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import numpy as np
import math
import time
import utility
from scipy import signal
# =============================================================
# function: dbayer_mhc
# demosaicing using Malvar-He-Cutler algorithm
# http://www.ipol.im/pub/art/2011/g_mhcd/
# =============================================================
def debayer_mhc(raw, bayer_pattern="rggb", clip_range=[0, 65535], timeshow=False):
# convert to float32 in case it was not
raw = np.float32(raw)
# dimensions
width, height = utility.helpers(raw).get_width_height()
# number of pixels to pad
no_of_pixel_pad = 2
raw = np.pad(raw, \
(no_of_pixel_pad, no_of_pixel_pad),\
'reflect') # reflect would not repeat the border value
# allocate space for the R, G, B planes
R = np.empty( (height + no_of_pixel_pad * 2, width + no_of_pixel_pad * 2), dtype = np.float32 )
G = np.empty( (height + no_of_pixel_pad * 2, width + no_of_pixel_pad * 2), dtype = np.float32 )
B = np.empty( (height + no_of_pixel_pad * 2, width + no_of_pixel_pad * 2), dtype = np.float32 )
# create a RGB output
demosaic_out = np.empty( (height, width, 3), dtype = np.float32 )
# fill up the directly available values according to the Bayer pattern
if (bayer_pattern == "rggb"):
G[::2, 1::2] = raw[::2, 1::2]
G[1::2, ::2] = raw[1::2, ::2]
R[::2, ::2] = raw[::2, ::2]
B[1::2, 1::2] = raw[1::2, 1::2]
# Green channel
for i in range(no_of_pixel_pad, height + no_of_pixel_pad):
# to display progress
t0 = time.process_time()
for j in range(no_of_pixel_pad, width + no_of_pixel_pad):
# G at Red location
if (((i % 2) == 0) and ((j % 2) == 0)):
G[i, j] = 0.125 * np.sum([-1. * R[i-2, j], \
2. * G[i-1, j], \
-1. * R[i, j-2], 2. * G[i, j-1], 4. * R[i,j], 2. * G[i, j+1], -1. * R[i, j+2],\
2. * G[i+1, j], \
-1. * R[i+2, j]])
# G at Blue location
elif (((i % 2) != 0) and ((j % 2) != 0)):
G[i, j] = 0.125 * np.sum([-1. * B[i-2, j], \
2. * G[i-1, j], \
-1. * B[i, j-2], 2. * G[i, j-1], 4. * B[i,j], 2. * G[i, j+1], -1. * B[i, j+2], \
2. * G[i+1, j],\
-1. * B[i+2, j]])
if (timeshow):
elapsed_time = time.process_time() - t0
print("Green: row index: " + str(i-1) + " of " + str(height) + \
" | elapsed time: " + "{:.3f}".format(elapsed_time) + " seconds")
# Red and Blue channel
for i in range(no_of_pixel_pad, height + no_of_pixel_pad):
# to display progress
t0 = time.process_time()
for j in range(no_of_pixel_pad, width + no_of_pixel_pad):
# Green locations in Red rows
if (((i % 2) == 0) and ((j % 2) != 0)):
# R at Green locations in Red rows
R[i, j] = 0.125 * np.sum([.5 * G[i-2, j],\
-1. * G[i-1, j-1], -1. * G[i-1, j+1], \
-1. * G[i, j-2], 4. * R[i, j-1], 5. * G[i,j], 4. * R[i, j+1], -1. * G[i, j+2], \
-1. * G[i+1, j-1], -1. * G[i+1, j+1], \
.5 * G[i+2, j]])
# B at Green locations in Red rows
B[i, j] = 0.125 * np.sum([-1. * G[i-2, j], \
-1. * G[i-1, j-1], 4. * B[i-1, j], -1. * G[i-1, j+1], \
.5 * G[i, j-2], 5. * G[i,j], .5 * G[i, j+2], \
-1. * G[i+1, j-1], 4. * B[i+1,j], -1. * G[i+1, j+1], \
-1. * G[i+2, j]])
# Green locations in Blue rows
elif (((i % 2) != 0) and ((j % 2) == 0)):
# R at Green locations in Blue rows
R[i, j] = 0.125 * np.sum([-1. * G[i-2, j], \
-1. * G[i-1, j-1], 4. * R[i-1, j], -1. * G[i-1, j+1], \
.5 * G[i, j-2], 5. * G[i,j], .5 * G[i, j+2], \
-1. * G[i+1, j-1], 4. * R[i+1, j], -1. * G[i+1, j+1], \
-1. * G[i+2, j]])
# B at Green locations in Blue rows
B[i, j] = 0.125 * np.sum([.5 * G[i-2, j], \
-1. * G [i-1, j-1], -1. * G[i-1, j+1], \
-1. * G[i, j-2], 4. * B[i, j-1], 5. * G[i,j], 4. * B[i, j+1], -1. * G[i, j+2], \
-1. * G[i+1, j-1], -1. * G[i+1, j+1], \
.5 * G[i+2, j]])
# R at Blue locations
elif (((i % 2) != 0) and ((j % 2) != 0)):
R[i, j] = 0.125 * np.sum([-1.5 * B[i-2, j], \
2. * R[i-1, j-1], 2. * R[i-1, j+1], \
-1.5 * B[i, j-2], 6. * B[i,j], -1.5 * B[i, j+2], \
2. * R[i+1, j-1], 2. * R[i+1, j+1], \
-1.5 * B[i+2, j]])
# B at Red locations
elif (((i % 2) == 0) and ((j % 2) == 0)):
B[i, j] = 0.125 * np.sum([-1.5 * R[i-2, j], \
2. * B[i-1, j-1], 2. * B[i-1, j+1], \
-1.5 * R[i, j-2], 6. * R[i,j], -1.5 * R[i, j+2], \
2. * B[i+1, j-1], 2. * B[i+1, j+1], \
-1.5 * R[i+2, j]])
if (timeshow):
elapsed_time = time.process_time() - t0
print("Red/Blue: row index: " + str(i-1) + " of " + str(height) + \
" | elapsed time: " + "{:.3f}".format(elapsed_time) + " seconds")
elif (bayer_pattern == "gbrg"):
G[::2, ::2] = raw[::2, ::2]
G[1::2, 1::2] = raw[1::2, 1::2]
R[1::2, ::2] = raw[1::2, ::2]
B[::2, 1::2] = raw[::2, 1::2]
# Green channel
for i in range(no_of_pixel_pad, height + no_of_pixel_pad):
# to display progress
t0 = time.process_time()
for j in range(no_of_pixel_pad, width + no_of_pixel_pad):
# G at Red location
if (((i % 2) != 0) and ((j % 2) == 0)):
G[i, j] = 0.125 * np.sum([-1. * R[i-2, j], \
2. * G[i-1, j], \
-1. * R[i, j-2], 2. * G[i, j-1], 4. * R[i,j], 2. * G[i, j+1], -1. * R[i, j+2],\
2. * G[i+1, j], \
-1. * R[i+2, j]])
# G at Blue location
elif (((i % 2) == 0) and ((j % 2) != 0)):
G[i, j] = 0.125 * np.sum([-1. * B[i-2, j], \
2. * G[i-1, j], \
-1. * B[i, j-2], 2. * G[i, j-1], 4. * B[i,j], 2. * G[i, j+1], -1. * B[i, j+2], \
2. * G[i+1, j],\
-1. * B[i+2, j]])
if (timeshow):
elapsed_time = time.process_time() - t0
print("Green: row index: " + str(i-1) + " of " + str(height) + \
" | elapsed time: " + "{:.3f}".format(elapsed_time) + " seconds")
# Red and Blue channel
for i in range(no_of_pixel_pad, height + no_of_pixel_pad):
# to display progress
t0 = time.process_time()
for j in range(no_of_pixel_pad, width + no_of_pixel_pad):
# Green locations in Red rows
if (((i % 2) != 0) and ((j % 2) != 0)):
# R at Green locations in Red rows
R[i, j] = 0.125 * np.sum([.5 * G[i-2, j],\
