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7873319 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 | #!/usr/bin/env python3
# Copyright (c) 2020-2022, NVIDIA CORPORATION. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification, are permitted
# provided that the following conditions are met:
# * Redistributions of source code must retain the above copyright notice, this list of
# conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright notice, this list of
# conditions and the following disclaimer in the documentation and/or other materials
# provided with the distribution.
# * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
# to endorse or promote products derived from this software without specific prior written
# permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
# IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
# FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
# OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
# STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import imageio
import numpy as np
import os
import struct
ROOT_DIR = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
def mse2psnr(x):
return -10.*np.log(x)/np.log(10.)
def write_image_imageio(img_file, img, quality):
img = (np.clip(img, 0.0, 1.0) * 255.0 + 0.5).astype(np.uint8)
kwargs = {}
if os.path.splitext(img_file)[1].lower() in [".jpg", ".jpeg"]:
if img.ndim >= 3 and img.shape[2] > 3:
img = img[:,:,:3]
kwargs["quality"] = quality
kwargs["subsampling"] = 0
imageio.imwrite(img_file, img, **kwargs)
def read_image_imageio(img_file):
img = imageio.imread(img_file)
img = np.asarray(img).astype(np.float32)
if len(img.shape) == 2:
img = img[:,:,np.newaxis]
return img / 255.0
def srgb_to_linear(img):
limit = 0.04045
return np.where(img > limit, np.power((img + 0.055) / 1.055, 2.4), img / 12.92)
def linear_to_srgb(img):
limit = 0.0031308
return np.where(img > limit, 1.055 * (img ** (1.0 / 2.4)) - 0.055, 12.92 * img)
def read_image(file):
if os.path.splitext(file)[1] == ".bin":
with open(file, "rb") as f:
bytes = f.read()
h, w = struct.unpack("ii", bytes[:8])
img = np.frombuffer(bytes, dtype=np.float16, count=h*w*4, offset=8).astype(np.float32).reshape([h, w, 4])
else:
img = read_image_imageio(file)
if img.shape[2] == 4:
img[...,0:3] = srgb_to_linear(img[...,0:3])
# Premultiply alpha
img[...,0:3] *= img[...,3:4]
else:
img = srgb_to_linear(img)
return img
def write_image(file, img, quality=95):
if os.path.splitext(file)[1] == ".bin":
if img.shape[2] < 4:
img = np.dstack((img, np.ones([img.shape[0], img.shape[1], 4 - img.shape[2]])))
with open(file, "wb") as f:
f.write(struct.pack("ii", img.shape[0], img.shape[1]))
f.write(img.astype(np.float16).tobytes())
else:
if img.shape[2] == 4:
img = np.copy(img)
# Unmultiply alpha
img[...,0:3] = np.divide(img[...,0:3], img[...,3:4], out=np.zeros_like(img[...,0:3]), where=img[...,3:4] != 0)
img[...,0:3] = linear_to_srgb(img[...,0:3])
else:
img = linear_to_srgb(img)
write_image_imageio(file, img, quality)
def trim(error, skip=0.000001):
error = np.sort(error.flatten())
size = error.size
skip = int(skip * size)
return error[skip:size-skip].mean()
def luminance(a):
a = np.maximum(0, a)**0.4545454545
return 0.2126 * a[:,:,0] + 0.7152 * a[:,:,1] + 0.0722 * a[:,:,2]
def L1(img, ref):
return np.abs(img - ref)
def APE(img, ref):
return L1(img, ref) / (1e-2 + ref)
def SAPE(img, ref):
return L1(img, ref) / (1e-2 + (ref + img) / 2.)
def L2(img, ref):
return (img - ref)**2
def RSE(img, ref):
return L2(img, ref) / (1e-2 + ref**2)
def rgb_mean(img):
return np.mean(img, axis=2)
def compute_error_img(metric, img, ref):
img[np.logical_not(np.isfinite(img))] = 0
img = np.maximum(img, 0.)
if metric == "MAE":
return L1(img, ref)
elif metric == "MAPE":
return APE(img, ref)
elif metric == "SMAPE":
return SAPE(img, ref)
elif metric == "MSE":
return L2(img, ref)
elif metric == "MScE":
return L2(np.clip(img, 0.0, 1.0), np.clip(ref, 0.0, 1.0))
elif metric == "MRSE":
return RSE(img, ref)
elif metric == "MtRSE":
return trim(RSE(img, ref))
elif metric == "MRScE":
return RSE(np.clip(img, 0, 100), np.clip(ref, 0, 100))
raise ValueError(f"Unknown metric: {metric}.")
def compute_error(metric, img, ref):
metric_map = compute_error_img(metric, img, ref)
metric_map[np.logical_not(np.isfinite(metric_map))] = 0
if len(metric_map.shape) == 3:
metric_map = np.mean(metric_map, axis=2)
mean = np.mean(metric_map)
return mean
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