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import numpy as np
from fractions import Fraction
import cv2
import numpy as np
import exifread
from exifread.utils import Ratio
import struct
import json
import torch
import time
Temp = np.ones([1536,2048]).astype(np.float32)
Timg = np.ones([768,1024,3]).astype(np.float32)
def apply_gamma(x):
# return x ** (1.0 / 2.2)
x = x.copy()
idx = x <= 0.0031308
x[idx] *= 12.92
x[idx == False] = (x[idx == False] ** (1.0 / 2.4)) * 1.055 - 0.055
return x
def binning(img,data):
if data['cfa_pattern'] == [0,1,1,2]:
ch_R = img[0::2, 0::2]
ch_G = (img[1::2, 0::2]+img[0::2,1::2])/2
ch_B = img[1::2, 1::2]
out = np.dstack((ch_R, ch_G, ch_B))
if data['cfa_pattern'] == [2,1,1,0]:
ch_R = img[1::2, 1::2]
ch_G = (img[1::2, 0::2]+img[0::2,1::2])/2
ch_B = img[0::2, 0::2]
out = np.dstack((ch_R, ch_G, ch_B))
return out
def Four2One(img):
Temp[0::2,0::2] = img[:,:,0]
Temp[1::2,0::2] = img[:,:,1]
Temp[0::2,1::2] = img[:,:,1]
Temp[1::2,1::2] = img[:,:,2]
return Temp
def One2Four(Temp):
Timg[:,:,0] = Temp[0::2,0::2]
Timg[:,:,1] = (Temp[1::2,0::2]+Temp[0::2,1::2])/2
Timg[:,:,2] = Temp[1::2,1::2]
return Timg
def white_balance(demosaic_img, as_shot_neutral):
if type(as_shot_neutral[0]) is Ratio:
as_shot_neutral = ratios2floats(as_shot_neutral)
as_shot_neutral = np.asarray(as_shot_neutral)
# transform vector into matrix
if as_shot_neutral.shape == (3,):
as_shot_neutral = np.diag(1. / as_shot_neutral)
assert as_shot_neutral.shape == (3, 3)
white_balanced_image = np.dot(demosaic_img, as_shot_neutral.T)
white_balanced_image = np.clip(white_balanced_image, 0.0, 1.0)
return white_balanced_image
def apply_color_space_transform(demosaiced_image, color_matrix):
xyz2cam = np.reshape(np.asarray(color_matrix), (3, 3))
# normalize rows (needed?)
xyz2cam = xyz2cam / np.sum(xyz2cam, axis=1, keepdims=True)
# inverse
cam2xyz = np.linalg.inv(xyz2cam)
# simplified matrix multiplication
xyz_image = cam2xyz[np.newaxis, np.newaxis, :, :] * \
demosaiced_image[:, :, np.newaxis, :]
xyz_image = np.sum(xyz_image, axis=-1)
xyz_image = np.clip(xyz_image, 0.0, 1.0)
return xyz_image
def transform_xyz_to_srgb(xyz_image):
xyz2srgb = np.array([[3.2404542, -1.5371385, -0.4985314],
[-0.9692660, 1.8760108, 0.0415560],
[0.0556434, -0.2040259, 1.0572252]])
# normalize rows (needed?)
xyz2srgb = xyz2srgb / np.sum(xyz2srgb, axis=-1, keepdims=True)
srgb_image = xyz2srgb[np.newaxis, np.newaxis, :, :] * xyz_image[:, :, np.newaxis, :]
srgb_image = np.sum(srgb_image, axis=-1)
srgb_image = np.clip(srgb_image, 0.0, 1.0)
return srgb_image
def fix_orientation(image, orientation):
# 1 = Horizontal(normal)
# 2 = Mirror horizontal
# 3 = Rotate 180
# 4 = Mirror vertical
# 5 = Mirror horizontal and rotate 270 CW
# 6 = Rotate 90 CW
# 7 = Mirror horizontal and rotate 90 CW
# 8 = Rotate 270 CW
if type(orientation) is list:
orientation = orientation[0]
if orientation == "Horizontal(normal)":
pass
elif orientation == "Mirror horizonta":
image = cv2.flip(image, 0)
elif orientation == "Rotate 180":
image = cv2.rotate(image, cv2.ROTATE_180)
elif orientation == "Mirror vertical":
image = cv2.flip(image, 1)
elif orientation == "Mirror horizontal and rotate 270 CW":
image = cv2.flip(image, 0)
image = cv2.rotate(image, cv2.ROTATE_90_COUNTERCLOCKWISE)
elif orientation == "Rotate 90 CW":
image = cv2.rotate(image, cv2.ROTATE_90_CLOCKWISE)
elif orientation == "Mirror horizontal and rotate 90 CW":
image = cv2.flip(image, 0)
image = cv2.rotate(image, cv2.ROTATE_90_CLOCKWISE)
elif orientation == "Rotate 270 CW":
image = cv2.rotate(image, cv2.ROTATE_90_COUNTERCLOCKWISE)
return image |