-1. * G[i-1, j-1], -1. * G[i-1, j+1], \
-1. * G[i, j-2], 4. * R[i, j-1], 5. * G[i,j], 4. * R[i, j+1], -1. * G[i, j+2], \
-1. * G[i+1, j-1], -1. * G[i+1, j+1], \
.5 * G[i+2, j]])
# B at Green locations in Red rows
B[i, j] = 0.125 * np.sum([-1. * G[i-2, j], \
-1. * G[i-1, j-1], 4. * B[i-1, j], -1. * G[i-1, j+1], \
.5 * G[i, j-2], 5. * G[i,j], .5 * G[i, j+2], \
-1. * G[i+1, j-1], 4. * B[i+1,j], -1. * G[i+1, j+1], \
-1. * G[i+2, j]])
# Green locations in Blue rows
elif (((i % 2) == 0) and ((j % 2) == 0)):
# R at Green locations in Blue rows
R[i, j] = 0.125 * np.sum([-1. * G[i-2, j], \
-1. * G[i-1, j-1], 4. * R[i-1, j], -1. * G[i-1, j+1], \
.5 * G[i, j-2], 5. * G[i,j], .5 * G[i, j+2], \
-1. * G[i+1, j-1], 4. * R[i+1, j], -1. * G[i+1, j+1], \
-1. * G[i+2, j]])
# B at Green locations in Blue rows
B[i, j] = 0.125 * np.sum([.5 * G[i-2, j], \
-1. * G [i-1, j-1], -1. * G[i-1, j+1], \
-1. * G[i, j-2], 4. * B[i, j-1], 5. * G[i,j], 4. * B[i, j+1], -1. * G[i, j+2], \
-1. * G[i+1, j-1], -1. * G[i+1, j+1], \
.5 * G[i+2, j]])
# R at Blue locations
elif (((i % 2) == 0) and ((j % 2) != 0)):
R[i, j] = 0.125 * np.sum([-1.5 * B[i-2, j], \
2. * R[i-1, j-1], 2. * R[i-1, j+1], \
-1.5 * B[i, j-2], 6. * B[i,j], -1.5 * B[i, j+2], \
2. * R[i+1, j-1], 2. * R[i+1, j+1], \
-1.5 * B[i+2, j]])
# B at Red locations
elif (((i % 2) != 0) and ((j % 2) == 0)):
B[i, j] = 0.125 * np.sum([-1.5 * R[i-2, j], \
2. * B[i-1, j-1], 2. * B[i-1, j+1], \
-1.5 * R[i, j-2], 6. * R[i,j], -1.5 * R[i, j+2], \
2. * B[i+1, j-1], 2. * B[i+1, j+1], \
-1.5 * R[i+2, j]])
if (timeshow):
elapsed_time = time.process_time() - t0
print("Red/Blue: row index: " + str(i-1) + " of " + str(height) + \
" | elapsed time: " + "{:.3f}".format(elapsed_time) + " seconds")
elif (bayer_pattern == "grbg"):
G[::2, ::2] = raw[::2, ::2]
G[1::2, 1::2] = raw[1::2, 1::2]
R[::2, 1::2] = raw[::2, 1::2]
B[1::2, ::2] = raw[1::2, ::2]
# Green channel
for i in range(no_of_pixel_pad, height + no_of_pixel_pad):
# to display progress
t0 = time.process_time()
for j in range(no_of_pixel_pad, width + no_of_pixel_pad):
# G at Red location
if (((i % 2) == 0) and ((j % 2) != 0)):
G[i, j] = 0.125 * np.sum([-1. * R[i-2, j], \
2. * G[i-1, j], \
-1. * R[i, j-2], 2. * G[i, j-1], 4. * R[i,j], 2. * G[i, j+1], -1. * R[i, j+2],\
2. * G[i+1, j], \
-1. * R[i+2, j]])
# G at Blue location
elif (((i % 2) != 0) and ((j % 2) == 0)):
G[i, j] = 0.125 * np.sum([-1. * B[i-2, j], \
2. * G[i-1, j], \
-1. * B[i, j-2], 2. * G[i, j-1], 4. * B[i,j], 2. * G[i, j+1], -1. * B[i, j+2], \
2. * G[i+1, j],\
-1. * B[i+2, j]])
if (timeshow):
elapsed_time = time.process_time() - t0
print("Green: row index: " + str(i-1) + " of " + str(height) + \
" | elapsed time: " + "{:.3f}".format(elapsed_time) + " seconds")
# Red and Blue channel
for i in range(no_of_pixel_pad, height + no_of_pixel_pad):
# to display progress
t0 = time.process_time()
for j in range(no_of_pixel_pad, width + no_of_pixel_pad):
# Green locations in Red rows
if (((i % 2) == 0) and ((j % 2) == 0)):
# R at Green locations in Red rows
R[i, j] = 0.125 * np.sum([.5 * G[i-2, j],\
-1. * G[i-1, j-1], -1. * G[i-1, j+1], \
-1. * G[i, j-2], 4. * R[i, j-1], 5. * G[i,j], 4. * R[i, j+1], -1. * G[i, j+2], \
-1. * G[i+1, j-1], -1. * G[i+1, j+1], \
.5 * G[i+2, j]])
# B at Green locations in Red rows
B[i, j] = 0.125 * np.sum([-1. * G[i-2, j], \
-1. * G[i-1, j-1], 4. * B[i-1, j], -1. * G[i-1, j+1], \
.5 * G[i, j-2], 5. * G[i,j], .5 * G[i, j+2], \
-1. * G[i+1, j-1], 4. * B[i+1,j], -1. * G[i+1, j+1], \
-1. * G[i+2, j]])
# Green locations in Blue rows
elif (((i % 2) != 0) and ((j % 2) != 0)):
# R at Green locations in Blue rows
R[i, j] = 0.125 * np.sum([-1. * G[i-2, j], \
-1. * G[i-1, j-1], 4. * R[i-1, j], -1. * G[i-1, j+1], \
.5 * G[i, j-2], 5. * G[i,j], .5 * G[i, j+2], \
-1. * G[i+1, j-1], 4. * R[i+1, j], -1. * G[i+1, j+1], \
-1. * G[i+2, j]])
# B at Green locations in Blue rows
B[i, j] = 0.125 * np.sum([.5 * G[i-2, j], \
-1. * G [i-1, j-1], -1. * G[i-1, j+1], \
-1. * G[i, j-2], 4. * B[i, j-1], 5. * G[i,j], 4. * B[i, j+1], -1. * G[i, j+2], \
-1. * G[i+1, j-1], -1. * G[i+1, j+1], \
.5 * G[i+2, j]])
# R at Blue locations
elif (((i % 2) != 0) and ((j % 2) == 0)):
R[i, j] = 0.125 * np.sum([-1.5 * B[i-2, j], \
2. * R[i-1, j-1], 2. * R[i-1, j+1], \
-1.5 * B[i, j-2], 6. * B[i,j], -1.5 * B[i, j+2], \
2. * R[i+1, j-1], 2. * R[i+1, j+1], \
-1.5 * B[i+2, j]])
# B at Red locations
elif (((i % 2) == 0) and ((j % 2) != 0)):
B[i, j] = 0.125 * np.sum([-1.5 * R[i-2, j], \
2. * B[i-1, j-1], 2. * B[i-1, j+1], \
-1.5 * R[i, j-2], 6. * R[i,j], -1.5 * R[i, j+2], \
2. * B[i+1, j-1], 2. * B[i+1, j+1], \
-1.5 * R[i+2, j]])
if (timeshow):
elapsed_time = time.process_time() - t0
print("Red/Blue: row index: " + str(i-1) + " of " + str(height) + \
" | elapsed time: " + "{:.3f}".format(elapsed_time) + " seconds")
elif (bayer_pattern == "bggr"):
G[::2, 1::2] = raw[::2, 1::2]
G[1::2, ::2] = raw[1::2, ::2]
R[1::2, 1::2] = raw[1::2, 1::2]
B[::2, ::2] = raw[::2, ::2]
# Green channel
for i in range(no_of_pixel_pad, height + no_of_pixel_pad):
# to display progress
t0 = time.process_time()
for j in range(no_of_pixel_pad, width + no_of_pixel_pad):
# G at Red location
if (((i % 2) != 0) and ((j % 2) != 0)):
G[i, j] = 0.125 * np.sum([-1. * R[i-2, j], \
2. * G[i-1, j], \
-1. * R[i, j-2], 2. * G[i, j-1], 4. * R[i,j], 2. * G[i, j+1], -1. * R[i, j+2],\
2. * G[i+1, j], \
-1. * R[i+2, j]])
# G at Blue location
elif (((i % 2) == 0) and ((j % 2) == 0)):
G[i, j] = 0.125 * np.sum([-1. * B[i-2, j], \
2. * G[i-1, j], \
-1. * B[i, j-2], 2. * G[i, j-1], 4. * B[i,j], 2. * G[i, j+1], -1. * B[i, j+2], \
2. * G[i+1, j],\
-1. * B[i+2, j]])
if (timeshow):
elapsed_time = time.process_time() - t0
