def __init__(self):
#self.image_dir = "../../OCT/beam_scanning/Data set/pic/NORMAL-BACKSIDE-center/"
#self.roi_dir = "../../OCT/beam_scanning/Data set/seg label/NORMAL-BACKSIDE-center/"
#self.database_root = "../../OCT/beam_scanning/Data Set Reorganize/NORMAL/"
#self.database_root = "../../OCT/beam_scanning/Data Set Reorganize/NORMAL-BACKSIDE-center/"
#self.database_root = "../../OCT/beam_scanning/Data Set Reorganize/NORMAL-BACKSIDE/"
# check the cuda device
pth_save_dir = "../out/sheathCGAN_coordinates3/"
# the portion of attated image to 2 sides
self.attatch_rate = 0.2
jason_tmp_dir = "D:/Deep learning/dataset/original/phantom/1/label/0.json"
# read th jso fie in hte start :
with open(jason_tmp_dir) as dir:
self.jason_tmp = JSON.load(dir)
self.shapeTmp = self.jason_tmp["shapes"]
self.coordinates0 = self.jason_tmp["shapes"][1][
"points"] # remember add finding corred label 1!!!
self.co_len = len(self.coordinates0)
#self.database_root = "D:/Deep learning/dataset/original/phantom/2/"
#self.database_root = "D:/Deep learning/dataset/original/dots/3/"
self.database_root = "D:/Deep learning/dataset/original/new_catheter_ruler/2/"
self.database_root = "D:/Deep learning/dataset/original/phantom_2th_march_2021/1/"
self.database_root = "D:/Deep learning/dataset/original/paper_with_strong_shadow/1/"
self.database_root = "D:/Deep learning/dataset/original/animal_tissue/2/"
#self.database_root = "D:/Deep learning/dataset/original/animal_tissue/1/"
#self.database_root = "D:/Deep learning/dataset/original/IVOCT/1/"
self.f_downsample_factor = 30
self.all_dir = self.database_root + "pic_all/"
self.image_dir = self.database_root + "pic/"
self.json_dir = self.database_root + "label/" # for this class sthis dir ist save the modified json
self.json_save_dir = self.database_root + "label_generate/"
self.img_num = 0
self.contours_x = [] # no predefines # predefine there are 4 contours
self.contours_y = [] # predefine there are 4 contours
#self.seger = Seg_One_1()
self.saver = Save_Contour_pkl()
self.display_flag = True
# deep learning model
print(torch.cuda.current_device())
print(torch.cuda.device(0))
print(torch.cuda.device_count())
print(torch.cuda.get_device_name(0))
print(torch.cuda.is_available())
torch.set_num_threads(2)
gancreator = cGAN_build2.CGAN_creator(
) # the Cgan for the segmentation
self.GANmodel = gancreator.creat_cgan() # G and D are created here
# for the detection just use the Gnets
self.GANmodel.netG.load_state_dict(
torch.load(pth_save_dir + 'cGANG_epoch_2.pth'))
self.GANmodel.netG.cuda()
def __init__(self):
#self.image_dir = "../../OCT/beam_scanning/Data set/pic/NORMAL-BACKSIDE-center/"
#self.roi_dir = "../../OCT/beam_scanning/Data set/seg label/NORMAL-BACKSIDE-center/"
#self.database_root = "../../OCT/beam_scanning/Data Set Reorganize/NORMAL/"
#self.database_root = "../../OCT/beam_scanning/Data Set Reorganize/NORMAL-BACKSIDE-center/"
#self.database_root = "../../OCT/beam_scanning/Data Set Reorganize/NORMAL-BACKSIDE/"
self.database_root = "../../OCT/beam_scanning/Data Set Reorganize/VARY/"
self.image_dir = self.database_root + "pic/"
self.roi_dir = self.database_root + "seg label/"
self.img_num = 0
self.contours_x = [] # predefine there are 4 contours
self.contours_y = [] # predefine there are 4 contours
self.saver = Save_Contour_pkl()
self.display_flag = True
def __init__(self):
#self.image_dir = "../../OCT/beam_scanning/Data set/pic/NORMAL-BACKSIDE-center/"
#self.roi_dir = "../../OCT/beam_scanning/Data set/seg label/NORMAL-BACKSIDE-center/"
#self.database_root = "../../OCT/beam_scanning/Data Set Reorganize/NORMAL/"
#self.database_root = "../../OCT/beam_scanning/Data Set Reorganize/NORMAL-BACKSIDE-center/"
#self.database_root = "../../OCT/beam_scanning/Data Set Reorganize/NORMAL-BACKSIDE/"
sub_folder = "8/"
self.database_root = "D:/Deep learning/dataset/label data/"
self.image_dir = self.database_root + "img/" + sub_folder
self.json_dir = self.database_root + "label/" + sub_folder
self.save_dir = self.database_root + "seg label pkl/" + sub_folder
self.img_num = 0
self.contours_x = [] # no predefines # predefine there are 4 contours
self.contours_y = [] # predefine there are 4 contours
self.saver = Save_Contour_pkl()
