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格式化 #23

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255 changes: 156 additions & 99 deletions isp_pipeline.py
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2 changes: 1 addition & 1 deletion model/aaf.py
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Expand Up @@ -2,6 +2,7 @@
import numpy as np
from scipy.ndimage import correlate


class AAF:
'Anti-aliasing Filter'

Expand All @@ -23,4 +24,3 @@ def execute(self):
[1, 0, 1, 0, 1]])/16)
self.img = aaf_img
return self.img

1 change: 1 addition & 0 deletions model/awb.py
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@@ -1,6 +1,7 @@
#!/usr/bin/python
import numpy as np


class WBGC:
'Auto White Balance Gain Control'

Expand Down
1 change: 1 addition & 0 deletions model/bcc.py
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@@ -1,6 +1,7 @@
#!/usr/bin/python
import numpy as np


class BCC:
'Brightness Contrast Control'

Expand Down
4 changes: 2 additions & 2 deletions model/blc.py
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@@ -1,6 +1,7 @@
#!/usr/bin/python
import numpy as np


class BLC:
'Black Level Compensation'

Expand All @@ -23,7 +24,7 @@ def execute(self):
beta = self.parameter[5]
raw_h = self.img.shape[0]
raw_w = self.img.shape[1]
blc_img = np.empty((raw_h,raw_w), np.int16)
blc_img = np.empty((raw_h, raw_w), np.int16)
if self.bayer_pattern == 'rggb':
r = self.img[::2, ::2] + bl_r
b = self.img[1::2, 1::2] + bl_b
Expand Down Expand Up @@ -62,4 +63,3 @@ def execute(self):
blc_img[1::2, 1::2] = gb
self.img = blc_img
return self.clipping()

26 changes: 14 additions & 12 deletions model/bnf.py
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Original file line number Diff line number Diff line change
@@ -1,6 +1,7 @@
#!/usr/bin/python
import numpy as np


class BNF:
'Bilateral Noise Filtering'

Expand All @@ -25,22 +26,23 @@ def execute(self):
raw_h = self.img.shape[0]
raw_w = self.img.shape[1]
bnf_img = np.empty((raw_h, raw_w), np.uint16)
rdiff = np.zeros((5,5), dtype='uint16')
rdiff = np.zeros((5, 5), dtype='uint16')
for y in range(img_pad.shape[0] - 4):
for x in range(img_pad.shape[1] - 4):
for i in range(5):
for j in range(5):
rdiff[i,j] = abs(img_pad[y+i,x+j] - img_pad[y+2, x+2])
if rdiff[i,j] >= self.rthres[0]:
rdiff[i,j] = self.rw[0]
elif rdiff[i,j] < self.rthres[0] and rdiff[i,j] >= self.rthres[1]:
rdiff[i,j] = self.rw[1]
elif rdiff[i,j] < self.rthres[1] and rdiff[i,j] >= self.rthres[2]:
rdiff[i,j] = self.rw[2]
elif rdiff[i,j] < self.rthres[2]:
rdiff[i,j] = self.rw[3]
rdiff[i, j] = abs(
img_pad[y+i, x+j] - img_pad[y+2, x+2])
if rdiff[i, j] >= self.rthres[0]:
rdiff[i, j] = self.rw[0]
elif rdiff[i, j] < self.rthres[0] and rdiff[i, j] >= self.rthres[1]:
rdiff[i, j] = self.rw[1]
elif rdiff[i, j] < self.rthres[1] and rdiff[i, j] >= self.rthres[2]:
rdiff[i, j] = self.rw[2]
elif rdiff[i, j] < self.rthres[2]:
rdiff[i, j] = self.rw[3]
weights = np.multiply(rdiff, self.dw)
bnf_img[y,x] = np.sum(np.multiply(img_pad[y:y+5,x:x+5], weights[:,:])) / np.sum(weights)
bnf_img[y, x] = np.sum(np.multiply(
img_pad[y:y+5, x:x+5], weights[:, :])) / np.sum(weights)
self.img = bnf_img
return self.clipping()

11 changes: 6 additions & 5 deletions model/ccm.py
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@@ -1,6 +1,7 @@
#!/usr/bin/python
import numpy as np


class CCM:
'Color Correction Matrix'