print("Green: row index: " + str(i-1) + " of " + str(height) + \
" | elapsed time: " + "{:.3f}".format(elapsed_time) + " seconds")
# Red and Blue channel
for i in range(no_of_pixel_pad, height + no_of_pixel_pad):
# to display progress
t0 = time.process_time()
for j in range(no_of_pixel_pad, width + no_of_pixel_pad):
# Green locations in Red rows
if (((i % 2) != 0) and ((j % 2) == 0)):
# R at Green locations in Red rows
R[i, j] = 0.125 * np.sum([.5 * G[i-2, j],\
-1. * G[i-1, j-1], -1. * G[i-1, j+1], \
-1. * G[i, j-2], 4. * R[i, j-1], 5. * G[i,j], 4. * R[i, j+1], -1. * G[i, j+2], \
-1. * G[i+1, j-1], -1. * G[i+1, j+1], \
.5 * G[i+2, j]])
# B at Green locations in Red rows
B[i, j] = 0.125 * np.sum([-1. * G[i-2, j], \
-1. * G[i-1, j-1], 4. * B[i-1, j], -1. * G[i-1, j+1], \
.5 * G[i, j-2], 5. * G[i,j], .5 * G[i, j+2], \
-1. * G[i+1, j-1], 4. * B[i+1,j], -1. * G[i+1, j+1], \
-1. * G[i+2, j]])
# Green locations in Blue rows
elif (((i % 2) == 0) and ((j % 2) != 0)):
# R at Green locations in Blue rows
R[i, j] = 0.125 * np.sum([-1. * G[i-2, j], \
-1. * G[i-1, j-1], 4. * R[i-1, j], -1. * G[i-1, j+1], \
.5 * G[i, j-2], 5. * G[i,j], .5 * G[i, j+2], \
-1. * G[i+1, j-1], 4. * R[i+1, j], -1. * G[i+1, j+1], \
-1. * G[i+2, j]])
# B at Green locations in Blue rows
B[i, j] = 0.125 * np.sum([.5 * G[i-2, j], \
-1. * G [i-1, j-1], -1. * G[i-1, j+1], \
-1. * G[i, j-2], 4. * B[i, j-1], 5. * G[i,j], 4. * B[i, j+1], -1. * G[i, j+2], \
-1. * G[i+1, j-1], -1. * G[i+1, j+1], \
.5 * G[i+2, j]])
# R at Blue locations
elif (((i % 2) == 0) and ((j % 2) == 0)):
R[i, j] = 0.125 * np.sum([-1.5 * B[i-2, j], \
2. * R[i-1, j-1], 2. * R[i-1, j+1], \
-1.5 * B[i, j-2], 6. * B[i,j], -1.5 * B[i, j+2], \
2. * R[i+1, j-1], 2. * R[i+1, j+1], \
-1.5 * B[i+2, j]])
# B at Red locations
elif (((i % 2) != 0) and ((j % 2) != 0)):
B[i, j] = 0.125 * np.sum([-1.5 * R[i-2, j], \
2. * B[i-1, j-1], 2. * B[i-1, j+1], \
-1.5 * R[i, j-2], 6. * R[i,j], -1.5 * R[i, j+2], \
2. * B[i+1, j-1], 2. * B[i+1, j+1], \
-1.5 * R[i+2, j]])
if (timeshow):
elapsed_time = time.process_time() - t0
print("Red/Blue: row index: " + str(i-1) + " of " + str(height) + \
" | elapsed time: " + "{:.3f}".format(elapsed_time) + " seconds")
else:
print("Invalid bayer pattern. Valid pattern can be rggb, gbrg, grbg, bggr")
return demosaic_out # This will be all zeros
# Fill up the RGB output with interpolated values
demosaic_out[0:height, 0:width, 0] = R[no_of_pixel_pad : height + no_of_pixel_pad, \
no_of_pixel_pad : width + no_of_pixel_pad]
demosaic_out[0:height, 0:width, 1] = G[no_of_pixel_pad : height + no_of_pixel_pad, \
no_of_pixel_pad : width + no_of_pixel_pad]
demosaic_out[0:height, 0:width, 2] = B[no_of_pixel_pad : height + no_of_pixel_pad, \
no_of_pixel_pad : width + no_of_pixel_pad]
demosaic_out = np.clip(demosaic_out, clip_range[0], clip_range[1])
return demosaic_out
def fill_channel_directional_weight(data, bayer_pattern):
#== Calculate the directional weights (weight_N, weight_E, weight_S, weight_W.
# where N, E, S, W stand for north, east, south, and west.)
data = np.asarray(data)
v = np.asarray(signal.convolve2d(data, [[1],[0],[-1]], mode="same", boundary="symm"))
h = np.asarray(signal.convolve2d(data, [[1, 0, -1]], mode="same", boundary="symm"))
weight_N = np.zeros(np.shape(data), dtype=np.float32)
weight_E = np.zeros(np.shape(data), dtype=np.float32)
weight_S = np.zeros(np.shape(data), dtype=np.float32)
weight_W = np.zeros(np.shape(data), dtype=np.float32)
value_N = np.zeros(np.shape(data), dtype=np.float32)
value_E = np.zeros(np.shape(data), dtype=np.float32)
value_S = np.zeros(np.shape(data), dtype=np.float32)
value_W = np.zeros(np.shape(data), dtype=np.float32)
if ((bayer_pattern == "rggb") or (bayer_pattern == "bggr")):
# note that in the following the locations in the comments are given
# assuming the bayer_pattern rggb
#== CALCULATE WEIGHTS IN B LOCATIONS
weight_N[1::2, 1::2] = np.abs(v[1::2, 1::2]) + np.abs(v[::2, 1::2])
# repeating the column before the last to the right so that sampling
# does not cause any dimension mismatch
temp_h_b = np.hstack((h, np.atleast_2d(h[:, -2]).T))
weight_E[1::2, 1::2] = np.abs(h[1::2, 1::2]) + np.abs(temp_h_b[1::2, 2::2])
# repeating the row before the last row to the bottom so that sampling
# does not cause any dimension mismatch
temp_v_b = np.vstack((v, v[-1]))
weight_S[1::2, 1::2] = np.abs(v[1::2, 1::2]) + np.abs(temp_v_b[2::2, 1::2])
weight_W[1::2, 1::2] = np.abs(h[1::2, 1::2]) + np.abs(h[1::2, ::2])
#== CALCULATE WEIGHTS IN R LOCATIONS
# repeating the second row at the top of matrix so that sampling does
# not cause any dimension mismatch, also remove the bottom row
temp_v_r = np.delete(np.vstack((v[1], v)), -1, 0)
weight_N[::2, ::2] = np.abs(v[::2, ::2]) + np.abs(temp_v_r[::2, ::2])
weight_E[::2, ::2] = np.abs(h[::2, ::2]) + np.abs(h[::2, 1::2])
weight_S[::2, ::2] = np.abs(v[::2, ::2]) + np.abs(v[1::2, ::2])
# repeating the second column at the left of matrix so that sampling
# does not cause any dimension mismatch, also remove the rightmost
# column
temp_h_r = np.delete(np.hstack((np.atleast_2d(h[:, 1]).T, h)), -1, 1)
weight_W[::2, ::2] = np.abs(h[::2, ::2]) + np.abs(temp_h_r[::2, ::2])
weight_N = np.divide(1., 1. + weight_N)
weight_E = np.divide(1., 1. + weight_E)
weight_S = np.divide(1., 1. + weight_S)
weight_W = np.divide(1., 1. + weight_W)
#== CALCULATE DIRECTIONAL ESTIMATES IN B LOCATIONS
value_N[1::2, 1::2] = data[::2, 1::2] + v[::2, 1::2] / 2.
# repeating the column before the last to the right so that sampling
# does not cause any dimension mismatch
temp = np.hstack((data, np.atleast_2d(data[:, -2]).T))
value_E[1::2, 1::2] = temp[1::2, 2::2] - temp_h_b[1::2, 2::2] / 2.
