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import streamlit as st |
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from transformers import pipeline |
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from PIL import Image, ImageDraw |
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import numpy as np |
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import colorsys |
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st.set_page_config( |
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page_title="Fraktur Detektion", |
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layout="wide", |
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initial_sidebar_state="collapsed" |
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) |
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st.markdown(""" |
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<style> |
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.stApp { |
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background: #f0f2f5 !important; |
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} |
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.block-container { |
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padding-top: 0 !important; |
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padding-bottom: 0 !important; |
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max-width: 1400px !important; |
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} |
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.upload-container { |
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background: white; |
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padding: 1.5rem; |
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border-radius: 10px; |
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box-shadow: 0 2px 4px rgba(0,0,0,0.1); |
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margin-bottom: 1rem; |
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text-align: center; |
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} |
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.results-container { |
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background: white; |
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padding: 1.5rem; |
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border-radius: 10px; |
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box-shadow: 0 2px 4px rgba(0,0,0,0.1); |
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} |
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.result-box { |
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background: #f8f9fa; |
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padding: 0.75rem; |
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border-radius: 8px; |
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margin: 0.5rem 0; |
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border: 1px solid #e9ecef; |
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} |
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h1, h2, h3, h4, p { |
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color: #1a1a1a !important; |
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margin: 0.5rem 0 !important; |
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} |
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.stImage { |
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background: white; |
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padding: 0.5rem; |
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border-radius: 8px; |
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box-shadow: 0 1px 3px rgba(0,0,0,0.1); |
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} |
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.stImage > img { |
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max-height: 300px !important; |
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width: auto !important; |
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margin: 0 auto !important; |
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display: block !important; |
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} |
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[data-testid="stFileUploader"] { |
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width: 100% !important; |
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} |
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.stButton > button { |
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width: 200px; |
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background-color: #0066cc !important; |
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color: white !important; |
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border: none !important; |
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padding: 0.5rem 1rem !important; |
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border-radius: 5px !important; |
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transition: all 0.3s ease !important; |
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} |
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.stButton > button:hover { |
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background-color: #0052a3 !important; |
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transform: translateY(-1px); |
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} |
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#MainMenu, footer, header, [data-testid="stToolbar"] { |
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display: none !important; |
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} |
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/* Hide deprecation warning */ |
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[data-testid="stExpander"], .element-container:has(>.stAlert) { |
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display: none !important; |
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} |
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</style> |
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""", unsafe_allow_html=True) |
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@st.cache_resource |
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def load_models(): |
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return { |
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"KnochenAuge": pipeline("object-detection", model="D3STRON/bone-fracture-detr"), |
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"KnochenWächter": pipeline("image-classification", model="Heem2/bone-fracture-detection-using-xray"), |
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"RöntgenMeister": pipeline("image-classification", |
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model="nandodeomkar/autotrain-fracture-detection-using-google-vit-base-patch-16-54382127388") |
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} |
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def translate_label(label): |
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translations = { |
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"fracture": "Knochenbruch", |
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"no fracture": "Kein Knochenbruch", |
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"normal": "Normal", |
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"abnormal": "Auffällig", |
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"F1": "Knochenbruch", |
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"NF": "Kein Knochenbruch" |
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} |
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return translations.get(label.lower(), label) |
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def create_heatmap_overlay(image, box, score): |
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overlay = Image.new('RGBA', image.size, (0, 0, 0, 0)) |
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draw = ImageDraw.Draw(overlay) |
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def get_temp_color(value): |
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if value > 0.8: |
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return (255, 0, 0) |
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elif value > 0.6: |
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return (255, 69, 0) |
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elif value > 0.4: |
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return (255, 165, 0) |
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else: |
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return (255, 255, 0) |
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x1, y1 = box['xmin'], box['ymin'] |
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x2, y2 = box['xmax'], box['ymax'] |
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width = x2 - x1 |
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height = y2 - y1 |
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steps = 30 |
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for i in range(steps): |
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alpha = int(255 * (1 - (i / steps)) * 0.7) |
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base_color = get_temp_color(score) |
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color = base_color + (alpha,) |
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shrink_x = (i * width) / (steps * 2) |
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shrink_y = (i * height) / (steps * 2) |
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draw.rectangle( |
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[x1 + shrink_x, y1 + shrink_y, x2 - shrink_x, y2 - shrink_y], |
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fill=color, |
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outline=None |
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) |
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border_color = get_temp_color(score) + (200,) |
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draw.rectangle([x1, y1, x2, y2], outline=border_color, width=2) |
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return overlay |
