diff --git a/src/main/java/ai/nets/samj/communication/model/SAMModel.java b/src/main/java/ai/nets/samj/communication/model/SAMModel.java
index 7f8eab5..6bbea8f 100644
--- a/src/main/java/ai/nets/samj/communication/model/SAMModel.java
+++ b/src/main/java/ai/nets/samj/communication/model/SAMModel.java
@@ -175,9 +175,14 @@ public void setReturnOnlyBiggest(boolean onlyBiggest) {
 		this.onlyBiggest = onlyBiggest;
 	}
 
-	public List<Mask> processBatchOfPoints(List<long[]> points) throws IOException, RuntimeException, InterruptedException {
-		//return samj.processBathcOfPoints(points, !onlyBiggest);
-		return null;
+	public List<Mask> processBatchOfPoints(List<int[]> points) throws IOException, RuntimeException, InterruptedException {
+		return samj.processBatchOfPoints(points, !onlyBiggest);
+	}
+
+	public <T extends RealType<T> & NativeType<T>>
+	List<Mask> processBatchOfPrompts(List<int[]> points, List<Rectangle> rects, RandomAccessibleInterval<T> rai) 
+			throws IOException, RuntimeException, InterruptedException {
+		return samj.processBatchOfPrompts(points, rects, rai, !onlyBiggest);
 	}
 
 	/**
diff --git a/src/main/java/ai/nets/samj/models/AbstractSamJ.java b/src/main/java/ai/nets/samj/models/AbstractSamJ.java
index 192d5d7..db612e2 100644
--- a/src/main/java/ai/nets/samj/models/AbstractSamJ.java
+++ b/src/main/java/ai/nets/samj/models/AbstractSamJ.java
@@ -167,8 +167,8 @@ public interface DebugTextPrinter { void printText(String text); }
 	
 	protected abstract void cellSAM(List<int[]> grid, boolean returnAll);
 	
-	protected abstract <T extends RealType<T> & NativeType<T>>  void
-	processPromptsBatchWithSAM(List<int[]> points, List<Rectangle> rects, RandomAccessibleInterval<T> rai, boolean returnAll);
+	protected abstract void
+	processPromptsBatchWithSAM(List<int[]> points, List<Rectangle> rects, SharedMemoryArray shmArr, boolean returnAll);
 	
 	protected abstract void processPointsWithSAM(int nPoints, int nNegPoints, boolean returnAll);
 	
@@ -178,8 +178,6 @@ public interface DebugTextPrinter { void printText(String text); }
 	
 	protected abstract <T extends RealType<T> & NativeType<T>> void createEncodeImageScript();
 	
-	abstract protected void processMasksWithSam(SharedMemoryArray shmArr, boolean returnAll);
-	
 	protected abstract <T extends RealType<T> & NativeType<T>> void createSHMArray(RandomAccessibleInterval<T> imShared);
 
 	@Override
@@ -418,29 +416,49 @@ List<Mask> processBatchOfPrompts(List<int[]> points, List<Rectangle> rects, Rand
 	public <T extends RealType<T> & NativeType<T>>
 	List<Mask> processBatchOfPrompts(List<int[]> pointsList, List<Rectangle> rects, RandomAccessibleInterval<T> rai, boolean returnAll) 
 			throws IOException, RuntimeException, InterruptedException {
-		checkPrompts(pointsList, rects, rai);
 		if ((pointsList == null || pointsList.size() == 0) && (rects == null || rects.size() == 0) && (rai == null))
 			return new ArrayList<Mask>();
+		checkPrompts(pointsList, rects, rai);
 
+		// TODO adapt to reencoding for big images, ideally it should process points close together together
 		pointsList = adaptPointPrompts(pointsList);
+		// TODO adapt rect prompts
 		this.script = "";
-		processPromptsBatchWithSAM(pointsList, null, null, returnAll);
-		printScript(script, "Points and negative points inference");
-		List<Mask> polys = processAndRetrieveContours(null);
-		recalculatePolys(polys, encodeCoords);
-		return polys;
+		SharedMemoryArray maskShma = null;
+		if (rai != null)
+			maskShma = SharedMemoryArray.createSHMAFromRAI(rai, false, false);
+
+		try {
+			processPromptsBatchWithSAM(pointsList, rects, maskShma, returnAll);
+			printScript(script, "Batch of prompts inference");
+			List<Mask> polys = processAndRetrieveContours(null);
+			recalculatePolys(polys, encodeCoords);
+			return polys;
+		} catch (IOException | RuntimeException | InterruptedException ex) {
+			maskShma.close();
+			throw ex;
+		}
 	}
 	
