mirrors/yolov5
1
0
Fork 0
mirror of https://github.com/ultralytics/yolov5.git synced 2025-06-03 14:49:29 +08:00
Code Issues Projects Releases Wiki Activity

clean_str() function addition (#1674)

Browse Source 
* 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
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
3 changed files with 13 additions and 7 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

9
detect.py
View File

@ -81,12 +81,13 @@ def detect(save_img=False):
# Process detections # Process detections
for i, det in enumerate(pred): # detections per image for i, det in enumerate(pred): # detections per image
if webcam: # batch_size >= 1 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: 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) p = Path(p) # to Path
txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') 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 s += '%gx%g ' % img.shape[2:] # print string
gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
if len(det): if len(det):

4
utils/datasets.py
View File

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

7
utils/general.py
View File

@ -2,6 +2,7 @@
import glob import glob
import logging import logging
import math
import os import os
import platform import platform
import random import random
@ -11,7 +12,6 @@ import time
from pathlib import Path from pathlib import Path
import cv2 import cv2
import math
import numpy as np import numpy as np
import torch import torch
import torchvision import torchvision
@ -97,6 +97,11 @@ def make_divisible(x, divisor):
return math.ceil(x / divisor) * 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): def labels_to_class_weights(labels, nc=80):
# Get class weights (inverse frequency) from training labels # Get class weights (inverse frequency) from training labels
if labels[0] is None: # no labels loaded if labels[0] is None: # no labels loaded