DICOM Supported

app.py

@@ -17,6 +17,8 @@ import io
 import uuid
 from datetime import datetime, timedelta
 import base64
+import pydicom
+import os
 
 # Configuration
 MAX_FILE_SIZE = 10 * 1024 * 1024  # 10MB
@@ -118,7 +120,54 @@ def process_predictions(predictions):
         decoded.append([(class_names[i], float(pred[i])) for i in top_indices])
     return decoded
 
-def preprocess_image(file_bytes):
+def preprocess_dicom(file_bytes):
+    """Process DICOM format images for the model."""
+    # Save the bytes to a temporary file
+    temp_file = "temp_dicom.dcm"
+    with open(temp_file, "wb") as f:
+        f.write(file_bytes)
+    
+    try:
+        # Read the DICOM file
+        dicom_data = pydicom.dcmread(temp_file)
+        
+        # Convert DICOM pixel data to numpy array
+        img = dicom_data.pixel_array
+        
+        # DICOM images are often 16-bit or higher, normalize to 8-bit for visualization
+        if img.dtype != np.uint8:
+            # Scale to [0, 255] for 8-bit representation
+            img = img.astype(np.float32)
+            img = (img - img.min()) / (img.max() - img.min()) * 255.0
+            img = img.astype(np.uint8)
+        
+        # Check if grayscale and convert to RGB
+        if len(img.shape) == 2:
+            img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
+        elif len(img.shape) == 3 and img.shape[2] == 1:
+            img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
+            
+        # Resize for model input
+        img = cv2.resize(img, (540, 540), interpolation=cv2.INTER_AREA)
+        
+        return img
+    finally:
+        # Clean up temporary file
+        if os.path.exists(temp_file):
+            os.remove(temp_file)
+
+def preprocess_image(file_bytes, content_type=None):
+    """Process images for the model, handling both DICOM and standard formats."""
+    # Check if the file is a DICOM file
+    if content_type and ('dicom' in content_type.lower() or content_type.lower() == 'application/octet-stream'):
+        try:
+            return preprocess_dicom(file_bytes)
+        except Exception as e:
+            print(f"DICOM processing error: {e}")
+            # Fall back to standard image processing if DICOM processing fails
+            pass
+    
+    # Process as standard image format
     img = cv2.imdecode(np.frombuffer(file_bytes, np.uint8), cv2.IMREAD_COLOR)
     img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
     return cv2.resize(img, (540, 540), interpolation=cv2.INTER_AREA)
@@ -129,15 +178,18 @@ async def analyze_image(
     request: Request,
     file: UploadFile = File(...)
 ):
-    if not file.content_type.startswith('image/'):
-        raise HTTPException(400, "Only image files accepted")
+    # Accept both standard image formats and DICOM files
+    if not (file.content_type.startswith('image/') or 
+            'dicom' in file.content_type.lower() or 
+            file.content_type == 'application/octet-stream'):
+        raise HTTPException(400, "Only image or DICOM files accepted")
     
     if file.size > MAX_FILE_SIZE:
         raise HTTPException(413, f"File too large (max {MAX_FILE_SIZE//1024//1024}MB)")
     
     try:
         contents = await file.read()
-        img = preprocess_image(contents)
+        img = preprocess_image(contents, file.content_type)
         
         img_array = img_to_array(img)
         img_array = np.expand_dims(img_array, axis=0)
@@ -165,7 +217,10 @@ async def analyze_image(
 async def health_check():
     return {
         "status": "healthy",
-        "timestamp": datetime.now().isoformat()
+        "timestamp": datetime.now().isoformat(),
+        "features": {
+            "dicom_support": True
+        }
     }
 
 if __name__ == "__main__":

requirements.txt

@@ -7,4 +7,5 @@ matplotlib==3.7.1
 python-multipart==0.0.6
 uvicorn==0.22.0
 slowapi==0.1.8
-redis==4.5.5
+redis==4.5.5
+pydicom==3.0.1