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sample_out_viz.py
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sample_out_viz.py
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import numpy as np
from scipy.io import loadmat
import matplotlib.pyplot as plt
def img_plot(mat_path, img_save_path):
data = loadmat(mat_path)
orig_imgs, seg, pred = data["all_test_images"], data["all_test_labels"], data["all_test_outputs"]
# Choose four random indices from the length of the batch
indices = np.random.choice(len(orig_imgs), 4, replace=False)
fig, axs = plt.subplots(4, 7, figsize=(30, 15)) # Create a 4x3 subplot grid
for i, idx in enumerate(indices):
slice_id = np.random.randint(30, 91)
# Plotting original image
axs[i, 0].imshow(orig_imgs[idx][1,1,slice_id], cmap="gray")
axs[i, 0].set_title("Original Image")
# Plotting segmentation mask
axs[i, 1].imshow(seg[idx][1,0,slice_id]>0, cmap="gray")
axs[i, 1].set_title("Segmentation Mask (Whole Tumor)")
# Plotting prediction
axs[i, 2].imshow(pred[idx][1,0,slice_id]>0, cmap="gray")
axs[i, 2].set_title("Prediction for Whole Tumor Mask")
# Plotting segmentation mask
axs[i, 3].imshow(((seg[idx][1,0,slice_id]==1) + (seg[idx][1,0,slice_id]==3))>0, cmap="gray")
axs[i, 3].set_title("Segmentation Mask (Tumor Core)")
# Plotting prediction
axs[i, 4].imshow(pred[idx][1,1,slice_id]>0, cmap="gray")
axs[i, 4].set_title("Prediction for Tumor Core Mask")
# Plotting segmentation mask
axs[i, 5].imshow(seg[idx][1,0,slice_id]==3, cmap="gray")
axs[i, 5].set_title("Segmentation Mask (Enhancing Tumor)")
# Plotting prediction
axs[i, 6].imshow(pred[idx][1,2,slice_id]>0, cmap="gray")
axs[i, 6].set_title("Prediction for Enhancing Tumor Mask")
# Hide x and y ticks for all subplots
for ax in axs.flatten():
ax.axis('off')
plt.tight_layout()
plt.savefig(img_save_path)
def img_plot_deep_supervision(mat_path, img_save_path):
data = loadmat(mat_path)
orig_imgs, seg, pred, ds_outs = data["all_test_images"], data["all_test_labels"], data["all_test_outputs"], data["ds_outs"]
# Choose four random indices from the length of the batch
indices = np.random.choice(len(orig_imgs), 4, replace=False)
fig, axs = plt.subplots(4, 13, figsize=(90, 20)) # Create a 4x3 subplot grid
for i, idx in enumerate(indices):
slice_id = np.random.randint(30, 91)
# Plotting original image
axs[i, 0].imshow(orig_imgs[idx][1,1,slice_id], cmap="gray")
axs[i, 0].set_title("Original Image")
# Plotting segmentation mask
axs[i, 1].imshow(seg[idx][1,0,slice_id]>0, cmap="gray")
axs[i, 1].set_title("Segmentation Mask (Whole Tumor)")
# Plotting prediction
axs[i, 2].imshow(ds_outs[idx][2][1,0,slice_id], cmap="gray")
axs[i, 2].set_title("ds2 for Whole Tumor Mask")
axs[i, 3].imshow(ds_outs[idx][1][1,0,slice_id], cmap="gray")
axs[i, 3].set_title("ds1 for Whole Tumor Mask")
axs[i, 4].imshow(pred[idx][1,0,slice_id]>0, cmap="gray")
axs[i, 4].set_title("Prediction for Whole Tumor Mask")
# Plotting segmentation mask
axs[i, 5].imshow(((seg[idx][1,0,slice_id]==1) + (seg[idx][1,0,slice_id]==3))>0, cmap="gray")
axs[i, 5].set_title("Segmentation Mask (Tumor Core)")
# Plotting prediction
axs[i, 6].imshow(ds_outs[idx][2][1,1,slice_id], cmap="gray")
