In dataset.py crop_img function sometimes returning negative vertices

[Ashish0091]

In the below code, I tried to print the output of crop_img function which does random cropping augmentation on input image. I tried to print the vertices variables and got negative vertices. My question is:

1. What is the use of negative vertices. How these negative vertices are useful during training
2. If this function is cropping non text region also does the model learns from background also

`def crop_img(img, vertices, labels, length):
'''crop img patches to obtain batch and augment
Input:
img : PIL Image
vertices : vertices of text regions <numpy.ndarray, (n,8)>
labels : 1->valid, 0->ignore, <numpy.ndarray, (n,)>
length : length of cropped image region
Output:
region : cropped image region
new_vertices: new vertices in cropped region
'''
h, w = img.height, img.width
# confirm the shortest side of image >= length
if h >= w and w < length:
img = img.resize((length, int(h * length / w)), Image.BILINEAR)
elif h < w and h < length:
img = img.resize((int(w * length / h), length), Image.BILINEAR)
ratio_w = img.width / w
ratio_h = img.height / h
assert(ratio_w >= 1 and ratio_h >= 1)

new_vertices = np.zeros(vertices.shape)
if vertices.size > 0:
	new_vertices[:,[0,2,4,6]] = vertices[:,[0,2,4,6]] * ratio_w
	new_vertices[:,[1,3,5,7]] = vertices[:,[1,3,5,7]] * ratio_h

# find random position
remain_h = img.height - length
remain_w = img.width - length
flag = True
cnt = 0
while flag and cnt < 1000:
	cnt += 1
	start_w = int(np.random.rand() * remain_w)
	start_h = int(np.random.rand() * remain_h)
	flag = is_cross_text([start_w, start_h], length, new_vertices[labels==1,:])
box = (start_w, start_h, start_w + length, start_h + length)
region = img.crop(box)
if new_vertices.size == 0:
	return region, new_vertices	

new_vertices[:,[0,2,4,6]] -= start_w
new_vertices[:,[1,3,5,7]] -= start_h
return region, new_vertices

`