# SPDX-License-Identifier: BSD-2-Clause
#
# Copyright (C) 2019, Raspberry Pi Ltd
#
# ctt_alsc.py - camera tuning tool for ALSC (auto lens shading correction)
from ctt_image_load import *
import matplotlib.pyplot as plt
from matplotlib import cm
from mpl_toolkits.mplot3d import Axes3D
"""
preform alsc calibration on a set of images
"""
def alsc_all(Cam, do_alsc_colour, plot):
imgs_alsc = Cam.imgs_alsc
"""
create list of colour temperatures and associated calibration tables
"""
list_col = []
list_cr = []
list_cb = []
list_cg = []
for Img in imgs_alsc:
col, cr, cb, cg, size = alsc(Cam, Img, do_alsc_colour, plot)
list_col.append(col)
list_cr.append(cr)
list_cb.append(cb)
list_cg.append(cg)
Cam.log += '\n'
Cam.log += '\nFinished processing images'
w, h, dx, dy = size
Cam.log += '\nChannel dimensions: w = {} h = {}'.format(int(w), int(h))
Cam.log += '\n16x12 grid rectangle size: w = {} h = {}'.format(dx, dy)
"""
convert to numpy array for data manipulation
"""
list_col = np.array(list_col)
list_cr = np.array(list_cr)
list_cb = np.array(list_cb)
list_cg = np.array(list_cg)
cal_cr_list = []
cal_cb_list = []
"""
only do colour calculations if required
"""
if do_alsc_colour:
Cam.log += '\nALSC colour tables'
for ct in sorted(set(list_col)):
Cam.log += '\nColour temperature: {} K'.format(ct)
"""
average tables for the same colour temperature
"""
indices = np.where(list_col == ct)
ct = int(ct)
t_r = np.mean(list_cr[indices], axis=0)
t_b = np.mean(list_cb[indices], axis=0)
"""
force numbers to be stored to 3dp.... :(
"""
t_r = np.where((100*t_r) % 1 <= 0.05, t_r+0.001, t_r)
t_b = np.where((100*t_b) % 1 <= 0.05, t_b+0.001, t_b)
t_r = np.where((100*t_r) % 1 >= 0.95, t_r-0.001, t_r)
t_b = np.where((100*t_b) % 1 >= 0.95, t_b-0.001, t_b)
t_r = np.round(t_r, 3)
t_b = np.round(t_b, 3)
r_corners = (t_r[0], t_r[15], t_r[-1], t_r[-16])
b_corners = (t_b[0], t_b[15], t_b[-1], t_b[-16])
r_cen = t_r[5*16+7]+t_r[5*16+8]+t_r[6*16+7]+t_r[6*16+8]
r_cen = round(r_cen/4, 3)
b_cen = t_b[5*16+7]+t_b[5*16+8]+t_b[6*16+7]+t_b[6*16+8]
b_cen = round(b_cen/4, 3)
Cam.log += '\nRed table corners: {}'.format(r_corners)
Cam.log += '\nRed table centre: {}'.format(r_cen)
Cam.log += '\nBlue table corners: {}'.format(b_corners)
Cam.log += '\nBlue table centre: {}'.format(b_cen)
cr_dict = {
'ct': ct,
'table': list(t_r)
}
cb_dict = {
'ct': ct,
'table': list(t_b)
}
cal_cr_list.append(cr_dict)
cal_cb_list.append(cb_dict)
Cam.log += '\n'
else:
cal_cr_list, cal_cb_list = None, None
"""
average all values for luminance shading and return one table for all temperatures
"""
lum_lut = np.mean(list_cg, axis=0)
lum_lut = np.where((100*lum_lut) % 1 <= 0.05, lum_lut+0.001, lum_lut)
lum_lut = np.where((100*lum_lut) % 1 >= 0.95, lum_lut-0.001, lum_lut)
lum_lut = list(np.round(lum_lut, 3))
"""
calculate average corner for lsc gain calculation further on
"""
corners = (lum_lut[0], lum_lut[15], lum_lut[-1], lum_lut[-16])
Cam.log += '\nLuminance table corners: {}'.format(corners)
l_cen = lum_lut[5*16+7]+lum_lut[5*16+8]+lum_lut[6*16+7]+lum_lut[6*16+8]
l_cen = round(l_cen/4, 3)
Cam.log += '\nLuminance table centre: {}'.format(l_cen)
av_corn = np.sum(corners)/4
return cal_cr_list, cal_cb_list, lum_lut, av_corn
"""
calculate g/r and g/b for 32x32 points arranged in a grid for a single image
"""
def alsc(Cam, Img, do_alsc_colour, plot=False):
Cam.log += '\nProcessing image: ' + Img.name
"""
get channel in correct order
"""
channels = [Img.channels[i] for i in Img.order]
"""
calculate size of single rectangle.