Expand All @@ -15,10 +16,10 @@ def execute(self):
ccm_img = np.empty((img_h, img_w, img_c), np.uint32)
for y in range(img_h):
for x in range(img_w):
mulval = self.ccm[:,0:3] * self.img[y,x,:]
ccm_img[y,x,0] = np.sum(mulval[0]) + self.ccm[0,3]
ccm_img[y,x,1] = np.sum(mulval[1]) + self.ccm[1,3]
ccm_img[y,x,2] = np.sum(mulval[2]) + self.ccm[2,3]
ccm_img[y,x,:] = ccm_img[y,x,:] / 1024
mulval = self.ccm[:, 0:3] * self.img[y, x, :]
ccm_img[y, x, 0] = np.sum(mulval[0]) + self.ccm[0, 3]
ccm_img[y, x, 1] = np.sum(mulval[1]) + self.ccm[1, 3]
ccm_img[y, x, 2] = np.sum(mulval[2]) + self.ccm[2, 3]
ccm_img[y, x, :] = ccm_img[y, x, :] / 1024
self.img = ccm_img.astype(np.uint8)
return self.img
121 changes: 72 additions & 49 deletions model/cfa.py
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Original file line number Diff line number Diff line change
@@ -1,6 +1,7 @@
#!/usr/bin/python
import numpy as np


class CFA:
'Color Filter Array Interpolation'

