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1. If image suffix is in the label's name, label's path will be error 2. Deprecated alias "np.int" #438

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1. If image suffix is in the label's name, label's path will be error 2. Deprecated alias "np.int" #438

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Jan 27, 2024
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Jan 27, 2024
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shyhyawJou Jan 27, 2024
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15 changes: 8 additions & 7 deletions utils/datasets.py
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Original file line number Diff line number Diff line change
Expand Up @@ -366,8 +366,9 @@ def __init__(self, path, img_size=640, batch_size=16, augment=False, hyp=None, r

def img2label_paths(img_paths):
# Define label paths as a function of image paths
from pathlib import Path
sa, sb = os.sep + 'images' + os.sep, os.sep + 'labels' + os.sep # /images/, /labels/ substrings
return [x.replace(sa, sb, 1).replace(x.split('.')[-1], 'txt') for x in img_paths]
return [Path(x.replace(sa, sb, 1)).with_suffix('.txt') for x in img_paths]

try:
f = [] # image files
Expand Down Expand Up @@ -406,7 +407,7 @@ def img2label_paths(img_paths):
self.label_files = img2label_paths(cache.keys()) # update

n = len(shapes) # number of images
bi = np.floor(np.arange(n) / batch_size).astype(np.int) # batch index
bi = np.floor(np.arange(n) / batch_size).astype(np.int64) # batch index
nb = bi[-1] + 1 # number of batches
self.batch = bi # batch index of image
self.n = n
Expand All @@ -433,7 +434,7 @@ def img2label_paths(img_paths):
elif mini > 1:
shapes[i] = [1, 1 / mini]

self.batch_shapes = np.ceil(np.array(shapes) * img_size / stride + pad).astype(np.int) * stride
self.batch_shapes = np.ceil(np.array(shapes) * img_size / stride + pad).astype(np.int64) * stride

# Check labels
create_datasubset, extract_bounding_boxes, labels_loaded = False, False, False
Expand Down Expand Up @@ -479,7 +480,7 @@ def img2label_paths(img_paths):
b = x[1:] * [w, h, w, h] # box
b[2:] = b[2:].max() # rectangle to square
b[2:] = b[2:] * 1.3 + 30 # pad
b = xywh2xyxy(b.reshape(-1, 4)).ravel().astype(np.int)
b = xywh2xyxy(b.reshape(-1, 4)).ravel().astype(np.int64)

b[[0, 2]] = np.clip(b[[0, 2]], 0, w) # clip boxes outside of image
b[[1, 3]] = np.clip(b[[1, 3]], 0, h)
Expand Down Expand Up @@ -689,7 +690,7 @@ def img2label_paths(img_paths):
self.label_files = img2label_paths(cache.keys()) # update

n = len(shapes) # number of images
bi = np.floor(np.arange(n) / batch_size).astype(np.int) # batch index
bi = np.floor(np.arange(n) / batch_size).astype(np.int64) # batch index
nb = bi[-1] + 1 # number of batches
self.batch = bi # batch index of image
self.n = n
Expand All @@ -716,7 +717,7 @@ def img2label_paths(img_paths):
elif mini > 1:
shapes[i] = [1, 1 / mini]

self.batch_shapes = np.ceil(np.array(shapes) * img_size / stride + pad).astype(np.int) * stride
self.batch_shapes = np.ceil(np.array(shapes) * img_size / stride + pad).astype(np.int64) * stride

# Check labels
create_datasubset, extract_bounding_boxes, labels_loaded = False, False, False
Expand Down Expand Up @@ -762,7 +763,7 @@ def img2label_paths(img_paths):
b = x[1:] * [w, h, w, h] # box
b[2:] = b[2:].max() # rectangle to square
b[2:] = b[2:] * 1.3 + 30 # pad
b = xywh2xyxy(b.reshape(-1, 4)).ravel().astype(np.int)
b = xywh2xyxy(b.reshape(-1, 4)).ravel().astype(np.int64)

b[[0, 2]] = np.clip(b[[0, 2]], 0, w) # clip boxes outside of image
b[[1, 3]] = np.clip(b[[1, 3]], 0, h)
Expand Down