feat: incremental assembly for large panoramas

Replace parallel chunk strategy with sequential incremental assembly:
1. Start with first 20 images, full cpfind + optimize
2. Add 5 new images at a time with 5-image bridge to existing set
3. cpfind only on bridge+new group (small, fast)
4. Remap CPs to global indices and accumulate

This ensures spatial coherence: each new batch is constrained by
the already-stable panorama, preventing orientation flips that
occurred with the parallel chunk approach.

Images are sorted by azimuth, so consecutive indices = spatial
neighbors (guaranteed contiguity).

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

pipeline/panorama_pipeline.py

@@ -329,77 +329,116 @@ def run_pipeline(work_dir, clahe_images, original_images, azs, els,
         timings["cpclean"] = time.time() - t0
 
     else:
-        # Large panorama: chunked cpfind strategy
-        # Split into overlapping chunks, cpfind each, merge CPs
-        step = chunk_size - chunk_overlap
-        chunks = []
-        i = 0
-        while i < n_images:
-            end = min(i + chunk_size, n_images)
-            chunks.append((i, end))
-            if end >= n_images:
-                break
-            i += step
+        # Large panorama: incremental assembly strategy
+        # Images are sorted by azimuth — consecutive indices = spatial neighbors.
+        # We build the panorama progressively:
+        #   1. Start with first initial_size images, full cpfind + optimize
+        #   2. Add batch_size new images at a time
+        #   3. cpfind only between new images and the tail of existing set
+        #   4. Optimize incrementally (existing images constrain new ones)
+        initial_size = chunk_size  # first group size
+        batch_size = chunk_overlap  # images to add per iteration
+        bridge_size = 5  # how many existing images to overlap with new ones
 
-        print(f"  Chunked cpfind: {n_images} images -> {len(chunks)} chunks of ~{chunk_size} "
-              f"(overlap={chunk_overlap})", flush=True)
+        print(f"  Incremental assembly: {n_images} images, "
+              f"initial={initial_size}, batch={batch_size}, bridge={bridge_size}",
+              flush=True)
 
         all_cp_lines = []
         t0_total = time.time()
 
-        for chunk_idx, (start, end) in enumerate(chunks):
-            chunk_clahe = clahe_images[start:end]
-            chunk_azs = azs[start:end]
-            chunk_els = els[start:end]
+        # Phase 1: initial group — full cpfind
+        init_end = min(initial_size, n_images)
+        init_pto = os.path.join(work_dir, f"{output_name}_init.pto")
+        build_pto(init_pto, clahe_images[:init_end],
+                  azs[:init_end], els[:init_end], img_w, img_h, fov)
 
-            # Build chunk PTO
-            chunk_pto = os.path.join(work_dir, f"{output_name}_chunk{chunk_idx}.pto")
-            build_pto(chunk_pto, chunk_clahe, chunk_azs, chunk_els, img_w, img_h, fov)
-
-            # cpfind on chunk
+        print(f"  Phase 1: cpfind on images 0-{init_end-1} ({init_end} images)...",
+              flush=True)
         t0 = time.time()
         r = run_hugin("cpfind",
             f"--multirow --celeste "
             f"--sieve1width 20 --sieve1height 20 --sieve1size 1000 "
             f"--sieve2width 10 --sieve2height 10 --sieve2size 5 "
             f"--minmatches 3 --ransaciter 2000 --ransacdist 50 "
-                f'-o "{chunk_pto}" "{chunk_pto}"',
+            f'-o "{init_pto}" "{init_pto}"',
             cwd=work_dir)
-            cp_time = time.time() - t0
+        run_hugin("cpclean", f'-o "{init_pto}" "{init_pto}"', cwd=work_dir)
 
-            # cpclean on chunk
-            run_hugin("cpclean", f'-o "{chunk_pto}" "{chunk_pto}"', cwd=work_dir)
-
-            # Read CPs and remap indices to global
-            with open(chunk_pto) as f:
+        # Read initial CPs (local indices = global indices for first group)
+        with open(init_pto) as f:
+            for line in f:
+                if line.startswith("c "):
+                    all_cp_lines.append(line)
+        os.remove(init_pto)
+
+        init_time = time.time() - t0
+        print(f"    Initial: {init_time:.0f}s, CPs: {len(all_cp_lines)}", flush=True)
+
+        # Phase 2: add batches incrementally
+        cursor = init_end
+        batch_num = 0
+        while cursor < n_images:
+            batch_num += 1
+            batch_end = min(cursor + batch_size, n_images)
+            new_count = batch_end - cursor
+
+            # Bridge: last bridge_size images from existing set + new images
+            bridge_start = max(0, cursor - bridge_size)
+            group_start = bridge_start
+            group_end = batch_end
+            group_size = group_end - group_start
+
+            # Build PTO for this bridge+new group
+            batch_pto = os.path.join(work_dir, f"{output_name}_batch{batch_num}.pto")
+            build_pto(batch_pto,
+                      clahe_images[group_start:group_end],
+                      azs[group_start:group_end],
+                      els[group_start:group_end],
+                      img_w, img_h, fov)
+
+            # cpfind on bridge+new group
+            t0 = time.time()
+            r = run_hugin("cpfind",
+                f"--multirow --celeste "
+                f"--sieve1width 20 --sieve1height 20 --sieve1size 1000 "
+                f"--sieve2width 10 --sieve2height 10 --sieve2size 5 "
+                f"--minmatches 3 --ransaciter 2000 --ransacdist 50 "
+                f'-o "{batch_pto}" "{batch_pto}"',
+                cwd=work_dir)
+            run_hugin("cpclean", f'-o "{batch_pto}" "{batch_pto}"', cwd=work_dir)
+            batch_time = time.time() - t0
+
+            # Read CPs and remap local indices to global
+            batch_cps = 0
+            with open(batch_pto) as f:
                 for line in f:
                     if line.startswith("c "):
-                        # Remap local image indices to global
                         parts = line.split()
                         new_parts = []
                         for p in parts:
                             if p.startswith("n") and not p.startswith("N"):
                                 local_idx = int(p[1:])
-                                new_parts.append(f"n{start + local_idx}")
+                                new_parts.append(f"n{group_start + local_idx}")
                             elif p.startswith("N"):
                                 local_idx = int(p[1:])
-                                new_parts.append(f"N{start + local_idx}")
+                                new_parts.append(f"N{group_start + local_idx}")
                             else:
                                 new_parts.append(p)
                         all_cp_lines.append(" ".join(new_parts) + "\n")
+                        batch_cps += 1
 
-            # Cleanup chunk PTO
-            os.remove(chunk_pto)
+            os.remove(batch_pto)
+            print(f"    Batch {batch_num}: imgs {cursor}-{batch_end-1} "
+                  f"(bridge from {bridge_start}): {batch_time:.0f}s, "
+                  f"+{batch_cps} CPs, total: {len(all_cp_lines)}", flush=True)
 
-            chunk_cps = len([l for l in all_cp_lines])  # running total
-            print(f"    Chunk {chunk_idx+1}/{len(chunks)} "
-                  f"(imgs {start}-{end-1}): {cp_time:.0f}s, "
-                  f"total CPs so far: {len(all_cp_lines)}", flush=True)
+            cursor = batch_end
 
         timings["cpfind"] = time.time() - t0_total
-        timings["cpclean"] = 0  # included in per-chunk processing
+        timings["cpclean"] = 0  # included in per-batch processing
 
-        # Write CPs back to the full CLAHE PTO
+        # Write all CPs to the full CLAHE PTO
         with open(pto_clahe, "a") as f:
             f.writelines(all_cp_lines)