From 2ed2ae3d16136542bb78de724d1a25608aac977c Mon Sep 17 00:00:00 2001
From: Ferdinand <ferdinand@MacBook-Pro.local>
Date: Tue, 7 Apr 2026 13:34:46 +0200
Subject: [PATCH] feat: analyze_folder orchestrates all checks

---
 analyzer.py            | 57 +++++++++++++++++++++++++++++++++++++++++-
 tests/test_analyzer.py | 21 ++++++++++++++++
 2 files changed, 77 insertions(+), 1 deletion(-)

diff --git a/analyzer.py b/analyzer.py
index 9b20114..9654746 100644
--- a/analyzer.py
+++ b/analyzer.py
@@ -2,7 +2,8 @@ import cv2
 import numpy as np
 from PIL import Image
 import imagehash
-from typing import List
+import os
+from typing import List, Optional
 
 
 def is_blurry(path: str, threshold: float = 100.0) -> bool:
@@ -63,3 +64,57 @@ def find_duplicates(paths: List[str], threshold: int = 8) -> List[List[str]]:
             groups.append(group)
 
     return groups
+
+
+SUPPORTED_EXTENSIONS = {".jpg", ".jpeg", ".png"}
+
+
+def analyze_folder(
+    folder: str,
+    blur_threshold: float = 100.0,
+    over_threshold: float = 240.0,
+    under_threshold: float = 30.0,
+    dup_threshold: int = 8,
+    use_ai: bool = False,
+    api_key: Optional[str] = None,
+) -> List[dict]:
+    """
+    Analysiert alle Bilder im Ordner.
+    Gibt Liste zurueck: [{"path": "/foo/bar.jpg", "reasons": ["unscharf"]}, ...]
+    Nur Bilder mit mindestens einem Grund werden zurueckgegeben.
+    """
+    paths = [
+        os.path.join(folder, f)
+        for f in os.listdir(folder)
+        if os.path.splitext(f)[1].lower() in SUPPORTED_EXTENSIONS
+    ]
+
+    results: dict = {path: [] for path in paths}
+
+    for path in paths:
+        try:
+            if is_blurry(path, blur_threshold):
+                results[path].append("unscharf")
+            if is_overexposed(path, over_threshold):
+                results[path].append("ueberbelichtet")
+            if is_underexposed(path, under_threshold):
+                results[path].append("unterbelichtet")
+        except Exception:
+            continue
+
+    dup_groups = find_duplicates(paths, dup_threshold)
+    for group in dup_groups:
+        original = os.path.basename(group[0])
+        for dup_path in group[1:]:
+            results[dup_path].append(f"Duplikat von {original}")
+
+    if use_ai and api_key:
+        ai_results = _analyze_with_ai(paths, api_key)
+        for path, ai_reasons in ai_results.items():
+            results[path].extend(ai_reasons)
+
+    return [
+        {"path": path, "reasons": reasons}
+        for path, reasons in results.items()
+        if reasons
+    ]
diff --git a/tests/test_analyzer.py b/tests/test_analyzer.py
index 264dfbe..909b937 100644
--- a/tests/test_analyzer.py
+++ b/tests/test_analyzer.py
@@ -88,3 +88,24 @@ def test_different_images_are_not_duplicates(tmp_path):
     p2 = make_diagonal_image(tmp_path, "diagonal.jpg")
     groups = find_duplicates([p1, p2], threshold=8)
     assert len(groups) == 0
+
+
+from analyzer import analyze_folder
+
+
+def test_analyze_folder_returns_results(tmp_path):
+    make_test_image(tmp_path, color=(128, 128, 128))
+    from PIL import Image
+    white = tmp_path / "white.jpg"
+    Image.new("RGB", (100, 100), color=(255, 255, 255)).save(white)
+
+    results = analyze_folder(
+        folder=str(tmp_path),
+        blur_threshold=100,
+        over_threshold=240,
+        under_threshold=30,
+        dup_threshold=8,
+        use_ai=False,
+    )
+    reasons_flat = [r for item in results for r in item["reasons"]]
+    assert "ueberbelichtet" in reasons_flat