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import os
os.environ["OPENCV_IO_ENABLE_OPENEXR"] = "1"
import argparse
import cv2
import matplotlib.pyplot as plt
import numpy as np
import torch
from matplotlib import colors
from tqdm import tqdm
# flake8: noqa
# mypy: ignore-errors
class tonemap:
def __init__(self):
pass
def process(self, img):
return img
def inv_process(self, img):
return img
# Log correction
class log_tonemap(tonemap):
# Constructor
# Base of log
# Scale of tonemapped
# Offset
def __init__(self, base, scale=1, offset=1):
self.base = base
self.scale = scale
self.offset = offset
def process(self, img):
tonemapped = (np.log(img + self.offset) / np.log(self.base)) * self.scale
return tonemapped
def inv_process(self, img):
inverse_tonemapped = np.power(self.base, (img) / self.scale) - self.offset
return inverse_tonemapped
class log_tonemap_clip(tonemap):
# Constructor
# Base of log
# Scale of tonemapped
# Offset
def __init__(self, base, scale=1, offset=1):
self.base = base
self.scale = scale
self.offset = offset
def process(self, img):
tonemapped = np.clip((np.log(img * self.scale + self.offset) / np.log(self.base)), 0, 2) - 1
return tonemapped
def inv_process(self, img):
inverse_tonemapped = (np.power(self.base, (img + 1)) - self.offset) / self.scale
return inverse_tonemapped
# Gamma Tonemap
class gamma_tonemap(tonemap):
def __init__(
self,
gamma,
):
self.gamma = gamma
def process(self, img):
tonemapped = np.power(img, 1 / self.gamma)
return tonemapped
def inv_process(self, img):
inverse_tonemapped = np.power(img, self.gamma)
return inverse_tonemapped
class linear_clip(tonemap):
def __init__(self, scale, mean):
self.scale = scale
self.mean = mean
def process(self, img):
tonemapped = np.clip((img - self.mean) / self.scale, -1, 1)
return tonemapped
def inv_process(self, img):
inverse_tonemapped = img * self.scale + self.mean
return inverse_tonemapped
def make_tonemap_HDR(opt):
if opt.mode == "luminance":
res_tonemap = log_tonemap_clip(10, 1.0, 1.0)
else: # temperature
res_tonemap = linear_clip(5000.0, 5000.0)
return res_tonemap
class LDRfromHDR:
def __init__(
self, tonemap="none", orig_scale=False, clip=True, quantization=0, color_jitter=0, noise=0
):
self.tonemap_str, val = tonemap
if tonemap[0] == "gamma":
self.tonemap = gamma_tonemap(val)
elif tonemap[0] == "log10":
self.tonemap = log_tonemap(val)
else:
print("Warning: No tonemap specified, using linear")
self.clip = clip
self.orig_scale = orig_scale
self.bits = quantization
self.jitter = color_jitter
self.noise = noise
self.wbModel = None
def process(self, HDR):
LDR, normalized_scale = self.rescale(HDR)
LDR = self.apply_clip(LDR)
LDR = self.apply_scale(LDR, normalized_scale)
LDR = self.apply_tonemap(LDR)
LDR = self.colorJitter(LDR)
LDR = self.gaussianNoise(LDR)
LDR = self.quantize(LDR)
LDR = self.apply_white_balance(LDR)
return LDR, normalized_scale
def rescale(self, img, percentile=90, max_mapping=0.8):
r_percentile = np.percentile(img, percentile)
alpha = max_mapping / (r_percentile + 1e-10)
img_reexposed = img * alpha
normalized_scale = normalizeScale(1 / alpha)
return img_reexposed, normalized_scale
def rescaleAlpha(self, img, percentile=90, max_mapping=0.8):
r_percentile = np.percentile(img, percentile)
alpha = max_mapping / (r_percentile + 1e-10)
return alpha
def apply_clip(self, img):
