eschmidbauer/DL-Art-School
1
0
Fork 0
forked from mrq/DL-Art-School
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Update extract_square_images

Browse Source
This commit is contained in:
James Betker 2021-01-08 13:16:34 -07:00
parent 5a8156026a
commit 41b7d50944
1 changed files with 23 additions and 9 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

32
codes/scripts/extract_square_images.py
View File

@ -13,16 +13,17 @@ import torch
def main():
split_img = False
opt = {}
opt['n_thread'] = 4
opt['compression_level'] = 98 # JPEG compression quality rating.
opt['n_thread'] = 7
opt['compression_level'] = 95 # JPEG compression quality rating.
# CV_IMWRITE_PNG_COMPRESSION from 0 to 9. A higher value means a smaller size and longer
# compression time. If read raw images during training, use 0 for faster IO speed.
opt['dest'] = 'file'
opt['input_folder'] = ['F:\\4k6k\\datasets\\ns_images\\imagesets\\imageset_1024_square_with_new']
opt['save_folder'] = 'F:\\4k6k\\datasets\\ns_images\\imagesets\\imageset_384_full'
opt['imgsize'] = 384
#opt['bottom_crop'] = 120
opt['input_folder'] = ['F:\\4k6k\\datasets\\ns_images\\imagesets\\pn_coven\\working']
opt['save_folder'] = 'F:\\4k6k\\datasets\\ns_images\\imagesets\\pn_coven\\cropped'
opt['imgsize'] = 1024
opt['bottom_crop'] = .1
opt['keep_folder'] = True
save_folder = opt['save_folder']
if not osp.exists(save_folder):
@ -58,11 +59,14 @@ class TiledDataset(data.Dataset):
# Perform explicit crops first. These are generally used to get rid of watermarks so we dont even want to
# consider these areas of the image.
if 'bottom_crop' in self.opt.keys():
img = img[:-self.opt['bottom_crop'], :, :]
bc = self.opt['bottom_crop']
if bc > 0 and bc < 1:
bc = int(bc * img.shape[0])
img = img[:-bc, :, :]
h, w, c = img.shape
# Uncomment to filter any image that doesnt meet a threshold size.
if min(h,w) < 512:
if min(h,w) < self.opt['imgsize']:
print("Skipping due to threshold")
return None
@ -71,7 +75,17 @@ class TiledDataset(data.Dataset):
# Crop the image so that only the center is left, since this is often the most salient part of the image.
img = img[(h - dim) // 2:dim + (h - dim) // 2, (w - dim) // 2:dim + (w - dim) // 2, :]
img = cv2.resize(img, (self.opt['imgsize'], self.opt['imgsize']), interpolation=cv2.INTER_AREA)
cv2.imwrite(osp.join(self.opt['save_folder'], basename + ".jpg"), img, [cv2.IMWRITE_JPEG_QUALITY, self.opt['compression_level']])
output_folder = self.opt['save_folder']
if self.opt['keep_folder']:
# Attempt to find the folder name one level above opt['input_folder'] and use that.
pts = [os.path.dirname(path)]
while pts[0] != self.opt['input_folder'][0]:
pts = os.path.split(pts[0])
output_folder = osp.join(self.opt['save_folder'], pts[-1])
os.makedirs(output_folder, exist_ok=True)
if not basename.endswith(".jpg"):
basename = basename + ".jpg"
cv2.imwrite(osp.join(output_folder, basename + ".jpg"), img, [cv2.IMWRITE_JPEG_QUALITY, self.opt['compression_level']])
return None
def __len__(self):