# repeating the row before the last row to the bottom so that sampling
# does not cause any dimension mismatch
temp = np.vstack((data, data[-1]))
value_S[1::2, 1::2] = temp[2::2, 1::2] - temp_v_b[2::2, 1::2] / 2.
value_W[1::2, 1::2] = data[1::2, ::2] + h[1::2, ::2] / 2.
#== CALCULATE DIRECTIONAL ESTIMATES IN R LOCATIONS
# repeating the second row at the top of matrix so that sampling does
# not cause any dimension mismatch, also remove the bottom row
temp = np.delete(np.vstack((data[1], data)), -1, 0)
value_N[::2, ::2] = temp[::2, ::2] + temp_v_r[::2, ::2] / 2.
value_E[::2, ::2] = data[::2, 1::2] - h[::2, 1::2] / 2.
value_S[::2, ::2] = data[1::2, ::2] - v[1::2, ::2] / 2.
# repeating the second column at the left of matrix so that sampling
# does not cause any dimension mismatch, also remove the rightmost
# column
temp = np.delete(np.hstack((np.atleast_2d(data[:, 1]).T, data)), -1, 1)
value_W[::2, ::2] = temp[::2, ::2] + temp_h_r[::2, ::2] / 2.
output = np.zeros(np.shape(data), dtype=np.float32)
output = np.divide((np.multiply(value_N, weight_N) + \
np.multiply(value_E, weight_E) + \
np.multiply(value_S, weight_S) + \
np.multiply(value_W, weight_W)),\
(weight_N + weight_E + weight_S + weight_W))
output[::2, 1::2] = data[::2, 1::2]
output[1::2, ::2] = data[1::2, ::2]
return output
elif ((bayer_pattern == "gbrg") or (bayer_pattern == "grbg")):
# note that in the following the locations in the comments are given
# assuming the bayer_pattern gbrg
#== CALCULATE WEIGHTS IN B LOCATIONS
# repeating the second row at the top of matrix so that sampling does
# not cause any dimension mismatch, also remove the bottom row
temp_v_b = np.delete(np.vstack((v[1], v)), -1, 0)
weight_N[::2, 1::2] = np.abs(v[::2, 1::2]) + np.abs(temp_v_b[::2, 1::2])
# repeating the column before the last to the right so that sampling
# does not cause any dimension mismatch
temp_h_b = np.hstack((h, np.atleast_2d(h[:, -2]).T))
weight_E[::2, 1::2] = np.abs(h[::2, 1::2]) + np.abs(temp_h_b[::2, 2::2])
# repeating the row before the last row to the bottom so that sampling
# does not cause any dimension mismatch
weight_S[::2, 1::2] = np.abs(v[::2, 1::2]) + np.abs(v[1::2, 1::2])
weight_W[::2, 1::2] = np.abs(h[::2, 1::2]) + np.abs(h[::2, ::2])
#== CALCULATE WEIGHTS IN R LOCATIONS
weight_N[1::2, ::2] = np.abs(v[1::2, ::2]) + np.abs(v[::2, ::2])
weight_E[1::2, ::2] = np.abs(h[1::2, ::2]) + np.abs(h[1::2, 1::2])
# repeating the row before the last row to the bottom so that sampling
# does not cause any dimension mismatch
temp_v_r = np.vstack((v, v[-1]))
weight_S[1::2, ::2] = np.abs(v[1::2, ::2]) + np.abs(temp_v_r[2::2, ::2])
# repeating the second column at the left of matrix so that sampling
# does not cause any dimension mismatch, also remove the rightmost
# column
temp_h_r = np.delete(np.hstack((np.atleast_2d(h[:, 1]).T, h)), -1, 1)
weight_W[1::2, ::2] = np.abs(h[1::2, ::2]) + np.abs(temp_h_r[1::2, ::2])
weight_N = np.divide(1., 1. + weight_N)
weight_E = np.divide(1., 1. + weight_E)
weight_S = np.divide(1., 1. + weight_S)
weight_W = np.divide(1., 1. + weight_W)
#== CALCULATE DIRECTIONAL ESTIMATES IN B LOCATIONS
# repeating the second row at the top of matrix so that sampling does
# not cause any dimension mismatch, also remove the bottom row
temp = np.delete(np.vstack((data[1], data)), -1, 0)
value_N[::2, 1::2] = temp[::2, 1::2] + temp_v_b[::2, 1::2] / 2.
# repeating the column before the last to the right so that sampling
# does not cause any dimension mismatch
temp = np.hstack((data, np.atleast_2d(data[:, -2]).T))
value_E[::2, 1::2] = temp[::2, 2::2] - temp_h_b[::2, 2::2] / 2.
# repeating the row before the last row to the bottom so that sampling
# does not cause any dimension mismatch
value_S[::2, 1::2] = data[1::2, 1::2] - v[1::2, 1::2] / 2.
value_W[::2, 1::2] = data[::2, ::2] + h[::2, ::2] / 2.
#== CALCULATE DIRECTIONAL ESTIMATES IN R LOCATIONS
# repeating the second row at the top of matrix so that sampling does
# not cause any dimension mismatch, also remove the bottom row
value_N[1::2, ::2] = data[::2, ::2] + v[::2, ::2] / 2.
value_E[1::2, ::2] = data[1::2, 1::2] - h[1::2, 1::2] / 2.
# repeating the row before the last row to the bottom so that sampling
# does not cause any dimension mismatch
temp = np.vstack((data, data[-1]))
value_S[1::2, ::2] = temp[2::2, ::2] - temp_v_r[2::2, ::2] / 2.
# repeating the second column at the left of matrix so that sampling
# does not cause any dimension mismatch, also remove the rightmost
# column
temp = np.delete(np.hstack((np.atleast_2d(data[:, 1]).T, data)), -1, 1)
value_W[1::2, ::2] = temp[1::2, ::2] + temp_h_r[1::2, ::2] / 2.
output = np.zeros(np.shape(data), dtype=np.float32)
output = np.divide((np.multiply(value_N, weight_N) + \
np.multiply(value_E, weight_E) + \
np.multiply(value_S, weight_S) + \
np.multiply(value_W, weight_W)),\
(weight_N + weight_E + weight_S + weight_W))
output[::2, ::2] = data[::2, ::2]
output[1::2, 1::2] = data[1::2, 1::2]
return output
def fill_br_locations(data, G, bayer_pattern):
# Fill up the B/R values interpolated at R/B locations
B = np.zeros(np.shape(data), dtype=np.float32)
R = np.zeros(np.shape(data), dtype=np.float32)
data = np.asarray(data)
G = np.asarray(G)
d1 = np.asarray(signal.convolve2d(data, [[-1, 0, 0],[0, 0, 0], [0, 0, 1]], mode="same", boundary="symm"))
d2 = np.asarray(signal.convolve2d(data, [[0, 0, 1], [0, 0, 0], [-1, 0, 0]], mode="same", boundary="symm"))
df_NE = np.asarray(signal.convolve2d(G, [[0, 0, 0], [0, 1, 0], [-1, 0, 0]], mode="same", boundary="symm"))
df_SE = np.asarray(signal.convolve2d(G, [[-1, 0, 0], [0, 1, 0], [0, 0, 0]], mode="same", boundary="symm"))
df_SW = np.asarray(signal.convolve2d(G, [[0, 0, -1], [0, 1, 0], [0, 0, 0]], mode="same", boundary="symm"))
df_NW = np.asarray(signal.convolve2d(G, [[0, 0, 0], [0, 1, 0], [0, 0, -1]], mode="same", boundary="symm"))
weight_NE = np.zeros(np.shape(data), dtype=np.float32)
weight_SE = np.zeros(np.shape(data), dtype=np.float32)
weight_SW = np.zeros(np.shape(data), dtype=np.float32)
weight_NW = np.zeros(np.shape(data), dtype=np.float32)
value_NE = np.zeros(np.shape(data), dtype=np.float32)
value_SE = np.zeros(np.shape(data), dtype=np.float32)
value_SW = np.zeros(np.shape(data), dtype=np.float32)
value_NW = np.zeros(np.shape(data), dtype=np.float32)
if ((bayer_pattern == "rggb") or (bayer_pattern == "bggr")):
#== weights for B in R locations
weight_NE[::2, ::2] = np.abs(d2[::2, ::2]) + np.abs(df_NE[::2, ::2])
weight_SE[::2, ::2] = np.abs(d1[::2, ::2]) + np.abs(df_SE[::2, ::2])
weight_SW[::2, ::2] = np.abs(d2[::2, ::2]) + np.abs(df_SW[::2, ::2])
weight_NW[::2, ::2] = np.abs(d1[::2, ::2]) + np.abs(df_NW[::2, ::2])
#== weights for R in B locations
weight_NE[1::2, 1::2] = np.abs(d2[1::2, 1::2]) + np.abs(df_NE[1::2, 1::2])
weight_SE[1::2, 1::2] = np.abs(d1[1::2, 1::2]) + np.abs(df_SE[1::2, 1::2])
weight_SW[1::2, 1::2] = np.abs(d2[1::2, 1::2]) + np.abs(df_SW[1::2, 1::2])
weight_NW[1::2, 1::2] = np.abs(d1[1::2, 1::2]) + np.abs(df_NW[1::2, 1::2])
weight_NE = np.divide(1., 1. + weight_NE)
weight_SE = np.divide(1., 1. + weight_SE)
weight_SW = np.divide(1., 1. + weight_SW)
weight_NW = np.divide(1., 1. + weight_NW)
#== directional estimates of B in R locations
# repeating the second row at the top of matrix so that sampling does
# not cause any dimension mismatch, also remove the bottom row
temp = np.delete(np.vstack((data[1], data)), -1, 0)
value_NE[::2, ::2] = temp[::2, 1::2] + df_NE[::2, ::2] / 2.
