def main():
opt = TestOptions()
args = opt.initialize()
if not os.path.exists(args.save):
os.makedirs(args.save)
model = CreateSSLModel(args)
model.eval()
model.cuda()
targetloader = CreateTrgDataSSLLoader(args)
predicted_label = np.zeros((len(targetloader), 1634, 1634))
predicted_prob = np.zeros((len(targetloader), 1634, 1634))
image_name = []
for index, batch in enumerate(targetloader):
if index % 100 == 0:
print('%d processd' % index)
image, _, name = batch
image = Variable(image).cuda()
_, output, _, _ = model(image, ssl=True)
output = nn.functional.softmax(output, dim=1)
output = nn.functional.upsample(
output, (1634, 1634), mode='bilinear',
align_corners=True).cpu().data[0].numpy()
output = output.transpose(1, 2, 0)
#import pdb;pdb.set_trace()
label, prob = np.argmax(output, axis=2), np.max(output, axis=2)
predicted_label[index] = label.copy()
predicted_prob[index] = prob.copy()
image_name.append(name[0])
thres = []
for i in range(3):
x = predicted_prob[predicted_label == i]
if len(x) == 0:
thres.append(0)
continue
x = np.sort(x)
thres.append(x[np.int(np.round(len(x) * 0.5))])
thres = np.array(thres)
thres[thres > 0.9] = 0.9
color_labels = {0: 0, 128: 1, 255: 2}
for index in range(len(targetloader)):
name = image_name[index]
label = predicted_label[index]
prob = predicted_prob[index]
for color, i in color_labels.items():
label[(prob > thres[i]) * (label == i)] = color
output = np.asarray(label, dtype=np.uint8)
output = Image.fromarray(output)
name = name.split('.')[0] + '.png'
# arr = name.split('/')[-1].split('.')[0] + '.npy'
# np.save('%s/%s' % (args.save, arr), prob)
output.save('%s/%s' % (args.save, name))
def main():
opt = TestOptions()
args = opt.initialize()
if not os.path.exists(args.save):
os.makedirs(args.save)
model = CreateSSLModel(args)
model.eval()
model.cuda()
targetloader = CreateTrgDataSSLLoader(args)
predicted_label = np.zeros((len(targetloader), 512, 1024))
predicted_prob = np.zeros((len(targetloader), 512, 1024))
image_name = []
for index, batch in enumerate(targetloader):
if index % 100 == 0:
print '%d processd' % index
image, _, name = batch
output = model(Variable(image).cuda(), ssl=True)
output = nn.functional.softmax(output, dim=1)
output = nn.functional.upsample(
output, (512, 1024), mode='bilinear',
align_corners=True).cpu().data[0].numpy()
output = output.transpose(1, 2, 0)
label, prob = np.argmax(output, axis=2), np.max(output, axis=2)
predicted_label[index] = label.copy()
predicted_prob[index] = prob.copy()
image_name.append(name[0])
thres = []
for i in range(19):
x = predicted_prob[predicted_label == i]
if len(x) == 0:
thres.append(0)
continue
x = np.sort(x)
thres.append(x[np.int(np.round(len(x) * 0.5))])
print thres
thres = np.array(thres)
thres[thres > 0.9] = 0.9
print thres
for index in range(len(targetloader)):
name = image_name[index]
label = predicted_label[index]
prob = predicted_prob[index]
for i in range(19):
label[(prob < thres[i]) * (label == i)] = 255
output = np.asarray(label, dtype=np.uint8)
output = Image.fromarray(output)
name = name.split('/')[-1]
output.save('%s/%s' % (args.save, name))
def main():
opt = TestOptions()
args = opt.initialize()
os.environ["CUDA_VISIBLE_DEVICES"] = args.GPU
if not os.path.exists(args.save):
os.makedirs(args.save)
args.restore_from = args.restore_opt1
model1 = CreateSSLModel(args)
model1.eval()
model1.cuda()
args.restore_from = args.restore_opt2
model2 = CreateSSLModel(args)
model2.eval()
model2.cuda()
args.restore_from = args.restore_opt3
model3 = CreateSSLModel(args)
model3.eval()
model3.cuda()
targetloader = CreateTrgDataSSLLoader(args)
# change the mean for different dataset
IMG_MEAN = np.array((104.00698793, 116.66876762, 122.67891434),
