From 40421f51bfd5bb6485240012845ed29e61f64d72 Mon Sep 17 00:00:00 2001
From: Zeecka <alexgarrido@protonmail.com>
Date: Fri, 2 Jan 2026 17:29:35 +0100
Subject: [PATCH] decomposer is now working

---
 aperisolve/analyzers/decomposer.py | 148 ++++++++++++++++-------------
 1 file changed, 81 insertions(+), 67 deletions(-)

diff --git a/aperisolve/analyzers/decomposer.py b/aperisolve/analyzers/decomposer.py
index cff14d5..8cee810 100644
--- a/aperisolve/analyzers/decomposer.py
+++ b/aperisolve/analyzers/decomposer.py
@@ -4,78 +4,92 @@
 """Bits Decomposer Analyzer for Image Submissions."""
 
 from pathlib import Path
+from typing import Any, Optional
 
 import numpy as np
 from PIL import Image
 
-from .utils import update_data
+from .base_analyzer import SubprocessAnalyzer
+
+
+class DecomposerAnalyzer(SubprocessAnalyzer):
+    """Analyzer for bits decomposer."""
+
+    def __init__(self, *args):
+        super().__init__("decomposer", *args)
+
+    def get_results(self, _: Optional[str] = None) -> dict[str, Any]:
+        """Analyze an image submission using bits decomposition."""
+        try:
+            img = Image.open(self.input_img)
+            converted = False
+
+            if img.mode == "P":
+                img = img.convert("RGB")
+                converted = True
+
+            img_np = np.array(img)
+
+            # Handle grayscale, RGB, RGBA, etc.
+            if len(img_np.shape) == 2:
+                channels = 1
+            else:
+                channels = img_np.shape[2]
+
+            if channels > 1:
+                channel_names = ["Red", "Green", "Blue", "Alpha"]
+            else:
+                channel_names = ["Grayscale"]
+
+            image_json = {}
+
+            # Add Superimposed RGB bit planes
+            if channels >= 3:
+                superimposed_json = []
+                for bit in range(8):
+                    bit_mask = 1 << bit
+                    rgb_planes = [((img_np[..., c] & bit_mask) >> bit) * 255 for c in range(3)]
+                    rgb_image = np.stack(rgb_planes, axis=-1).astype(np.uint8)
+                    rgb_img = Image.fromarray(rgb_image, mode="RGB")
+                    img_name = f"superimposed_bit_{bit}.png"
+                    out_path = self.output_dir / img_name
+                    dl_path = Path(self.output_dir.name) / img_name
+                    superimposed_json.append("/image/" + str(dl_path))
+                    rgb_img.save(out_path)
+
+                image_json["Superimposed"] = superimposed_json
+
+            # Process individual channels
+            for c in range(channels):
+                channel_data = img_np[..., c] if channels > 1 else img_np
+                channel_label = channel_names[c]
+                channel_json = []
+                for bit in range(8):
+                    bit_mask = 1 << bit
+                    bit_plane = ((channel_data & bit_mask) >> bit) * 255
+                    bit_img = Image.fromarray(bit_plane.astype(np.uint8), mode="L")
+                    img_name = f"{channel_label}_bit_{bit}.png"
+                    out_path = self.output_dir / img_name
+                    dl_path = Path(self.output_dir.name) / img_name
+                    channel_json.append("/image/" + str(dl_path))
+                    bit_img.save(out_path)
+                image_json[channel_label] = channel_json
+
+            output = {
+                "status": "ok",
+                "images": image_json,
+            }
+            if converted:
+                output["note"] = (
+                    "Image contains a color palette and was converted to RGB for processing."
+                )
+
+            return output
+        except Exception as e:
+            return {"status": "error", "error": str(e)}
 
 
 def analyze_decomposer(input_img: Path, output_dir: Path) -> None:
     """Analyze an image submission using bits decomposition."""
-    img = Image.open(input_img)
-    converted = False
-
-    if img.mode == "P":
-        img = img.convert("RGB")
-        converted = True
-
-    img_np = np.array(img)
-
-    # Handle grayscale, RGB, RGBA, etc.
-    if len(img_np.shape) == 2:
-        channels = 1
-    else:
-        channels = img_np.shape[2]
-
-    if channels > 1:
-        channel_names = ["Red", "Green", "Blue", "Alpha"]
-    else:
-        channel_names = ["Grayscale"]
-
-    image_json = {}
-
-    # Add Superimposed RGB bit planes
-    if channels >= 3:
-        superimposed_json = []
-        for bit in range(8):
-            bit_mask = 1 << bit
-            rgb_planes = [((img_np[..., c] & bit_mask) >> bit) * 255 for c in range(3)]
-            rgb_image = np.stack(rgb_planes, axis=-1).astype(np.uint8)
-            rgb_img = Image.fromarray(rgb_image, mode="RGB")
-            img_name = f"superimposed_bit_{bit}.png"
-            out_path = output_dir / img_name
-            dl_path = Path(output_dir.name) / img_name
-            superimposed_json.append("/image/" + str(dl_path))
-            rgb_img.save(out_path)
-
-        image_json["Superimposed"] = superimposed_json
-
-    # Process individual channels
-    for c in range(channels):
-        channel_data = img_np[..., c] if channels > 1 else img_np
-        channel_label = channel_names[c]
-        channel_json = []
-        for bit in range(8):
-            bit_mask = 1 << bit
-            bit_plane = ((channel_data & bit_mask) >> bit) * 255
-            bit_img = Image.fromarray(bit_plane.astype(np.uint8), mode="L")
-            img_name = f"{channel_label}_bit_{bit}.png"
-            out_path = output_dir / img_name
-            dl_path = Path(output_dir.name) / img_name
-            channel_json.append("/image/" + str(dl_path))
-            bit_img.save(out_path)
-        image_json[channel_label] = channel_json
-
-    output = {
-        "decomposer": {
-            "status": "ok",
-            "images": image_json,
-        }
-    }
-    if converted:
-        output["decomposer"][
-            "note"
-        ] = "Image contains a color palette and was converted to RGB for processing."
-
-    update_data(output_dir, output)
+    analyzer = DecomposerAnalyzer(input_img, output_dir)
+    analyzer.analyze()