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-rw-r--r--utils/raspberrypi/ctt/ctt_image_load.py116
1 files changed, 58 insertions, 58 deletions
diff --git a/utils/raspberrypi/ctt/ctt_image_load.py b/utils/raspberrypi/ctt/ctt_image_load.py
index dd7adb16..7d097118 100644
--- a/utils/raspberrypi/ctt/ctt_image_load.py
+++ b/utils/raspberrypi/ctt/ctt_image_load.py
@@ -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
generated by cgit v1.2.1 (git 2.18.0) at 2024-11-05 02:11:57 +0000