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import math
import cv2
import numpy as np
import torch
from torch import nn
from ..image_processor import convert_to_gray, image_binarize, min_max_normalize
class GaussianFilter(nn.Module):
def __init__(self, module_size: int, filter_thres: float = 0.1) -> None:
super().__init__()
self.module_size = module_size
self.filter_thres = filter_thres
self.conv = nn.Conv2d(
in_channels=1,
out_channels=1,
kernel_size=module_size,
stride=module_size,
padding=0,
bias=False,
groups=1,
)
self._setup_filter_weights()
def _setup_filter_weights(self) -> None:
filter_1d = cv2.getGaussianKernel(
ksize=self.module_size,
sigma=1.5,
ktype=cv2.CV_32F
)
filter_2d = filter_1d * filter_1d.T
filter_2d = min_max_normalize(filter_2d)
filter_2d[filter_2d < self.filter_thres] = .0
gaussian_filter_init = torch.tensor(filter_2d, dtype=torch.float32)
self.conv.weight = nn.Parameter(
gaussian_filter_init.reshape(1, 1, *gaussian_filter_init.shape),
requires_grad=False,
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.conv(x)
class RegionMeanFilter(nn.Module):
def __init__(self, module_size: int) -> None:
super().__init__()
self.module_size = module_size
self.conv = nn.Conv2d(
in_channels=1,
out_channels=1,
kernel_size=module_size,
stride=module_size,
padding=0,
bias=None,
groups=1,
)
self._setup_kernel_weights()
def _setup_kernel_weights(self) -> None:
module_center = int(self.module_size / 2)
radius = math.ceil(self.module_size / 6)
center_filter = torch.zeros((1, 1, self.module_size, self.module_size))
center_filter[
:, :,
module_center-radius : module_center+radius,
module_center-radius : module_center+radius,
] = 1.0
self.conv.weight = nn.Parameter(
center_filter / center_filter.sum(),
requires_grad=False,
)
@torch.no_grad()
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.conv(x)
class CenterPixelExtractor(nn.Module):
def __init__(self, module_size: int) -> None:
super().__init__()
self.module_size = module_size
self.conv = nn.Conv2d(
in_channels=1,
out_channels=1,
kernel_size=module_size,
stride=module_size,
padding=0,
bias=None,
groups=1,
)
self._setup_kernel_weights()
def _setup_kernel_weights(self) -> None:
module_center = int(self.module_size / 2) + 1
center_filter = torch.zeros((1, 1, self.module_size, self.module_size))
center_filter[:, :, module_center, module_center] = 1.0
self.conv.weight = nn.Parameter(center_filter, requires_grad=False)
@torch.no_grad()
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.conv(x)
class QRCodeErrorExtractor(nn.Module):
def __init__(self, module_size: int) -> None:
super().__init__()
self.module_size = module_size
self.region_mean_filter = RegionMeanFilter(module_size)
self.center_pixel_extractor = CenterPixelExtractor(module_size=module_size)
def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
x_center_mean = self.region_mean_filter(x)
y_center_pixel = self.center_pixel_extractor(y)
error_mask = (y_center_pixel == 0) & (x_center_mean > 0.45) | \
(y_center_pixel == 1) & (x_center_mean < 0.65)
return error_mask.float()
class ScanningRobustLoss(nn.Module):
def __init__(self, module_size: int) -> None:
super().__init__()
self.gaussian_filter = GaussianFilter(module_size=module_size)
self.center_filter = RegionMeanFilter(module_size=module_size)
self.module_error_extractor = QRCodeErrorExtractor(module_size=module_size)
def _compute_error(self, image: torch.Tensor, qrcode: torch.Tensor) -> torch.Tensor:
gray_image = convert_to_gray(image)
error0 = 2 * torch.relu(gray_image - 0.45) * (1 - qrcode)
error1 = 2 * torch.relu(0.65 - gray_image) * qrcode
return error0 + error1
def _compute_ealy_stopping_mask(self, image: torch.Tensor, qrcode: torch.Tensor) -> torch.Tensor:
return self.module_error_extractor(
convert_to_gray(image.clone().detach()),
image_binarize(qrcode),
)
def forward(self, image: torch.Tensor, qrcode: torch.Tensor) -> torch.Tensor:
error = self._compute_error(image, qrcode)
sample_error = self.gaussian_filter(error)
mask = self._compute_ealy_stopping_mask(image, qrcode)
return torch.mean(sample_error * mask)
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