dtype=np.float32)
IMG_MEAN = torch.reshape(torch.from_numpy(IMG_MEAN), (1, 3, 1, 1))
mean_img = torch.zeros(1, 1)
predicted_label = np.zeros((len(targetloader), 512, 1024))
predicted_prob = np.zeros((len(targetloader), 512, 1024))
image_name = []
with torch.no_grad():
for index, batch in enumerate(targetloader):
if index % 100 == 0:
print('%d processd' % index)
image, _, name = batch
if mean_img.shape[-1] < 2:
B, C, H, W = image.shape
mean_img = IMG_MEAN.repeat(B, 1, H, W)
image = image.clone() - mean_img
image = Variable(image).cuda()
# forward
output1 = model1(image)
output1 = nn.functional.softmax(output1, dim=1)
output2 = model2(image)
output2 = nn.functional.softmax(output2, dim=1)
output3 = model3(image)
output3 = nn.functional.softmax(output3, dim=1)
a, b = 0.3333, 0.3333
output = a * output1 + b * output2 + (1.0 - a - b) * output3
output = nn.functional.interpolate(
output, (512, 1024), mode='bilinear',
align_corners=True).cpu().data[0].numpy()
output = output.transpose(1, 2, 0)
label, prob = np.argmax(output, axis=2), np.max(output, axis=2)
predicted_label[index] = label.copy()
predicted_prob[index] = prob.copy()
image_name.append(name[0])
thres = []
for i in range(19):
x = predicted_prob[predicted_label == i]
if len(x) == 0:
thres.append(0)
continue
x = np.sort(x)
thres.append(x[np.int(np.round(len(x) * 0.66))])
print(thres)
thres = np.array(thres)
thres[thres > 0.9] = 0.9
print(thres)
for index in range(len(targetloader)):
name = image_name[index]
label = predicted_label[index]
prob = predicted_prob[index]
for i in range(19):
label[(prob < thres[i]) * (label == i)] = 255
output = np.asarray(label, dtype=np.uint8)
output = Image.fromarray(output)
name = name.split('/')[-1]
output.save('%s/%s' % (args.save, name))
def main():
opt = TestOptions()
args = opt.initialize()
if not os.path.exists(args.save):
os.makedirs(args.save)
model = CreateSSLModel(args)
cudnn.enabled = True
cudnn.benchmark = True
model.train()
model.cuda()
targetloader = CreateTrgDataSSLLoader(args)
#predicted_label = np.zeros((len(targetloader), 1634, 1634))
predicted_label = np.zeros((len(targetloader), 2056, 2124))
#predicted_prob = np.zeros((len(targetloader), 1634, 1634))
predicted_prob = np.zeros((len(targetloader), 2056, 2124))
image_name = []
for index, batch in enumerate(targetloader):
if index % 10 == 0:
print('%d processd' % index)
image, _, name = batch
image = Variable(image).cuda()
_, output, _, _ = model(image, ssl=True)
output = nn.functional.softmax(output / args.alpha, dim=1)
#output = nn.functional.upsample(output, (1634, 1634), mode='bilinear', align_corners=True).cpu().data[0].numpy()
output = nn.functional.upsample(
output, (2056, 2124), mode='bilinear',
align_corners=True).cpu().data[0].numpy()
output = output.transpose(1, 2, 0)
label, prob = np.argmax(output, axis=2), np.max(output, axis=2)
cup_mask = get_bool(label, 0)
disc_mask = get_bool(label, 0) + get_bool(label, 1)
disc_mask = disc_mask.astype(np.uint8)
cup_mask = cup_mask.astype(np.uint8)
for i in range(5):
disc_mask = scipy.signal.medfilt2d(disc_mask, 19)
cup_mask = scipy.signal.medfilt2d(cup_mask, 19)
disc_mask = morphology.binary_erosion(disc_mask,
morphology.diamond(7)).astype(
np.uint8) # return 0,1
cup_mask = morphology.binary_erosion(cup_mask,
morphology.diamond(7)).astype(
np.uint8) # return 0,1
disc_mask = get_largest_fillhole(disc_mask)
cup_mask = get_largest_fillhole(cup_mask)
disc_mask = morphology.binary_dilation(disc_mask,
morphology.diamond(7)).astype(
np.uint8) # return 0,1
cup_mask = morphology.binary_dilation(cup_mask,
morphology.diamond(7)).astype(
np.uint8) # return 0,1
