Updated Burns

SkinBurns_Classification.py

@@ -129,24 +129,9 @@ def FullFeautures(pathF):
 
     fv = []
 
-    # split color histogram into two halves and extend
-    _cl = len(ch)
-    fv.extend(ch[:_cl//2])
-    fv.extend(ch[_cl//2:])
-
-    # split LBP histogram into two halves and extend
-    _ll = len(lbph)
-    fv.extend(lbph[:_ll//2])
-    fv.extend(lbph[_ll//2:])
-
-    # split GLCM features into two halves and extend
-    _gl = len(glc)
-    fv.extend(glc[:_gl//2])
-    fv.extend(glc[_gl//2:])
-
-    # split Hu moments into two halves and extend
-    _hl = len(hu)
-    fv.extend(hu[:_hl//2])
-    fv.extend(hu[_hl//2:])
+    fv.extend(ch)
+    fv.extend(lbph)
+    fv.extend(glc)
+    fv.extend(hu)
 
     return _np.array(fv) 

app.py

@@ -44,8 +44,8 @@ import signal
 
 app = FastAPI()
 
-SCREENSHOTS_DIR = "screenshots"  # Folder containing screenshots to upload
-OUTPUT_DIR = "Output"            # Folder containing the .mp4 video and graph .png
+SCREENSHOTS_DIR = "screenshots"  
+OUTPUT_DIR = "Output"          
 UPLOAD_DIR = "uploads"
 os.makedirs(UPLOAD_DIR, exist_ok=True)
 
@@ -59,7 +59,7 @@ except Exception as e:
 
 
 
-# ✅ Cloudinary config
+# Cloudinary config
 cloudinary.config( 
     cloud_name = "darumyfpl", 
     api_key = "493972437417214", 
@@ -75,30 +75,21 @@ def greet_json():
 @app.post("/predict_burn")
 async def predict_burn(file: UploadFile = File(...)):
     try:
-        #  Save the uploaded file temporarily
         temp_file_path = f"temp_{file.filename}"
         with open(temp_file_path, "wb") as temp_file:
             temp_file.write(await file.read())
 
+        loaded_svm = joblib.load('svm_model.pkl')
 
-        # Load the saved SVM model
-        with open('svm_model.pkl', 'rb') as model_file:
-            loaded_svm = pickle.load(model_file)
-
-        # Extract features from the uploaded image
+        print("SVM model loaded successfully")
         features = FullFeautures(temp_file_path)
 
-        # Remove the temporary file
         os.remove(temp_file_path)
 
         if features is None:
             return JSONResponse(content={"error": "Failed to extract features from the image."}, status_code=400)
 
-        # Reshape features to match the SVM model's expected input
-        features = features.reshape(1, -1)
-
-        # Predict the class 
-        prediction = loaded_svm.predict(features)
+        prediction = loaded_svm.predict(features.reshape(1, -1))
         prediction_label = "Burn" if prediction[0] == 1 else "No Burn"
 
         if prediction[0] == 1:
@@ -118,22 +109,18 @@ async def predict_burn(file: UploadFile = File(...)):
 @app.post("/segment_burn")
 async def segment_burn_endpoint(reference: UploadFile = File(...), patient: UploadFile = File(...)):
     try:
-        # Save the reference image temporarily
         reference_path = f"temp_ref_{reference.filename}"
         reference_bytes = await reference.read()
         with open(reference_path, "wb") as ref_file:
             ref_file.write(reference_bytes)
 
-        # Save the patient image temporarily
         patient_path = f"temp_patient_{patient.filename}"
         patient_bytes = await patient.read()
         with open(patient_path, "wb") as pat_file:
             pat_file.write(patient_bytes)
 
-        # Call the segmentation logic
         burn_crop_clean, burn_crop_debug = segment_burn(patient_path, reference_path)
 
-        # Save the cropped outputs
         burn_crop_clean_path = f"temp_burn_crop_clean_{uuid.uuid4()}.png"
         burn_crop_debug_path = f"temp_burn_crop_debug_{uuid.uuid4()}.png"
   
@@ -141,13 +128,10 @@ async def segment_burn_endpoint(reference: UploadFile = File(...), patient: Uplo
         plt.imsave(burn_crop_clean_path, burn_crop_clean)
         plt.imsave(burn_crop_debug_path, burn_crop_debug)
 
-        # Upload to Cloudinary
         crop_clean_upload = cloudinary.uploader.upload(burn_crop_clean_path, public_id=f"ref_{reference.filename}")
         crop_debug_upload = cloudinary.uploader.upload(burn_crop_debug_path, public_id=f"pat_{patient.filename}")
         crop_clean_url = crop_clean_upload["secure_url"]
         crop_debug_url = crop_debug_upload["secure_url"]
-
-        # Clean up temp files
         
         os.remove(burn_crop_clean_path)
         os.remove(burn_crop_debug_path)
@@ -165,18 +149,14 @@ async def segment_burn_endpoint(reference: UploadFile = File(...), patient: Uplo
 @app.post("/classify-ecg")
 async def classify_ecg_endpoint(file: UploadFile = File(...)):
     model = joblib.load('voting_classifier.pkl')
-    # Load the model
 
     try:
-        # Save the uploaded file temporarily
         temp_file_path = f"temp_{file.filename}"
         with open(temp_file_path, "wb") as temp_file:
             temp_file.write(await file.read())
 
-        # Call the ECG classification function
         result = classify_ecg(temp_file_path, model, debug=True, is_pdf=True)
 
