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import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
class CustomTverskyLoss(nn.Module):
def __init__(self, alpha=0.1, beta=0.9, size_average=True):
super(CustomTverskyLoss, self).__init__()
self.alpha = alpha
self.beta = beta
self.size_average = size_average
def forward(self, inputs, targets, smooth=1):
# If your model contains a sigmoid or equivalent activation layer, comment this line
# inputs = F.sigmoid(inputs)
# Check if the input tensors are of expected shape
if inputs.shape != targets.shape:
raise ValueError("Shape mismatch: inputs and targets must have the same shape")
# Compute Tversky loss for each sample in the batch
tversky_loss_values = []
for input_sample, target_sample in zip(inputs, targets):
# Flatten tensors for each sample
input_sample = input_sample.view(-1)
target_sample = target_sample.view(-1)
# Calculate the true positives, false positives, and false negatives
true_positives = (input_sample * target_sample).sum()
false_positives = (input_sample * (1 - target_sample)).sum()
false_negatives = ((1 - input_sample) * target_sample).sum()
# Compute the Tversky index for each sample
tversky_index = (true_positives + smooth) / (true_positives + self.alpha * false_positives + self.beta * false_negatives + smooth)
tversky_loss_values.append(1 - tversky_index)
# Convert list of Tversky loss values to a tensor
tversky_loss_values = torch.stack(tversky_loss_values)
# If you want the average loss over the batch to be returned
if self.size_average:
return tversky_loss_values.mean()
else:
# If you want individual losses for each sample in the batch
return tversky_loss_values
class CustomDiceLoss(nn.Module):
def __init__(self, weight=None, size_average=True):
super(CustomDiceLoss, self).__init__()
self.size_average = size_average
def forward(self, inputs, targets, smooth=1):
# If your model contains a sigmoid or equivalent activation layer, comment this line
#inputs = F.sigmoid(inputs)
# Check if the input tensors are of expected shape
if inputs.shape != targets.shape:
raise ValueError("Shape mismatch: inputs and targets must have the same shape")
# Compute Dice loss for each sample in the batch
dice_loss_values = []
for input_sample, target_sample in zip(inputs, targets):
# Flatten tensors for each sample
input_sample = input_sample.view(-1)
target_sample = target_sample.view(-1)
intersection = (input_sample * target_sample).sum()
dice = (2. * intersection + smooth) / (input_sample.sum() + target_sample.sum() + smooth)
dice_loss_values.append(1 - dice)
# Convert list of Dice loss values to a tensor
dice_loss_values = torch.stack(dice_loss_values)
# If you want the average loss over the batch to be returned
if self.size_average:
return dice_loss_values.mean()
else:
# If you want individual losses for each sample in the batch
return dice_loss_values
def smooth_heaviside(phi, alpha, epsilon):
# Scale and shift phi for the sigmoid function
scaled_phi = (phi - alpha) / epsilon
# Apply the sigmoid function
H = torch.sigmoid(scaled_phi)
return H
def calc_Phi(variable, LSgrid):
device = variable.device # Get the device of the variable
x0 = variable[0]
y0 = variable[1]
L = variable[2]
t = variable[3] # Constant thickness
angle = variable[4]
# Rotation
st = torch.sin(angle)
ct = torch.cos(angle)
x1 = ct * (LSgrid[0][:, None].to(device) - x0) + st * (LSgrid[1][:, None].to(device) - y0)
y1 = -st * (LSgrid[0][:, None].to(device) - x0) + ct * (LSgrid[1][:, None].to(device) - y0)
# Regularized hyperellipse equation
a = L / 2 # Semi-major axis
b = t / 2 # Constant semi-minor axis
small_constant = 1e-9 # To avoid division by zero
temp = ((x1 / (a + small_constant))**6) + ((y1 / (b + small_constant))**6)
# # Ensuring the hyperellipse shape
allPhi = 1 - (temp + small_constant)**(1/6)
# # Call Heaviside function with allPhi
alpha = torch.tensor(0.0, device=device, dtype=torch.float32)
epsilon = torch.tensor(0.001, device=device, dtype=torch.float32)
H_phi = smooth_heaviside(allPhi, alpha, epsilon)
return allPhi, H_phi
# utils.py
import torch
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
from matplotlib.colors import TwoSlopeNorm
def preprocess_image(image_path, threshold_value=0.9, upscale=False, upscale_factor=2.0):
image = Image.open(image_path).convert('L')
image = image.point(lambda x: 255 if x > threshold_value * 255 else 0, '1')
if upscale:
image = image.resize(