axs[i, 6].set_title("ds2 for Whole Tumor Mask")
axs[i, 7].imshow(ds_outs[idx][1][1,1,slice_id], cmap="gray")
axs[i, 7].set_title("ds1 for Whole Tumor Mask")
axs[i, 8].imshow(pred[idx][1,1,slice_id]>0, cmap="gray")
axs[i, 8].set_title("Prediction for Whole Tumor Mask")
# Plotting segmentation mask
axs[i, 9].imshow(seg[idx][1,0,slice_id]==3, cmap="gray")
axs[i, 9].set_title("Segmentation Mask (Enhancing Tumor)")
# Plotting prediction
axs[i, 10].imshow(ds_outs[idx][2][1,2,slice_id], cmap="gray")
axs[i, 10].set_title("ds2 for Whole Tumor Mask")
axs[i, 11].imshow(ds_outs[idx][1][1,2,slice_id], cmap="gray")
axs[i, 11].set_title("ds1 for Whole Tumor Mask")
axs[i, 12].imshow(pred[idx][1,2,slice_id]>0, cmap="gray")
axs[i, 12].set_title("Prediction for Whole Tumor Mask")
# Hide x and y ticks for all subplots
for ax in axs.flatten():
ax.axis('off')
plt.tight_layout()
plt.savefig(img_save_path)
def img_plot_deep_supervision2(mat_path, img_save_path):
data = loadmat(mat_path)
orig_imgs, seg, pred, ds_outs = data["all_test_images"], data["all_test_labels"], data["all_test_outputs"], data["ds_outs"]
idx = np.random.randint(len(orig_imgs)) # Choose a random index
slice_id = np.random.randint(30, 91)
fig, axs = plt.subplots(3, 4, figsize=(40, 30)) # Create a 3x4 subplot grid
# Plotting for Whole Tumor
axs[0, 0].imshow(seg[idx][1, 0, slice_id] > 0, cmap="gray")
axs[0, 0].axis('off')
axs[0, 1].imshow(ds_outs[idx][2][1, 0, slice_id], cmap="gray")
axs[0, 1].axis('off')
axs[0, 2].imshow(ds_outs[idx][1][1, 0, slice_id], cmap="gray")
axs[0, 2].axis('off')
axs[0, 3].imshow(pred[idx][1, 0, slice_id] > 0, cmap="gray")
axs[0, 3].axis('off')
# Plotting for Tumor Core
axs[1, 0].imshow(((seg[idx][1, 0, slice_id] == 1) + (seg[idx][1, 0, slice_id] == 3)) > 0, cmap="gray")
axs[1, 0].axis('off')
axs[1, 1].imshow(ds_outs[idx][2][1, 1, slice_id], cmap="gray")
axs[1, 1].axis('off')
axs[1, 2].imshow(ds_outs[idx][1][1, 1, slice_id], cmap="gray")
axs[1, 2].axis('off')
axs[1, 3].imshow(pred[idx][1, 1, slice_id] > 0, cmap="gray")
axs[1, 3].axis('off')
# Plotting for Enhancing Tumor
axs[2, 0].imshow(seg[idx][1, 0, slice_id] == 3, cmap="gray")
axs[2, 0].axis('off')
axs[2, 1].imshow(ds_outs[idx][2][1, 2, slice_id], cmap="gray")
axs[2, 1].axis('off')
axs[2, 2].imshow(ds_outs[idx][1][1, 2, slice_id], cmap="gray")
axs[2, 2].axis('off')
axs[2, 3].imshow(pred[idx][1, 2, slice_id] > 0, cmap="gray")
axs[2, 3].axis('off')
for ax, col in zip(axs[0], ['Segmentation mask', 'First DS Output', 'Second DS Output', 'Prediction']):
ax.set_title(col, fontsize=60)
for ax, row in zip(axs[:,0], ['Class Whole Tumor', 'Class Tumor Core', 'Class Enhancing Tumor']):
ax.set_ylabel(row, fontsize=60, labelpad=1000)
plt.subplots_adjust(wspace=5, hspace=5) # Adjust spacing between subplots
plt.tight_layout()
# Save the grid of subplots
plt.savefig(img_save_path + "_grid.png", bbox_inches='tight')
# Show the original image separately
plt.figure(figsize=(5, 5))
plt.imshow(orig_imgs[idx][1, 1, slice_id], cmap="gray")
plt.title("Original Image")
plt.axis('off')
# Save the original image separately
plt.savefig(img_save_path + "_original.png", bbox_inches='tight')