initial commit

2026-07-12 10:17:17 -04:00
commit dab5a4ebc6
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+#!/usr/bin/env python3
+"""
+Immich YOLO Photo Classifier Pipeline
+Classifies photos as: has-people, scenic, or neither.
+Moves non-people/non-scenic photos to NO PEOPLE PHOTOS folder.
+
+Usage:
+  /mnt/storage/yolo_venv/bin/python3 this_script.py
+
+Requirements:
+  - /mnt/storage/yolo_venv/ with ultralytics installed
+  - ~/yolov8n_openvino_model/ (exported from yolov8n.pt)
+  - Immich PostgreSQL container running
+  - Photos on /mnt/wd-passport/immich/photos/
+"""
+import multiprocessing, os, time, warnings, subprocess, shutil
+warnings.filterwarnings("ignore")
+
+PHOTO_BASE = "/mnt/wd-passport/immich/photos"
+NO_PEOPLE_DIR = "/mnt/wd-passport/immich/NO PEOPLE PHOTOS"
+DB_QUERY = '''docker exec immich_postgres psql -U postgres -d immich -t -A -c "SELECT \\\"originalPath\\\" FROM asset WHERE type='IMAGE' AND visibility='timeline' AND \\\"originalPath\\\" LIKE '/data/library/%' ORDER BY \\\"originalPath\\\";" 2>/dev/null'''
+
+PERSON_CLASS = 0
+NATURE_CLASSES = {16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 58, 77, 80}
+MOVE_CLASSES = {56, 57, 59, 60, 61, 62, 63, 64, 65, 67, 70, 71, 72, 73, 74, 75, 76, 78, 79,
+                1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}
+
+def worker(args):
+    model_path, paths = args
+    from ultralytics import YOLO
+    import warnings
+    warnings.filterwarnings("ignore")
+    model = YOLO(model_path, task="detect")
+    results = []
+    for ap in paths:
+        try:
+            r = model(ap, verbose=False)
+            has_person = has_nature = has_move = False
+            for r2 in r:
+                if r2.boxes:
+                    for box in r2.boxes:
+                        cls = int(box.cls[0])
+                        if cls == PERSON_CLASS:           has_person = True
+                        elif cls in NATURE_CLASSES:        has_nature = True
+                        elif cls in MOVE_CLASSES:          has_move = True
+            if has_person:        results.append((ap, "keep_people"))
+            elif has_nature:      results.append((ap, "keep_scenic"))
+            elif has_move:        results.append((ap, "move"))
+            else:                 results.append((ap, "check_size"))
+        except Exception as e:
+            results.append((ap, f"error:{e}"))
+    return results
+
+if __name__ == "__main__":
+    multiprocessing.set_start_method("forkserver", force=True)
+    num_workers = min(multiprocessing.cpu_count(), 8)
+    os.makedirs(NO_PEOPLE_DIR, exist_ok=True)
+    model_path = os.path.expanduser("~/yolov8n_openvino_model/")
+
+    print(f"[1] Querying DB...", flush=True)
+    result = subprocess.run(DB_QUERY, shell=True, capture_output=True, text=True, timeout=120)
+    db_paths = [p.strip() for p in result.stdout.strip().split('\n') if p.strip()]
+
+    print(f"[2] Mapping {len(db_paths)} paths...", flush=True)
+    actual = []
+    for p in db_paths:
+        ap = PHOTO_BASE + "/" + p[6:]
+        if os.path.exists(ap):
+            actual.append(ap)
+    print(f"  {len(actual)} accessible files", flush=True)
+
+    print(f"[3] Classifying with {num_workers} workers...", flush=True)
+    chunks = [actual[i::num_workers] for i in range(num_workers)]
+    chunk_args = [(model_path, c) for c in chunks]
+    t_start = time.time()
+    with multiprocessing.Pool(num_workers) as pool:
+        all_results = pool.map(worker, chunk_args)
+    elapsed = time.time() - t_start
+
+    keep_people = keep_scenic = errors = 0
+    to_move, check_size = [], []
+    for chunk in all_results:
+        for ap, d in chunk:
+            if d == "keep_people":    keep_people += 1
+            elif d == "keep_scenic":  keep_scenic += 1
+            elif d == "move":         to_move.append(ap)
+            elif d == "check_size":   check_size.append(ap)
+            else:                     errors += 1
+
+    print(f"\n  YOLO: {elapsed/60:.1f}min ({len(actual)/elapsed:.1f} img/s)", flush=True)
+    print(f"  People:{keep_people} Scenic:{keep_scenic} Move:{len(to_move)} Check:{len(check_size)} Err:{errors}", flush=True)
+
+    print(f"[4] Size check on {len(check_size)} undetected...", flush=True)
+    for ap in check_size:
+        try:
+            if os.path.getsize(ap) < 400000:
+                to_move.append(ap)
+        except: pass
+    print(f"  Now {len(to_move)} to move", flush=True)
+
+    print(f"[5] Moving {len(to_move)} photos...", flush=True)
+    moved = file_errors = 0
+    for ap in to_move:
+        try:
+            rel = os.path.relpath(ap, PHOTO_BASE)
+            dest = os.path.join(NO_PEOPLE_DIR, rel)
+            os.makedirs(os.path.dirname(dest), exist_ok=True)
+            shutil.move(ap, dest)
+            moved += 1
+        except Exception as e:
+            file_errors += 1
+
+    print(f"\nDONE! Moved {moved} photos", flush=True)
+    print(f"  Total:{len(actual)} Kept(people):{keep_people} Kept(scenic):{keep_scenic} Moved:{moved} Errors:{file_errors}", flush=True)
+    print(f"  Time: {(time.time()-t_start)/60:.1f}min", flush=True)
+    print(f"  Output: {NO_PEOPLE_DIR}", flush=True)