File size: 7,988 Bytes
f700114 cc165f9 f700114 1a77416 a72cc82 69c9de9 005d8cf a72cc82 f700114 a72cc82 f700114 383659b 1a77416 383659b 8d54860 952e5d1 383659b 1a77416 dc9c960 8375e6c dc9c960 8375e6c dc9c960 8375e6c dc9c960 1a77416 e59f527 1a77416 dc9c960 383659b 8d54860 1d4ce47 dc9c960 8d54860 1a77416 dc9c960 9f81278 dc9c960 1d4ce47 9f81278 dc9c960 9f81278 dc9c960 9f81278 8d54860 1a77416 f700114 a72cc82 69c9de9 a72cc82 de4364a 8375e6c a72cc82 de4364a a72cc82 8375e6c a72cc82 8375e6c a72cc82 8375e6c a72cc82 8375e6c a72cc82 8375e6c a72cc82 8375e6c 383659b f700114 268bd19 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 |
import streamlit as st
from transformers import pipeline
from PIL import Image, ImageDraw
import numpy as np
# Configuration de la page
if 'page_config' not in st.session_state:
st.set_page_config(
page_title="Fraktur Detektion",
layout="wide",
initial_sidebar_state="collapsed",
menu_items=None
)
st.session_state.page_config = True
@st.cache_resource
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():
st.markdown("""
<style>
.stApp {background: #f0f2f5}
div[data-testid="stToolbar"] {display: none}
#MainMenu {visibility: hidden}
footer {visibility: hidden}
header {visibility: hidden}
.result-box {
background: #f8f9fa;
padding: 0.75rem;
border-radius: 8px;
margin: 0.5rem 0;
border: 1px solid #e9ecef;
}
</style>
""", unsafe_allow_html=True)
try:
models = load_models()
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
)
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">
<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">
<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">
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() |