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def cal_IoU(img, lab, nb_class): | |
for i in range(nb_class): | |
mask1 = (img == (i + 1)).astype('uint8') | |
mask2 = (lab == (i + 1)).astype('uint8') | |
mask_and = cv2.bitwise_and(mask1, mask2) | |
mask_or = cv2.bitwise_or(mask1, mask2) | |
s1 = np.sum(mask_and) | |
s2 = np.sum(mask_or) |
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import re | |
def is_img_file(file_name): | |
file_name = str(file_name) | |
file_name = file_name.lower() | |
return re.search('\.bmp$', file_name) or re.search('\.png$', file_name) or \ | |
re.search('\.jpg$', file_name) or re.search('\.jpeg', file_name) or re.search('\.tiff', file_name) |
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import pandas as pd | |
import numpy as np | |
def load_train_data(path='inputFiles/train.csv'): | |
x = [] | |
y = [] | |
data = pd.read_csv(path) | |
data = data.values | |
data_num = len(data) | |
for i in range(0, data_num): |
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def rgb2gray(img): | |
assert np.ndim(img) == 3 | |
img = color.rgb2gray(img) * 255 | |
img = np.uint8(img) | |
return img |
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def interval_peek(arr): | |
''' | |
Find all no-zero continuous interval, then in every interval, set all element 0 except the peek. | |
:param arr: | |
:return: | |
''' | |
left = 0 | |
arr_len = len(arr) | |
res = arr[:] | |
while left < arr_len: |
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import os, sys | |
curPath = os.path.abspath(os.path.dirname(__file__)) | |
rootPath = os.path.split(curPath)[0] | |
print('rootPath:{}'.format(rootPath)) | |
sys.path.append(rootPath) |
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