|
|
import streamlit as st |
|
|
from transformers import pipeline |
|
|
from PIL import Image, ImageDraw |
|
|
import numpy as np |
|
|
import colorsys |
|
|
import os |
|
|
|
|
|
|
|
|
os.environ['STREAMLIT_SERVER_WEBSOCKET_HEADERS'] = '{"Access-Control-Allow-Origin": "*"}' |
|
|
os.environ['STREAMLIT_SERVER_ENABLE_CORS'] = 'true' |
|
|
|
|
|
|
|
|
st.set_page_config( |
|
|
page_title="Fraktur Detektion", |
|
|
layout="wide", |
|
|
initial_sidebar_state="collapsed" |
|
|
) |
|
|
|
|
|
|
|
|
st.markdown(""" |
|
|
<script> |
|
|
if (window.WebSocket && navigator.userAgent.indexOf("Edge") > -1) { |
|
|
const originalWebSocket = window.WebSocket; |
|
|
window.WebSocket = function(url, protocols) { |
|
|
if (url.includes('_stcore/stream')) { |
|
|
url = url.replace('wss://', 'ws://'); |
|
|
} |
|
|
return new originalWebSocket(url, protocols); |
|
|
}; |
|
|
} |
|
|
</script> |
|
|
""", unsafe_allow_html=True) |
|
|
|
|
|
st.markdown(""" |
|
|
<style> |
|
|
.stApp { |
|
|
background: #f0f2f5 !important; |
|
|
} |
|
|
|
|
|
.block-container { |
|
|
padding-top: 0 !important; |
|
|
padding-bottom: 0 !important; |
|
|
max-width: 1400px !important; |
|
|
} |
|
|
|
|
|
.upload-container { |
|
|
background: white; |
|
|
padding: 1.5rem; |
|
|
border-radius: 10px; |
|
|
box-shadow: 0 2px 4px rgba(0,0,0,0.1); |
|
|
margin-bottom: 1rem; |
|
|
text-align: center; |
|
|
} |
|
|
|
|
|
.results-container { |
|
|
background: white; |
|
|
padding: 1.5rem; |
|
|
border-radius: 10px; |
|
|
box-shadow: 0 2px 4px rgba(0,0,0,0.1); |
|
|
} |
|
|
|
|
|
.result-box { |
|
|
background: #f8f9fa; |
|
|
padding: 0.75rem; |
|
|
border-radius: 8px; |
|
|
margin: 0.5rem 0; |
|
|
border: 1px solid #e9ecef; |
|
|
} |
|
|
|
|
|
h1, h2, h3, h4, p { |
|
|
color: #1a1a1a !important; |
|
|
margin: 0.5rem 0 !important; |
|
|
} |
|
|
|
|
|
.stImage { |
|
|
background: white; |
|
|
padding: 0.5rem; |
|
|
border-radius: 8px; |
|
|
box-shadow: 0 1px 3px rgba(0,0,0,0.1); |
|
|
} |
|
|
|
|
|
.stImage > img { |
|
|
max-height: 300px !important; |
|
|
width: auto !important; |
|
|
margin: 0 auto !important; |
|
|
display: block !important; |
|
|
} |
|
|
|
|
|
[data-testid="stFileUploader"] { |
|
|
width: 100% !important; |
|
|
} |
|
|
|
|
|
.stFileUploaderFileName { |
|
|
color: #1a1a1a !important; |
|
|
} |
|
|
|
|
|
.stButton > button { |
|
|
width: 200px; |
|
|
background-color: #f8f9fa !important; |
|
|
color: #1a1a1a !important; |
|
|
border: 1px solid #e9ecef !important; |
|
|
padding: 0.5rem 1rem !important; |
|
|
border-radius: 5px !important; |
|
|
transition: all 0.3s ease !important; |
|
|
} |
|
|
|
|
|
.stButton > button:hover { |
|
|
background-color: #e9ecef !important; |
|
|
transform: translateY(-1px); |
|
|
} |
|
|
|
|
|
#MainMenu, footer, header, [data-testid="stToolbar"] { |
|
|
display: none !important; |
|
|
} |
|
|
|
|
|
/* Hide deprecation warning */ |
|
|
[data-testid="stExpander"], .element-container:has(>.stAlert) { |
|
|
display: none !important; |
|
|
} |
|
|
|
|
|
/* Fix for WebSocket connection issues */ |
|
|
iframe { |
|
|
visibility: hidden; |