if self.clip:
img = np.clip(img, 0, 1)
return img
def apply_scale(self, img, scale):
if self.orig_scale:
scale = unNormalizeScale(scale)
img = img * scale
return img
def apply_tonemap(self, img):
if self.tonemap_str == "none":
return img
gammaed = self.tonemap.process(img)
return gammaed
def quantize(self, img):
if self.bits == 0:
return img
max_val = np.power(2, self.bits)
img = img * max_val
img = np.floor(img)
img = img / max_val
return img
def colorJitter(self, img):
if self.jitter == 0:
return img
hsv = colors.rgb_to_hsv(img)
hue_offset = np.random.normal(0, self.jitter, 1)
hsv[:, :, 0] = (hsv[:, :, 0] + hue_offset) % 1.0
rgb = colors.hsv_to_rgb(hsv)
return rgb
def gaussianNoise(self, img):
if self.noise == 0:
return img
noise_amount = np.random.uniform(0, self.noise, 1)
noise_img = np.random.normal(0, noise_amount, img.shape)
img = img + noise_img
img = np.clip(img, 0, 1).astype(np.float32)
return img
def apply_white_balance(self, img):
if self.wbModel is None:
return img
img = self.wbModel.correctImage(img)
return img.copy()
def make_LDRfromHDR(opt):
LDR_from_HDR = LDRfromHDR(
opt.tonemap_LDR, opt.orig_scale, opt.clip, opt.quantization, opt.color_jitter, opt.noise
)
return LDR_from_HDR
def torchnormalizeEV(EV, mean=5.12, scale=6, clip=True):
# Normalize based on the computed distribution between -1 1
EV -= mean
EV = EV / scale
if clip:
EV = torch.clip(EV, min=-1, max=1)
return EV
def torchnormalizeEV0(EV, mean=5.12, scale=6, clip=True):
# Normalize based on the computed distribution between 0 1
EV -= mean
EV = EV / scale
if clip:
EV = torch.clip(EV, min=-1, max=1)
EV += 0.5
EV = EV / 2
return EV
def normalizeScale(x, scale=4):
x = np.log10(x + 1)
x = x / (scale / 2)
x = x - 1
return x
def unNormalizeScale(x, scale=4):
x = x + 1
x = x * (scale / 2)
x = np.power(10, x) - 1
return x
def normalizeIlluminance(x, scale=5):
x = np.log10(x + 1)
x = x / (scale / 2)
x = x - 1
return x
def unNormalizeIlluminance(x, scale=5):
x = x + 1
x = x * (scale / 2)
x = np.power(10, x) - 1
return x
def main(args):
processor = LDRfromHDR(
# tonemap=("log10", 10),
tonemap=("gamma", args.gamma),
orig_scale=False,
clip=True,
quantization=0,
color_jitter=0,
noise=0,
)
img_list = list(os.listdir(args.hdr_dir))
img_list = [f for f in img_list if f.endswith(args.extension)]
img_list = [f for f in img_list if not f.startswith("._")]
if not os.path.exists(args.out_dir):
os.makedirs(args.out_dir)
for fname in tqdm(img_list):
fname_out = ".".join(fname.split(".")[:-1])
out = os.path.join(args.out_dir, f"{fname_out}.jpg")
if os.path.exists(out) and not args.overwrite:
continue
fpath = os.path.join(args.hdr_dir, fname)
img = cv2.imread(fpath, cv2.IMREAD_ANYCOLOR | cv2.IMREAD_ANYDEPTH)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
ldr, scale = processor.process(img)
ldr = (ldr * 255).astype(np.uint8)
ldr = cv2.cvtColor(ldr, cv2.COLOR_RGB2BGR)
cv2.imwrite(out, ldr)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--hdr_dir", type=str, default="hdr")
parser.add_argument("--out_dir", type=str, default="ldr")
parser.add_argument("--extension", type=str, default=".exr")
parser.add_argument("--overwrite", action="store_true")
parser.add_argument("--gamma", type=float, default=2)
args = parser.parse_args()
main(args)
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