-(-(w-1)//32) is a ceiling division. w-1 is to deal robustly with the case
where w is a multiple of 32.
"""
w, h = Img.w/2, Img.h/2
dx, dy = int(-(-(w-1)//16)), int(-(-(h-1)//12))
"""
average the green channels into one
"""
av_ch_g = np.mean((channels[1:3]), axis=0)
if do_alsc_colour:
"""
obtain 16x12 grid of intensities for each channel and subtract black level
"""
g = get_16x12_grid(av_ch_g, dx, dy) - Img.blacklevel_16
r = get_16x12_grid(channels[0], dx, dy) - Img.blacklevel_16
b = get_16x12_grid(channels[3], dx, dy) - Img.blacklevel_16
"""
calculate ratios as 32 bit in order to be supported by medianBlur function
"""
cr = np.reshape(g/r, (12, 16)).astype('float32')
cb = np.reshape(g/b, (12, 16)).astype('float32')
cg = np.reshape(1/g, (12, 16)).astype('float32')
"""
median blur to remove peaks and save as float 64
"""
cr = cv2.medianBlur(cr, 3).astype('float64')
cb = cv2.medianBlur(cb, 3).astype('float64')
cg = cv2.medianBlur(cg, 3).astype('float64')
cg = cg/np.min(cg)
"""
debugging code showing 2D surface plot of vignetting. Quite useful for
for sanity check
"""
if plot:
hf = plt.figure(figsize=(8, 8))
ha = hf.add_subplot(311, projection='3d')
"""
note Y is plotted as -Y so plot has same axes as image
"""
X, Y = np.meshgrid(range(16), range(12))
ha.plot_surface(X, -Y, cr, cmap=cm.coolwarm, linewidth=0)
ha.set_title('ALSC Plot\nImg: {}\n\ncr'.format(Img.str))
hb = hf.add_subplot(312, projection='3d')
hb.plot_surface(X, -Y, cb, cmap=cm.coolwarm, linewidth=0)
hb.set_title('cb')
hc = hf.add_subplot(313, projection='3d')
hc.plot_surface(X, -Y, cg, cmap=cm.coolwarm, linewidth=0)
hc.set_title('g')
# print(Img.str)
plt.show()
return Img.col, cr.flatten(), cb.flatten(), cg.flatten(), (w, h, dx, dy)
else:
"""
only perform calculations for luminance shading
"""
g = get_16x12_grid(av_ch_g, dx, dy) - Img.blacklevel_16
cg = np.reshape(1/g, (12, 16)).astype('float32')
cg = cv2.medianBlur(cg, 3).astype('float64')
cg = cg/np.min(cg)
if plot:
hf = plt.figure(figssize=(8, 8))
ha = hf.add_subplot(1, 1, 1, projection='3d')
X, Y = np.meashgrid(range(16), range(12))
ha.plot_surface(X, -Y, cg, cmap=cm.coolwarm, linewidth=0)
ha.set_title('ALSC Plot (Luminance only!)\nImg: {}\n\ncg').format(Img.str)
plt.show()
return Img.col, None, None, cg.flatten(), (w, h, dx, dy)
"""
Compresses channel down to a 16x12 grid
"""
def get_16x12_grid(chan, dx, dy):
grid = []
"""
since left and bottom border will not necessarily have rectangles of
dimension dx x dy, the 32nd iteration has to be handled separately.