|
|
} |
|
|
</style> |
|
|
|
|
|
<script> |
|
|
// Fix for WebSocket connection |
|
|
window.addEventListener('load', function() { |
|
|
setTimeout(function() { |
|
|
const frames = document.getElementsByTagName('iframe'); |
|
|
for (let frame of frames) { |
|
|
frame.style.visibility = 'visible'; |
|
|
} |
|
|
}, 1000); |
|
|
}); |
|
|
</script> |
|
|
""", unsafe_allow_html=True) |
|
|
|
|
|
|
|
|
@st.cache_resource(show_spinner=False) |
|
|
def load_models(): |
|
|
return { |
|
|
"KnochenAuge": pipeline("object-detection", model="D3STRON/bone-fracture-detr"), |
|
|
"KnochenWächter": pipeline("image-classification", model="Heem2/bone-fracture-detection-using-xray"), |
|
|
"RöntgenMeister": pipeline("image-classification", |
|
|
model="nandodeomkar/autotrain-fracture-detection-using-google-vit-base-patch-16-54382127388") |
|
|
} |
|
|
|
|
|
def translate_label(label): |
|
|
translations = { |
|
|
"fracture": "Knochenbruch", |
|
|
"no fracture": "Kein Knochenbruch", |
|
|
"normal": "Normal", |
|
|
"abnormal": "Auffällig", |
|
|
"F1": "Knochenbruch", |
|
|
"NF": "Kein Knochenbruch" |
|
|
} |
|
|
return translations.get(label.lower(), label) |
|
|
|
|
|
def create_heatmap_overlay(image, box, score): |
|
|
overlay = Image.new('RGBA', image.size, (0, 0, 0, 0)) |
|
|
draw = ImageDraw.Draw(overlay) |
|
|
|
|
|
x1, y1 = box['xmin'], box['ymin'] |
|
|
x2, y2 = box['xmax'], box['ymax'] |
|
|
|
|
|
if score > 0.8: |
|
|
fill_color = (255, 0, 0, 100) |
|
|
border_color = (255, 0, 0, 255) |
|
|
elif score > 0.6: |
|
|
fill_color = (255, 165, 0, 100) |
|
|
border_color = (255, 165, 0, 255) |
|
|
else: |
|
|
fill_color = (255, 255, 0, 100) |
|
|
border_color = (255, 255, 0, 255) |
|
|
|
|
|
draw.rectangle([x1, y1, x2, y2], fill=fill_color) |
|
|
draw.rectangle([x1, y1, x2, y2], outline=border_color, width=2) |
|
|
|
|
|
return overlay |
|
|
|
|
|
def draw_boxes(image, predictions): |
|
|
result_image = image.copy().convert('RGBA') |
|
|
|
|
|
for pred in predictions: |
|
|
box = pred['box'] |
|
|
score = pred['score'] |
|
|
|
|
|
overlay = create_heatmap_overlay(image, box, score) |
|
|
result_image = Image.alpha_composite(result_image, overlay) |
|
|
|
|
|
draw = ImageDraw.Draw(result_image) |
|
|
temp = 36.5 + (score * 2.5) |
|
|
label = f"{translate_label(pred['label'])} ({score:.1%} • {temp:.1f}°C)" |
|
|
|
|
|
text_bbox = draw.textbbox((box['xmin'], box['ymin']-20), label) |
|
|
draw.rectangle(text_bbox, fill=(0, 0, 0, 180)) |
|
|
|
|
|
draw.text( |
|
|
(box['xmin'], box['ymin']-20), |
|
|
label, |
|
|
fill=(255, 255, 255, 255) |
|
|
) |
|
|
|
|
|
return result_image |
|
|
|
|
|
def main(): |
|
|
try: |
|
|
models = load_models() |
|
|
|
|
|
with st.container(): |
|
|
st.write("### 📤 Röntgenbild hochladen") |
|
|
uploaded_file = st.file_uploader("Bild auswählen", type=['png', 'jpg', 'jpeg'], label_visibility="collapsed") |
|
|
|
|
|
col1, col2 = st.columns([2, 1]) |
|
|
with col1: |
|
|
conf_threshold = st.slider( |
|
|
"Konfidenzschwelle", |
|
|
min_value=0.0, max_value=1.0, |
|
|
value=0.60, step=0.05, |
|
|
