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d7546f7 371a737 d7546f7 371a737 d7546f7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 | import torch
import numpy as np
import json
import random
device = "cuda" if torch.cuda.is_available() else "cpu"
def pack_raw(raw):
# pack Bayer image to 4 channels
im = raw.raw_image_visible.astype(np.float32)
im = np.maximum(im - 512, 0) / (16383 - 512) # subtract the black level
im = np.expand_dims(im, axis=2)
img_shape = im.shape
H = img_shape[0]
W = img_shape[1]
out = np.concatenate((im[0:H:2, 0:W:2, :],
im[0:H:2, 1:W:2, :],
im[1:H:2, 1:W:2, :],
im[1:H:2, 0:W:2, :]), axis=2)
return out
def CV72fillCurve(img, first_index, end_index):
with open('customs/k_table_AMBA_4k0111_update.json') as f:
K_tableCV72_load_json = json.load(f)
with open('customs/gaussian_table_AMBA_4k0111_update.json') as f:
GS_tableCV72_load_json = json.load(f)
img = torch.clone(img)
imgPoisson = img
imgPoisson = torch.clamp(imgPoisson, 0, 1)
gain_index = str(random.randrange(int(first_index), int(end_index)))
h, w, c = img.shape
for i in range(c):
syn_noise_R = torch.zeros([h, w], device=img.device)
channel = str(i)
pu_K = K_tableCV72_load_json[gain_index][channel]['K']
pu_std = GS_tableCV72_load_json[gain_index][channel]['std']
pu_row_std = GS_tableCV72_load_json[gain_index][channel]['row_std']# std of row noise
generated_row = torch.normal(0, pu_row_std, size=(h, 1))
for j in range(h):
gen_row_noisy = torch.normal(float(generated_row[j]), torch.tensor(pu_std), size=(1, w))
syn_noise_R[j] = gen_row_noisy
img[:, :, i] = img[:, :, i] + (imgPoisson[:, :, i] - torch.poisson(imgPoisson[:, :, i] / pu_K) * pu_K) ## Don't add 200
img[:, :, i] += syn_noise_R
img[:, :, i] = torch.clamp(img[:, :, i], 0, 1)
return img
def CV72fillCurve_np(img, first_index, end_index):
with open('customs/k_table_AMBA_4k0111_update.json') as f:
K_tableCV72_load_json = json.load(f)
with open('customs/gaussian_table_AMBA_4k0111_update.json') as f:
GS_tableCV72_load_json = json.load(f)
img = np.copy(img)
imgPoisson = img
imgPoisson = np.clip(imgPoisson, 0, 1)
gain_index = str(random.randrange(int(first_index), int(end_index)))
h, w, c = img.shape
for i in range(c):
syn_noise_R = np.zeros([h, w])
channel = str(i)
pu_K = K_tableCV72_load_json[gain_index][channel]['K']
pu_std = GS_tableCV72_load_json[gain_index][channel]['std']
pu_row_std = GS_tableCV72_load_json[gain_index][channel]['row_std']# std of row noise
generated_row = np.random.normal(0, pu_row_std, size=(h, 1))
for j in range(h):
gen_row_noisy = np.random.normal(float(generated_row[j]), pu_std, size=(1, w))
syn_noise_R[j] = gen_row_noisy
img[:, :, i] = img[:, :, i] + (imgPoisson[:, :, i] - np.random.poisson(imgPoisson[:, :, i] / pu_K) * pu_K) ## Don't add 200
img[:, :, i] += syn_noise_R
img[:, :, i] = np.clip(img[:, :, i], 0, 1)
return img
def rgb2rggb(img):
red = img[:, :, 0]
green1 = img[:, :, 1]
green2 = img[:, :, 1]
blue = img[:, :, 2]
rggb = torch.stack([red, green1, green2, blue], dim=2)
return rggb
def rggb2rgb(img):
red = img[:, :, 0]
green = img[:, :, 1] / 2 + img[:, :, 2] / 2
blue = img[:, :, 3]
rgb = torch.stack([red, green, blue], dim=2)
return rgb
def rggb2rgb_np(img):
red = img[:, :, 0]
green = img[:, :, 1] / 2 + img[:, :, 2] / 2
blue = img[:, :, 3]
rgb = np.stack([red, green, blue], axis=2)
return rgb
def rgb2rggb_np(img):
red = img[:, :, 0]
green1 = img[:, :, 1]
green2 = img[:, :, 1]
blue = img[:, :, 2]
rggb = np.stack([red, green1, green2, blue], axis=2)
return rggb |