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