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step4_Merge.py
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step4_Merge.py
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import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
from tnscui_utils.TNSUCI_util import *
def get_pic(file_path):
GT = Image.open(file_path)
GT = np.asarray(GT)
GT = GT.astype(np.float32)
return GT
merge_path = []
merge_path.append(r'/media/root/s1_fold51_s2_fold51')
merge_path.append(r'/media/root/s1_fold51_s2_fold52')
merge_path.append(r'/media/root/s1_fold51_s2_fold53')
merge_path.append(r'/media/root/s1_fold51_s2_fold54')
merge_path.append(r'/media/root/s1_fold51_s2_fold55')
merge_path.append(r'/media/root/s1_fold52_s2_fold51')
merge_path.append(r'/media/root/s1_fold52_s2_fold52')
merge_path.append(r'/media/root/s1_fold52_s2_fold53')
merge_path.append(r'/media/root/s1_fold52_s2_fold54')
merge_path.append(r'/media/root/s1_fold52_s2_fold55')
merge_path.append(r'/media/root/s1_fold53_s2_fold51')
merge_path.append(r'/media/root/s1_fold53_s2_fold52')
merge_path.append(r'/media/root/s1_fold53_s2_fold53')
merge_path.append(r'/media/root/s1_fold53_s2_fold54')
merge_path.append(r'/media/root/s1_fold53_s2_fold55')
merge_path.append(r'/media/root/s1_fold54_s2_fold51')
merge_path.append(r'/media/root/s1_fold54_s2_fold52')
merge_path.append(r'/media/root/s1_fold54_s2_fold53')
merge_path.append(r'/media/root/s1_fold54_s2_fold54')
merge_path.append(r'/media/root/s1_fold54_s2_fold55')
merge_path.append(r'/media/root/s1_fold55_s2_fold51')
merge_path.append(r'/media/root/s1_fold55_s2_fold52')
merge_path.append(r'/media/root/s1_fold55_s2_fold53')
merge_path.append(r'/media/root/s1_fold55_s2_fold54')
merge_path.append(r'/media/root/s1_fold55_s2_fold55')
save_path = r'/media/root/merge'
if not os.path.exists(save_path):
os.makedirs(save_path)
mask_list = get_filelist_frompath(merge_path[0],'PNG')
for indd, file in enumerate(mask_list):
print(indd,file)
file_name = file.split(sep)[-1]
pic_list = [get_pic(_path+sep+file_name) for _path in merge_path]
pic_list_array = np.array(pic_list)
pic_list_array_mean = np.mean(pic_list_array,0)
final_mask = (pic_list_array_mean > 0.485*255)
final_mask = final_mask.astype(np.float32)
# final_mask = largestConnectComponent(final_mask.astype(np.int))
final_mask = final_mask.astype(np.uint8)
final_mask = final_mask*255
if False:
plt.subplot(1, 2, 1)
plt.imshow(pic_list_array_mean,cmap=plt.cm.gray)
plt.subplot(1, 2, 2)
plt.imshow(final_mask,cmap=plt.cm.gray)
plt.show()
# 保存图像
if True:
final_savepath = save_path + sep + file_name
im = Image.fromarray(final_mask)
im.save(final_savepath)