"""
for i in range(11):
for j in range(15):
grid.append(np.mean(chan[dy*i:dy*(1+i), dx*j:dx*(1+j)]))
grid.append(np.mean(chan[dy*i:dy*(1+i), 15*dx:]))
for j in range(15):
grid.append(np.mean(chan[11*dy:, dx*j:dx*(1+j)]))
grid.append(np.mean(chan[11*dy:, 15*dx:]))
"""
return as np.array, ready for further manipulation
"""
return np.array(grid)
"""
obtains sigmas for red and blue, effectively a measure of the 'error'
"""
def get_sigma(Cam, cal_cr_list, cal_cb_list):
Cam.log += '\nCalculating sigmas'
"""
provided colour alsc tables were generated for two different colour
temperatures sigma is calculated by comparing two calibration temperatures
adjacent in colour space
"""
"""
create list of colour temperatures
"""
cts = [cal['ct'] for cal in cal_cr_list]
# print(cts)
"""
calculate sigmas for each adjacent cts and return worst one
"""
sigma_rs = []
sigma_bs = []
for i in range(len(cts)-1):
sigma_rs.append(calc_sigma(cal_cr_list[i]['table'], cal_cr_list[i+1]['table']))
sigma_bs.append(calc_sigma(cal_cb_list[i]['table'], cal_cb_list[i+1]['table']))
Cam.log += '\nColour temperature interval {} - {} K'.format(cts[i], cts[i+1])
Cam.log += '\nSigma red: {}'.format(sigma_rs[-1])
Cam.log += '\nSigma blue: {}'.format(sigma_bs[-1])
"""
return maximum sigmas, not necessarily from the same colour temperature
interval
"""
sigma_r = max(sigma_rs) if sigma_rs else 0.005
sigma_b = max(sigma_bs) if sigma_bs else 0.005
Cam.log += '\nMaximum sigmas: Red = {} Blue = {}'.format(sigma_r, sigma_b)
# print(sigma_rs, sigma_bs)
# print(sigma_r, sigma_b)
return sigma_r, sigma_b
"""
calculate sigma from two adjacent gain tables
"""
def calc_sigma(g1, g2):
"""
reshape into 16x12 matrix
"""
g1 = np.reshape(g1, (12, 16))
g2 = np.reshape(g2, (12, 16))
"""
apply gains to gain table
"""
gg = g1/g2
if np.mean(gg) < 1:
gg = 1/gg
"""
for each internal patch, compute average difference between it and its 4
neighbours, then append to list
"""
diffs = []
for i in range(10):
for j in range(14):
"""
note indexing is incremented by 1 since all patches on borders are
not counted
"""
diff = np.abs(gg[i+1][j+1]-gg[i][j+1])
diff += np.abs(gg[i+1][j+1]-gg[i+2][j+1])
diff += np.abs(gg[i+1][j+1]-gg[i+1][j])
diff += np.abs(gg[i+1][j+1]-gg[i+1][j+2])
diffs.append(diff/4)
"""
return mean difference
"""
mean_diff = np.mean(diffs)
return(np.round(mean_diff, 5))
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# SPDX-License-Identifier: BSD-2-Clause
#
# Copyright (C) 2019-2020, Raspberry Pi Ltd
#
# camera tuning tool image loading
from ctt_tools import *
from ctt_macbeth_locator import *
import json
import pyexiv2 as pyexif
import rawpy as raw
"""
Image class load image from raw data and extracts metadata.
Once image is extracted from data, it finds 24 16x16 patches for each
channel, centred at the macbeth chart squares
"""
class Image:
def __init__(self, buf):
self.buf = buf
self.patches = None
self.saturated = False
'''
obtain metadata from buffer
'''
def get_meta(self):
self.ver = ba_to_b(self.buf[4:5])
self.w = ba_to_b(self.buf[0xd0:0xd2])
self.h = ba_to_b(self.buf[0xd2:0xd4])
