CODEWITHSUNDEEP
pythonimage-processing
oldbhoy
oldbhoy

Reputation: 25

Converting byte array to 16 bit grayscale image in Python

I'm working with large image datasets stored in a non-standard image format (.Tsm). Essentially it's a binary file with some headers at the start, very similar to FITS standard except stored in little-endian as opposed to FITS big-endian.

After reading the file header and formatting the metadata, I can read a single image using the following code

    def __read_slice(self, file, img_num, dimensions):
        """Read a single image slice from .tsm file"""

        pixel_range = self.metadata["pixel range"]
        bytes_to_read = self.metadata["bytes to read"]

        # position file pointer to correct byte
        file.seek(self.HEADER_TOTAL_LEN + (bytes_to_read * img_num), 0)

        all_bytes = file.read(bytes_to_read)  # read image bytes
        img = np.empty(len(pixels), dtype='uint16')  # preallocate image vector

        byte_idx = 0
        for idx, pixel in enumerate(pixel_range):
            img[idx] = (all_bytes[byte_idx + 1] << 8) + all_bytes[byte_idx]
            byte_idx += 2

        return np.reshape(img, (dimensions[1], dimensions[0]))  # reshape array to correct dimensions

the trouble is the images can be very large (2048x2048) so even just loading in 20-30 frames for processing can take a significant amount of time. I'm new to python so i'm guessing the code here is pretty inefficient, especially the loop.

Is there a more efficient way to convert the byte data into 16bit integers?

Upvotes: 0

Views: 1282

Answers (1)

tzot
tzot

Reputation: 95941

You can try:

img= np.frombuffer(all_bytes, dtype='uint16')

Example:

>>> np.frombuffer(b'\x01\x02\x03\x04', dtype='uint16')
array([ 513, 1027], dtype=uint16)

Upvotes: 2

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