# 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)}")