value_SE[::2, ::2] = data[1::2, 1::2] + df_SE[::2, ::2] / 2.
# repeating the second column at the left of matrix so that sampling
# does not cause any dimension mismatch, also remove the rightmost
# column
temp = np.delete(np.hstack((np.atleast_2d(data[:, 1]).T, data)), -1, 1)
value_SW[::2, ::2] = temp[1::2, ::2] + df_SW[::2, ::2] / 2.
# repeating the second row at the top of matrix so that sampling does
# not cause any dimension mismatch, also remove the bottom row
temp = np.delete(np.vstack((data[1], data)), -1, 0)
# repeating the second column at the left of matrix so that sampling
# does not cause any dimension mismatch, also remove the rightmost
# column
temp = np.delete(np.hstack((np.atleast_2d(temp[:, 1]).T, temp)), -1, 1)
value_NW[::2, ::2] = temp[::2, ::2] + df_NW[::2, ::2]
#== directional estimates of R in B locations
# repeating the column before the last to the right so that sampling
# does not cause any dimension mismatch
temp = np.hstack((data, np.atleast_2d(data[:, -2]).T))
value_NE[1::2, 1::2] = temp[::2, 2::2] + df_NE[1::2, 1::2] / 2.
# repeating the column before the last to the right so that sampling
# does not cause any dimension mismatch
temp = np.hstack((data, np.atleast_2d(data[:, -2]).T))
# repeating the row before the last row to the bottom so that sampling
# does not cause any dimension mismatch
temp = np.vstack((temp, temp[-1]))
value_SE[1::2, 1::2] = temp[2::2, 2::2] + df_SE[1::2, 1::2] / 2.
# repeating the row before the last row to the bottom so that sampling
# does not cause any dimension mismatch
temp = np.vstack((data, data[-1]))
value_SW[1::2, 1::2] = temp[2::2, ::2] + df_SW[1::2, 1::2] / 2.
value_NW[1::2, 1::2] = data[::2, ::2] + df_NW[1::2, 1::2] / 2.
RB = np.divide(np.multiply(weight_NE, value_NE) + \
np.multiply(weight_SE, value_SE) + \
np.multiply(weight_SW, value_SW) + \
np.multiply(weight_NW, value_NW),\
(weight_NE + weight_SE + weight_SW + weight_NW))
if (bayer_pattern == "rggb"):
R[1::2, 1::2] = RB[1::2, 1::2]
R[::2, ::2] = data[::2, ::2]
B[::2, ::2] = RB[::2, ::2]
B[1::2, 1::2] = data[1::2, 1::2]
elif (bayer_pattern == "bggr"):
R[::2, ::2] = RB[::2, ::2]
R[1::2, 1::2] = data[1::2, 1::2]
B[1::2, 1::2] = RB[1::2, 1::2]
B[::2, ::2] = data[::2, ::2]
R[1::2, ::2] = G[1::2, ::2]
R[::2, 1::2] = G[::2, 1::2]
R = fill_channel_directional_weight(R, "gbrg")
B[1::2, ::2] = G[1::2, ::2]
B[::2, 1::2] = G[::2, 1::2]
B = fill_channel_directional_weight(B, "gbrg")
elif ((bayer_pattern == "grbg") or (bayer_pattern == "gbrg")):
#== weights for B in R locations
weight_NE[::2, 1::2] = np.abs(d2[::2, 1::2]) + np.abs(df_NE[::2, 1::2])
weight_SE[::2, 1::2] = np.abs(d1[::2, 1::2]) + np.abs(df_SE[::2, 1::2])
weight_SW[::2, 1::2] = np.abs(d2[::2, 1::2]) + np.abs(df_SW[::2, 1::2])
weight_NW[::2, 1::2] = np.abs(d1[::2, 1::2]) + np.abs(df_NW[::2, 1::2])
#== weights for R in B locations
weight_NE[1::2, ::2] = np.abs(d2[1::2, ::2]) + np.abs(df_NE[1::2, ::2])
weight_SE[1::2, ::2] = np.abs(d1[1::2, ::2]) + np.abs(df_SE[1::2, ::2])
weight_SW[1::2, ::2] = np.abs(d2[1::2, ::2]) + np.abs(df_SW[1::2, ::2])
weight_NW[1::2, ::2] = np.abs(d1[1::2, ::2]) + np.abs(df_NW[1::2, ::2])
weight_NE = np.divide(1., 1. + weight_NE)
weight_SE = np.divide(1., 1. + weight_SE)
weight_SW = np.divide(1., 1. + weight_SW)
weight_NW = np.divide(1., 1. + weight_NW)
#== directional estimates of B in R locations
# repeating the second row at the top of matrix so that sampling does
# not cause any dimension mismatch, also remove the bottom row
temp = np.delete(np.vstack((data[1], data)), -1, 0)
# repeating the column before the last to the right so that sampling
# does not cause any dimension mismatch
temp = np.hstack((temp, np.atleast_2d(temp[:, -2]).T))
value_NE[::2, 1::2] = temp[::2, 2::2] + df_NE[::2, 1::2] / 2.
# repeating the column before the last to the right so that sampling
# does not cause any dimension mismatch
temp = np.hstack((data, np.atleast_2d(data[:, -2]).T))
value_SE[::2, 1::2] = temp[1::2, 2::2] + df_SE[::2, 1::2] / 2.
value_SW[::2, 1::2] = data[1::2, ::2] + df_SW[::2, 1::2] / 2.
# repeating the second row at the top of matrix so that sampling does
# not cause any dimension mismatch, also remove the bottom row
temp = np.delete(np.vstack((data[1], data)), -1, 0)
value_NW[::2, 1::2] = temp[::2, ::2] + df_NW[::2, 1::2]
#== directional estimates of R in B locations
value_NE[1::2, ::2] = data[::2, 1::2] + df_NE[1::2, ::2] / 2.
# repeating the column before the last to the right so that sampling
# does not cause any dimension mismatch
temp = np.hstack((data, np.atleast_2d(data[:, -2]).T))
# repeating the row before the last row to the bottom so that sampling
# does not cause any dimension mismatch
temp = np.vstack((temp, temp[-1]))
value_SE[1::2, ::2] = temp[2::2, 1::2] + df_SE[1::2, ::2] / 2.
# repeating the row before the last row to the bottom so that sampling
# does not cause any dimension mismatch
temp = np.vstack((data, data[-1]))
# repeating the second column at the left of matrix so that sampling
# does not cause any dimension mismatch, also remove the rightmost
# column
temp = np.delete(np.hstack((np.atleast_2d(temp[:, 1]).T, temp)), -1, 1)
value_SW[1::2, ::2] = temp[2::2, ::2] + df_SW[1::2, ::2] / 2.
# repeating the second column at the left of matrix so that sampling
# does not cause any dimension mismatch, also remove the rightmost
# column
temp = np.delete(np.hstack((np.atleast_2d(data[:, 1]).T, data)), -1, 1)
value_NW[1::2, ::2] = temp[::2, ::2] + df_NW[1::2, ::2] / 2.