 	private <T extends RealType<T> & NativeType<T>>
 	void checkPrompts(List<int[]> pointsList, List<Rectangle> rects, RandomAccessibleInterval<T> rai) {
+		long[] dims;
 		if ((pointsList == null || pointsList.size() == 0)
 				&& (rects == null || rects.size() == 0)
-				&& !(rai.getType() instanceof IntegerType)) 
+				&& rai != null && !(rai.getType() instanceof IntegerType)) {
 			throw new IllegalArgumentException("The mask provided should be of any integer type.");
-		else if ((pointsList == null || pointsList.size() == 0)
+		} else if ((pointsList == null || pointsList.size() == 0)
 				&& (rects == null || rects.size() == 0)
-				&& !(rai.getType() instanceof IntegerType)) {
-			throw new IllegalArgumentException("The mask provided should be of the same size as the image of interest.");
+				&& rai != null) {
+			dims = rai.dimensionsAsLongArray();
+			if ((dims.length == 2 || (dims.length == 3 && dims[2] == 1)) 
+					&& dims[1] == this.shma.getOriginalShape()[0] && dims[0] == this.shma.getOriginalShape()[1]) {
+				rai = Views.permute(rai, 0, 1);
+			} else if (dims[0] != this.shma.getOriginalShape()[0] && dims[1] != this.shma.getOriginalShape()[1]
+					|| (dims.length == 3 && dims[2] != 1) || dims.length > 3) {
+				throw new IllegalArgumentException("The provided mask should be a 2d image with just one channel of width "
+						+ this.shma.getOriginalShape()[1] + " and height " + this.shma.getOriginalShape()[0]);
+			}
 		}
 	}
 	
@@ -730,32 +748,9 @@ List<Mask> processMask(RandomAccessibleInterval<T> img)
 	 * @throws InterruptedException if the process in interrupted
 	 */
 	public <T extends RealType<T> & NativeType<T>>
-	List<Mask> processMask(RandomAccessibleInterval<T> img, boolean returnAll) 
+	List<Mask> processMask(RandomAccessibleInterval<T> rai, boolean returnAll) 
 				throws IOException, RuntimeException, InterruptedException {
-		long[] dims = img.dimensionsAsLongArray();
-		if ((dims.length == 2 || (dims.length == 3 && dims[2] == 1)) 
-				&& dims[1] == this.shma.getOriginalShape()[0] && dims[0] == this.shma.getOriginalShape()[1]) {
-			img = Views.permute(img, 0, 1);
-		} else if (dims[0] != this.shma.getOriginalShape()[0] && dims[1] != this.shma.getOriginalShape()[1]
-				|| (dims.length == 3 && dims[2] != 1) || dims.length > 3) {
-			throw new IllegalArgumentException("The provided mask should be a 2d image with just one channel of width "
-					+ this.shma.getOriginalShape()[1] + " and height " + this.shma.getOriginalShape()[0]);
-		}
-		SharedMemoryArray maskShma = SharedMemoryArray.createSHMAFromRAI(img, false, false);
-		try {
-			return processMask(maskShma, returnAll);
-		} catch (IOException | RuntimeException | InterruptedException ex) {
-			maskShma.close();
-			throw ex;
-		}
-	}
-	
-	private List<Mask> processMask(SharedMemoryArray shmArr, boolean returnAll) 
-				throws IOException, RuntimeException, InterruptedException {
-		this.script = "";
-		processMasksWithSam(shmArr, returnAll);
-		printScript(script, "Pre-computed mask inference");
-		List<Mask> polys = processAndRetrieveContours(null);
+		List<Mask> polys = processBatchOfPrompts(null, null, rai, returnAll);
 		debugPrinter.printText("processMask() obtained " + polys.size() + " polygons");
 		return polys;
 	}
diff --git a/src/main/java/ai/nets/samj/models/EfficientSamJ.java b/src/main/java/ai/nets/samj/models/EfficientSamJ.java
index 53db12f..18a2ecf 100644
--- a/src/main/java/ai/nets/samj/models/EfficientSamJ.java
+++ b/src/main/java/ai/nets/samj/models/EfficientSamJ.java
@@ -226,64 +226,6 @@ protected void createEncodeImageScript() {
 				+ "_ = predictor.get_image_embeddings(im[None, ...])" + System.lineSeparator();
 	}
 