-        # Remove the temporary file
         os.remove(temp_file_path)
 
         return {"result": result}
@@ -220,14 +200,11 @@ async def diagnose_ecg(file: UploadFile = File(...)):
 
 
 def clean_warning_name(filename: str) -> str:
-    """
-    Remove frame index and underscores from filename base
-    E.g. "posture_001.png" -> "posture"
-    """
+
     name, _ = os.path.splitext(filename)
-    # Remove trailing underscore + digits
+    
     cleaned = re.sub(r'_\d+$', '', name)
-    # Remove all underscores in the name for description
+    
     cleaned_desc = cleaned.replace('_', ' ')
     return cleaned, cleaned_desc
 
@@ -253,27 +230,22 @@ async def process_video(file: UploadFile = File(...)):
                 if os.path.isfile(file_path):
                     os.remove(file_path)
 
-    # Save uploaded video file
     video_path = os.path.join(UPLOAD_DIR, file.filename)
     with open(video_path, "wb") as buffer:
         shutil.copyfileobj(file.file, buffer)
 
     print(f"\n[API] CPR Analysis Started on {video_path}")
 
-    # Prepare output paths for the analyzer
     video_output_path = os.path.join(OUTPUT_DIR, "Myoutput.mp4")
     plot_output_path = os.path.join(OUTPUT_DIR, "Myoutput.png")
 
-    # Initialize analyzer with input video and output paths
     start_time = time.time()
     analyzer = OfflineAnalyzer(video_path, video_output_path, plot_output_path, requested_fps=30)
 
-    # Run the analysis (choose your method)
     chunks = analyzer.run_analysis_video()
 
-    warnings = []  # Start empty list
+    warnings = []  
 
-    # Upload screenshots and build warnings list with descriptions and URLs
     if os.path.exists(SCREENSHOTS_DIR):
         for filename in os.listdir(SCREENSHOTS_DIR):
             if filename.lower().endswith(('.png', '.jpg', '.jpeg')):
@@ -333,97 +305,6 @@ async def process_video(file: UploadFile = File(...)):
     })
 
 
-# @app.websocket("/ws/process_video")
-# async def websocket_process_video(websocket: WebSocket):
-
-#     await websocket.accept()
-
-#     frame_buffer = []
-#     frame_limit = 50
-#     frame_size = (640, 480)  # Adjust if needed
-#     fps = 30  # Adjust if needed
-#     loop = asyncio.get_event_loop()
-
-#     # Progress reporting during analysis
-#     async def progress_callback(data):
-#         await websocket.send_text(json.dumps(data))
-
-#     def sync_callback(data):
-#         asyncio.run_coroutine_threadsafe(progress_callback(data), loop)
-
-#     def save_frames_to_video(frames, path):
-#         out = cv2.VideoWriter(path, cv2.VideoWriter_fourcc(*'mp4v'), fps, frame_size)
-#         for frame in frames:
-#             resized = cv2.resize(frame, frame_size)
-#             out.write(resized)
-#         out.release()
-
-#     def run_analysis_on_buffer(frames):
-#         try:
-#             tmp_path = "temp_video.mp4"
-#             save_frames_to_video(frames, tmp_path)
-
-#             # Notify: video saved
-#             asyncio.run_coroutine_threadsafe(
-#                 websocket.send_text(json.dumps({
-#                     "status": "info",
-#                     "message": "Video saved. Starting CPR analysis..."
-#                 })),
-#                 loop
-#             )
-
-#             # Run analysis
-#             analyzer = CPRAnalyzer(video_path=tmp_path)
-#             analyzer.run_analysis(progress_callback=sync_callback)
-
-#         except Exception as e:
-#             asyncio.run_coroutine_threadsafe(
-#                 websocket.send_text(json.dumps({"error": str(e)})),
-#                 loop
-#             )
-
-#     try:
-#         while True:
-#             data: bytes = await websocket.receive_bytes()
-#             np_arr = np.frombuffer(data, np.uint8)
-#             frame = cv2.imdecode(np_arr, cv2.IMREAD_COLOR)
-#             if frame is None:
-#                 continue
-
-#             frame_buffer.append(frame)
-#             print(f"Frame added to buffer: {len(frame_buffer)}")
-
-#             if len(frame_buffer) == frame_limit:
-#                 # Notify Flutter that we're switching to processing
-#                 await websocket.send_text(json.dumps({
-#                     "status": "ready",
-#                     "message": "Prepare Right CPR: First 150 frames received. Starting processing."
-#                 }))
-
-#                 # Copy and clear buffer
-#                 buffer_copy = frame_buffer[:]
-#                 frame_buffer.clear()
-
-#                 # Launch background processing
-#                 executor = concurrent.futures.ThreadPoolExecutor()
-#                 loop.run_in_executor(executor, run_analysis_on_buffer, buffer_copy)
-#             else:
-#                 # Tell Flutter to send the next frame
-#                 await websocket.send_text(json.dumps({
-#                     "status": "continue",
-#                     "message": f"Frame {len(frame_buffer)} received. Send next."
-#                 }))
-
-#     except WebSocketDisconnect:
-#         print("Client disconnected")
-
-#     except Exception as e:
-#         await websocket.send_text(json.dumps({"error": str(e)}))
-
-#     finally:
-#         cv2.destroyAllWindows()
-
-
 logger = logging.getLogger("cpr_logger")
 clients = set()
 analyzer_thread = None

svm_model.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:acf55c90d6a4067d1a83c0e15764fb47b0197a1f2cfa2abccb580260eff0316a
-size 337615
+oid sha256:2fefa4a94a2c6b329e1b5cfe0f61959489ed5d582835cf0c44a141eca980acbf
+size 503059