Detect and handle duplicate Immich people with the same name

When Immich has multiple person records sharing a name (e.g. unmerged
face clusters), winnow would previously run separate jobs for each,
with the second job wiping the first job's output directory — resulting
in far fewer training images than expected.

New behaviour:
- At startup, duplicate names are detected and a warning is printed
  showing asset counts for each duplicate.
- By default (MERGE_DUPLICATE_PEOPLE=false), only the person with the
  most assets is processed; smaller duplicates are skipped cleanly.
- With MERGE_DUPLICATE_PEOPLE=true, the duplicates are permanently
  merged inside Immich via PUT /api/people/{id}/merge (keeps the
  largest), then the people list is re-fetched before jobs run.

Also adds an explicit comment in executor.py confirming that replacement
targets come exclusively from tracker-mapped files, so manually-added
Frigate training images are never selected for deletion.

winnow/config.py

@@ -36,6 +36,7 @@ class _Config:
 
     # People filtering
     MIN_FACE_COUNT: int = 0
+    MERGE_DUPLICATE_PEOPLE: bool = False
 
     # Output quality
     FACE_MARGIN: float = 0.15
@@ -60,6 +61,7 @@ class _Config:
         self.YEARS_FILTER = int(os.getenv("YEARS_FILTER", "10"))
         self.MIN_FACE_WIDTH = int(os.getenv("MIN_FACE_WIDTH", "90"))
         self.MIN_FACE_COUNT = int(os.getenv("MIN_FACE_COUNT", "0"))
+        self.MERGE_DUPLICATE_PEOPLE = os.getenv("MERGE_DUPLICATE_PEOPLE", "false").lower() in ("true", "1", "yes")
         self.BLUR_THRESHOLD = float(os.getenv("BLUR_THRESHOLD", "120.0"))
         self.MIN_CONFIDENCE = float(os.getenv("MIN_CONFIDENCE", "0.7"))
         self.MAX_AUTO_IMAGES = int(os.getenv("MAX_AUTO_IMAGES", "80"))