-	@Override
-	protected void processMasksWithSam(SharedMemoryArray shmArr, boolean returnAll) {
-		String code = "";
-		code += "shm_mask = shared_memory.SharedMemory(name='" + shmArr.getNameForPython() + "')" + System.lineSeparator();
-		code += "mask = np.frombuffer(buffer=shm_mask.buf, dtype='" + shmArr.getOriginalDataType() + "').reshape([";
-		for (long l : shmArr.getOriginalShape()) 
-			code += l + ",";
-		code += "])" + System.lineSeparator();
-		code += "different_mask_vals = np.unique(mask)" + System.lineSeparator();
-		//code += "print(different_mask_vals)" + System.lineSeparator();
-		code += "cont_x = []" + System.lineSeparator();
-		code += "cont_y = []" + System.lineSeparator();
-		code += "rle_masks = []" + System.lineSeparator();
-		code += "for val in different_mask_vals:" + System.lineSeparator()
-			  + "  if val < 1:" + System.lineSeparator()
-			  + "    continue" + System.lineSeparator()
-			  + "  locations = np.where(mask == val)" + System.lineSeparator()
-			  + "  input_points_pos = np.zeros((locations[0].shape[0], 2))" + System.lineSeparator()
-			  + "  input_labels_pos = np.ones((locations[0].shape[0]))" + System.lineSeparator()
-			  + "  locations_neg = np.where((mask != val) & (mask != 0))" + System.lineSeparator()
-			  + "  input_points_neg = np.zeros((locations_neg[0].shape[0], 2))" + System.lineSeparator()
-			  + "  input_labels_neg = np.zeros((locations_neg[0].shape[0]))" + System.lineSeparator()
-			  + "  input_points_pos[:, 0] = locations[0]" + System.lineSeparator()
-			  + "  input_points_pos[:, 1] = locations[1]" + System.lineSeparator()
-			  + "  input_points_neg[:, 0] = locations_neg[0]" + System.lineSeparator()
-			  + "  input_points_neg[:, 1] = locations_neg[1]" + System.lineSeparator()
-			  + "  input_points = np.concatenate((input_points_pos.reshape(-1, 2), input_points_neg.reshape(-1, 2)), axis=0)" + System.lineSeparator()
-			  + "  input_label = np.concatenate((input_labels_pos, input_labels_neg * 0), axis=0)" + System.lineSeparator()
-			  + "  input_points = torch.reshape(torch.tensor(input_points), [1, 1, -1, 2])" + System.lineSeparator()
-			  + "  input_label = torch.reshape(torch.tensor(input_label), [1, 1, -1])" + System.lineSeparator()
-			  + "  predicted_logits, predicted_iou = predictor.predict_masks(predictor.encoded_images," + System.lineSeparator()
-			  + "    input_points," + System.lineSeparator()
-			  + "    input_label," + System.lineSeparator()
-			  + "    multimask_output=True," + System.lineSeparator()
-			  + "    input_h=input_h," + System.lineSeparator()
-			  + "    input_w=input_w," + System.lineSeparator()
-			  + "    output_h=input_h," + System.lineSeparator()
-			  + "    output_w=input_w,)" + System.lineSeparator()
-			  //+ "np.save('/temp/aa.npy', mask)" + System.lineSeparator()
-			  + "  sorted_ids = torch.argsort(predicted_iou, dim=-1, descending=True)" + System.lineSeparator()
-			  + "  predicted_iou = torch.take_along_dim(predicted_iou, sorted_ids, dim=2)" + System.lineSeparator()
-			  + "  predicted_logits = torch.take_along_dim(predicted_logits, sorted_ids[..., None, None], dim=2)" + System.lineSeparator()
-			  + "  mask_val = torch.ge(predicted_logits[0, 0, 0, :, :], 0).cpu().detach().numpy()" + System.lineSeparator()
-				+ (this.isIJROIManager ? "mask_val[1:, 1:] += mask_val[:-1, :-1]" : "") + System.lineSeparator()
-			  + "  cont_x_val,cont_y_val,rle_val = get_polygons_from_binary_mask(mask_val, only_biggest=" + (!returnAll ? "True" : "False") + ")" + System.lineSeparator()
-			  + "  cont_x += cont_x_val" + System.lineSeparator()
-			  + "  cont_y += cont_y_val" + System.lineSeparator()
-			  + "  rle_masks += rle_val" + System.lineSeparator()
-			  + "task.update('all contours traced')" + System.lineSeparator()
-			  + "task.outputs['contours_x'] = cont_x" + System.lineSeparator()
-			  + "task.outputs['contours_y'] = cont_y" + System.lineSeparator()
-			  + "task.outputs['rle'] = rle_masks" + System.lineSeparator();
-		code += "mask = 0" + System.lineSeparator();
-		code += "shm_mask.close()" + System.lineSeparator();
-		code += "shm_mask.unlink()" + System.lineSeparator();
-		this.script = code;
-	}
-
 	@Override
 	protected void processPointsWithSAM(int nPoints, int nNegPoints, boolean returnAll) {
 		String code = "" + System.lineSeparator()
@@ -445,9 +387,61 @@ public String deleteEncodingScript(String encodingName) {
 	}
 
