def hard_sum(img_path1,img_path2):
whole_data=[]
img_name_list1,label_name_list1=tool.generate_list(img_path1,img_path1)
img_name_list2,label_name_list2=tool.generate_list(img_path2,img_path2)
number=len(img_name_list1)
name_list=[]
sum_hard=[]
sum_mic=[]
sum_heam=[]
for i in range(number):
img=image.imread(img_name_list1[i])
img1=image.imread(img_name_list2[i])
x,y,z=img.shape
imgr=tool.imresize(img1)
stamp,prob_map=tool.optic_disc_seg(imgr,net_vessel,crop_size=256,stride=128,ctx=ctx1,threshold=0.5)
stamp=cv2.resize(stamp, (y, x), interpolation=cv2.INTER_NEAREST)
prob_map=cv2.resize(prob_map.asnumpy(), (y, x), interpolation=cv2.INTER_NEAREST)
hardpr=prob_map_get(img,net_hard,ctx=ctx2,crop_size=256,stride=128,threshold=0.5)
heampr=prob_map_get(img,net_heam,ctx=ctx4,crop_size=256,stride=128,threshold=0.5)
micpr=prob_map_get(img,net_mic,ctx=ctx3,crop_size=256,stride=128,threshold=0.5)
hard_mask=(hardpr.asnumpy()*(1-stamp)*(1-prob_map))>0.5
mic_mask=((1-stamp)*(1-heampr.asnumpy())*micpr.asnumpy())>0.5
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(10,10))
mic_mask = cv2.morphologyEx((mic_mask*np.array([1])).astype('uint8'), cv2.MORPH_OPEN, kernel)
heam_mask=((1-stamp)*(1-prob_map)*heampr.asnumpy())>0.5
hardpr1=hardpr.asnumpy()*hard_mask
micpr1=micpr.asnumpy()*mic_mask
heampr1=heampr.asnumpy()*heam_mask
img_name=img_name_list1[i].split('/')[-1]
print(img_name)
name_list.append(img_name)
sum_hard.append(int(np.sum(hardpr1)))
sum_mic.append(int(np.sum(micpr1)))
sum_heam.append(int(np.sum(heampr1)))
whole_data.append([img_name,int(np.sum(hardpr1)),int(np.sum(micpr1)),int(np.sum(heampr1))])
return whole_data
def load_data(
batch_size,
is_reversed=False,
img_path="/data/dataset/SiZe/hard_e_datasets/train_patch/",
label_path="/data/dataset/SiZe/hard_e_datasets/train_label_patch/"):
img_in_list = []
img_out_list = []
img_name_list, label_name_list = tools.generate_list(img_path, label_path)
n = len(img_name_list)
for i in range(n // 20):
print(i)
img_in = image.imread(img_name_list[i]).astype(np.float32) / 127.5 - 1
label_out = image.imread(label_name_list[i])
label_out = image2label(label_out)
img_in = nd.transpose(img_in, (2, 0, 1))
img_in = img_in.reshape((1, ) + img_in.shape)
label_out = label_out.reshape((1, ) + label_out.shape)
img_in_list.append(img_in)
img_out_list.append(label_out)
return mx.io.NDArrayIter(
data=[nd.concat(*img_in_list, dim=0),
nd.concat(*img_out_list, dim=0)],
batch_size=batch_size)
def read_images(
img_path="/data/dataset/SiZe/hard_e_datasets/train_patch/",
label_path="/data/dataset/SiZe/hard_e_datasets/train_label_patch/"):
img_name_list, label_name_list = tools.generate_list(img_path, label_path)
n = len(img_name_list)
n1 = len(label_path)
data, label = [None] * (n // 4), [None] * (n // 4)
for i in range(n // 4):
print(i)
data[i] = image.imread(img_name_list[i])
label[i] = image.imread(label_name_list[i])
return data, label
def h5_maker(img_path, label_path):
img_name_list, label_name_list = tools.generate_list(img_path, label_path)
num1 = len(img_name_list)
num2 = len(label_name_list)
if num1 != num2:
print('data and label is not match')
return
img_list = []
label_list = []
for i in range(num1):
img_t = image.imread(img_name_list[i])
img_t = img_t.transpose((2, 0, 1))
img_list.append(img_t)
label_img_t = image.imread(label_name_list[i])
label_t = image2label(label_img_t)
label_list.append(label_t)
with h5py.File("exudates.h5", "w") as f:
f.create_dataset("img", data=img_list)
f.create_dataset("label", data=label_list)
classes = ['background', 'target']
colormap = [[0, 0, 0], [255, 255, 255]]
cm2lbl = np.zeros(256**3)
for i, cm in enumerate(colormap):
cm2lbl[(cm[0] * 256 + cm[1]) * 256 + cm[2]] = i
def image2label(im):
data = im.astype('int32').asnumpy()
idx = (data[:, :, 0] * 256 + data[:, :, 1]) * 256 + data[:, :, 2]
return nd.array(cm2lbl[idx])
img_path = "/data/dataset/SiZe/hard_e_datasets/train_patch/"
label_path = "/data/dataset/SiZe/hard_e_datasets/train_label_patch/"
img_name_list, label_name_list = tools.generate_list(img_path, label_path)
def creat_h5_file(img_name_list, label_name_list):
for times in range(101):
print(times)
if times == 0:
h5f = h5py.File("train_data.h5", 'w')
x = h5f.create_dataset("data", (1000, 3, 256, 256),
maxshape=(None, 3, 256, 256),
dtype=np.uint8)
y = h5f.create_dataset("label", (1000, 256, 256),
maxshape=(None, 256, 256),
dtype=np.float32)
h5f = h5py.File("train_data.h5", "a") #add mode
x = h5f["data"]