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LIENWEIYING/T1/visualize_medical_compare.py
Furen c792f5eb0b Initial commit
2026-07-22 14:50:09 +08:00

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Python
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# import nibabel as nib
# import matplotlib.pyplot as plt
# import numpy as np
# import os
# def load_nifti(file_path):
# """載入NIfTI檔案"""
# return nib.load(file_path).get_fdata()
# def create_colored_mask(data):
# """創建彩色遮罩"""
# color_dict = {
# 1: [144/255, 238/255, 144/255], # Brainstem - 綠色
# 2: [255/255, 218/255, 150/255], # Right_Eye - 淺黃色
# 3: [205/255, 170/255, 125/255], # Left_Eye - 棕色
# 4: [135/255, 206/255, 235/255], # Optic_Chiasm - 藍色
# 5: [255/255, 99/255, 71/255], # Right_Optic_Nerve - 亮珊瑚紅
# 6: [255/255, 160/255, 122/255] # Left_Optic_Nerve - 淺珊瑚紅
# }
# mask = np.zeros((*data.shape, 4))
# for label, color in color_dict.items():
# organ_mask = (data == label)
# if np.any(organ_mask):
# mask[organ_mask] = [*color, 1.0]
# return mask
# def get_brain_bounds(image):
# """獲取腦部區域的邊界"""
# mask = image > np.percentile(image, 1)
# rows = np.any(mask, axis=1)
# cols = np.any(mask, axis=0)
# rmin, rmax = np.where(rows)[0][[0, -1]]
# cmin, cmax = np.where(cols)[0][[0, -1]]
# center_r = (rmin + rmax) // 2
# center_c = (cmin + cmax) // 2
# radius = int(max(rmax - rmin, cmax - cmin) * 0.55)
# margin = int(radius * 0.15)
# rmin = max(center_r - radius - margin, 0)
# rmax = min(center_r + radius + margin, image.shape[0])
# cmin = max(center_c - radius - margin, 0)
# cmax = min(center_c + radius + margin, image.shape[1])
# return rmin, rmax, cmin, cmax
# def check_organs_in_slice(slice_data):
# """檢查切片中存在的器官標籤"""
# unique_labels = set(np.unique(slice_data))
# if 0 in unique_labels:
# unique_labels.remove(0)
# organ_dict = {
# 1: "Brainstem",
# 2: "Right_Eye",
# 3: "Left_Eye",
# 4: "Optic_Chiasm",
# 5: "Right_Optic_Nerve",
# 6: "Left_Optic_Nerve"
# }
# return {organ_dict[label] for label in unique_labels if label in organ_dict}
# def save_slice(image, ground_truth, prediction, slice_num, save_path):
# """保存指定切片的比較圖"""
# plt.clf()
# fig, axes = plt.subplots(1, 3, figsize=(30, 10))
# plt.subplots_adjust(wspace=0.01)
# for ax in axes:
# ax.axis('off')
# img_slice = np.rot90(image[:, :, slice_num])
# rmin, rmax, cmin, cmax = get_brain_bounds(img_slice)
# # 調整圖像對比度
# p2, p98 = np.percentile(img_slice, (2, 98))
# img_slice = np.clip(img_slice, p2, p98)
# img_slice = (img_slice - p2) / (p98 - p2)
# # 原始圖像
# axes[0].imshow(img_slice[rmin:rmax, cmin:cmax], cmap='gray')
# axes[0].set_title('Original Image', fontsize=32, pad=20, weight='bold')
# # Ground Truth
# gt_slice = np.rot90(ground_truth[:, :, slice_num])
# gt_mask = create_colored_mask(gt_slice)
# axes[1].imshow(img_slice[rmin:rmax, cmin:cmax], cmap='gray', zorder=1)
# axes[1].imshow(gt_mask[rmin:rmax, cmin:cmax], zorder=2)
# axes[1].set_title('Ground Truth', fontsize=32, pad=20, weight='bold')
# # Prediction
# pred_slice = np.rot90(prediction[:, :, slice_num])
# pred_mask = create_colored_mask(pred_slice)
# axes[2].imshow(img_slice[rmin:rmax, cmin:cmax], cmap='gray', zorder=1)
# axes[2].imshow(pred_mask[rmin:rmax, cmin:cmax], zorder=2)
# axes[2].set_title('Prediction', fontsize=32, pad=20, weight='bold')
# plt.savefig(save_path, bbox_inches='tight', dpi=300, pad_inches=0.05)