 	@Override
-	protected <T extends RealType<T> & NativeType<T>> void processPromptsBatchWithSAM(List<int[]> points,
-			List<Rectangle> rects, RandomAccessibleInterval<T> rai, boolean returnAll) {
-		// TODO Auto-generated method stub
-		
+	protected void processPromptsBatchWithSAM(List<int[]> points, List<Rectangle> rects, SharedMemoryArray shmArr,
+			boolean returnAll) {
+		String code = "";
+		code += "shm_mask = shared_memory.SharedMemory(name='" + shmArr.getNameForPython() + "')" + System.lineSeparator();
+		code += "mask = np.frombuffer(buffer=shm_mask.buf, dtype='" + shmArr.getOriginalDataType() + "').reshape([";
+		for (long l : shmArr.getOriginalShape()) 
+			code += l + ",";
+		code += "])" + System.lineSeparator();
+		code += "different_mask_vals = np.unique(mask)" + System.lineSeparator();
+		//code += "print(different_mask_vals)" + System.lineSeparator();
+		code += "cont_x = []" + System.lineSeparator();
+		code += "cont_y = []" + System.lineSeparator();
+		code += "rle_masks = []" + System.lineSeparator();
+		code += "for val in different_mask_vals:" + System.lineSeparator()
+			  + "  if val < 1:" + System.lineSeparator()
+			  + "    continue" + System.lineSeparator()
+			  + "  locations = np.where(mask == val)" + System.lineSeparator()
+			  + "  input_points_pos = np.zeros((locations[0].shape[0], 2))" + System.lineSeparator()
+			  + "  input_labels_pos = np.ones((locations[0].shape[0]))" + System.lineSeparator()
+			  + "  locations_neg = np.where((mask != val) & (mask != 0))" + System.lineSeparator()
+			  + "  input_points_neg = np.zeros((locations_neg[0].shape[0], 2))" + System.lineSeparator()
+			  + "  input_labels_neg = np.zeros((locations_neg[0].shape[0]))" + System.lineSeparator()
+			  + "  input_points_pos[:, 0] = locations[0]" + System.lineSeparator()
+			  + "  input_points_pos[:, 1] = locations[1]" + System.lineSeparator()
+			  + "  input_points_neg[:, 0] = locations_neg[0]" + System.lineSeparator()
+			  + "  input_points_neg[:, 1] = locations_neg[1]" + System.lineSeparator()
+			  + "  input_points = np.concatenate((input_points_pos.reshape(-1, 2), input_points_neg.reshape(-1, 2)), axis=0)" + System.lineSeparator()
+			  + "  input_label = np.concatenate((input_labels_pos, input_labels_neg * 0), axis=0)" + System.lineSeparator()
+			  + "  input_points = torch.reshape(torch.tensor(input_points), [1, 1, -1, 2])" + System.lineSeparator()
+			  + "  input_label = torch.reshape(torch.tensor(input_label), [1, 1, -1])" + System.lineSeparator()
+			  + "  predicted_logits, predicted_iou = predictor.predict_masks(predictor.encoded_images," + System.lineSeparator()
+			  + "    input_points," + System.lineSeparator()
+			  + "    input_label," + System.lineSeparator()
+			  + "    multimask_output=True," + System.lineSeparator()
+			  + "    input_h=input_h," + System.lineSeparator()
+			  + "    input_w=input_w," + System.lineSeparator()
+			  + "    output_h=input_h," + System.lineSeparator()
+			  + "    output_w=input_w,)" + System.lineSeparator()
+			  //+ "np.save('/temp/aa.npy', mask)" + System.lineSeparator()
+			  + "  sorted_ids = torch.argsort(predicted_iou, dim=-1, descending=True)" + System.lineSeparator()
+			  + "  predicted_iou = torch.take_along_dim(predicted_iou, sorted_ids, dim=2)" + System.lineSeparator()
+			  + "  predicted_logits = torch.take_along_dim(predicted_logits, sorted_ids[..., None, None], dim=2)" + System.lineSeparator()
+			  + "  mask_val = torch.ge(predicted_logits[0, 0, 0, :, :], 0).cpu().detach().numpy()" + System.lineSeparator()
+				+ (this.isIJROIManager ? "mask_val[1:, 1:] += mask_val[:-1, :-1]" : "") + System.lineSeparator()
+			  + "  cont_x_val,cont_y_val,rle_val = get_polygons_from_binary_mask(mask_val, only_biggest=" + (!returnAll ? "True" : "False") + ")" + System.lineSeparator()
+			  + "  cont_x += cont_x_val" + System.lineSeparator()
+			  + "  cont_y += cont_y_val" + System.lineSeparator()
+			  + "  rle_masks += rle_val" + System.lineSeparator()
+			  + "task.update('all contours traced')" + System.lineSeparator()
+			  + "task.outputs['contours_x'] = cont_x" + System.lineSeparator()
+			  + "task.outputs['contours_y'] = cont_y" + System.lineSeparator()
+			  + "task.outputs['rle'] = rle_masks" + System.lineSeparator();
+		code += "mask = 0" + System.lineSeparator();
+		code += "shm_mask.close()" + System.lineSeparator();
+		code += "shm_mask.unlink()" + System.lineSeparator();
+		this.script = code;
 	}
 }
diff --git a/src/main/java/ai/nets/samj/models/EfficientViTSamJ.java b/src/main/java/ai/nets/samj/models/EfficientViTSamJ.java
index 0b191da..0c63fcd 100644
--- a/src/main/java/ai/nets/samj/models/EfficientViTSamJ.java
+++ b/src/main/java/ai/nets/samj/models/EfficientViTSamJ.java
@@ -333,54 +333,6 @@ protected <T extends RealType<T> & NativeType<T>> void createSHMArray(RandomAcce
 		adaptImageToModel(imageToBeSent, shma.getSharedRAI());
 	}
 