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def draw_boxes(image, predictions): |
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result_image = image.copy().convert('RGBA') |
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sorted_predictions = sorted(predictions, key=lambda x: x['score']) |
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for pred in sorted_predictions: |
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box = pred['box'] |
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score = pred['score'] |
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heatmap = create_heatmap_overlay(image, box, score) |
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result_image = Image.alpha_composite(result_image, heatmap) |
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draw = ImageDraw.Draw(result_image) |
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temp = 36.5 + (score * 2.5) |
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label = f"{translate_label(pred['label'])} ({score:.1%}) • {temp:.1f}°C" |
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text_bbox = draw.textbbox((box['xmin'], box['ymin']-25), label) |
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padding = 3 |
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text_bbox = ( |
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text_bbox[0]-padding, text_bbox[1]-padding, |
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text_bbox[2]+padding, text_bbox[3]+padding |
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) |
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draw.rectangle(text_bbox, fill="#000000CC") |
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draw.text( |
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(box['xmin'], box['ymin']-25), |
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label, |
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fill="#FFFFFF", |
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stroke_width=1, |
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stroke_fill="#000000" |
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) |
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return result_image |
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def main(): |
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models = load_models() |
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with st.container(): |
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st.write("### 📤 Röntgenbild hochladen") |
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uploaded_file = st.file_uploader("Bild auswählen", type=['png', 'jpg', 'jpeg'], label_visibility="collapsed") |
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col1, col2 = st.columns([2, 1]) |
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with col1: |
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conf_threshold = st.slider( |
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"Konfidenzschwelle", |
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min_value=0.0, max_value=1.0, |
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value=0.60, step=0.05, |
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label_visibility="visible" |
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) |
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with col2: |
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analyze_button = st.button("Analysieren") |
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if uploaded_file and analyze_button: |
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with st.spinner("Bild wird analysiert..."): |
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image = Image.open(uploaded_file) |
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results_container = st.container() |
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predictions_watcher = models["KnochenWächter"](image) |
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predictions_master = models["RöntgenMeister"](image) |
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predictions_locator = models["KnochenAuge"](image) |
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has_fracture = False |
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max_fracture_score = 0 |
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filtered_locations = [p for p in predictions_locator |
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if p['score'] >= conf_threshold |
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and 'fracture' in p['label'].lower()] |
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for pred in predictions_watcher: |
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if pred['score'] >= conf_threshold and 'fracture' in pred['label'].lower(): |
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has_fracture = True |
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max_fracture_score = max(max_fracture_score, pred['score']) |
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with results_container: |
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st.write("### 🔍 Analyse Ergebnisse") |
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col1, col2 = st.columns(2) |
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with col1: |
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st.write("#### 🤖 KI-Diagnose") |
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st.write("##### 🛡️ KnochenWächter") |
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for pred in predictions_watcher: |
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if pred['score'] >= conf_threshold: |
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confidence_color = '#0066cc' if pred['score'] > 0.7 else '#ffa500' |
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label_lower = pred['label'].lower() |
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if 'fracture' in label_lower: |
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has_fracture = True |
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max_fracture_score = max(max_fracture_score, pred['score']) |
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st.markdown(f""" |
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<div class="result-box" style="color: #1a1a1a;"> |
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<span style="color: {confidence_color}; font-weight: 500;"> |
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{pred['score']:.1%} |
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</span> - {translate_label(pred['label'])} |
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</div> |
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""", unsafe_allow_html=True) |
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st.write("#### 🎓 RöntgenMeister") |
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for pred in predictions_master: |
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if pred['score'] >= conf_threshold: |
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confidence_color = '#0066cc' if pred['score'] > 0.7 else '#ffa500' |
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st.markdown(f""" |
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<div class="result-box" style="color: #1a1a1a;"> |
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<span style="color: {confidence_color}; font-weight: 500;"> |
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{pred['score']:.1%} |
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</span> - {translate_label(pred['label'])} |
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</div> |
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""", unsafe_allow_html=True) |
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if max_fracture_score > 0: |
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st.write("#### 📊 Wahrscheinlichkeit") |
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no_fracture_prob = 1 - max_fracture_score |
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st.markdown(f""" |
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<div class="result-box" style="color: #1a1a1a;"> |
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Knochenbruch: <strong style="color: #0066cc">{max_fracture_score:.1%}</strong><br> |
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Kein Knochenbruch: <strong style="color: #ffa500">{no_fracture_prob:.1%}</strong> |
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</div> |
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""", unsafe_allow_html=True) |
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with col2: |
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predictions = models["KnochenAuge"](image) |
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st.write("Debug - Toutes les prédictions:") |
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for p in predictions: |
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st.write(f"Label: {p['label']}, Score: {p['score']}") |
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filtered_preds = [p for p in predictions if p['score'] >= conf_threshold |
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and 'fracture' in p['label'].lower()] |
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st.write("Debug - Prédictions filtrées:") |
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for p in filtered_preds: |
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st.write(f"Label: {p['label']}, Score: {p['score']}, Box: {p['box']}") |
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if filtered_preds: |
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st.write("#### 🎯 Fraktur Lokalisation") |
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result_image = draw_boxes(image, filtered_preds) |
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st.image(result_image, use_container_width=True) |
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else: |
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st.write("#### 🖼️ Röntgenbild") |
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st.image(image, use_container_width=True) |
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if __name__ == "__main__": |
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main() |