self.pad = ba_to_b(self.buf[0xd4:0xd6])
self.fmt = self.buf[0xf5]
self.sigbits = 2*self.fmt + 4
self.pattern = self.buf[0xf4]
self.exposure = ba_to_b(self.buf[0x90:0x94])
self.againQ8 = ba_to_b(self.buf[0x94:0x96])
self.againQ8_norm = self.againQ8/256
camName = self.buf[0x10:0x10+128]
camName_end = camName.find(0x00)
self.camName = self.buf[0x10:0x10+128][:camName_end].decode()
"""
Channel order depending on bayer pattern
"""
bayer_case = {
0: (0, 1, 2, 3), # red
1: (2, 0, 3, 1), # green next to red
2: (3, 2, 1, 0), # green next to blue
3: (1, 0, 3, 2), # blue
128: (0, 1, 2, 3) # arbitrary order for greyscale casw
}
self.order = bayer_case[self.pattern]
'''
manual blacklevel - not robust
'''
if 'ov5647' in self.camName:
self.blacklevel = 16
else:
self.blacklevel = 64
self.blacklevel_16 = self.blacklevel << (6)
return 1
'''
print metadata for debug
'''
def print_meta(self):
print('\nData:')
print(' ver = {}'.format(self.ver))
print(' w = {}'.format(self.w))
print(' h = {}'.format(self.h))
print(' pad = {}'.format(self.pad))
print(' fmt = {}'.format(self.fmt))
print(' sigbits = {}'.format(self.sigbits))
print(' pattern = {}'.format(self.pattern))
print(' exposure = {}'.format(self.exposure))
print(' againQ8 = {}'.format(self.againQ8))
print(' againQ8_norm = {}'.format(self.againQ8_norm))
print(' camName = {}'.format(self.camName))
print(' blacklevel = {}'.format(self.blacklevel))
print(' blacklevel_16 = {}'.format(self.blacklevel_16))
return 1
"""
get image from raw scanline data
"""
def get_image(self, raw):
self.dptr = []
"""
check if data is 10 or 12 bits
"""
if self.sigbits == 10:
"""
calc length of scanline
"""
lin_len = ((((((self.w+self.pad+3)>>2)) * 5)+31)>>5) * 32
"""
stack scan lines into matrix
"""
raw = np.array(raw).reshape(-1, lin_len).astype(np.int64)[:self.h, ...]
"""
separate 5 bits in each package, stopping when w is satisfied
"""
ba0 = raw[..., 0:5*((self.w+3)>>2):5]
ba1 = raw[..., 1:5*((self.w+3)>>2):5]
ba2 = raw[..., 2:5*((self.w+3)>>2):5]
ba3 = raw[..., 3:5*((self.w+3)>>2):5]
ba4 = raw[..., 4:5*((self.w+3)>>2):5]
"""
assemble 10 bit numbers
"""
ch0 = np.left_shift((np.left_shift(ba0, 2) + (ba4 % 4)), 6)
ch1 = np.left_shift((np.left_shift(ba1, 2) + (np.right_shift(ba4, 2) % 4)), 6)
ch2 = np.left_shift((np.left_shift(ba2, 2) + (np.right_shift(ba4, 4) % 4)), 6)
ch3 = np.left_shift((np.left_shift(ba3, 2) + (np.right_shift(ba4, 6) % 4)), 6)
"""
interleave bits
"""
mat = np.empty((self.h, self.w), dtype=ch0.dtype)
mat[..., 0::4] = ch0
mat[..., 1::4] = ch1
mat[..., 2::4] = ch2
mat[..., 3::4] = ch3
"""
There is som eleaking memory somewhere in the code. This code here
seemed to make things good enough that the code would run for
reasonable numbers of images, however this is techincally just a
workaround. (sorry)
"""
ba0, ba1, ba2, ba3, ba4 = None, None, None, None, None
del ba0, ba1, ba2, ba3, ba4
ch0, ch1, ch2, ch3 = None, None, None, None
del ch0, ch1, ch2, ch3
"""
same as before but 12 bit case
"""
elif self.sigbits == 12:
lin_len = ((((((self.w+self.pad+1)>>1)) * 3)+31)>>5) * 32
raw = np.array(raw).reshape(-1, lin_len).astype(np.int64)[:self.h, ...]