RB = np.divide(np.multiply(weight_NE, value_NE) + \
np.multiply(weight_SE, value_SE) + \
np.multiply(weight_SW, value_SW) + \
np.multiply(weight_NW, value_NW),\
(weight_NE + weight_SE + weight_SW + weight_NW))
if (bayer_pattern == "grbg"):
R[1::2, ::2] = RB[1::2, ::2]
R[::2, 1::2] = data[::2, 1::2]
B[::2, 1::2] = RB[::2, 1::2]
B[1::2, ::2] = data[1::2, ::2]
elif (bayer_pattern == "gbrg"):
R[::2, 1::2] = RB[::2, 1::2]
R[1::2, ::2] = data[1::2, ::2]
B[1::2, ::2] = RB[1::2, ::2]
B[::2, 1::2] = data[::2, 1::2]
R[::2, ::2] = G[::2, ::2]
R[1::2, 1::2] = G[1::2, 1::2]
R = fill_channel_directional_weight(R, "rggb")
B[1::2, 1::2] = G[1::2, 1::2]
B[::2, ::2] = G[::2, ::2]
B = fill_channel_directional_weight(B, "rggb")
return B, R
# # =============================================================
# # function: dbayer_mhc_fast
# # demosaicing using Malvar-He-Cutler algorithm
# # http://www.ipol.im/pub/art/2011/g_mhcd/
# # =============================================================
# def debayer_mhc_fast(raw, bayer_pattern="rggb", clip_range=[0, 65535], timeshow=False):
#
# # convert to float32 in case it was not
# raw = np.float32(raw)
#
# # dimensions
# width, height = utility.helpers(raw).get_width_height()
#
# # allocate space for the R, G, B planes
# R = np.empty((height, width), dtype = np.float32)
# G = np.empty((height, width), dtype = np.float32)
# B = np.empty((height, width), dtype = np.float32)
#
# # create a RGB output
# demosaic_out = np.empty( (height, width, 3), dtype = np.float32 )
#
# # define the convolution kernels
# kernel_g_at_rb = [[0., 0., -1., 0., 0.],\
# [0., 0., 2., 0., 0.],\
# [-1., 2., 4., 2., -1.],\
# [0., 0., 2., 0., 0.],\
# [0., 0., -1., 0., 0.]] * .125
#
# kernel_r_at_gr = [[0., 0., .5, 0., 0.],\
# [0., -1., 0., -1., 0.],\
# [-1., 4., 5., 4., -1.],\
# [0., -1., 0., -1., 0.],\
# [0., 0., .5, 0., 0.]] * .125
#
# kernel_b_at_gr = [[0., 0., -1., 0., 0.],\
# [0., -1., 4., -1., 0.],\
# [.5., 0., 5., 0., .5],\
# [0., -1., 4., -1., 0],\
# [0., 0., -1., 0., 0.]] * .125
#
# kernel_r_at_gb = [[0., 0., -1., 0., 0.],\
# [0., -1., 4., -1., 0.],\
# [.5, 0., 5., 0., .5],\
# [0., -1., 4., -1., 0.],\
# [0., 0., -1., 0., 0.]] * .125
#
# kernel_b_at_gb = [[0., 0., .5, 0., 0.],\
# [0., -1., 0., -1., 0.],\
# [-1., 4., 5., 4., -1.],\
# [0., -1., 0., -1., 0.],\
# [0., 0., .5, 0., 0.]] * .125
#
# kernel_r_at_b = [[0., 0., -1.5, 0., 0.],\
# [0., 2., 0., 2., 0.],\
# [-1.5, 0., 6., 0., -1.5],\
# [0., 2., 0., 2., 0.],\
# [0., 0., -1.5, 0., 0.]] * .125
#
# kernel_b_at_r = [[0., 0., -1.5, 0., 0.],\
# [0., 2., 0., 2., 0.],\
# [-1.5, 0., 6., 0., -1.5],\
# [0., 2., 0., 2., 0.],\
# [0., 0., -1.5, 0., 0.]] * .125
#
#
#
# # fill up the directly available values according to the Bayer pattern
# if (bayer_pattern == "rggb"):
#
# G[::2, 1::2] = raw[::2, 1::2]
# G[1::2, ::2] = raw[1::2, ::2]
# R[::2, ::2] = raw[::2, ::2]
# B[1::2, 1::2] = raw[1::2, 1::2]
#
# # Green channel
# for i in range(no_of_pixel_pad, height + no_of_pixel_pad):
#
# # to display progress
# t0 = time.process_time()
#
# for j in range(no_of_pixel_pad, width + no_of_pixel_pad):
#
# # G at Red location
# if (((i % 2) == 0) and ((j % 2) == 0)):
# G[i, j] = 0.125 * np.sum([-1. * R[i-2, j], \
# 2. * G[i-1, j], \
# -1. * R[i, j-2], 2. * G[i, j-1], 4. * R[i,j], 2. * G[i, j+1], -1. * R[i, j+2],\
# 2. * G[i+1, j], \
# -1. * R[i+2, j]])
# # G at Blue location
# elif (((i % 2) != 0) and ((j % 2) != 0)):
# G[i, j] = 0.125 * np.sum([-1. * B[i-2, j], \
# 2. * G[i-1, j], \
# -1. * B[i, j-2], 2. * G[i, j-1], 4. * B[i,j], 2. * G[i, j+1], -1. * B[i, j+2], \
# 2. * G[i+1, j],\
# -1. * B[i+2, j]])
# if (timeshow):
# elapsed_time = time.process_time() - t0
# print("Green: row index: " + str(i-1) + " of " + str(height) + \
# " | elapsed time: " + "{:.3f}".format(elapsed_time) + " seconds")
#
# # Red and Blue channel
# for i in range(no_of_pixel_pad, height + no_of_pixel_pad):
#
# # to display progress
# t0 = time.process_time()
#
# for j in range(no_of_pixel_pad, width + no_of_pixel_pad):
#
# # Green locations in Red rows
# if (((i % 2) == 0) and ((j % 2) != 0)):
# # R at Green locations in Red rows
# R[i, j] = 0.125 * np.sum([.5 * G[i-2, j],\
# -1. * G[i-1, j-1], -1. * G[i-1, j+1], \
# -1. * G[i, j-2], 4. * R[i, j-1], 5. * G[i,j], 4. * R[i, j+1], -1. * G[i, j+2], \
# -1. * G[i+1, j-1], -1. * G[i+1, j+1], \
# .5 * G[i+2, j]])
#
# # B at Green locations in Red rows
# B[i, j] = 0.125 * np.sum([-1. * G[i-2, j], \
# -1. * G[i-1, j-1], 4. * B[i-1, j], -1. * G[i-1, j+1], \
# .5 * G[i, j-2], 5. * G[i,j], .5 * G[i, j+2], \
# -1. * G[i+1, j-1], 4. * B[i+1,j], -1. * G[i+1, j+1], \
# -1. * G[i+2, j]])
#
# # Green locations in Blue rows
# elif (((i % 2) != 0) and ((j % 2) == 0)):
#
# # R at Green locations in Blue rows
# R[i, j] = 0.125 * np.sum([-1. * G[i-2, j], \
# -1. * G[i-1, j-1], 4. * R[i-1, j], -1. * G[i-1, j+1], \
# .5 * G[i, j-2], 5. * G[i,j], .5 * G[i, j+2], \
# -1. * G[i+1, j-1], 4. * R[i+1, j], -1. * G[i+1, j+1], \
# -1. * G[i+2, j]])
#
# # B at Green locations in Blue rows
# B[i, j] = 0.125 * np.sum([.5 * G[i-2, j], \
# -1. * G [i-1, j-1], -1. * G[i-1, j+1], \
# -1. * G[i, j-2], 4. * B[i, j-1], 5. * G[i,j], 4. * B[i, j+1], -1. * G[i, j+2], \
# -1. * G[i+1, j-1], -1. * G[i+1, j+1], \
# .5 * G[i+2, j]])
#
# # R at Blue locations
# elif (((i % 2) != 0) and ((j % 2) != 0)):
# R[i, j] = 0.125 * np.sum([-1.5 * B[i-2, j], \
# 2. * R[i-1, j-1], 2. * R[i-1, j+1], \
# -1.5 * B[i, j-2], 6. * B[i,j], -1.5 * B[i, j+2], \
# 2. * R[i+1, j-1], 2. * R[i+1, j+1], \
# -1.5 * B[i+2, j]])
#
# # B at Red locations
# elif (((i % 2) == 0) and ((j % 2) == 0)):
# B[i, j] = 0.125 * np.sum([-1.5 * R[i-2, j], \
# 2. * B[i-1, j-1], 2. * B[i-1, j+1], \
# -1.5 * R[i, j-2], 6. * R[i,j], -1.5 * R[i, j+2], \
# 2. * B[i+1, j-1], 2. * B[i+1, j+1], \
# -1.5 * R[i+2, j]])
#
# if (timeshow):
# elapsed_time = time.process_time() - t0
# print("Red/Blue: row index: " + str(i-1) + " of " + str(height) + \
# " | elapsed time: " + "{:.3f}".format(elapsed_time) + " seconds")
#
#
# elif (bayer_pattern == "gbrg"):
#
# G[::2, ::2] = raw[::2, ::2]
# G[1::2, 1::2] = raw[1::2, 1::2]
# R[1::2, ::2] = raw[1::2, ::2]
# B[::2, 1::2] = raw[::2, 1::2]
#
# # Green channel
# for i in range(no_of_pixel_pad, height + no_of_pixel_pad):
#
# # to display progress
# t0 = time.process_time()
#
# for j in range(no_of_pixel_pad, width + no_of_pixel_pad):
#
# # G at Red location
# if (((i % 2) != 0) and ((j % 2) == 0)):
# G[i, j] = 0.125 * np.sum([-1. * R[i-2, j], \
# 2. * G[i-1, j], \
# -1. * R[i, j-2], 2. * G[i, j-1], 4. * R[i,j], 2. * G[i, j+1], -1. * R[i, j+2],\
# 2. * G[i+1, j], \
# -1. * R[i+2, j]])
# # G at Blue location
# elif (((i % 2) == 0) and ((j % 2) != 0)):
# G[i, j] = 0.125 * np.sum([-1. * B[i-2, j], \
# 2. * G[i-1, j], \
# -1. * B[i, j-2], 2. * G[i, j-1], 4. * B[i,j], 2. * G[i, j+1], -1. * B[i, j+2], \
# 2. * G[i+1, j],\
# -1. * B[i+2, j]])
# if (timeshow):
# elapsed_time = time.process_time() - t0
# print("Green: row index: " + str(i-1) + " of " + str(height) + \
# " | elapsed time: " + "{:.3f}".format(elapsed_time) + " seconds")
#
# # Red and Blue channel
# for i in range(no_of_pixel_pad, height + no_of_pixel_pad):
#
# # to display progress
# t0 = time.process_time()
#
# for j in range(no_of_pixel_pad, width + no_of_pixel_pad):
#
# # Green locations in Red rows
# if (((i % 2) != 0) and ((j % 2) != 0)):
# # R at Green locations in Red rows
# R[i, j] = 0.125 * np.sum([.5 * G[i-2, j],\
# -1. * G[i-1, j-1], -1. * G[i-1, j+1], \
# -1. * G[i, j-2], 4. * R[i, j-1], 5. * G[i,j], 4. * R[i, j+1], -1. * G[i, j+2], \
# -1. * G[i+1, j-1], -1. * G[i+1, j+1], \
# .5 * G[i+2, j]])
#
# # B at Green locations in Red rows
# B[i, j] = 0.125 * np.sum([-1. * G[i-2, j], \
# -1. * G[i-1, j-1], 4. * B[i-1, j], -1. * G[i-1, j+1], \
# .5 * G[i, j-2], 5. * G[i,j], .5 * G[i, j+2], \
# -1. * G[i+1, j-1], 4. * B[i+1,j], -1. * G[i+1, j+1], \
# -1. * G[i+2, j]])
#
# # Green locations in Blue rows
# elif (((i % 2) == 0) and ((j % 2) == 0)):
#
# # R at Green locations in Blue rows
# R[i, j] = 0.125 * np.sum([-1. * G[i-2, j], \
# -1. * G[i-1, j-1], 4. * R[i-1, j], -1. * G[i-1, j+1], \
# .5 * G[i, j-2], 5. * G[i,j], .5 * G[i, j+2], \
# -1. * G[i+1, j-1], 4. * R[i+1, j], -1. * G[i+1, j+1], \
# -1. * G[i+2, j]])
#
# # B at Green locations in Blue rows
# B[i, j] = 0.125 * np.sum([.5 * G[i-2, j], \
# -1. * G [i-1, j-1], -1. * G[i-1, j+1], \
# -1. * G[i, j-2], 4. * B[i, j-1], 5. * G[i,j], 4. * B[i, j+1], -1. * G[i, j+2], \
# -1. * G[i+1, j-1], -1. * G[i+1, j+1], \
# .5 * G[i+2, j]])
#
# # R at Blue locations
# elif (((i % 2) == 0) and ((j % 2) != 0)):
# R[i, j] = 0.125 * np.sum([-1.5 * B[i-2, j], \
# 2. * R[i-1, j-1], 2. * R[i-1, j+1], \
# -1.5 * B[i, j-2], 6. * B[i,j], -1.5 * B[i, j+2], \
# 2. * R[i+1, j-1], 2. * R[i+1, j+1], \
# -1.5 * B[i+2, j]])
#
# # B at Red locations
# elif (((i % 2) != 0) and ((j % 2) == 0)):
# B[i, j] = 0.125 * np.sum([-1.5 * R[i-2, j], \
# 2. * B[i-1, j-1], 2. * B[i-1, j+1], \
# -1.5 * R[i, j-2], 6. * R[i,j], -1.5 * R[i, j+2], \
# 2. * B[i+1, j-1], 2. * B[i+1, j+1], \
# -1.5 * R[i+2, j]])
#
# if (timeshow):
# elapsed_time = time.process_time() - t0
# print("Red/Blue: row index: " + str(i-1) + " of " + str(height) + \
# " | elapsed time: " + "{:.3f}".format(elapsed_time) + " seconds")
#
# elif (bayer_pattern == "grbg"):
#
# G[::2, ::2] = raw[::2, ::2]
# G[1::2, 1::2] = raw[1::2, 1::2]
# R[::2, 1::2] = raw[::2, 1::2]
# B[1::2, ::2] = raw[1::2, ::2]
#
# # Green channel
# for i in range(no_of_pixel_pad, height + no_of_pixel_pad):
#
# # to display progress
# t0 = time.process_time()
#
# for j in range(no_of_pixel_pad, width + no_of_pixel_pad):
#
# # G at Red location
# if (((i % 2) == 0) and ((j % 2) != 0)):
# G[i, j] = 0.125 * np.sum([-1. * R[i-2, j], \
# 2. * G[i-1, j], \
# -1. * R[i, j-2], 2. * G[i, j-1], 4. * R[i,j], 2. * G[i, j+1], -1. * R[i, j+2],\
# 2. * G[i+1, j], \
# -1. * R[i+2, j]])
# # G at Blue location
# elif (((i % 2) != 0) and ((j % 2) == 0)):
# G[i, j] = 0.125 * np.sum([-1. * B[i-2, j], \
# 2. * G[i-1, j], \
# -1. * B[i, j-2], 2. * G[i, j-1], 4. * B[i,j], 2. * G[i, j+1], -1. * B[i, j+2], \
# 2. * G[i+1, j],\
# -1. * B[i+2, j]])
# if (timeshow):
# elapsed_time = time.process_time() - t0
# print("Green: row index: " + str(i-1) + " of " + str(height) + \
# " | elapsed time: " + "{:.3f}".format(elapsed_time) + " seconds")
#
# # Red and Blue channel
# for i in range(no_of_pixel_pad, height + no_of_pixel_pad):
#
# # to display progress
# t0 = time.process_time()
#
# for j in range(no_of_pixel_pad, width + no_of_pixel_pad):
#
# # Green locations in Red rows
# if (((i % 2) == 0) and ((j % 2) == 0)):
# # R at Green locations in Red rows
# R[i, j] = 0.125 * np.sum([.5 * G[i-2, j],\
# -1. * G[i-1, j-1], -1. * G[i-1, j+1], \
# -1. * G[i, j-2], 4. * R[i, j-1], 5. * G[i,j], 4. * R[i, j+1], -1. * G[i, j+2], \
# -1. * G[i+1, j-1], -1. * G[i+1, j+1], \
# .5 * G[i+2, j]])
#
# # B at Green locations in Red rows
# B[i, j] = 0.125 * np.sum([-1. * G[i-2, j], \
# -1. * G[i-1, j-1], 4. * B[i-1, j], -1. * G[i-1, j+1], \
