129 lines
4.4 KiB
Python
129 lines
4.4 KiB
Python
import base64
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import torch
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import numpy as np
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def b64decode(data: str, encoding: str = "utf-8") -> bytes:
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return base64.b64decode(data.encode(encoding))
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def b64encode(data: bytes, encoding: str = "utf-8") -> str:
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return base64.b64encode(data).decode(encoding)
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def encode_steganography(data_bytes: bytes, width: int = None, height: int = None, use_alpha: bool = True):
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"""
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Encode bytes into a steganography image.
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Args:
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data_bytes: Bytes to encode
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width: Optional width of output image (auto-calculated if not provided)
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height: Optional height of output image (auto-calculated if not provided)
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use_alpha: Whether to use RGBA (4 channels) or RGB (3 channels) format
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Returns:
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Image tensor with encoded data
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"""
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data_length = len(data_bytes)
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channels = 4 if use_alpha else 3
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# Calculate image dimensions if not provided
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if width is None or height is None:
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# Calculate square-ish dimensions
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# We need 4 bytes per pixel (RGBA) or 3 bytes per pixel (RGB) to store data
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# Add header: 4 bytes for length
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total_bytes_needed = data_length + 4
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pixels_needed = (total_bytes_needed + channels - 1) // channels # Round up
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if width is None and height is None:
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# Calculate square dimensions
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side = int(np.ceil(np.sqrt(pixels_needed)))
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width = height = side
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elif width is None:
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width = (pixels_needed + height - 1) // height # Round up
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else: # height is None
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height = (pixels_needed + width - 1) // width # Round up
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total_pixels = width * height
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total_capacity = total_pixels * channels
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# Check if data fits
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if data_length + 4 > total_capacity:
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raise ValueError(f"Data too large ({data_length} bytes) for image size {width}x{height} with {channels} channels (capacity: {total_capacity - 4} bytes)")
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# Create header with data length (4 bytes)
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header = data_length.to_bytes(4, byteorder='big')
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# Combine header and data
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full_data = header + data_bytes
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# Pad with random noise to fill the image
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remaining_bytes = total_capacity - len(full_data)
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noise = np.random.randint(0, 256, remaining_bytes, dtype=np.uint8)
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full_data_array = np.frombuffer(full_data, dtype=np.uint8)
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full_array = np.concatenate([full_data_array, noise])
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# Reshape to image format
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if use_alpha:
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# RGBA format (height, width, 4 channels)
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image_array = full_array.reshape(height, width, 4)
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else:
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# RGB format (height, width, 3 channels)
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image_array = full_array.reshape(height, width, 3)
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# Convert to float32 [0, 1] range for ComfyUI
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image_tensor = torch.from_numpy(image_array.astype(np.float32) / 255.0)
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# Add batch dimension
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image_tensor = image_tensor.unsqueeze(0)
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return image_tensor
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def decode_steganography(image):
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"""
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Decode bytes from a steganography image.
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Args:
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image: Image tensor with encoded data
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Returns:
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Decoded bytes
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"""
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# Handle tensor input
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if isinstance(image, torch.Tensor):
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image = image.detach().cpu().numpy()
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# Remove batch dimension if present
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if image.ndim == 4:
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image = image[0]
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# Convert from float32 [0, 1] to uint8 [0, 255]
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if image.dtype == np.float32 or image.dtype == np.float64:
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image_uint8 = np.clip(image * 255, 0, 255).astype(np.uint8)
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else:
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image_uint8 = image.astype(np.uint8)
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# Handle different channel configurations
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if image_uint8.ndim == 2:
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# Grayscale - can't decode, need at least RGB
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raise ValueError("Cannot decode from grayscale image, need at least 3 channels")
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elif image_uint8.shape[2] == 3:
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# RGB - use 3 bytes per pixel
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pass
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elif image_uint8.shape[2] == 4:
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# RGBA - use all 4 channels
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pass
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else:
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raise ValueError(f"Unexpected number of channels: {image_uint8.shape[2]}")
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# Flatten the image to get byte array
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byte_array = image_uint8.flatten()
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# Read header (first 4 bytes = data length)
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data_length = int.from_bytes(byte_array[:4].tobytes(), byteorder='big')
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# Validate data length
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if data_length < 0 or data_length > len(byte_array) - 4:
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raise ValueError(f"Invalid data length in image header: {data_length}")
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# Extract data bytes
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data_bytes = byte_array[4:4 + data_length].tobytes()
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return data_bytes |