Expand All @@ -21,33 +22,39 @@ def clipping(self):
def malvar(self, is_color, center, y, x, img):
if is_color == 'r':
r = center
g = 4 * img[y,x] - img[y-2,x] - img[y,x-2] - img[y+2,x] - img[y,x+2] \
+ 2 * (img[y+1,x] + img[y,x+1] + img[y-1,x] + img[y,x-1])
b = 6 * img[y,x] - 3 * (img[y-2,x] + img[y,x-2] + img[y+2,x] + img[y,x+2]) / 2 \
+ 2 * (img[y-1,x-1] + img[y-1,x+1] + img[y+1,x-1] + img[y+1,x+1])
g = 4 * img[y, x] - img[y-2, x] - img[y, x-2] - img[y+2, x] - img[y, x+2] \
+ 2 * (img[y+1, x] + img[y, x+1] + img[y-1, x] + img[y, x-1])
b = 6 * img[y, x] - 3 * (img[y-2, x] + img[y, x-2] + img[y+2, x] + img[y, x+2]) / 2 \
+ 2 * (img[y-1, x-1] + img[y-1, x+1] +
img[y+1, x-1] + img[y+1, x+1])
g = g / 8
b = b / 8
elif is_color == 'gr':
r = 5 * img[y,x] - img[y,x-2] - img[y-1,x-1] - img[y+1,x-1] - img[y-1,x+1] - img[y+1,x+1] - img[y,x+2] \
+ (img[y-2,x] + img[y+2,x]) / 2 + 4 * (img[y,x-1] + img[y,x+1])
r = 5 * img[y, x] - img[y, x-2] - img[y-1, x-1] - img[y+1, x-1] - img[y-1, x+1] - img[y+1, x+1] - img[y, x+2] \
+ (img[y-2, x] + img[y+2, x]) / 2 + \
4 * (img[y, x-1] + img[y, x+1])
g = center
b = 5 * img[y,x] - img[y-2,x] - img[y-1,x-1] - img[y-1,x+1] - img[y+2,x] - img[y+1,x-1] - img[y+1,x+1] \
+ (img[y,x-2] + img[y,x+2]) / 2 + 4 * (img[y-1,x] + img[y+1,x])
b = 5 * img[y, x] - img[y-2, x] - img[y-1, x-1] - img[y-1, x+1] - img[y+2, x] - img[y+1, x-1] - img[y+1, x+1] \
+ (img[y, x-2] + img[y, x+2]) / 2 + \
4 * (img[y-1, x] + img[y+1, x])
r = r / 8
b = b / 8
elif is_color == 'gb':
r = 5 * img[y,x] - img[y-2,x] - img[y-1,x-1] - img[y-1,x+1] - img[y+2,x] - img[y+1,x-1] - img[y+1,x+1] \
+ (img[y,x-2] + img[y,x+2]) / 2 + 4 * (img[y-1,x] + img[y+1,x])
r = 5 * img[y, x] - img[y-2, x] - img[y-1, x-1] - img[y-1, x+1] - img[y+2, x] - img[y+1, x-1] - img[y+1, x+1] \
+ (img[y, x-2] + img[y, x+2]) / 2 + \
4 * (img[y-1, x] + img[y+1, x])
g = center
b = 5 * img[y,x] - img[y,x-2] - img[y-1,x-1] - img[y+1,x-1] - img[y-1,x+1] - img[y+1,x+1] - img[y,x+2] \
+ (img[y-2,x] + img[y+2,x]) / 2 + 4 * (img[y,x-1] + img[y,x+1])
b = 5 * img[y, x] - img[y, x-2] - img[y-1, x-1] - img[y+1, x-1] - img[y-1, x+1] - img[y+1, x+1] - img[y, x+2] \
+ (img[y-2, x] + img[y+2, x]) / 2 + \
4 * (img[y, x-1] + img[y, x+1])
r = r / 8
b = b / 8
elif is_color == 'b':
r = 6 * img[y,x] - 3 * (img[y-2,x] + img[y,x-2] + img[y+2,x] + img[y,x+2]) / 2 \
+ 2 * (img[y-1,x-1] + img[y-1,x+1] + img[y+1,x-1] + img[y+1,x+1])
g = 4 * img[y,x] - img[y-2,x] - img[y,x-2] - img[y+2,x] - img[y,x+2] \
+ 2 * (img[y+1,x] + img[y,x+1] + img[y-1,x] + img[y,x-1])
r = 6 * img[y, x] - 3 * (img[y-2, x] + img[y, x-2] + img[y+2, x] + img[y, x+2]) / 2 \
+ 2 * (img[y-1, x-1] + img[y-1, x+1] +
img[y+1, x-1] + img[y+1, x+1])
g = 4 * img[y, x] - img[y-2, x] - img[y, x-2] - img[y+2, x] - img[y, x+2] \
+ 2 * (img[y+1, x] + img[y, x+1] + img[y-1, x] + img[y, x-1])
b = center
r = r / 8
g = g / 8
Expand All @@ -62,44 +69,60 @@ def execute(self):
for y in range(0, img_pad.shape[0]-4-1, 2):
for x in range(0, img_pad.shape[1]-4-1, 2):
if self.bayer_pattern == 'rggb':
r = img_pad[y+2,x+2]
gr = img_pad[y+2,x+3]
gb = img_pad[y+3,x+2]
b = img_pad[y+3,x+3]
r = img_pad[y+2, x+2]
gr = img_pad[y+2, x+3]
gb = img_pad[y+3, x+2]
b = img_pad[y+3, x+3]
if self.mode == 'malvar':
cfa_img[y,x,:] = self.malvar('r', r, y+2,x+2, img_pad)
cfa_img[y,x+1,:] = self.malvar('gr', gr, y+2,x+3, img_pad)
cfa_img[y+1,x,:] = self.malvar('gb', gb, y+3,x+2, img_pad)
cfa_img[y+1,x+1,:] = self.malvar('b', b, y+3,x+3, img_pad)
cfa_img[y, x, :] = self.malvar(
'r', r, y+2, x+2, img_pad)
cfa_img[y, x+1,
:] = self.malvar('gr', gr, y+2, x+3, img_pad)
cfa_img[y+1, x,
:] = self.malvar('gb', gb, y+3, x+2, img_pad)
cfa_img[y+1, x+1,
:] = self.malvar('b', b, y+3, x+3, img_pad)
elif self.bayer_pattern == 'bggr':
b = img_pad[y+2,x+2]
gb = img_pad[y+2,x+3]
gr = img_pad[y+3,x+2]
r = img_pad[y+3,x+3]
b = img_pad[y+2, x+2]
gb = img_pad[y+2, x+3]
gr = img_pad[y+3, x+2]
r = img_pad[y+3, x+3]
if self.mode == 'malvar':
cfa_img[y,x,:] = self.malvar('b', b, y+2,x+2, img_pad)
cfa_img[y,x+1,:] = self.malvar('gb', gb, y+2,x+3, img_pad)
cfa_img[y+1,x,:] = self.malvar('gr', gr, y+3,x+2, img_pad)
cfa_img[y+1,x+1,:] = self.malvar('r', r, y+3,x+3, img_pad)
cfa_img[y, x, :] = self.malvar(
'b', b, y+2, x+2, img_pad)
cfa_img[y, x+1,
:] = self.malvar('gb', gb, y+2, x+3, img_pad)
cfa_img[y+1, x,
:] = self.malvar('gr', gr, y+3, x+2, img_pad)
cfa_img[y+1, x+1,
:] = self.malvar('r', r, y+3, x+3, img_pad)
elif self.bayer_pattern == 'gbrg':
gb = img_pad[y+2,x+2]
b = img_pad[y+2,x+3]
r = img_pad[y+3,x+2]
gr = img_pad[y+3,x+3]
gb = img_pad[y+2, x+2]
b = img_pad[y+2, x+3]
r = img_pad[y+3, x+2]
gr = img_pad[y+3, x+3]
if self.mode == 'malvar':
cfa_img[y,x,:] = self.malvar('gb', gb, y+2,x+2, img_pad)
cfa_img[y,x+1,:] = self.malvar('b', b, y+2,x+3, img_pad)
cfa_img[y+1,x,:] = self.malvar('r', r, y+3,x+2, img_pad)
cfa_img[y+1,x+1,:] = self.malvar('gr', gr, y+3,x+3, img_pad)
cfa_img[y, x, :] = self.malvar(
'gb', gb, y+2, x+2, img_pad)
cfa_img[y, x+1,
:] = self.malvar('b', b, y+2, x+3, img_pad)
cfa_img[y+1, x,
:] = self.malvar('r', r, y+3, x+2, img_pad)
cfa_img[y+1, x+1,
:] = self.malvar('gr', gr, y+3, x+3, img_pad)
elif self.bayer_pattern == 'grbg':
gr = img_pad[y+2,x+2]
r = img_pad[y+2,x+3]
b = img_pad[y+3,x+2]
gb = img_pad[y+3,x+3]
gr = img_pad[y+2, x+2]
r = img_pad[y+2, x+3]
b = img_pad[y+3, x+2]
gb = img_pad[y+3, x+3]
if self.mode == 'malvar':
cfa_img[y,x,:] = self.malvar('gr', gr, y+2,x+2, img_pad)
cfa_img[y,x+1,:] = self.malvar('r', r, y+2,x+3, img_pad)
cfa_img[y+1,x,:] = self.malvar('b', b, y+3,x+2, img_pad)
cfa_img[y+1,x+1,:] = self.malvar('gb', gb, y+3,x+3, img_pad)
cfa_img[y, x, :] = self.malvar(
'gr', gr, y+2, x+2, img_pad)
cfa_img[y, x+1,
:] = self.malvar('r', r, y+2, x+3, img_pad)
cfa_img[y+1, x,
:] = self.malvar('b', b, y+3, x+2, img_pad)
cfa_img[y+1, x+1,
:] = self.malvar('gb', gb, y+3, x+3, img_pad)
self.img = cfa_img
return self.clipping()
return self.clipping()
18 changes: 11 additions & 7 deletions model/cnf.py
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Original file line number Diff line number Diff line change
@@ -1,6 +1,7 @@
#!/usr/bin/python
import numpy as np