disc_mask = get_largest_fillhole(disc_mask).astype(
np.uint8) # return 0,1
cup_mask = get_largest_fillhole(cup_mask).astype(np.uint8)
label = disc_mask + cup_mask
predicted_label[index] = label.copy()
predicted_prob[index] = prob.copy()
image_name.append(name[0])
thres = []
for i in range(3):
x = predicted_prob[predicted_label == i]
if len(x) == 0:
thres.append(0)
continue
x = np.sort(x)
#print(len(x))
print(i, x)
print(np.sum(x < 0.5), '/', len(x), np.sum(x < 0.5) / len(x))
#thres.append(x[np.int(np.round(len(x) *int(args.p[i])))])
thres.append(0.5)
thres = np.array(thres)
print(thres)
# thres[thres > 0.6] = 0.6
color_labels = {0: 255, 1: 128, 2: 0}
#import pdb;pdb.set_trace()
for index in range(len(targetloader)):
name = image_name[index]
label = predicted_label[index] # label_min:0 label_max=2
#print(label)
prob = predicted_prob[index] # prob_min:0.5 prob_max=1.0
#print(prob)
for i, color in color_labels.items():
label[label == i] = color
#print(thres[i])
label[(prob > thres[i]) * (label == i)] = color
output = np.asarray(label, dtype=np.uint8)
output = Image.fromarray(output.astype(np.uint8))
#name = name.split('.')[0] + '.png'
name = name.split('_')[0] + '.bmp'
output.save('%s/%s' % (args.save, name))
disc_dice, cup_dice = calculate_dice(args.gt_dir, args.save,
args.devkit_dir)
print('===> disc_dice:' + str(round(disc_dice, 3)) + '\t' + 'cup_dice:' +
str(round(cup_dice, 3)))
def main():
opt = TestOptions()
args = opt.initialize()
os.environ["CUDA_VISIBLE_DEVICES"] = args.GPU
if not os.path.exists(args.save):
os.makedirs(args.save)
args.restore_from = args.restore_opt1
model1 = CreateSSLModel(args)
model1.eval()
model1.cuda()
args.restore_from = args.restore_opt2
model2 = CreateSSLModel(args)
model2.eval()
model2.cuda()
args.restore_from = args.restore_opt3
model3 = CreateSSLModel(args)
model3.eval()
model3.cuda()
targetloader = CreateTrgDataSSLLoader(args)
# change the mean for different dataset
IMG_MEAN = np.array((104.00698793, 116.66876762, 122.67891434), dtype=np.float32)
IMG_MEAN = torch.reshape( torch.from_numpy(IMG_MEAN), (1,3,1,1) )
mean_img = torch.zeros(1, 1)
image_name = []
x = [None]*19 # x values for all 19 classes
cachepath = "../cache" # Directory to save the numpy files as cache.
with torch.no_grad():
for index, batch in enumerate(targetloader):
if index % 1 == 0:
print( '%d processd' % index )
image, _, name = batch
if mean_img.shape[-1] < 2:
B, C, H, W = image.shape
mean_img = IMG_MEAN.repeat(B,1,H,W)
image = image.clone() - mean_img
image = Variable(image).cuda()
# forward
output1 = model1(image)
output1 = nn.functional.softmax(output1, dim=1)
output2 = model2(image)
output2 = nn.functional.softmax(output2, dim=1)
output3 = model3(image)
output3 = nn.functional.softmax(output3, dim=1)
a, b = 0.3333, 0.3333
output = a*output1 + b*output2 + (1.0-a-b)*output3
output = nn.functional.interpolate(output, (512, 1024), mode='bilinear', align_corners=True).cpu().data[0].numpy()
output = output.transpose(1,2,0)
label, prob = np.argmax(output, axis=2), np.max(output, axis=2)
# Saving the prob and label files for each index seperately so that while loading the whole array need not be loaded in turn saving a lot of CPU ram.
f1 = gzip.GzipFile(os.path.join(cachepath, "label_" + str(index))+'.npy.gz', "w")
f2 = gzip.GzipFile(os.path.join(cachepath, "prob_" + str(index))+'.npy.gz', "w")
np.save(f1,label)
np.save(f2, prob)
f1.close()
f2.close()
for i in range(19):
d = prob[label==i]
if(len(d)==0):
continue
if x[i] is None:
x[i]=d
else:
x[i]= np.concatenate((x[i],d))
image_name.append(name[0])
thres = []
thres = []
for i in range(19):
if x[i] is None:
thres.append(0)