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utils: raspberrypi: ctt: Fix pycodestyle E231

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E231 missing whitespace after ','
E231 missing whitespace after ':'

Signed-off-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com>
Reviewed-by: Kieran Bingham <kieran.bingham@ideasonboard.com>
Reviewed-by: David Plowman <david.plowman@raspberrypi.com>
This commit is contained in:
Laurent Pinchart 2020-05-02 03:32:00 +03:00
parent 7a653369cb
commit 93a133fb17
11 changed files with 493 additions and 493 deletions
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116
utils/raspberrypi/ctt/ctt_image_load.py
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@ -18,7 +18,7 @@ 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):
def __init__(self, buf):
self.buf = buf
self.patches = None
self.saturated = False
@ -45,11 +45,11 @@ class Image:
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
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]
@ -87,7 +87,7 @@ class Image:
"""
get image from raw scanline data
"""
def get_image(self,raw):
def get_image(self, raw):
self.dptr = []
"""
check if data is 10 or 12 bits
@ -100,31 +100,31 @@ class Image:
"""
stack scan lines into matrix
"""
raw = np.array(raw).reshape(-1,lin_len).astype(np.int64)[:self.h,...]
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]
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)
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 = 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
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
@ -132,25 +132,25 @@ class Image:
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
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
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:
"""
@ -162,21 +162,21 @@ class Image:
"""
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]
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):
def get_patches(self, cen_coords, size=16):
"""
obtain channel widths and heights
"""
ch_w,ch_h = self.w,self.h
ch_w, ch_h = self.w, self.h
cen_coords = list(np.array((cen_coords[0])).astype(np.int32))
self.cen_coords = cen_coords
"""
@ -184,10 +184,10 @@ class Image:
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
'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:
@ -199,7 +199,7 @@ class Image:
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 = 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
@ -218,7 +218,7 @@ def brcm_load_image(Cam, im_str):
"""
create byte array
"""
with open(im_str,'rb') as image:
with open(im_str, 'rb') as image:
f = image.read()
b = bytearray(f)
"""
@ -249,7 +249,7 @@ def brcm_load_image(Cam, im_str):
"""
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)]
indices = [m.start()//2 for m in re.finditer(match_str, b_str)]
# print(indices)
try:
start = indices[0] + 3
@ -325,10 +325,10 @@ def dng_load_image(Cam, im_str):
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)
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]
except:
@ -347,7 +347,7 @@ 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):
def load_image(Cam, im_str, mac_config=None, show=False, mac=True, show_meta=False):
"""
check image is correct filetype
"""
@ -363,7 +363,7 @@ def load_image(Cam,im_str,mac_config=None,show=False,mac=True,show_meta=False):
"""
find macbeth centres, discarding images that are too dark or light
"""
av_chan = (np.mean(np.array(Img.channels),axis=0)/(2**16))
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:
@ -371,7 +371,7 @@ def load_image(Cam,im_str,mac_config=None,show=False,mac=True,show_meta=False):
print('\nError: Image too dark!')
Cam.log += '\nWARNING: Image too dark!'
else:
macbeth = find_macbeth(Cam,av_chan,mac_config)
macbeth = find_macbeth(Cam, av_chan, mac_config)
"""
if no macbeth found return error
@ -405,8 +405,8 @@ def load_image(Cam,im_str,mac_config=None,show=False,mac=True,show_meta=False):
"""
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)
copy = np.reshape(copy, (Img.h//2, Img.w//2)).astype(np.float64)
copy, _ = reshape(copy, 800)
represent(copy)
return Img