class CNF:
'Chroma Noise Filtering'

Expand Down Expand Up @@ -89,13 +90,14 @@ def cnd(self, y, x, img):
for i in range(y - 4, y + 4, 1):
for j in range(x - 4, x + 4, 1):
if i % 2 == 1 and j % 2 == 0:
avgG = avgG + img[i,j]
avgG = avgG + img[i, j]
elif i % 2 == 0 and j % 2 == 1:
avgG = avgG + img[i, j]
elif i % 2 == 0 and j % 2 == 0:
avgC1 = avgC1 + img[i,j] # weights are equal, could be as gaussian dist
# weights are equal, could be as gaussian dist
avgC1 = avgC1 + img[i, j]
elif i % 2 == 1 and j % 2 == 1:
avgC2 = avgC2 + img[i,j]
avgC2 = avgC2 + img[i, j]
avgG = avgG / 40
avgC1 = avgC1 / 25
avgC2 = avgC2 / 16
Expand All @@ -112,9 +114,9 @@ def cnd(self, y, x, img):
def cnf(self, is_color, y, x, img):
is_noise, avgG, avgC1, avgC2 = self.cnd(y, x, img)
if is_noise:
pix_out = self.cnc(is_color, img[y,x], avgG, avgC1, avgC2)
pix_out = self.cnc(is_color, img[y, x], avgG, avgC1, avgC2)
else:
pix_out = img[y,x]
pix_out = img[y, x]
return pix_out

