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f.read().strip() (#1577)

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Glenn Jocher 2020-11-29 12:01:42 +01:00 committed by GitHub
parent bc5c898c93
commit fed9451454
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1 changed files with 4 additions and 4 deletions
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utils/datasets.py
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@ -261,7 +261,7 @@ class LoadStreams: # multiple IP or RTSP cameras
if os.path.isfile(sources): if os.path.isfile(sources):
with open(sources, 'r') as f: with open(sources, 'r') as f:
sources = [x.strip() for x in f.read().splitlines() if len(x.strip())] sources = [x.strip() for x in f.read().strip().splitlines() if len(x.strip())]
else: else:
sources = [sources] sources = [sources]
@ -353,7 +353,7 @@ class LoadImagesAndLabels(Dataset): # for training/testing
f += glob.glob(str(p / '**' / '*.*'), recursive=True) f += glob.glob(str(p / '**' / '*.*'), recursive=True)
elif p.is_file(): # file elif p.is_file(): # file
with open(p, 'r') as t: with open(p, 'r') as t:
t = t.read().splitlines() t = t.read().strip().splitlines()
parent = str(p.parent) + os.sep parent = str(p.parent) + os.sep
f += [x.replace('./', parent) if x.startswith('./') else x for x in t] # local to global path f += [x.replace('./', parent) if x.startswith('./') else x for x in t] # local to global path
else: else:
@ -450,7 +450,7 @@ class LoadImagesAndLabels(Dataset): # for training/testing
if os.path.isfile(lb_file): if os.path.isfile(lb_file):
nf += 1 # label found nf += 1 # label found
with open(lb_file, 'r') as f: with open(lb_file, 'r') as f:
l = np.array([x.split() for x in f.read().splitlines()], dtype=np.float32) # labels l = np.array([x.split() for x in f.read().strip().splitlines()], dtype=np.float32) # labels
if len(l): if len(l):
assert l.shape[1] == 5, 'labels require 5 columns each' assert l.shape[1] == 5, 'labels require 5 columns each'
assert (l >= 0).all(), 'negative labels' assert (l >= 0).all(), 'negative labels'
@ -897,7 +897,7 @@ def extract_boxes(path='../coco128/'): # from utils.datasets import *; extract_
lb_file = Path(img2label_paths([str(im_file)])[0]) lb_file = Path(img2label_paths([str(im_file)])[0])
if Path(lb_file).exists(): if Path(lb_file).exists():
with open(lb_file, 'r') as f: with open(lb_file, 'r') as f:
lb = np.array([x.split() for x in f.read().splitlines()], dtype=np.float32) # labels lb = np.array([x.split() for x in f.read().strip().splitlines()], dtype=np.float32) # labels
for j, x in enumerate(lb): for j, x in enumerate(lb):
c = int(x[0]) # class c = int(x[0]) # class