# .5 * G[i, j-2], 5. * G[i,j], .5 * G[i, j+2], \
# -1. * G[i+1, j-1], 4. * B[i+1,j], -1. * G[i+1, j+1], \
# -1. * G[i+2, j]])
#
# # Green locations in Blue rows
# elif (((i % 2) != 0) and ((j % 2) != 0)):
#
# # R at Green locations in Blue rows
# R[i, j] = 0.125 * np.sum([-1. * G[i-2, j], \
# -1. * G[i-1, j-1], 4. * R[i-1, j], -1. * G[i-1, j+1], \
# .5 * G[i, j-2], 5. * G[i,j], .5 * G[i, j+2], \
# -1. * G[i+1, j-1], 4. * R[i+1, j], -1. * G[i+1, j+1], \
# -1. * G[i+2, j]])
#
# # B at Green locations in Blue rows
# B[i, j] = 0.125 * np.sum([.5 * G[i-2, j], \
# -1. * G [i-1, j-1], -1. * G[i-1, j+1], \
# -1. * G[i, j-2], 4. * B[i, j-1], 5. * G[i,j], 4. * B[i, j+1], -1. * G[i, j+2], \
# -1. * G[i+1, j-1], -1. * G[i+1, j+1], \
# .5 * G[i+2, j]])
#
# # R at Blue locations
# elif (((i % 2) != 0) and ((j % 2) == 0)):
# R[i, j] = 0.125 * np.sum([-1.5 * B[i-2, j], \
# 2. * R[i-1, j-1], 2. * R[i-1, j+1], \
# -1.5 * B[i, j-2], 6. * B[i,j], -1.5 * B[i, j+2], \
# 2. * R[i+1, j-1], 2. * R[i+1, j+1], \
# -1.5 * B[i+2, j]])
#
# # B at Red locations
# elif (((i % 2) == 0) and ((j % 2) != 0)):
# B[i, j] = 0.125 * np.sum([-1.5 * R[i-2, j], \
# 2. * B[i-1, j-1], 2. * B[i-1, j+1], \
# -1.5 * R[i, j-2], 6. * R[i,j], -1.5 * R[i, j+2], \
# 2. * B[i+1, j-1], 2. * B[i+1, j+1], \
# -1.5 * R[i+2, j]])
#
# if (timeshow):
# elapsed_time = time.process_time() - t0
# print("Red/Blue: row index: " + str(i-1) + " of " + str(height) + \
# " | elapsed time: " + "{:.3f}".format(elapsed_time) + " seconds")
#
# elif (bayer_pattern == "bggr"):
#
# G[::2, 1::2] = raw[::2, 1::2]
# G[1::2, ::2] = raw[1::2, ::2]
# R[1::2, 1::2] = raw[1::2, 1::2]
# B[::2, ::2] = raw[::2, ::2]
#
# # Green channel
# for i in range(no_of_pixel_pad, height + no_of_pixel_pad):
#
# # to display progress
# t0 = time.process_time()
#
# for j in range(no_of_pixel_pad, width + no_of_pixel_pad):
#
# # G at Red location
# if (((i % 2) != 0) and ((j % 2) != 0)):
# G[i, j] = 0.125 * np.sum([-1. * R[i-2, j], \
# 2. * G[i-1, j], \
# -1. * R[i, j-2], 2. * G[i, j-1], 4. * R[i,j], 2. * G[i, j+1], -1. * R[i, j+2],\
# 2. * G[i+1, j], \
# -1. * R[i+2, j]])
# # G at Blue location
# elif (((i % 2) == 0) and ((j % 2) == 0)):
# G[i, j] = 0.125 * np.sum([-1. * B[i-2, j], \
# 2. * G[i-1, j], \
# -1. * B[i, j-2], 2. * G[i, j-1], 4. * B[i,j], 2. * G[i, j+1], -1. * B[i, j+2], \
# 2. * G[i+1, j],\
# -1. * B[i+2, j]])
# if (timeshow):
# elapsed_time = time.process_time() - t0
# print("Green: row index: " + str(i-1) + " of " + str(height) + \
# " | elapsed time: " + "{:.3f}".format(elapsed_time) + " seconds")
#
# # Red and Blue channel
# for i in range(no_of_pixel_pad, height + no_of_pixel_pad):
#
# # to display progress
# t0 = time.process_time()
#
# for j in range(no_of_pixel_pad, width + no_of_pixel_pad):
#
# # Green locations in Red rows
# if (((i % 2) != 0) and ((j % 2) == 0)):
# # R at Green locations in Red rows
# R[i, j] = 0.125 * np.sum([.5 * G[i-2, j],\
# -1. * G[i-1, j-1], -1. * G[i-1, j+1], \
# -1. * G[i, j-2], 4. * R[i, j-1], 5. * G[i,j], 4. * R[i, j+1], -1. * G[i, j+2], \
# -1. * G[i+1, j-1], -1. * G[i+1, j+1], \
# .5 * G[i+2, j]])
#
# # B at Green locations in Red rows
# B[i, j] = 0.125 * np.sum([-1. * G[i-2, j], \
# -1. * G[i-1, j-1], 4. * B[i-1, j], -1. * G[i-1, j+1], \
# .5 * G[i, j-2], 5. * G[i,j], .5 * G[i, j+2], \
# -1. * G[i+1, j-1], 4. * B[i+1,j], -1. * G[i+1, j+1], \
# -1. * G[i+2, j]])
#
# # Green locations in Blue rows
# elif (((i % 2) == 0) and ((j % 2) != 0)):
#
# # R at Green locations in Blue rows
# R[i, j] = 0.125 * np.sum([-1. * G[i-2, j], \
# -1. * G[i-1, j-1], 4. * R[i-1, j], -1. * G[i-1, j+1], \
# .5 * G[i, j-2], 5. * G[i,j], .5 * G[i, j+2], \
# -1. * G[i+1, j-1], 4. * R[i+1, j], -1. * G[i+1, j+1], \
# -1. * G[i+2, j]])
#
# # B at Green locations in Blue rows
# B[i, j] = 0.125 * np.sum([.5 * G[i-2, j], \
# -1. * G [i-1, j-1], -1. * G[i-1, j+1], \
# -1. * G[i, j-2], 4. * B[i, j-1], 5. * G[i,j], 4. * B[i, j+1], -1. * G[i, j+2], \
# -1. * G[i+1, j-1], -1. * G[i+1, j+1], \
# .5 * G[i+2, j]])
#
# # R at Blue locations
# elif (((i % 2) == 0) and ((j % 2) == 0)):
# R[i, j] = 0.125 * np.sum([-1.5 * B[i-2, j], \
# 2. * R[i-1, j-1], 2. * R[i-1, j+1], \
# -1.5 * B[i, j-2], 6. * B[i,j], -1.5 * B[i, j+2], \
# 2. * R[i+1, j-1], 2. * R[i+1, j+1], \
# -1.5 * B[i+2, j]])
#
# # B at Red locations
# elif (((i % 2) != 0) and ((j % 2) != 0)):
# B[i, j] = 0.125 * np.sum([-1.5 * R[i-2, j], \
# 2. * B[i-1, j-1], 2. * B[i-1, j+1], \
# -1.5 * R[i, j-2], 6. * R[i,j], -1.5 * R[i, j+2], \
# 2. * B[i+1, j-1], 2. * B[i+1, j+1], \
# -1.5 * R[i+2, j]])
#
# if (timeshow):
# elapsed_time = time.process_time() - t0
# print("Red/Blue: row index: " + str(i-1) + " of " + str(height) + \
# " | elapsed time: " + "{:.3f}".format(elapsed_time) + " seconds")
#
# else:
# print("Invalid bayer pattern. Valid pattern can be rggb, gbrg, grbg, bggr")
# return demosaic_out # This will be all zeros
#
# # Fill up the RGB output with interpolated values
# demosaic_out[0:height, 0:width, 0] = R[no_of_pixel_pad : height + no_of_pixel_pad, \
# no_of_pixel_pad : width + no_of_pixel_pad]
# demosaic_out[0:height, 0:width, 1] = G[no_of_pixel_pad : height + no_of_pixel_pad, \
# no_of_pixel_pad : width + no_of_pixel_pad]
# demosaic_out[0:height, 0:width, 2] = B[no_of_pixel_pad : height + no_of_pixel_pad, \
# no_of_pixel_pad : width + no_of_pixel_pad]
#
# demosaic_out = np.clip(demosaic_out, clip_range[0], clip_range[1])
# return demosaic_out
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