-	@Override
-	protected void processMasksWithSam(SharedMemoryArray shmArr, boolean returnAll) {
-		String code = "";
-		code += "shm_mask = shared_memory.SharedMemory(name='" + shmArr.getNameForPython() + "')" + System.lineSeparator();
-		code += "mask = np.frombuffer(buffer=shm_mask.buf, dtype='" + shmArr.getOriginalDataType() + "').reshape([";
-		for (long l : shmArr.getOriginalShape()) 
-			code += l + ",";
-		code += "])" + System.lineSeparator();
-		code += "different_mask_vals = np.unique(mask)" + System.lineSeparator();
-		code += "contours_x = []" + System.lineSeparator();
-		code += "contours_y = []" + System.lineSeparator();
-		code += "rle_masks = []" + System.lineSeparator();
-		code += "for val in different_mask_vals:" + System.lineSeparator()
-			  + "  if val < 1:" + System.lineSeparator()
-			  + "    continue" + System.lineSeparator()
-			  + "  locations = np.where(mask == val)" + System.lineSeparator()
-			  + "  input_points_pos = np.zeros((locations[0].shape[0], 2))" + System.lineSeparator()
-			  + "  input_labels_pos = np.ones((locations[0].shape[0]))" + System.lineSeparator()
-			  + "  locations_neg = np.where((mask != val) & (mask != 0))" + System.lineSeparator()
-			  + "  input_points_neg = np.zeros((locations_neg[0].shape[0], 2))" + System.lineSeparator()
-			  + "  input_labels_neg = np.zeros((locations_neg[0].shape[0]))" + System.lineSeparator()
-			  + "  input_points_pos[:, 0] = locations[0]" + System.lineSeparator()
-			  + "  input_points_pos[:, 1] = locations[1]" + System.lineSeparator()
-			  + "  input_points_neg[:, 0] = locations_neg[0]" + System.lineSeparator()
-			  + "  input_points_neg[:, 1] = locations_neg[1]" + System.lineSeparator()
-			  + "  input_points = np.concatenate((input_points_pos.reshape(-1, 2), input_points_neg.reshape(-1, 2)), axis=0)" + System.lineSeparator()
-			  + "  input_label = np.concatenate((input_labels_pos, input_labels_neg), axis=0)" + System.lineSeparator()
-			  + "  mask_val, _, _ = predictor.predict(" + System.lineSeparator()
-			  + "    point_coords=input_points," + System.lineSeparator()
-			  + "    point_labels=input_label," + System.lineSeparator()
-			  + "    multimask_output=False," + System.lineSeparator()
-			  + "    box=None,)" + System.lineSeparator()
-			  //+ "np.save('/temp/aa.npy', mask)" + System.lineSeparator()
-				+ (this.isIJROIManager ? "mask_val[0, 1:, 1:] += mask_val[0, :-1, :-1]" : "") + System.lineSeparator()
-			  + "  contours_x_val,contours_y_val,rle_val = get_polygons_from_binary_mask(mask_val[0], only_biggest=" + (!returnAll ? "True" : "False") + ")" + System.lineSeparator()
-			  + "  contours_x += contours_x_val" + System.lineSeparator()
-			  + "  contours_y += contours_y_val" + System.lineSeparator()
-			  + "  rle_masks += rle_val" + System.lineSeparator()
-			  + "task.update('all contours traced')" + System.lineSeparator()
-			  + "task.outputs['contours_x'] = contours_x" + System.lineSeparator()
-			  + "task.outputs['contours_y'] = contours_y" + System.lineSeparator()
-			  + "task.outputs['rle'] = rle_masks" + System.lineSeparator();
-		code += "mask = 0" + System.lineSeparator();
-		code += "shm_mask.close()" + System.lineSeparator();
-		code += "shm_mask.unlink()" + System.lineSeparator();
-		this.script = code;
-	}
-
 	@Override
 	protected void processPointsWithSAM(int nPoints, int nNegPoints, boolean returnAll) {
 		String code = "" + System.lineSeparator()
@@ -510,9 +462,51 @@ public String deleteEncodingScript(String encodingName) {
 	}
 
