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Dockerfile

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+FROM python:3.10
+
+WORKDIR /app
+
+COPY requirements.txt .
+
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+EXPOSE 7860
+
+CMD ["streamlit", "run", "app.py", "--server.port=7860", "--server.address=0.0.0.0"]

app.py

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+import streamlit as st
+import cv2
+import numpy as np
+from ultralytics import YOLO
+from PIL import Image
+
+st.set_page_config(layout="wide")
+st.title("OPG Segmentation + Midline + Sinus Detection")
+
+# -----------------------------
+# Load model (cached)
+# -----------------------------
+@st.cache_resource
+def load_model():
+    return YOLO("best.pt")
+
+model = load_model()
+
+# -----------------------------
+# Upload image
+# -----------------------------
+uploaded_file = st.file_uploader("Upload OPG Image", type=["jpg", "png", "jpeg"])
+
+# -----------------------------
+# Preprocessing Function
+# -----------------------------
+def preprocess_image(image):
+
+    # Convert to grayscale
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+
+    # Apply CLAHE
+    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
+    gray = clahe.apply(gray)
+
+    # Resize so max dimension = 2048
+    h, w = gray.shape
+    scale = 2048 / max(h, w)
+    gray = cv2.resize(gray, (int(w * scale), int(h * scale)))
+
+    # Convert back to 3-channel (YOLO expects 3 channels)
+    processed = cv2.cvtColor(gray, cv2.COLOR_GRAY2BGR)
+
+    return processed
+
+
+
+if uploaded_file is not None:
+
+    # Convert to OpenCV format
+    image = Image.open(uploaded_file).convert("RGB")
+    image = np.array(image)
+    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+
+    image = preprocess_image(image)
+
+    h, w, _ = image.shape
+
+    # -----------------------------
+    # Run prediction
+    # -----------------------------
+    results = model(image, conf=0.25)
+    result = results[0]
+
+    tooth_centers_x = []
+
+    # -----------------------------
+    # Collect tooth centers
+    # -----------------------------
+    for box, cls in zip(result.boxes.xywh, result.boxes.cls):
+        cls = int(cls)
+        if cls == 0:  # tooth class
+            x_center = box[0].item()
+            tooth_centers_x.append(x_center)
+
+    if len(tooth_centers_x) == 0:
+        st.warning("No teeth detected!")
+        st.stop()
+
+    # -----------------------------
+    # Compute midline
+    # -----------------------------
+    midline_x = int((min(tooth_centers_x) + max(tooth_centers_x)) / 2)
+
+    # -----------------------------
+    # Draw tooth masks
+    # -----------------------------
+    if result.masks is not None:
+        for mask, cls in zip(result.masks.xy, result.boxes.cls):
+            cls = int(cls)
+            if cls == 0:
+                polygon = np.array(mask, dtype=np.int32)
+                cv2.polylines(image, [polygon], True, (0, 255, 0), 2)
+
+    # -----------------------------
+    # Process sinus
+    # -----------------------------
+    for box, cls in zip(result.boxes.xywh, result.boxes.cls):
+        cls = int(cls)
+        if cls == 1:  # sinus class
+            x, y, bw, bh = box
+            x_center = x.item()
+
+            if x_center < midline_x:
+                label = "Right Sinus"
+                color = (255, 0, 0)
+            else:
+                label = "Left Sinus"
+                color = (0, 0, 255)
+
+            x1 = int(x - bw / 2)
+            y1 = int(y - bh / 2)
+            x2 = int(x + bw / 2)
+            y2 = int(y + bh / 2)
+
+            cv2.rectangle(image, (x1, y1), (x2, y2), color, 2)
+            cv2.putText(image, label, (x1, y1 - 10),
+                        cv2.FONT_HERSHEY_SIMPLEX, 0.7, color, 2)
+
+    # -----------------------------
+    # Draw midline
+    # -----------------------------
+    cv2.line(image, (midline_x, 0), (midline_x, h),
+             (0, 255, 255), 2)
+
+    # Convert back to RGB for display
+    display_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+    col1, col2 = st.columns(2)
+
+    with col1:
+        st.image(uploaded_file, caption="Original Image")
+
+    with col2:
+        st.image(display_image, caption="Segmented Output")
+
+    # -----------------------------
+    # Download button
+    # -----------------------------
+    result_pil = Image.fromarray(display_image)
+    st.download_button(
+        label="Download Result Image",
+        data=result_pil.tobytes(),
+        file_name="output_with_midline.jpg",
+        mime="image/jpeg"
+    )

best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:687d06e6ffb97dbf9d39b4a91cb7f9c8cdc36b3b8af660441170eca2d4d699f1
+size 54849237

requirements.txt

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+streamlit
+ultralytics
+opencv-python-headless
+numpy
+pillow
+torch