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import rawpy
import numpy as np
import math
import sys
from scipy import signal
""" Process RAW file into a image file.
Example usage:
raw = read("sample.ARW")
rgb = process(raw)
write(rgb, "output.ARW")
"""
def read(filename):
"""
Read RAW data from specified file. Currently supported formats are
ARW (Sony RAW format)
:param filename: path to the target RAW file
"""
return rawpy.imread(filename)
def process(raw, color_matrix=(1024, 0, 0, 0, 1024, 0, 0, 0, 1024)):
"""
This processes RAW data that was read by read() method.
Must be called after read() operation. No error is checked.
"""
raw_array = get_raw_array(raw)
blc_raw = black_level_correction(raw, raw_array)
wb_raw = white_balance_Bayer(raw, blc_raw)
dms_img = advanced_demosaic(raw, wb_raw)
img_ccm = color_correction_matrix(dms_img, color_matrix)
img_gamma = gamma_correction(img_ccm)
return img_gamma
def write(rgb_image, output_filename):
"""
Write the processed RGB image to a specified file as PNG format.
Thsi must be called after process(). No error is checked.
:param output_filename: path to the output file. Extension must be png.
"""
import imageio
outimg = rgb_image.copy()
outimg[outimg < 0] = 0
outimg = outimg / outimg.max() * 255
imageio.imwrite(output_filename, outimg.astype('uint8'))
def get_raw_array(raw):
h, w = raw.sizes.raw_height, raw.sizes.raw_width
raw_array = np.array(raw.raw_image).reshape((h, w)).astype('float')
return raw_array
def black_level_correction(raw, raw_array):
blc = raw.black_level_per_channel
bayer_pattern = raw.raw_pattern
blc_raw = raw_array.copy()
blc_raw[0::2, 0::2] -= blc[bayer_pattern[0, 0]]
blc_raw[0::2, 1::2] -= blc[bayer_pattern[0, 1]]
blc_raw[1::2, 0::2] -= blc[bayer_pattern[1, 0]]
blc_raw[1::2, 1::2] -= blc[bayer_pattern[1, 1]]
return blc_raw
def preview_demosaic(raw, raw_array):
bayer_pattern = raw.raw_pattern
h, w = raw.sizes.raw_height, raw.sizes.raw_width
shuffle = np.zeros((h // 2, w // 2, 4))
shuffle[:, :, bayer_pattern[0, 0]] = raw_array[0::2, 0::2]
shuffle[:, :, bayer_pattern[0, 1]] = raw_array[0::2, 1::2]
shuffle[:, :, bayer_pattern[1, 0]] = raw_array[1::2, 0::2]
shuffle[:, :, bayer_pattern[1, 1]] = raw_array[1::2, 1::2]
dms_img = np.zeros((h // 2, w // 2, 3))
dms_img[:, :, 0] = shuffle[:, :, 0]
dms_img[:, :, 1] = (shuffle[:, :, 1] + shuffle[:, :, 3]) / 2
dms_img[:, :, 2] = shuffle[:, :, 2]
return dms_img
def simple_demosaic(raw, raw_array):
h, w = raw_array.shape
dms_img2 = np.zeros((h, w, 3))
green = raw_array.copy()
green[(raw.raw_colors == 0) | (raw.raw_colors == 2)] = 0
g_flt = np.array([[0, 1 / 4, 0], [1 / 4, 1, 1 / 4], [0, 1 / 4, 0]])
dms_img2[:, :, 1] = signal.convolve2d(green, g_flt, boundary='symm', mode='same')
red = raw_array.copy()
red[raw.raw_colors != 0] = 0
rb_flt = np.array([[1 / 4, 1 / 2, 1 / 4], [1 / 2, 1, 1 / 2], [1 / 4, 1 / 2, 1 / 4]])
dms_img2[:, :, 0] = signal.convolve2d(red, rb_flt, boundary='symm', mode='same')
blue = raw_array.copy()
blue[raw.raw_colors != 2] = 0
rb_flt = np.array([[1 / 4, 1 / 2, 1 / 4], [1 / 2, 1, 1 / 2], [1 / 4, 1 / 2, 1 / 4]])
dms_img2[:, :, 2] = signal.convolve2d(blue, rb_flt, boundary='symm', mode='same')
return dms_img2
def advanced_demosaic(raw, dms_input):
# TODO: This assumes the top-left pixel is always RED. Need to support all combination
# generate basic FIR filters
hlpf = np.array([[1, 2, 3, 4, 3, 2, 1]]) / 16
vlpf = np.transpose(hlpf)
hhpf = np.array([[-1, 2, -3, 4, -3, 2, -1]]) / 16
