def main(args):
head, _ = os.path.splitext(os.path.basename(args.in_fn))
levels = ecvl.OpenSlideGetLevels(args.in_fn)
# for each level, extract a region with size = size of the last level
dims = [0, 0] + levels[-1] # [x, y, w, h] region to read
print("Reading %s" % args.in_fn)
for i in range(len(levels)):
img = ecvl.OpenSlideRead(args.in_fn, i, dims)
out_fn = "%s_level_%d.png" % (head, i)
print("Writing %s" % out_fn)
ecvl.ImWrite(out_fn, img)
def read_slide(slide_fn, level=4):
levels = ecvl.OpenSlideGetLevels(slide_fn)
dims = [0, 0] + levels[level]
img = ecvl.OpenSlideRead(slide_fn, level, dims)
t = ecvl.ImageToTensor(img)
t_np = t.getdata()
s = t_np.shape
t_np = t_np.transpose((1, 2, 0)).reshape(
(s[1] * s[2], 3)) # Channel last and reshape
t_eval = Tensor.fromarray(t_np)
print(t_eval.getShape())
return t_eval, s
def get_mask_tissue_from_slide(self,
slide_fn,
level=2,
use_openslide=False):
if use_openslide and not no_openslide:
# Using open slide
slide = openslide.open_slide(slide_fn)
dims = slide.level_dimensions
ds = [int(i) for i in slide.level_downsamples]
x0 = 0
y0 = 0
x1 = dims[0][0]
y1 = dims[0][1]
delta_x = x1 - x0
delta_y = y1 - y0
pil_img = slide.read_region(location=(x0, y0),
level=level,
size=(delta_x // ds[level],
delta_y // ds[level]))
np_img = np.array(pil_img)
msk_pred = self.get_tissue_mask(np_img,
channel_first=False,
BGR=False)
else:
## Using ECVL
levels = ecvl.OpenSlideGetLevels(slide_fn)
dims = [0, 0] + levels[level]
img = ecvl.OpenSlideRead(slide_fn, level, dims)
t = ecvl.ImageToTensor(img)
np_img = t.getdata(
) # Tensor to numpy array (ecvl read images with channel first)
msk_pred = self.get_tissue_mask(np_img,
channel_first=True,
BGR=True)
return msk_pred
def main(args):
slide_fn = args.slide_fn
level = args.level
t_dec = td.tissue_detector(model_fn=args.weights_fn, th=args.threshold)
mask = t_dec.get_mask_tissue_from_slide(slide_fn,
level=level,
use_openslide=args.use_openslide)
dimensions = ecvl.OpenSlideGetLevels(slide_fn)[0]
if args.more_info_output:
with open(args.output, 'wb') as f:
pickle.dump(
{
'mask': mask,
'dimensions': dimensions,
'extraction_level': args.level,
'threshold': args.threshold,
'input': args.slide_fn,
'weights': args.weights_fn,
}, f)
else:
np.save(args.output, mask)