self.display_flag = True
class Read_read_check_json_label(object):
def __init__(self):
#self.image_dir = "../../OCT/beam_scanning/Data set/pic/NORMAL-BACKSIDE-center/"
#self.roi_dir = "../../OCT/beam_scanning/Data set/seg label/NORMAL-BACKSIDE-center/"
#self.database_root = "../../OCT/beam_scanning/Data Set Reorganize/NORMAL/"
#self.database_root = "../../OCT/beam_scanning/Data Set Reorganize/NORMAL-BACKSIDE-center/"
#self.database_root = "../../OCT/beam_scanning/Data Set Reorganize/NORMAL-BACKSIDE/"
sub_folder = "8/"
self.database_root = "D:/Deep learning/dataset/label data/"
self.image_dir = self.database_root + "img/" + sub_folder
self.json_dir = self.database_root + "label/" + sub_folder
self.save_dir = self.database_root + "seg label pkl/" + sub_folder
self.img_num = 0
self.contours_x = [] # no predefines # predefine there are 4 contours
self.contours_y = [] # predefine there are 4 contours
self.saver = Save_Contour_pkl()
self.display_flag = True
def draw_coordinates_color(self, img1, vx, vy, color):
if color == 0:
painter = [254, 0, 0]
elif color == 1:
painter = [0, 254, 0]
elif color == 2:
painter = [0, 0, 254]
else:
painter = [0, 0, 0]
#path0 = signal.resample(path0, W)
H, W, _ = img1.shape
for j in range(len(vx)):
#path0l[path0x[j]]
dy = np.clip(vy[j], 2, H - 2)
dx = np.clip(vx[j], 2, W - 2)
img1[int(dy) + 1, int(dx), :] = img1[int(dy), int(dx), :] = painter
#img1[int(dy)+1,dx,:]=img1[int(dy)-1,dx,:]=img1[int(dy),dx,:]=painter
return img1
def check_one_folder(self):
for i in os.listdir(self.json_dir):
#for i in os.listdir("E:\\estimagine\\vs_project\\PythonApplication_data_au\\pic\\"):
# separath the name of json
a, b = os.path.splitext(i)
# if it is a json it will have corresponding image
if b == ".json":
img_path = self.image_dir + a + ".jpg"
img1 = cv2.imread(img_path)
if img1 is None:
print("no_img for this zip")
else:
json_dir = self.json_dir + a + b
with open(json_dir) as f_dir:
data = JSON.load(f_dir)
shape = data["shapes"]
num_line = len(shape)
len_list = num_line
#with ZipFile(json_dir, 'r') as zipObj:
# # Get list of files names in zip
# #listOfiles = zipObj.namelist()
# # this line of code is importanct sice the the formmer one will change the sequence
# listOfiles = zipObj.infolist()
# len_list = len(listOfiles)
#rois = read_roi_zip(roi_dir) # get all the coordinates
gray = cv2.cvtColor(
img1, cv2.COLOR_BGR2GRAY) # transfer into gray image
H, W = gray.shape
max_buffer = np.zeros(len_list)
contoursx = [None] * len_list
contoursy = [None] * len_list
for iter in range(len_list):
# get the name of one contour
# iter
coordinates = shape[iter]["points"]
coordinates = np.array(coordinates)
#line_name = os.path.splitext(listOfiles[iter].filename)
#line_name =line_name[0] #just use the former one
#pathy = rois[line_name]['y']
#pathx = rois[line_name]['x']
# delete the coordinate out side the boundary of image
len_ori, _ = coordinates.shape
target = 0
for j in range(len_ori):
if (coordinates[target, 0] < 0
or coordinates[target, 0] >
(W - 1)): # check the x coordinates
coordinates = np.delete(coordinates,
target,
axis=0)
else:
target += 1
pathy = coordinates[:, 1]
pathx = coordinates[:, 0]
num_points = len(pathx)
path_w = pathx[num_points - 1] - pathx[0]
path_w = int(path_w)
# sometimes the contour is plot in reversed direction
if path_w < 0:
path_w = -path_w
pathy = pathy[::-1]
pathx = pathx[::-1]
pathyl = np.ones(int(path_w)) * np.nan
for j in range(num_points):
#importante sometimes the start point is nnot the lestmost
this_index = np.clip(pathx[j] - pathx[0], 0,
path_w - 1)
pathyl[int(this_index)] = float(pathy[j] - 1)
add_3 = np.append(pathyl[::-1], pathyl,
axis=0) # cascade
add_3 = np.append(add_3, pathyl[::-1],
axis=0) # cascade
s = pd.Series(add_3)
pathyl = s.interpolate(method="linear")
pathyl = pathyl[path_w:2 * path_w].to_numpy()
pathxl = np.arange(int(pathx[0]),
int(pathx[0]) + path_w)
if len(pathxl
) > 0.96 * W: # correct the unperfect label
# rememver to add resacle later
pathyl = signal.resample(pathyl, W)
pathxl = np.arange(0, W)
contoursx[iter] = pathxl
contoursy[iter] = pathyl
#max_buffer[iter] = np.min(pathyl) # use the minimal value to detemine which contour as the 1st and 2nd .....
