Image Data Transcoder: Converting DAT Image Files to RAW Format

Image transcoding is a common requirement in digital image processing, particularly when converting standard format images (such as BMP or JPG) to RAW format. RAW format typically refers to uncompressed or unprocessed raw pixel data, commonly used in industrial inspection, embedded systems, or image processing scenarios for specific hardware devices.

### Core Logic Format Parsing: The program needs to read header information from standard formats like BMP/JPG and extract key parameters (width, height, bit depth, pixel arrangement). BMP files can directly parse pixel arrays, while JPG requires decoding into bitmap data first due to compression characteristics. Code implementation would involve using libraries like PIL (Python Imaging Library) or OpenCV to handle format-specific parsing and decompression routines.

Data Transformation: Color Space Processing: Rearrange RGB or RGBA data according to target requirements (e.g., converting to grayscale or adjusting channel order). Implementation typically uses matrix operations or dedicated color conversion functions (cv2.cvtColor in OpenCV for color space transformations). Byte Alignment: Adjust data storage methods based on RAW format's bit depth (8-bit/16-bit), which may involve big-endian/little-endian conversion. This requires bitwise operations and byte manipulation functions to ensure proper memory alignment and endianness handling.

Output Optimization: When generating RAW files, ensure no additional header information is included, preserving only continuous pixel data streams, while supporting custom output resolution and bit depth. The output routine should implement direct binary writing with configurable parameters for resolution and pixel format settings.

### Extended Applications Such tools can be integrated into automated testing pipelines or convert standard images into binary streams that can be directly processed by FPGA and DSP chips. This is suitable for computer vision algorithm verification stages that require raw data input. Integration examples include batch processing scripts using multiprocessing for efficiency, or generating hardware-specific memory maps for embedded vision systems.