 	@Override
-	protected <T extends RealType<T> & NativeType<T>> void processPromptsBatchWithSAM(List<int[]> points,
-			List<Rectangle> rects, RandomAccessibleInterval<T> rai, boolean returnAll) {
-		// TODO Auto-generated method stub
-		
+	protected void processPromptsBatchWithSAM(List<int[]> points, List<Rectangle> rects, SharedMemoryArray shmArr,
+			boolean returnAll) {
+		String code = "";
+		code += "shm_mask = shared_memory.SharedMemory(name='" + shmArr.getNameForPython() + "')" + System.lineSeparator();
+		code += "mask = np.frombuffer(buffer=shm_mask.buf, dtype='" + shmArr.getOriginalDataType() + "').reshape([";
+		for (long l : shmArr.getOriginalShape()) 
+			code += l + ",";
+		code += "])" + System.lineSeparator();
+		code += "different_mask_vals = np.unique(mask)" + System.lineSeparator();
+		code += "contours_x = []" + System.lineSeparator();
+		code += "contours_y = []" + System.lineSeparator();
+		code += "rle_masks = []" + System.lineSeparator();
+		code += "for val in different_mask_vals:" + System.lineSeparator()
+			  + "  if val < 1:" + System.lineSeparator()
+			  + "    continue" + System.lineSeparator()
+			  + "  locations = np.where(mask == val)" + System.lineSeparator()
+			  + "  input_points_pos = np.zeros((locations[0].shape[0], 2))" + System.lineSeparator()
+			  + "  input_labels_pos = np.ones((locations[0].shape[0]))" + System.lineSeparator()
+			  + "  locations_neg = np.where((mask != val) & (mask != 0))" + System.lineSeparator()
+			  + "  input_points_neg = np.zeros((locations_neg[0].shape[0], 2))" + System.lineSeparator()
+			  + "  input_labels_neg = np.zeros((locations_neg[0].shape[0]))" + System.lineSeparator()
+			  + "  input_points_pos[:, 0] = locations[0]" + System.lineSeparator()
+			  + "  input_points_pos[:, 1] = locations[1]" + System.lineSeparator()
+			  + "  input_points_neg[:, 0] = locations_neg[0]" + System.lineSeparator()
+			  + "  input_points_neg[:, 1] = locations_neg[1]" + System.lineSeparator()
+			  + "  input_points = np.concatenate((input_points_pos.reshape(-1, 2), input_points_neg.reshape(-1, 2)), axis=0)" + System.lineSeparator()
+			  + "  input_label = np.concatenate((input_labels_pos, input_labels_neg), axis=0)" + System.lineSeparator()
+			  + "  mask_val, _, _ = predictor.predict(" + System.lineSeparator()
+			  + "    point_coords=input_points," + System.lineSeparator()
+			  + "    point_labels=input_label," + System.lineSeparator()
+			  + "    multimask_output=False," + System.lineSeparator()
+			  + "    box=None,)" + System.lineSeparator()
+			  //+ "np.save('/temp/aa.npy', mask)" + System.lineSeparator()
+				+ (this.isIJROIManager ? "mask_val[0, 1:, 1:] += mask_val[0, :-1, :-1]" : "") + System.lineSeparator()
+			  + "  contours_x_val,contours_y_val,rle_val = get_polygons_from_binary_mask(mask_val[0], only_biggest=" + (!returnAll ? "True" : "False") + ")" + System.lineSeparator()
+			  + "  contours_x += contours_x_val" + System.lineSeparator()
+			  + "  contours_y += contours_y_val" + System.lineSeparator()
+			  + "  rle_masks += rle_val" + System.lineSeparator()
+			  + "task.update('all contours traced')" + System.lineSeparator()
+			  + "task.outputs['contours_x'] = contours_x" + System.lineSeparator()
+			  + "task.outputs['contours_y'] = contours_y" + System.lineSeparator()
+			  + "task.outputs['rle'] = rle_masks" + System.lineSeparator();
+		code += "mask = 0" + System.lineSeparator();
+		code += "shm_mask.close()" + System.lineSeparator();
+		code += "shm_mask.unlink()" + System.lineSeparator();
+		this.script = code;
 	}
 }
diff --git a/src/main/java/ai/nets/samj/models/Sam2.java b/src/main/java/ai/nets/samj/models/Sam2.java
index 9b1c210..ecdcd4a 100644
--- a/src/main/java/ai/nets/samj/models/Sam2.java
+++ b/src/main/java/ai/nets/samj/models/Sam2.java
@@ -316,64 +316,6 @@ protected <T extends RealType<T> & NativeType<T>> void createSHMArray(RandomAcce
 		adaptImageToModel(imageToBeSent, shma.getSharedRAI());
 	}
 
