|
from fastai.vision.all import * |
|
from io import BytesIO |
|
import requests |
|
import streamlit as st |
|
|
|
""" |
|
# U-Net |
|
This is a segmentation model for images of Brain MRI. |
|
""" |
|
|
|
def predict(img): |
|
st.image(img, caption="Your image", use_column_width=True) |
|
pred_mask = learn_inf.predict(img)[0] |
|
|
|
pred_mask = pred_mask.numpy() |
|
|
|
gray_ratio = int(255/pred_mask.max()) |
|
pred_mask = pred_mask*gray_ratio |
|
|
|
f""" |
|
### Prediction result: |
|
""" |
|
st.image(pred_mask, caption="Prediction Mask", use_column_width=True) |
|
|
|
def acc_camvid(inp, targ): |
|
void_code = 0 |
|
targ = targ.squeeze(1) |
|
mask = targ != void_code |
|
return (inp.argmax(dim=1)[mask]==targ[mask]).float().mean() |
|
|
|
def label_func(x): |
|
|
|
return x.parents[0] / (x.stem + '_mask' + '.png') |
|
|
|
path = "./" |
|
learn_inf = load_learner(path + "model-34-stage2") |
|
|
|
option = st.radio("", ["Upload Image", "Image URL"]) |
|
|
|
if option == "Upload Image": |
|
uploaded_file = st.file_uploader("Please upload an image.") |
|
|
|
if uploaded_file is not None: |
|
img = PILImage.create(uploaded_file) |
|
predict(img) |
|
|
|
else: |
|
url = st.text_input("Please input a url.") |
|
|
|
if url != "": |
|
try: |
|
response = requests.get(url) |
|
pil_img = PILImage.create(BytesIO(response.content)) |
|
predict(pil_img) |
|
|
|
except: |
|
st.text("Problem reading image from", url) |
|
|
|
|
