2:
plt.imshow(img[:, :, index], cmap='gray')
plt.axis('off')
png_path = './uploaded_img/' + generate_random_str() + '.png'
plt.savefig(png_path, bbox_inches='tight', pad_inches=0)
show_img(PIL.Image.open(png_path))
if __name__ == "__main__":
pywebio.platform.start_server(
applications=[zlzheimer_diagnostic_system, ], # applications 参数为一个可迭代对象（此处是列表），里面放要运行的主函数。
auto_open_webbrowser=False, # 不自动打开浏览器
cdn=False, # 不使用 cdn
debug=True, # 可以看到报错
port=6006 # 运行在 6006 端口
)
# start_server 函数启动一个 http 服务器