-	@Override
-	protected void processMasksWithSam(SharedMemoryArray shmArr, boolean returnAll) {
-		String code = "";
-		code += "shm_mask = shared_memory.SharedMemory(name='" + shmArr.getNameForPython() + "')" + System.lineSeparator();
-		code += "mask = np.frombuffer(buffer=shm_mask.buf, dtype='" + shmArr.getOriginalDataType() + "').reshape([";
-		for (long l : shmArr.getOriginalShape()) 
-			code += l + ",";
-		code += "])" + System.lineSeparator();
-		code += ""
-				+ "point_prompts = []" + System.lineSeparator()
-				+ "point_labels = []" + System.lineSeparator()
-				+ "labeled_array, num_features = label(mask)" + System.lineSeparator()
-				+ "contours_x = []" + System.lineSeparator()
-				+ "contours_y = []" + System.lineSeparator()
-				+ "rle_masks = []" + System.lineSeparator()
-				// TODO right now is geetting the mask after each prompt
-				// TODO test processing first every prompt and then getting the masks
-				+ "" + System.lineSeparator()
-				+ "for n_feat in range(num_features):" + System.lineSeparator()
-				+ "  inds = np.where(labeled_array == n_feat)" + System.lineSeparator()
-				+ "  n_points = np.min([3, inds[0].shape[0]])" + System.lineSeparator()
-				+ "  random_positions = np.random.choice(inds[0].shape[0], n_points, replace=False)" + System.lineSeparator()
-				+ "  for pp in range(n_points):" + System.lineSeparator()
-				+ "    point_prompts += [[inds[0][random_positions[pp]], inds[1][random_positions[pp]]]]" + System.lineSeparator()
-				+ "    point_labels += [n_feat]" + System.lineSeparator()
-				+ "" + System.lineSeparator()
-				+ "  mask, _, _ = predictor.predict(" + System.lineSeparator()
-				+ "    point_coords=point_prompts," + System.lineSeparator()
-				+ "    point_labels=point_labels," + System.lineSeparator()
-				+ "    multimask_output=False," + System.lineSeparator()
-				+ "    box=None,)" + System.lineSeparator()
-				+ "" + System.lineSeparator()
-				+ "" + System.lineSeparator()
-				+ "" + System.lineSeparator()
-				// TODO + "for b in range(num_features):" + System.lineSeparator()
-				// TODO + "  mm = mask[b]" + System.lineSeparator()
-				+ (this.isIJROIManager ? "  mask += mask[0, :-1, :-1]" : "") + System.lineSeparator()
-				+ "  c_x, c_y, r_m = get_polygons_from_binary_mask(mask[0], only_biggest=" + (!returnAll ? "True" : "False") + ")" + System.lineSeparator()
-				+ "  contours_x += c_x" + System.lineSeparator()
-				+ "  contours_y += c_Y" + System.lineSeparator()
-				+ "  rle_masks += r_m" + System.lineSeparator()
-				+ "" + System.lineSeparator()
-				+ "" + System.lineSeparator()
-				// TODO remove + "import matplotlib.pyplot as plt" + System.lineSeparator()
-				// TODO remove + "plt.imsave('/tmp/aa.jpg', mask[0], cmap='gray')" + System.lineSeparator()
-				//+ (this.isIJROIManager ? "mask[0, :, 1:] += mask[0, :, :-1]" : "") + System.lineSeparator()
-				//+ "np.save('/home/carlos/git/aa.npy', mask)" + System.lineSeparator()
-				+ "contours_x, contours_y, rle_masks = get_polygons_from_binary_mask(mask[0], only_biggest=" + (!returnAll ? "True" : "False") + ")" + System.lineSeparator()
-				+ "task.update('all contours traced')" + System.lineSeparator()
-				+ "task.outputs['contours_x'] = contours_x" + System.lineSeparator()
-				+ "task.outputs['contours_y'] = contours_y" + System.lineSeparator()
-				+ "task.outputs['rle'] = rle_masks" + System.lineSeparator();
-		code += "mask = 0" + System.lineSeparator();
-		code += "shm_mask.close()" + System.lineSeparator();
-		code += "shm_mask.unlink()" + System.lineSeparator();
-		this.script = code;
-	}
-
 	@Override
 	protected void processPointsWithSAM(int nPoints, int nNegPoints, boolean returnAll) {
 		String code = "" + System.lineSeparator()
@@ -504,9 +446,82 @@ public String deleteEncodingScript(String encodingName) {
 	}
 