def execute(self):
Expand All @@ -132,7 +134,8 @@ def execute(self):
cnf_img[y, x] = self.cnf('r', y + 4, x + 4, img_pad)
cnf_img[y, x + 1] = gr
cnf_img[y + 1, x] = gb
cnf_img[y + 1, x + 1] = self.cnf('b', y + 5, x + 5, img_pad)
cnf_img[y + 1, x +
1] = self.cnf('b', y + 5, x + 5, img_pad)
elif self.bayer_pattern == 'bggr':
b = img_pad[y + 4, x + 4]
gb = img_pad[y + 4, x + 5]
Expand All @@ -141,7 +144,8 @@ def execute(self):
cnf_img[y, x] = self.cnf('b', y + 4, x + 4, img_pad)
cnf_img[y, x + 1] = gb
cnf_img[y + 1, x] = gr
cnf_img[y + 1, x + 1] = self.cnf('r', y + 5, x + 5, img_pad)
cnf_img[y + 1, x +
1] = self.cnf('r', y + 5, x + 5, img_pad)
elif self.bayer_pattern == 'gbrg':
gb = img_pad[y + 4, x + 4]
b = img_pad[y + 4, x + 5]
Expand Down
13 changes: 10 additions & 3 deletions model/csc.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,6 +2,7 @@
import numpy as np
from scipy.ndimage import correlate


class CSC:
'Color Space Conversion'

Expand All @@ -22,9 +23,15 @@ def execute(self):
# csc_img[y,x,2] = np.sum(mulval[2]) + self.csc[2,3]
# csc_img[y,x,:] = csc_img[y,x,:] / 1024

csc_img[:, :, 0] = self.img[:, :, 0] * self.csc[0, 0] + self.img[:, :, 1] * self.csc[0, 1] + self.img[:, :, 2] * self.csc[0, 2] + self.csc[0, 3]
csc_img[:, :, 1] = self.img[:, :, 0] * self.csc[1, 0] + self.img[:, :, 1] * self.csc[1, 1] + self.img[:, :, 2] * self.csc[1, 2] + self.csc[1, 3]
csc_img[:, :, 2] = self.img[:, :, 0] * self.csc[2, 0] + self.img[:, :, 1] * self.csc[2, 1] + self.img[:, :, 2] * self.csc[2, 2] + self.csc[2, 3]
csc_img[:, :, 0] = self.img[:, :, 0] * self.csc[0, 0] + self.img[:, :, 1] * \
self.csc[0, 1] + self.img[:, :, 2] * \
self.csc[0, 2] + self.csc[0, 3]
csc_img[:, :, 1] = self.img[:, :, 0] * self.csc[1, 0] + self.img[:, :, 1] * \
self.csc[1, 1] + self.img[:, :, 2] * \
self.csc[1, 2] + self.csc[1, 3]
csc_img[:, :, 2] = self.img[:, :, 0] * self.csc[2, 0] + self.img[:, :, 1] * \
self.csc[2, 1] + self.img[:, :, 2] * \
self.csc[2, 2] + self.csc[2, 3]
csc_img = csc_img / 1024

self.img = csc_img.astype(np.uint8)
Expand Down
2 changes: 1 addition & 1 deletion model/dpc.py
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Original file line number Diff line number Diff line change
@@ -1,6 +1,7 @@
#!/usr/bin/python
import numpy as np


class DPC:
'Dead Pixel Correction'

Expand Down Expand Up @@ -55,4 +56,3 @@ def execute(self):
dpc_img[y, x] = p0
self.img = dpc_img
return self.clipping()

8 changes: 6 additions & 2 deletions model/eeh.py
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Original file line number Diff line number Diff line change
@@ -1,6 +1,7 @@
#!/usr/bin/python
import numpy as np


class EE:
'Edge Enhancement'

Expand Down Expand Up @@ -43,7 +44,10 @@ def execute(self):
em_img = np.empty((img_h, img_w), np.int16)
for y in range(img_pad.shape[0] - 2):
for x in range(img_pad.shape[1] - 4):
em_img[y,x] = np.sum(np.multiply(img_pad[y:y+3, x:x+5], self.edge_filter[:, :])) / 8
ee_img[y,x] = img_pad[y+1,x+2] + self.emlut(em_img[y,x], self.thres, self.gain, self.emclip)
em_img[y, x] = np.sum(np.multiply(
img_pad[y:y+3, x:x+5], self.edge_filter[:, :])) / 8
ee_img[y, x] = img_pad[y+1, x+2] + \
self.emlut(em_img[y, x], self.thres,
self.gain, self.emclip)
self.img = ee_img
return self.clipping(), em_img
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