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from PIL import Image
import warnings
import numpy as np
import struct
import io
__version__ = "1.3.0"
def load(data):
def consume(n):
nonlocal data
out, data = data[:n], data[n:]
if len(out) < n:
raise ValueError(f'File is truncated, tried to read {n} bytes, but only {len(out)} bytes remain.')
return out
header = consume(128)
for model, match in {
'c201': bytes([0xca, 0xac]),
'other': bytes([0xba, 0xab]),
'p200': bytes([0x04, 0xa0]),
}.items():
if (header[:2] + header[-2:]).startswith(match):
break
else:
raise ValueError(f'Header magic not found. Got header: {header[0]:02x} {header[1]:02x}')
_unk0, coarse_section_length, y_res, x_res,\
_zero0, _unk1, _zero1, fine_offset, _unk2, jpeg_length,\
y_res_2, x_res_2, _unk3, = struct.unpack('<HIHHHHHHHIHHI', header[2:34])
fine_temp_offset1, fine_temp_offset2, *rest, high_gain_mode_flag = struct.unpack('<11I', header[34:78])
if fine_temp_offset1 != fine_temp_offset2:
warnings.warn(f'File lists two different zero offsets for the fine image data {fine_temp_offset1} and {fine_temp_offset2}. Resulting radiometric data might be offset. Please report this with an example file to code@jaseg.de.')
fine_temp_offset = fine_temp_offset1 / 10000
# import textwrap
# print(textwrap.dedent(f'''
# {_unk0=}, {coarse_section_length=}, {y_res=}, {x_res=},
# {_zero0=}, {_unk1=}, {_zero1=}, {fine_offset=}, {_unk2=}, {jpeg_length=},
# {y_res_2=}, {x_res_2=}, {_unk3=}
# {fine_temp_offset1=} {fine_temp_offset1=} {rest=}, {high_gain_mode_flag=}'''))
if (x_res, y_res) != (x_res_2, y_res_2) and model != 'p200':
raise ValueError(f'Resolution mismatch in header: {x_res}*{y_res} != {x_res_2}*{y_res_2}')
if x_res*y_res != coarse_section_length:
raise ValueError('Resolution mismatch in header')
if model == 'c201':
if header[-2:] != bytes([0xac,0xca]):
raise ValueError(f'Header end marker not found. Got header: {header[-2]:02x} {header[-1]:02x}')
coarse_img = np.frombuffer(consume(coarse_section_length), dtype=np.uint8).reshape((y_res, x_res))
# 1/16th Kelvin steps
fine_img = np.frombuffer(consume(x_res*y_res*2), dtype=np.uint16).reshape((y_res, x_res))
fine_img = (fine_img / 16) - fine_temp_offset
vis_jpg = Image.open(io.BytesIO(consume(jpeg_length)))
elif model == 'other':
if header[-2:] != bytes([0xab,0xba]):
raise ValueError(f'Header end marker not found. Got header: {header[-2]:02x} {header[-1]:02x}')
coarse_img = np.frombuffer(consume(coarse_section_length), dtype=np.uint8).reshape((y_res, x_res))
# 0.1 Kelvin steps
fine_img = np.frombuffer(consume(x_res*y_res*2), dtype=np.uint16).reshape((y_res, x_res))
fine_img = fine_img / 10 - fine_temp_offset
vis_jpg = Image.open(io.BytesIO(data))
else:
header += consume(128)
coarse_img = np.frombuffer(consume(coarse_section_length), dtype=np.uint8).reshape((y_res, x_res))
fine_img = np.frombuffer(consume(x_res*y_res*2), dtype=np.uint16).reshape((y_res, x_res))
fine_img = fine_img / 10 - fine_temp_offset
# In my example file, data now contains the JSON '{"roi":[]}' and no JPG. We ignore that.
vis_jpg = None
return coarse_img, fine_img, vis_jpg
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