# plt.close()
# print(f"已保存切片 {slice_num} 至: {save_path}")
# if __name__ == "__main__":
# base_dir = "/mnt/1248/onlylian"
# image_path = os.path.join(base_dir, "nnUNet/nnUNet_raw/Dataset888/imagesTs/T1_069_0000.nii.gz")
# gt_path = os.path.join(base_dir, "nnUNet/nnUNet_raw/Dataset888/labelsTs/T1_069.nii.gz")
# pred_path = os.path.join(base_dir, "T1/output_predictions/T1_069.nii.gz")
# save_dir = os.path.join(base_dir, "T1")
# os.makedirs(save_dir, exist_ok=True)
# print("正在載入影像...")
# image = load_nifti(image_path)
# ground_truth = load_nifti(gt_path)
# prediction = load_nifti(pred_path)
# print("影像載入完成")
# print("\n分析切片中的器官...")
# total_slices = image.shape[2]
# for slice_num in range(total_slices):
# gt_slice = ground_truth[:, :, slice_num]
# pred_slice = prediction[:, :, slice_num]
# gt_organs = check_organs_in_slice(gt_slice)
# pred_organs = check_organs_in_slice(pred_slice)
# if gt_organs or pred_organs:
# print(f"\n切片 {slice_num}:")
# print(f"Ground Truth 包含: {', '.join(sorted(gt_organs))}")
# print(f"Prediction 包含: {', '.join(sorted(pred_organs))}")
# print("\n開始切片選擇和保存過程...")
# while True:
# try:
# slice_num = int(input("\n請輸入要保存的切片編號 (-1 退出): "))
# if slice_num == -1:
# print("程序結束")
# break
# if 0 <= slice_num < total_slices:
# save_path = os.path.join(save_dir, f'comparison_T1_069_{slice_num:03d}.png')
# save_slice(image, ground_truth, prediction, slice_num, save_path)
# else:
# print(f"切片編號必須在 0 到 {total_slices-1} 之間")
# except ValueError:
# print("請輸入有效的數字")
# except Exception as e:
# print(f"發生錯誤: {str(e)}")
import nibabel as nib
import matplotlib.pyplot as plt
import numpy as np
import os
def load_nifti(file_path):
"""載入NIfTI檔案"""
return nib.load(file_path).get_fdata()
def create_colored_mask(data):
"""創建彩色遮罩"""
color_dict = {
1: [144/255, 238/255, 144/255], # Brainstem - 綠色
2: [255/255, 218/255, 150/255], # Right_Eye - 淺黃色
3: [205/255, 170/255, 125/255], # Left_Eye - 棕色
4: [135/255, 206/255, 235/255], # Optic_Chiasm - 藍色
5: [255/255, 99/255, 71/255], # Right_Optic_Nerve - 亮珊瑚紅
6: [255/255, 160/255, 122/255] # Left_Optic_Nerve - 淺珊瑚紅
}
mask = np.zeros((*data.shape, 4))
for label, color in color_dict.items():
organ_mask = (data == label)
if np.any(organ_mask):
mask[organ_mask] = [*color, 1.0]
return mask
def get_brain_bounds(image):
"""獲取腦部區域的邊界，並調整顯示位置以避開底部基架"""
# 基本閾值處理
mask = image > np.percentile(image, 1)
# 獲取非零區域的邊界
rows = np.any(mask, axis=1)
cols = np.any(mask, axis=0)
# 確保有足夠的點
if not np.any(rows) or not np.any(cols):
return 0, image.shape[0], 0, image.shape[1]
row_indices = np.where(rows)[0]
col_indices = np.where(cols)[0]
# 獲取初始邊界
rmin, rmax = row_indices[[0, -1]]
cmin, cmax = col_indices[[0, -1]]
# 計算中心點，但略微上移以避開基架
center_r = (rmin + rmax) // 2 - int(image.shape[0] * 0.15) # 上移15%
center_c = (cmin + cmax) // 2
# 計算顯示範圍
height = image.shape[0]
width = image.shape[1]
display_size = int(min(height, width) * 0.7) # 使用70%的圖像大小
# 設置新的邊界，確保不會超出圖像範圍
rmin = max(center_r - display_size//2, 0)
rmax = min(center_r + display_size//2, height - int(height * 0.2)) # 留出底部空間
cmin = max(center_c - display_size//2, 0)
cmax = min(center_c + display_size//2, width)
# 如果上邊界太小，適當下移整個顯示區域
if rmin < height * 0.1:
shift = int(height * 0.1) - rmin
rmin += shift
rmax += shift
return rmin, rmax, cmin, cmax
def apply_brain_window(image, window_width=80, window_level=40):
