CLAUDE: Derive DNN temporal depth from ONNX input shape (8/9 swap)

CuasDnnInfer reads N (temporal frame count) from the model's input
shape [B,N,H,W] and exposes getNFrames(); the constructor arg is now
only a fallback hint. CuasDetectRT uses dnn.getNFrames() instead of the
hardcoded 8, so an 8- vs 9-frame ONNX swaps purely by changing
curt_dnn_model (N follows automatically).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

src/main/java/com/elphel/imagej/cuas/rt/CuasDetectRT.java

@@ -1162,15 +1162,15 @@ public class CuasDetectRT {
    		}                                                                                  // By Claude on 06/11/2026
    		// DNN front-end (-DNN): if curt_dnn_model is set, run the trained FCN per-pixel       // By Claude on 06/13/2026
    		// (stride-1, matching training) over the ROI -> (Vx,Vy,s) field = softmax(vel)*s,
-   		// same shape as the C5P output, saved as -DNN-RECT / -DNN-HYPER-RECT. N=8 frames
-   		// (training). Reuses curt_c5_levels for level gating. Recurrent feed is the next step
+   		// same shape as the C5P output, saved as -DNN-RECT / -DNN-HYPER-RECT. N (temporal
+   		// depth, 8 or 9) is read from the loaded ONNX. Reuses curt_c5_levels for level gating. Recurrent feed is the next step
    		// (the field is [0,1]-scaled, unlike the C5P matched-filter response - needs rescale).
    		if (!clt_parameters.imp.curt_dnn_model.isEmpty() && (curt_save_select != null)) {     // By Claude on 06/13/2026
-   			final int N_dnn = 8;                                                              // By Claude on 06/13/2026
    			final int vr_dnn = clt_parameters.imp.curt_vel_radius;                            // By Claude on 06/13/2026
    			final int dnn_stride = Math.max(1, clt_parameters.imp.curt_dnn_stride);           // 1 = every slice (testing), 4 = production 50% overlap // By Claude on 06/14/2026
    			try {                                                                             // By Claude on 06/13/2026
-   				CuasDnnInfer dnn = new CuasDnnInfer(clt_parameters.imp.curt_dnn_model, N_dnn); // By Claude on 06/13/2026
+   				CuasDnnInfer dnn = new CuasDnnInfer(clt_parameters.imp.curt_dnn_model, 8);    // 8 = fallback hint only // By Claude on 06/13/2026
+   				final int N_dnn = dnn.getNFrames();                                           // 8 or 9, read from the loaded ONNX input shape -> 8/9 models swap at runtime // By Claude on 06/15/2026
    				for (int nlev = 0; nlev < pyramid_levels; nlev++) {                           // By Claude on 06/13/2026
    					if (!c5LevelSelected(c5_levels, nlev)) continue;                          // level gating (curt_c5_levels) // By Claude on 06/13/2026
    					double [][] framesD = dpixels_pyramid[nlev];                              // By Claude on 06/13/2026

src/main/java/com/elphel/imagej/cuas/rt/CuasDnnInfer.java

@@ -34,16 +34,29 @@ public class CuasDnnInfer implements AutoCloseable {
 
     public CuasDnnInfer(String modelSpec, int nframes) throws Exception {
         String local = resolveModel(modelSpec);
-        this.nframes = nframes;
         this.env = OrtEnvironment.getEnvironment();
         OrtSession.SessionOptions opts = new OrtSession.SessionOptions();
         // CPU EP by default. GPU later: opts.addCUDA(deviceId) once cuDNN 9 is present.
         this.session = env.createSession(local, opts);
         this.inputName = session.getInputNames().iterator().next();  // "frames"
+        // Derive temporal depth (N frames) from the model's input shape [B, N, H, W] so an
+        // 8- vs 9-frame model swaps at runtime; the constructor arg is only a fallback hint.
+        // By Claude on 06/15/2026
+        int modelN = nframes;
+        try {
+            ai.onnxruntime.NodeInfo ni = session.getInputInfo().get(inputName);
+            long[] shape = ((ai.onnxruntime.TensorInfo) ni.getInfo()).getShape();
+            if ((shape != null) && (shape.length >= 2) && (shape[1] > 0)) modelN = (int) shape[1];
+        } catch (Exception e) { /* keep the hint value */ }
+        this.nframes = modelN;
         System.out.println("CuasDnnInfer: loaded " + local + "  input=" + inputName
-                + " outputs=" + session.getOutputNames());
+                + " outputs=" + session.getOutputNames() + " nframes=" + this.nframes
+                + ((modelN != nframes) ? " (hint was " + nframes + ")" : ""));
     }
 
+    /** Temporal depth the loaded model expects (N frames), read from its input shape. By Claude on 06/15/2026 */
+    public int getNFrames() { return nframes; }
+
     /** Run one patch. frames = [N][H][W] (newest first). Returns the raw output channels
      *  [outCh] for the center output pixel: [0]=det logit, [1..V*V]=vel logits, last 2 = offset. */
     public float[] inferRaw(float[][][] frames) throws Exception {