label_visibility="visible" |
|
|
) |
|
|
with col2: |
|
|
analyze_button = st.button("Analysieren") |
|
|
|
|
|
if uploaded_file and analyze_button: |
|
|
with st.spinner("Bild wird analysiert..."): |
|
|
image = Image.open(uploaded_file) |
|
|
results_container = st.container() |
|
|
|
|
|
predictions_watcher = models["KnochenWächter"](image) |
|
|
predictions_master = models["RöntgenMeister"](image) |
|
|
predictions_locator = models["KnochenAuge"](image) |
|
|
|
|
|
has_fracture = False |
|
|
max_fracture_score = 0 |
|
|
filtered_locations = [p for p in predictions_locator |
|
|
if p['score'] >= conf_threshold] |
|
|
|
|
|
for pred in predictions_watcher: |
|
|
if pred['score'] >= conf_threshold and 'fracture' in pred['label'].lower(): |
|
|
has_fracture = True |
|
|
max_fracture_score = max(max_fracture_score, pred['score']) |
|
|
|
|
|
with results_container: |
|
|
st.write("### 🔍 Analyse Ergebnisse") |
|
|
col1, col2 = st.columns(2) |
|
|
|
|
|
with col1: |
|
|
st.write("#### 🤖 KI-Diagnose") |
|
|
|
|
|
st.markdown("#### 🛡️ KnochenWächter") |
|
|
for pred in predictions_watcher: |
|
|
confidence_color = '#0066cc' if pred['score'] > 0.7 else '#ffa500' |
|
|
label_lower = pred['label'].lower() |
|
|
if pred['score'] >= conf_threshold and 'fracture' in label_lower: |
|
|
has_fracture = True |
|
|
max_fracture_score = max(max_fracture_score, pred['score']) |
|
|
st.markdown(f""" |
|
|
<div class="result-box" style="color: #1a1a1a;"> |
|
|
<span style="color: {confidence_color}; font-weight: 500;"> |
|
|
{pred['score']:.1%} |
|
|
</span> - {translate_label(pred['label'])} |
|
|
</div> |
|
|
""", unsafe_allow_html=True) |
|
|
|
|
|
st.markdown("#### 🎓 RöntgenMeister") |
|
|
for pred in predictions_master: |
|
|
confidence_color = '#0066cc' if pred['score'] > 0.7 else '#ffa500' |
|
|
st.markdown(f""" |
|
|
<div class="result-box" style="color: #1a1a1a;"> |
|
|
<span style="color: {confidence_color}; font-weight: 500;"> |
|
|
{pred['score']:.1%} |
|
|
</span> - {translate_label(pred['label'])} |
|
|
</div> |
|
|
""", unsafe_allow_html=True) |
|
|
|
|
|
if max_fracture_score > 0: |
|
|
st.write("#### 📊 Wahrscheinlichkeit") |
|
|
no_fracture_prob = 1 - max_fracture_score |
|
|
st.markdown(f""" |
|
|
<div class="result-box" style="color: #1a1a1a;"> |
|
|
Knochenbruch: <strong style="color: #0066cc">{max_fracture_score:.1%}</strong><br> |
|
|
Kein Knochenbruch: <strong style="color: #ffa500">{no_fracture_prob:.1%}</strong> |
|
|
</div> |
|
|
""", unsafe_allow_html=True) |
|
|
|
|
|
with col2: |
|
|
predictions = models["KnochenAuge"](image) |
|
|
filtered_preds = [p for p in predictions if p['score'] >= conf_threshold] |
|
|
|
|
|
if filtered_preds: |
|
|
st.write("#### 🎯 Fraktur Lokalisation") |
|
|
result_image = draw_boxes(image, filtered_preds) |
|
|
st.image(result_image, use_container_width=True) |
|
|
else: |
|
|
st.write("#### 🖼️ Röntgenbild") |
|
|
st.image(image, use_container_width=True) |
|
|
except Exception as e: |
|
|
st.error(f"Ein Fehler ist aufgetreten: {str(e)}") |
|
|
|
|
|
if __name__ == "__main__": |
|
|
main() |