ba0 = raw[..., 0:3*((self.w+1)>>1):3]
ba1 = raw[..., 1:3*((self.w+1)>>1):3]
ba2 = raw[..., 2:3*((self.w+1)>>1):3]
ch0 = np.left_shift((np.left_shift(ba0, 4) + ba2 % 16), 4)
ch1 = np.left_shift((np.left_shift(ba1, 4) + (np.right_shift(ba2, 4)) % 16), 4)
mat = np.empty((self.h, self.w), dtype=ch0.dtype)
mat[..., 0::2] = ch0
mat[..., 1::2] = ch1
else:
"""
data is neither 10 nor 12 or incorrect data
"""
print('ERROR: wrong bit format, only 10 or 12 bit supported')
return 0
"""
separate bayer channels
"""
c0 = mat[0::2, 0::2]
c1 = mat[0::2, 1::2]
c2 = mat[1::2, 0::2]
c3 = mat[1::2, 1::2]
self.channels = [c0, c1, c2, c3]
return 1
"""
obtain 16x16 patch centred at macbeth square centre for each channel
"""
def get_patches(self, cen_coords, size=16):
"""
obtain channel widths and heights
"""
ch_w, ch_h = self.w, self.h
cen_coords = list(np.array((cen_coords[0])).astype(np.int32))
self.cen_coords = cen_coords
"""
squares are ordered by stacking macbeth chart columns from
left to right. Some useful patch indices:
white = 3
black = 23
'reds' = 9, 10
'blues' = 2, 5, 8, 20, 22
'greens' = 6, 12, 17
greyscale = 3, 7, 11, 15, 19, 23
"""
all_patches = []
for ch in self.channels:
ch_patches = []
for cen in cen_coords:
'''
macbeth centre is placed at top left of central 2x2 patch
to account for rounding
Patch pixels are sorted by pixel brightness so spatial
information is lost.
'''
patch = ch[cen[1]-7:cen[1]+9, cen[0]-7:cen[0]+9].flatten()
patch.sort()
if patch[-5] == (2**self.sigbits-1)*2**(16-self.sigbits):
self.saturated = True
ch_patches.append(patch)
# print('\nNew Patch\n')
all_patches.append(ch_patches)
# print('\n\nNew Channel\n\n')
self.patches = all_patches
return 1
def brcm_load_image(Cam, im_str):
"""
Load image where raw data and metadata is in the BRCM format
"""
try:
"""
create byte array
"""
with open(im_str, 'rb') as image:
f = image.read()
b = bytearray(f)
"""
return error if incorrect image address
"""
except FileNotFoundError:
print('\nERROR:\nInvalid image address')
Cam.log += '\nWARNING: Invalid image address'
return 0
"""
return error if problem reading file
"""
if f is None:
print('\nERROR:\nProblem reading file')
Cam.log += '\nWARNING: Problem readin file'
return 0
# print('\nLooking for EOI and BRCM header')
"""
find end of image followed by BRCM header by turning
bytearray into hex string and string matching with regexp
"""
start = -1
match = bytearray(b'\xff\xd9@BRCM')
match_str = binascii.hexlify(match)
b_str = binascii.hexlify(b)
"""
note index is divided by two to go from string to hex
"""
indices = [m.start()//2 for m in re.finditer(match_str, b_str)]
# print(indices)
try:
start = indices[0] + 3
except IndexError:
print('\nERROR:\nNo Broadcom header found')
Cam.log += '\nWARNING: No Broadcom header found!'
return 0
"""
extract data after header
"""
# print('\nExtracting data after header')
buf = b[start:start+32768]
Img = Image(buf)
Img.str = im_str
# print('Data found successfully')
"""
obtain metadata
"""
# print('\nReading metadata')
Img.get_meta()
Cam.log += '\nExposure : {} us'.format(Img.exposure)
Cam.log += '\nNormalised gain : {}'.format(Img.againQ8_norm)
# print('Metadata read successfully')
"""
obtain raw image data
"""
# print('\nObtaining raw image data')
raw = b[start+32768:]
Img.get_image(raw)
"""
delete raw to stop memory errors
"""
raw = None
del raw
# print('Raw image data obtained successfully')
return Img
def dng_load_image(Cam, im_str):
try:
Img = Image(None)
# RawPy doesn't load all the image tags that we need, so we use py3exiv2
metadata = pyexif.ImageMetadata(im_str)
metadata.read()
Img.ver = 100 # random value
"""
The DNG and TIFF/EP specifications use different IFDs to store the raw
image data and the Exif tags. DNG stores them in a SubIFD and in an Exif
IFD respectively (named "SubImage1" and "Photo" by pyexiv2), while
TIFF/EP stores them both in IFD0 (name "Image"). Both are used in "DNG"
files, with libcamera-apps following the DNG recommendation and
applications based on picamera2 following TIFF/EP.
This code detects which tags are being used, and therefore extracts the
correct values.
