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import streamlit as st |
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from transformers import pipeline |
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from PIL import Image |
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import numpy as np |
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import cv2 |
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st.set_page_config(page_title="Détection de fractures osseuses") |
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st.title("Détection de fractures osseuses par rayons X") |
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@st.cache_resource |
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def load_model(): |
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return pipeline( |
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"object-detection", |
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model="anirban22/detr-resnet-50-med_fracture", |
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threshold=0.1 |
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) |
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model = load_model() |
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def enhance_image(img): |
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img_array = np.array(img) |
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gray = cv2.cvtColor(img_array, cv2.COLOR_RGB2GRAY) |
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clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8,8)) |
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enhanced = clahe.apply(gray) |
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return cv2.cvtColor(enhanced, cv2.COLOR_GRAY2RGB) |
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uploaded_file = st.file_uploader("Téléchargez une image radiographique", type=["jpg", "jpeg", "png"]) |
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if uploaded_file: |
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image = Image.open(uploaded_file).convert('RGB') |
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if image.size[0] > 800: |
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ratio = 800.0 / image.size[0] |
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size = (800, int(image.size[1] * ratio)) |
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image = image.resize(size, Image.Resampling.LANCZOS) |
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enhanced_image = enhance_image(image) |
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predictions = model(enhanced_image) |
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col1, col2 = st.columns(2) |
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with col1: |
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st.image(image, caption="Image originale", use_container_width=True) |
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with col2: |
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output_img = np.array(image).copy() |
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for pred in predictions: |
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box = pred['box'] |
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score = pred['score'] |
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x1, y1, x2, y2 = [int(i) for i in [box['xmin'], box['ymin'], box['xmax'], box['ymax']]] |
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cv2.rectangle(output_img, (x1, y1), (x2, y2), (255, 0, 0), 3) |
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text = f"Fracture: {score:.2f}" |
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cv2.putText(output_img, text, (x1, y1-10), |
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cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 0, 0), 2) |
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st.image(output_img, caption="Détection des fractures", use_container_width=True) |
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st.subheader("Résultats") |
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if predictions: |
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for idx, pred in enumerate(predictions, 1): |
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st.warning(f"⚠️ Fracture {idx} détectée (Confiance: {pred['score']*100:.1f}%)") |
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else: |
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st.warning("⚠️ Aucune fracture n'a été détectée avec certitude") |
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else: |
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st.info("Veuillez télécharger une image radiographique pour l'analyse.") |
