Be able to create dataset from annotated images only

Add the ability to create a dataset/splits only with images that have an annotation file, i.e a .txt file, associated to it. As we talked about this, the absence of a txt file could mean two things:

* either the image wasn't yet labelled by someone,
* either there is no object to detect.

When it's easy to create small datasets, when you have to create datasets with thousands of images (and more coming), it's hard to track where you at and you don't want to wait to have all of them annotated before starting to train. Which means some images would lack txt files and annotations, resulting in label inconsistency as you say in ultralytics#2313. By adding the annotated_only argument to the function, people could create, if they want to, datasets/splits only with images that were labelled, for sure.

utils/datasets.py

@@ -1033,19 +1033,32 @@ def extract_boxes(path='../coco128/'):  # from utils.datasets import *; extract_
                     assert cv2.imwrite(str(f), im[b[1]:b[3], b[0]:b[2]]), f'box failure in {f}'
 
 
-def autosplit(path='../coco128', weights=(0.9, 0.1, 0.0)):  # from utils.datasets import *; autosplit('../coco128')
+def autosplit(path='../coco128', weights=(0.9, 0.1, 0.0), annotated_only=False): # from utils.datasets import *; autosplit('../coco128')
+
     """ Autosplit a dataset into train/val/test splits and save path/autosplit_*.txt files
     # Arguments
-        path:       Path to images directory
-        weights:    Train, val, test weights (list)
+        path:           Path to images directory
+        weights:        Train, val, test weights (list)
+        annotated_only: Only use images with an annotated txt file
     """
+
     path = Path(path)  # images dir
-    files = list(path.rglob('*.*'))
+
+    # make sure we only work with images files
+    files = sum([list(path.rglob(f"*.{img_ext}")) for img_ext in img_formats], [])
     n = len(files)  # number of files
+
     indices = random.choices([0, 1, 2], weights=weights, k=n)  # assign each image to a split
+
     txt = ['autosplit_train.txt', 'autosplit_val.txt', 'autosplit_test.txt']  # 3 txt files
     [(path / x).unlink() for x in txt if (path / x).exists()]  # remove existing
+
+    if annotated_only:
+        print("Only annotated images with a .txt file associated will be used to create the dataset")
+
     for i, img in tqdm(zip(indices, files), total=n):
-        if img.suffix[1:] in img_formats:
+        # in case we want to use only annotated files
+        if not annotated_only or (annotated_only and Path(img2label_paths([str(img)])[0]).exists()):
             with open(path / txt[i], 'a') as f:
                 f.write(str(img) + '\n')  # add image to txt file
+