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clean_str() function addition (#1674)

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* clean_str() function addition

* cleanup

* add euro symbol €

* add closing exclamation (spanish)

* cleanup
This commit is contained in:
Glenn Jocher 2020-12-17 17:20:20 -08:00 committed by GitHub
parent 7e161d9774
commit d5289b54c4
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3 changed files with 13 additions and 7 deletions
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9
detect.py
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@ -81,12 +81,13 @@ def detect(save_img=False):
# Process detections
for i, det in enumerate(pred): # detections per image
if webcam: # batch_size >= 1
p, s, im0, frame = Path(path[i]), '%g: ' % i, im0s[i].copy(), dataset.count
p, s, im0, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.count
else:
p, s, im0, frame = Path(path), '', im0s, getattr(dataset, 'frame', 0)
p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0)
save_path = str(save_dir / p.name)
txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}')
p = Path(p) # to Path
save_path = str(save_dir / p.name) # img.jpg
txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # img.txt
s += '%gx%g ' % img.shape[2:] # print string
gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
if len(det):

4
utils/datasets.py
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@ -19,7 +19,7 @@ from PIL import Image, ExifTags
from torch.utils.data import Dataset
from tqdm import tqdm
from utils.general import xyxy2xywh, xywh2xyxy
from utils.general import xyxy2xywh, xywh2xyxy, clean_str
from utils.torch_utils import torch_distributed_zero_first
# Parameters
@ -267,7 +267,7 @@ class LoadStreams: # multiple IP or RTSP cameras
n = len(sources)
self.imgs = [None] * n
self.sources = sources
self.sources = [clean_str(x) for x in sources] # clean source names for later
for i, s in enumerate(sources):
# Start the thread to read frames from the video stream
print('%g/%g: %s... ' % (i + 1, n, s), end='')

7
utils/general.py
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@ -2,6 +2,7 @@
import glob
import logging
import math
import os
import platform
import random
@ -11,7 +12,6 @@ import time
from pathlib import Path
import cv2
import math
import numpy as np
import torch
import torchvision
@ -97,6 +97,11 @@ def make_divisible(x, divisor):
return math.ceil(x / divisor) * divisor
def clean_str(s):
# Cleans a string by replacing special characters with underscore _
return re.sub(pattern="[|@#!¡·$€%&()=?¿^*;:,¨´><+]", repl="_", string=s)
def labels_to_class_weights(labels, nc=80):
# Get class weights (inverse frequency) from training labels
if labels[0] is None: # no labels loaded