 	@Override
-	protected <T extends RealType<T> & NativeType<T>> void processPromptsBatchWithSAM(List<int[]> points,
-			List<Rectangle> rects, RandomAccessibleInterval<T> rai, boolean returnAll) {
-		// TODO Auto-generated method stub
-		
+	protected void processPromptsBatchWithSAM(List<int[]> points,
+			List<Rectangle> rects, SharedMemoryArray shmArr, boolean returnAll) {
+		String code = "";
+		code += "shm_mask = shared_memory.SharedMemory(name='" + shmArr.getNameForPython() + "')" + System.lineSeparator();
+		code += "mask = np.frombuffer(buffer=shm_mask.buf, dtype='" + shmArr.getOriginalDataType() + "').reshape([";
+		for (long l : shmArr.getOriginalShape()) 
+			code += l + ",";
+		code += "])" + System.lineSeparator();
+		code += ""
+				+ "point_prompts = []" + System.lineSeparator()
+				+ "point_labels = []" + System.lineSeparator()
+				+ "mask_prompts = []" + System.lineSeparator()
+				+ "mask_labels = []" + System.lineSeparator()
+				+ "labeled_array, num_features = label(mask)" + System.lineSeparator()
+				+ "contours_x = []" + System.lineSeparator()
+				+ "contours_y = []" + System.lineSeparator()
+				+ "rle_masks = []" + System.lineSeparator()
+				// TODO right now is geetting the mask after each prompt
+				// TODO test processing first every prompt and then getting the masks
+				+ "" + System.lineSeparator()
+				+ "for n_feat in range(num_features):" + System.lineSeparator()
+				+ "  inds = np.where(labeled_array == n_feat)" + System.lineSeparator()
+				+ "  n_points = np.min([3, inds[0].shape[0]])" + System.lineSeparator()
+				+ "  random_positions = np.random.choice(inds[0].shape[0], n_points, replace=False)" + System.lineSeparator()
+				+ "  for pp in range(n_points):" + System.lineSeparator()
+				+ "    point_prompts += [[inds[0][random_positions[pp]], inds[1][random_positions[pp]]]]" + System.lineSeparator()
+				+ "    point_labels += [n_feat]" + System.lineSeparator()
+				+ "  mask, _, _ = predictor.predict(" + System.lineSeparator()
+				+ "    point_coords=point_prompts," + System.lineSeparator()
+				+ "    point_labels=point_labels," + System.lineSeparator()
+				+ "    multimask_output=False," + System.lineSeparator()
+				+ "    box=None,)" + System.lineSeparator()
+				+ (this.isIJROIManager ? "  mask += mask[0, :-1, :-1]" : "") + System.lineSeparator()
+				+ "  c_x, c_y, r_m = get_polygons_from_binary_mask(mask[0], only_biggest=" + (!returnAll ? "True" : "False") + ")" + System.lineSeparator()
+				+ "  contours_x += c_x" + System.lineSeparator()
+				+ "  contours_y += c_Y" + System.lineSeparator()
+				+ "  rle_masks += r_m" + System.lineSeparator()
+				+ "" + System.lineSeparator()
+				+ "for p_prompt in range(len(point_prompts)):" + System.lineSeparator()
+				+ "  mask, _, _ = predictor.predict(" + System.lineSeparator()
+				+ "    point_coords=np.array(p_prompt).reshape(1, 2)," + System.lineSeparator()
+				+ "    point_labels=np.array([1])," + System.lineSeparator()
+				+ "    multimask_output=False," + System.lineSeparator()
+				+ "    box=None,)" + System.lineSeparator()
+				+ (this.isIJROIManager ? "  mask += mask[0, :-1, :-1]" : "") + System.lineSeparator()
+				+ "  c_x, c_y, r_m = get_polygons_from_binary_mask(mask[0], only_biggest=" + (!returnAll ? "True" : "False") + ")" + System.lineSeparator()
+				+ "  contours_x += c_x" + System.lineSeparator()
+				+ "  contours_y += c_Y" + System.lineSeparator()
+				+ "  rle_masks += r_m" + System.lineSeparator()
+				+ "" + System.lineSeparator()
+				+ "" + System.lineSeparator()
+				+ "for rect_prompt in range(len(rect_prompts)):" + System.lineSeparator()
+				+ "  input_box = np.array([[input_box[0], input_box[1]], [input_box[2], input_box[3]]])" + System.lineSeparator()
+				+ "  mask, _, _ = predictor.predict(" + System.lineSeparator()
+				+ "    point_coords=None," + System.lineSeparator()
+				+ "    point_labels=np.array([1])," + System.lineSeparator()
+				+ "    multimask_output=False," + System.lineSeparator()
+				+ "    box=input_box,)" + System.lineSeparator()
+				+ (this.isIJROIManager ? "  mask += mask[0, :-1, :-1]" : "") + System.lineSeparator()
+				+ "  c_x, c_y, r_m = get_polygons_from_binary_mask(mask[0], only_biggest=" + (!returnAll ? "True" : "False") + ")" + System.lineSeparator()
+				+ "  contours_x += c_x" + System.lineSeparator()
+				+ "  contours_y += c_Y" + System.lineSeparator()
+				+ "  rle_masks += r_m" + System.lineSeparator()
+				+ "" + System.lineSeparator()
+				+ "" + System.lineSeparator()
+				// TODO remove + "import matplotlib.pyplot as plt" + System.lineSeparator()
+				// TODO remove + "plt.imsave('/tmp/aa.jpg', mask[0], cmap='gray')" + System.lineSeparator()
+				//+ (this.isIJROIManager ? "mask[0, :, 1:] += mask[0, :, :-1]" : "") + System.lineSeparator()
+				//+ "np.save('/home/carlos/git/aa.npy', mask)" + System.lineSeparator()
+				+ "task.update('all contours traced')" + System.lineSeparator()
+				+ "task.outputs['contours_x'] = contours_x" + System.lineSeparator()
+				+ "task.outputs['contours_y'] = contours_y" + System.lineSeparator()
+				+ "task.outputs['rle'] = rle_masks" + System.lineSeparator();
+		code += "mask = 0" + System.lineSeparator();
+		code += "shm_mask.close()" + System.lineSeparator();
+		code += "shm_mask.unlink()" + System.lineSeparator();
+		this.script = code;
 	}
 }