"""應用brain window設置"""
window_min = window_level - window_width/2
window_max = window_level + window_width/2
windowed = np.clip(image, window_min, window_max)
windowed = (windowed - window_min) / (window_max - window_min)
return windowed
def check_organs_in_slice(slice_data):
"""檢查切片中存在的器官標籤"""
unique_labels = set(np.unique(slice_data))
if 0 in unique_labels:
unique_labels.remove(0)
organ_dict = {
1: "Brainstem",
2: "Right_Eye",
3: "Left_Eye",
4: "Optic_Chiasm",
5: "Right_Optic_Nerve",
6: "Left_Optic_Nerve"
}
return {organ_dict[label] for label in unique_labels if label in organ_dict}
def save_slice(image, ground_truth, prediction, slice_num, save_path):
"""保存指定切片的比較圖"""
plt.clf()
fig, axes = plt.subplots(1, 3, figsize=(24, 8))
plt.subplots_adjust(wspace=0.01)
for ax in axes:
ax.axis('off')
img_slice = np.rot90(image[:, :, slice_num])
rmin, rmax, cmin, cmax = get_brain_bounds(img_slice)
# 使用brain window設置
img_slice = apply_brain_window(img_slice)
# 原始圖像
axes[0].imshow(img_slice[rmin:rmax, cmin:cmax], cmap='gray')
axes[0].set_title('Original Image', fontsize=32, pad=20, weight='bold')
# Ground Truth
gt_slice = np.rot90(ground_truth[:, :, slice_num])
gt_mask = create_colored_mask(gt_slice)
axes[1].imshow(img_slice[rmin:rmax, cmin:cmax], cmap='gray', zorder=1)
axes[1].imshow(gt_mask[rmin:rmax, cmin:cmax], zorder=2)
axes[1].set_title('Ground Truth', fontsize=32, pad=20, weight='bold')
# Prediction
pred_slice = np.rot90(prediction[:, :, slice_num])
pred_mask = create_colored_mask(pred_slice)
axes[2].imshow(img_slice[rmin:rmax, cmin:cmax], cmap='gray', zorder=1)
axes[2].imshow(pred_mask[rmin:rmax, cmin:cmax], zorder=2)
axes[2].set_title('Prediction', fontsize=32, pad=20, weight='bold')
plt.savefig(save_path, bbox_inches='tight', dpi=300, pad_inches=0.05)
plt.close()
print(f"已保存切片 {slice_num} 至: {save_path}")
if __name__ == "__main__":
base_dir = "/mnt/1248/onlylian"
image_path = os.path.join(base_dir, "nnUNet/nnUNet_raw/Dataset888/imagesTs/T1_069_0000.nii.gz")
gt_path = os.path.join(base_dir, "nnUNet/nnUNet_raw/Dataset888/labelsTs/T1_069.nii.gz")
pred_path = os.path.join(base_dir, "T1/output_predictions/T1_069.nii.gz")
save_dir = os.path.join(base_dir, "T1")
os.makedirs(save_dir, exist_ok=True)
print("正在載入影像...")
image = load_nifti(image_path)
ground_truth = load_nifti(gt_path)
prediction = load_nifti(pred_path)
print("影像載入完成")
print("\n分析切片中的器官...")
total_slices = image.shape[2]
for slice_num in range(total_slices):
gt_slice = ground_truth[:, :, slice_num]
pred_slice = prediction[:, :, slice_num]
gt_organs = check_organs_in_slice(gt_slice)
pred_organs = check_organs_in_slice(pred_slice)
if gt_organs or pred_organs:
print(f"\n切片 {slice_num}:")
print(f"Ground Truth 包含: {', '.join(sorted(gt_organs))}")
print(f"Prediction 包含: {', '.join(sorted(pred_organs))}")
print("\n開始切片選擇和保存過程...")
while True:
try:
slice_num = int(input("\n請輸入要保存的切片編號 (-1 退出): "))
if slice_num == -1:
print("程序結束")
break
if 0 <= slice_num < total_slices:
save_path = os.path.join(save_dir, f'comparison_T1_069_{slice_num:03d}.png')
save_slice(image, ground_truth, prediction, slice_num, save_path)
else:
print(f"切片編號必須在 0 到 {total_slices-1} 之間")
except ValueError:
print("請輸入有效的數字")
except Exception as e:
print(f"發生錯誤: {str(e)}")
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