(int(image.width * upscale_factor), int(image.height * upscale_factor)),
resample=Image.BICUBIC
)
return image
def run_model(model, image, conf=0.05, iou=0.5, imgsz=640):
results = model(image, conf=conf, iou=iou, imgsz=imgsz)
return results
def save_results(results, filename='results.jpg'):
for r in results:
im_array = r.plot(boxes=True, labels=False, line_width=1)
im = Image.fromarray(im_array[..., ::-1])
im.save(filename)
def process_results(results, input_image):
diceloss = CustomDiceLoss()
tverskyloss = CustomTverskyLoss()
prediction_tensor = results[0].regression_preds.to('cpu').detach()
input_image_array = np.array(input_image.convert('L'))
input_image_array_tensor = torch.tensor(input_image_array) / 255.0
input_image_array_tensor = 1.0 - input_image_array_tensor
input_image_array_tensor = torch.flip(input_image_array_tensor, [0])
for r in results:
im_array = r.plot(boxes=True, labels=False, line_width=1)
seg_result = Image.fromarray(im_array[..., ::-1])
DH = input_image_array.shape[0] / min(input_image_array.shape[1], input_image_array.shape[0])
DW = input_image_array.shape[1] / min(input_image_array.shape[1], input_image_array.shape[0])
nelx = input_image_array.shape[1] - 1
nely = input_image_array.shape[0] - 1
x, y = torch.meshgrid(torch.linspace(0, DW, nelx+1), torch.linspace(0, DH, nely+1))
LSgrid = torch.stack((x.flatten(), y.flatten()), dim=0)
pred_bboxes = results[0].boxes.xyxyn.to('cpu').detach()
constant_tensor_02 = torch.full((pred_bboxes.shape[0],), 0.2)
constant_tensor_00 = torch.full((pred_bboxes.shape[0],), 0.001)
xmax = torch.stack([pred_bboxes[:,2]*(DW*1.0), pred_bboxes[:,3]*(DH*1.0), pred_bboxes[:,2]*(DW*1.0), pred_bboxes[:,3]*(DH*1.0), constant_tensor_02], dim=1)
xmin = torch.stack([pred_bboxes[:,0]*(DW*1.0), pred_bboxes[:,1]*(DH*1.0), pred_bboxes[:,0]*(DW*1.0), pred_bboxes[:,1]*(DH*1.0), constant_tensor_00], dim=1)
unnormalized_preds = prediction_tensor * (xmax - xmin) + xmin
x_center = (unnormalized_preds[:, 0] + unnormalized_preds[:, 2]) / 2
y_center = (unnormalized_preds[:, 1] + unnormalized_preds[:, 3]) / 2
L = torch.sqrt((unnormalized_preds[:, 0] - unnormalized_preds[:, 2])**2 +
(unnormalized_preds[:, 1] - unnormalized_preds[:, 3])**2)
L = L + 1e-4
t_1 = unnormalized_preds[:, 4]
epsilon = 1e-10
y_diff = unnormalized_preds[:, 3] - unnormalized_preds[:, 1] + epsilon
x_diff = unnormalized_preds[:, 2] - unnormalized_preds[:, 0] + epsilon
theta = torch.atan2(y_diff, x_diff)
formatted_variables = torch.cat((x_center.unsqueeze(1),
y_center.unsqueeze(1),
L.unsqueeze(1),
t_1.unsqueeze(1),
theta.unsqueeze(1)), dim=1)
pred_Phi, pred_H = calc_Phi(formatted_variables.T, LSgrid)
sum_pred_H = torch.sum(pred_H.detach().cpu(), dim=1)
sum_pred_H[sum_pred_H > 1] = 1
final_H = np.flipud(sum_pred_H.detach().numpy().reshape((nely+1, nelx+1), order='F'))
dice_loss = diceloss(torch.tensor(final_H.copy()), input_image_array_tensor)
tversky_loss = tverskyloss(torch.tensor(final_H.copy()), input_image_array_tensor)
return input_image_array_tensor, seg_result, pred_Phi, sum_pred_H, final_H, dice_loss, tversky_loss
def plot_results(input_image_array_tensor, seg_result, pred_Phi, sum_pred_H, final_H, dice_loss, tversky_loss, filename='combined_plots.png'):
nelx = input_image_array_tensor.shape[1] - 1
nely = input_image_array_tensor.shape[0] - 1
fig, axes = plt.subplots(2, 2, figsize=(8, 8))
axes[0, 0].imshow(input_image_array_tensor.squeeze(), origin='lower', cmap='gray_r')
axes[0, 0].set_title('Input Image')
axes[0, 0].axis('on')
axes[0, 1].imshow(seg_result)
axes[0, 1].set_title('Segmentation Result')
axes[0, 1].axis('off')
render_colors1 = ['yellow', 'g', 'r', 'c', 'm', 'y', 'black', 'orange', 'pink', 'cyan', 'slategrey', 'wheat', 'purple', 'mediumturquoise', 'darkviolet', 'orangered']
for i, color in zip(range(0, pred_Phi.shape[1]), render_colors1*100):
axes[1, 1].contourf(np.flipud(pred_Phi[:, i].numpy().reshape((nely+1, nelx+1), order='F')), [0, 1], colors=color)
axes[1, 1].set_title('Prediction contours')
axes[1, 1].set_aspect('equal')
axes[1, 0].imshow(np.flipud(sum_pred_H.detach().numpy().reshape((nely+1, nelx+1), order='F')), origin='lower', cmap='gray_r')
axes[1, 0].set_title('Prediction Projection')
plt.subplots_adjust(hspace=0.3, wspace=0.01)
plt.figtext(0.5, 0.05, f'Dice Loss: {dice_loss.item():.4f}', ha='center', fontsize=16)
fig.savefig(filename, dpi=600)
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