clean_str() function addition (#1674)

* clean_str() function addition

* cleanup

* add euro symbol €

* add closing exclamation (spanish)

* cleanup

detect.py

         # 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):

utils/datasets.py

 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
 
         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='')

utils/general.py

 
 import glob
 import logging
+import math
 import os
 import platform
 import random
 from pathlib import Path
 
 import cv2
-import math
 import numpy as np
 import torch
 import torchvision
     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