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读取图像的ground truth和每个round重建结果, 并且绘制error map.
"""
import os
import numpy as np
from PIL import Image
from skimage import io
from matplotlib import pyplot as plt
def normalize_image(image):
# return (image - image.min())/(image.max() - image.min())
image = image[40:200, 55:215]
# image = image[80:160, 95:175]
print("image shape:", image.shape)
return image/255.0
# return (image - image.min())/(image.max() - image.min())
def viz_diff_img(image, test_outputdir, image_name):
print("image range:", image.max(), image.min())
plt.axis('off')
# plt.imshow(image, cmap='jet',vmin=0, vmax=50)
plt.imshow(image, cmap='jet',vmin=0, vmax=30)
# plt.colorbar()
plt.savefig(os.path.join(test_outputdir, f'{image_name}'), bbox_inches='tight',pad_inches = 0)
# baseline = 'UNet_4X'
# baseline_list = ['DCAMSR_4X', 'MCCA_4X', 'MINet_4X', 'MTrans_4X', 'swinir_4X_']
baseline_list = ['DCAMSR_8X', 'MCCA_8X', 'MINet_8X', 'MTrans_8X', 'swinir_8X_']
# baseline_list = ['swinir_8X_']
baseline_list = ['our']
for baseline in baseline_list:
# root_dir = f"/data/qic99/recon_code/recon_2M/BRATS_baseline/model/{baseline}/result_case/"
root_dir = '/data/qic99/recon_code/recon_2M/BRATS_freq_multi_fusion_2_2_4/model/unet_wo_kspace_4X_lr1e-4/result_case/'
# root_dir = '/data/qic99/recon_code/recon_2M/BRATS_freq_multi_fusion_2_2_4/model/unet_wo_kspace_8X_lr1e-4/result_case/'
image_name = "301_t2"
dst_dir = "./error_map_8X"
# dst_dir = "./error_map_4X"
os.makedirs(dst_dir, exist_ok=True)
img_gt = normalize_image(np.array(Image.open(root_dir + image_name + ".png")))
img_in = normalize_image(np.array(Image.open(root_dir + image_name + "_out.png")))
img_lq = normalize_image(np.array(Image.open(root_dir + image_name + "_in.png")))
print(img_gt.max(), img_gt.min())
print(img_in.max(), img_in.min())
io.imsave(os.path.join(dst_dir, image_name + "_lq.png"), (img_lq*255).astype(np.uint8))
io.imsave(os.path.join(dst_dir, image_name + ".png"), (img_gt*255).astype(np.uint8))
io.imsave(os.path.join(dst_dir, baseline+'_'+image_name + "_out.png"), (img_in*255).astype(np.uint8))
viz_diff_img(np.abs(img_gt - img_in)*255, dst_dir, baseline+'_'+image_name + "_error_map.png")
print("input error:", np.mean(np.abs(img_gt - img_in)))
# break
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