vhpf = np.transpose(hhpf)
identity_filter = np.zeros((7, 7))
identity_filter[3, 3] = 1
# generate FIR filters to extract necessary components
FC1 = np.matmul(vhpf, hhpf)
FC2H = np.matmul(vlpf, hhpf)
FC2V = np.matmul(vhpf, hlpf)
FL = identity_filter - FC1 - FC2V - FC2H
# f_C1 at 4 corners
c1_mod = signal.convolve2d(dms_input, FC1, boundary='symm', mode='same')
# f_C1^1 at wy = 0, wx = +Pi/-Pi
c2h_mod = signal.convolve2d(dms_input, FC2H, boundary='symm', mode='same')
# f_C1^1 at wy = +Pi/-Pi, wx = 0
c2v_mod = signal.convolve2d(dms_input, FC2V, boundary='symm', mode='same')
# f_L at center
f_L = signal.convolve2d(dms_input, FL, boundary='symm', mode='same')
# Move c1 to the center by shifting by Pi in both x and y direction
# f_c1 = c1 * (-1)^x * (-1)^y
f_c1 = c1_mod.copy()
f_c1[:, 1::2] *= -1
f_c1[1::2, :] *= -1
# Move c2a to the center by shifting by Pi in x direction, same for c2b in y direction
c2h = c2h_mod.copy()
c2h[:, 1::2] *= -1
c2v = c2v_mod.copy()
c2v[1::2, :] *= -1
# f_c2 = (c2v_mod * x_mod + c2h_mod * y_mod) / 2
f_c2 = (c2v + c2h) / 2
# generate RGB channel using
# [R, G, B] = [[1, 1, 2], [1, -1, 0], [1, 1, - 2]] x [L, C1, C2]
height, width = dms_input.shape
dms_img = np.zeros((height, width, 3))
dms_img[:, :, 0] = f_L + f_c1 + 2 * f_c2;
dms_img[:, :, 1] = f_L - f_c1;
dms_img[:, :, 2] = f_L + f_c1 - 2 * f_c2;
return dms_img
def white_balance(raw, array):
s = array.shape
if len(s) == 3:
result = white_balance_rgb(raw, array)
else:
result = white_balance_Bayer(raw, array)
return result
def white_balance_rgb(raw, rgb_array):
wb = np.array(raw.camera_whitebalance)
img_wb = rgb_array.copy()
img_wb[:, :, 0] *= wb[0] / 1024
img_wb[:, :, 1] *= wb[1] / 1024
img_wb[:, :, 2] *= wb[2] / 1024
return img_wb
def white_balance_Bayer(raw, raw_array):
bayer_pattern = raw.raw_pattern
wb = np.array(raw.camera_whitebalance)
img_wb = raw_array.copy()
img_wb[0::2, 0::2] *= wb[bayer_pattern[0, 0]] / 1024
img_wb[0::2, 1::2] *= wb[bayer_pattern[0, 1]] / 1024
img_wb[1::2, 0::2] *= wb[bayer_pattern[1, 0]] / 1024
img_wb[1::2, 1::2] *= wb[bayer_pattern[1, 1]] / 1024
return img_wb
def color_correction_matrix(rgb_array, color_matrix):
img_ccm = np.zeros_like(rgb_array)
ccm = np.array(color_matrix).reshape((3, 3))
for c in (0, 1, 2):
img_ccm[:, :, c] = ccm[c, 0] * rgb_array[:, :, 0] + \
ccm[c, 1] * rgb_array[:, :, 1] + \
ccm[c, 2] * rgb_array[:, :, 2]
return img_ccm / 1024
def gamma_correction(rgb_array):
img_gamma = rgb_array.copy()
img_gamma[img_gamma < 0] = 0
img_gamma = img_gamma / img_gamma.max()
img_gamma = np.power(img_gamma, 1/2.2)
return img_gamma
def main(argv):
if (len(argv) < 2):
print("Usage: {} input_filename [output_filename] [color_matrix]".format(argv[0]))
print("\tDefault output_filename is output.png")
print("\tDefault matrix is identity matrix ([1024, 0, 0, 0, 1024, 0, 0, 0, 1024]")
print("\tExample: python3 {} sample.ARW sample.png \"1141, -205, 88, -52, 1229, -154, 70, -225, 1179\"".format(argv[0]))
print("\tSupported RAW format is ARW (Sony RAW)")
print("\tSupported output format is PNG only")
return
filename = argv[1]
output_filename = "output.png"
color_matrix = [1024, 0, 0, 0, 1024, 0, 0, 0, 1024]
if len(argv) > 2:
output_filename = argv[2]
if len(argv) > 3:
color_matrix = [int(value) for value in (argv[3]).split(',')]
color_matrix = [1024, 0, 0, 0, 1024, 0, 0, 0, 1024]
raw = read(filename)
rgb_image = process(raw, color_matrix)
write(rgb_image, output_filename)
if __name__ == "__main__":
main(sys.argv)