"""
try:
Img.w = metadata['Exif.SubImage1.ImageWidth'].value
subimage = "SubImage1"
photo = "Photo"
except KeyError:
Img.w = metadata['Exif.Image.ImageWidth'].value
subimage = "Image"
photo = "Image"
Img.pad = 0
Img.h = metadata[f'Exif.{subimage}.ImageLength'].value
white = metadata[f'Exif.{subimage}.WhiteLevel'].value
Img.sigbits = int(white).bit_length()
Img.fmt = (Img.sigbits - 4) // 2
Img.exposure = int(metadata[f'Exif.{photo}.ExposureTime'].value * 1000000)
Img.againQ8 = metadata[f'Exif.{photo}.ISOSpeedRatings'].value * 256 / 100
Img.againQ8_norm = Img.againQ8 / 256
Img.camName = metadata['Exif.Image.Model'].value
Img.blacklevel = int(metadata[f'Exif.{subimage}.BlackLevel'].value[0])
Img.blacklevel_16 = Img.blacklevel << (16 - Img.sigbits)
bayer_case = {
'0 1 1 2': (0, (0, 1, 2, 3)),
'1 2 0 1': (1, (2, 0, 3, 1)),
'2 1 1 0': (2, (3, 2, 1, 0)),
'1 0 2 1': (3, (1, 0, 3, 2))
}
cfa_pattern = metadata[f'Exif.{subimage}.CFAPattern'].value
Img.pattern = bayer_case[cfa_pattern][0]
Img.order = bayer_case[cfa_pattern][1]
# Now use RawPy tp get the raw Bayer pixels
raw_im = raw.imread(im_str)
raw_data = raw_im.raw_image
shift = 16 - Img.sigbits
c0 = np.left_shift(raw_data[0::2, 0::2].astype(np.int64), shift)
c1 = np.left_shift(raw_data[0::2, 1::2].astype(np.int64), shift)
c2 = np.left_shift(raw_data[1::2, 0::2].astype(np.int64), shift)
c3 = np.left_shift(raw_data[1::2, 1::2].astype(np.int64), shift)
Img.channels = [c0, c1, c2, c3]
Img.rgb = raw_im.postprocess()
except Exception:
print("\nERROR: failed to load DNG file", im_str)
print("Either file does not exist or is incompatible")
Cam.log += '\nERROR: DNG file does not exist or is incompatible'
raise
return Img
'''
load image from file location and perform calibration
check correct filetype
mac boolean is true if image is expected to contain macbeth chart and false
if not (alsc images don't have macbeth charts)
'''
def load_image(Cam, im_str, mac_config=None, show=False, mac=True, show_meta=False):
"""
check image is correct filetype
"""
if '.jpg' in im_str or '.jpeg' in im_str or '.brcm' in im_str or '.dng' in im_str:
if '.dng' in im_str:
Img = dng_load_image(Cam, im_str)
else:
Img = brcm_load_image(Cam, im_str)
"""
handle errors smoothly if loading image failed
"""
if Img == 0:
return 0
if show_meta:
Img.print_meta()
if mac:
"""
find macbeth centres, discarding images that are too dark or light
"""
av_chan = (np.mean(np.array(Img.channels), axis=0)/(2**16))
av_val = np.mean(av_chan)
# print(av_val)
if av_val < Img.blacklevel_16/(2**16)+1/64:
macbeth = None
print('\nError: Image too dark!')
Cam.log += '\nWARNING: Image too dark!'
else:
macbeth = find_macbeth(Cam, av_chan, mac_config)
"""
if no macbeth found return error
"""
if macbeth is None:
print('\nERROR: No macbeth chart found')
return 0
mac_cen_coords = macbeth[1]
# print('\nMacbeth centres located successfully')
"""
obtain image patches
"""
# print('\nObtaining image patches')
Img.get_patches(mac_cen_coords)
if Img.saturated:
print('\nERROR: Macbeth patches have saturated')
Cam.log += '\nWARNING: Macbeth patches have saturated!'
return 0
"""
clear memory
"""
Img.buf = None
del Img.buf
# print('Image patches obtained successfully')
"""
optional debug
"""
if show and __name__ == '__main__':
copy = sum(Img.channels)/2**18
copy = np.reshape(copy, (Img.h//2, Img.w//2)).astype(np.float64)
copy, _ = reshape(copy, 800)
represent(copy)
return Img
"""
return error if incorrect filetype
"""
else:
# print('\nERROR:\nInvalid file extension')
return 0
"""
bytearray splice to number little endian
"""
def ba_to_b(b):
total = 0
for i in range(len(b)):
total += 256**i * b[i]
return total
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