max_buffer[iter] = np.max(
pathyl
) # use the mean value to detemine which contour as the 1st and 2nd .....
pass
# use the minimal value to detemine which contour as the 1st and 2nd .....
new_index = np.argsort(max_buffer)
self.contours_x = [None] * len_list
self.contours_y = [None] * len_list
for iter in range(len_list):
self.contours_x[iter] = contoursx[new_index[iter]]
self.contours_y[iter] = contoursy[new_index[iter]]
if self.display_flag == True:
img1 = self.draw_coordinates_color(
img1, self.contours_x[iter],
self.contours_y[iter], iter)
#save this result
self.img_num = a
#self.contours_x = [path0ln, path1ln, path2ln, path3ln]
#self.saver.append_new_name_contour (self.img_num,self.contours,self.database_root)
self.saver.append_new_name_contour(self.img_num,
self.contours_x,
self.contours_y,
self.save_dir)
cv2.imshow('pic', img1)
print(str(a))
cv2.waitKey(10)
class Read_read_check_ROI_label(object):
def __init__(self):
#self.image_dir = "../../OCT/beam_scanning/Data set/pic/NORMAL-BACKSIDE-center/"
#self.roi_dir = "../../OCT/beam_scanning/Data set/seg label/NORMAL-BACKSIDE-center/"
#self.database_root = "../../OCT/beam_scanning/Data Set Reorganize/NORMAL/"
#self.database_root = "../../OCT/beam_scanning/Data Set Reorganize/NORMAL-BACKSIDE-center/"
#self.database_root = "../../OCT/beam_scanning/Data Set Reorganize/NORMAL-BACKSIDE/"
self.database_root = "../../OCT/beam_scanning/Data Set Reorganize/VARY/"
self.image_dir = self.database_root + "pic/"
self.roi_dir = self.database_root + "seg label/"
self.img_num = 0
self.contours_x = [] # predefine there are 4 contours
self.contours_y = [] # predefine there are 4 contours
self.saver = Save_Contour_pkl()
self.display_flag = True
def draw_coordinates_color(self, img1, vx, vy, color):
if color == 0:
painter = [254, 0, 0]
elif color == 1:
painter = [0, 254, 0]
elif color == 2:
painter = [0, 0, 254]
else:
painter = [0, 0, 0]
#path0 = signal.resample(path0, W)
H, W, _ = img1.shape
for j in range(len(vx)):
#path0l[path0x[j]]
dy = np.clip(vy[j], 2, H - 2)
dx = np.clip(vx[j], 2, W - 2)
img1[int(dy) + 1, dx, :] = img1[int(dy), dx, :] = painter
#img1[int(dy)+1,dx,:]=img1[int(dy)-1,dx,:]=img1[int(dy),dx,:]=painter
return img1
def check_one_folder(self):
for i in os.listdir(self.roi_dir):
#for i in os.listdir("E:\\estimagine\\vs_project\\PythonApplication_data_au\\pic\\"):
# 分离文件名与后缀
a, b = os.path.splitext(i)
# 如果后缀名是“.xml”就旋转related的图像
if b == ".zip" or b == ".ZIP":
img_path = self.image_dir + a + ".jpg"
img1 = cv2.imread(img_path)
if img1 is None:
print("no_img for this zip")
else:
roi_dir = self.roi_dir + a + b
with ZipFile(roi_dir, 'r') as zipObj:
# Get list of files names in zip
#listOfiles = zipObj.namelist()
# this line of code is importanct sice the the formmer one will change the sequence
listOfiles = zipObj.infolist()
len_list = len(listOfiles)
rois = read_roi_zip(roi_dir) # get all the coordinates
gray = cv2.cvtColor(
img1, cv2.COLOR_BGR2GRAY) # transfer into gray image
H, W = gray.shape
max_buffer = np.zeros(len_list)
contoursx = [None] * len_list
contoursy = [None] * len_list
for iter in range(len_list):
# get the name of one contour
# iter
line_name = os.path.splitext(listOfiles[iter].filename)
line_name = line_name[0] #just use the former one
pathy = rois[line_name]['y']
pathx = rois[line_name]['x']
num_points = len(pathx)
path_w = pathx[num_points - 1] - pathx[0]
# sometimes the contour is plot in reversed direction
if path_w < 0:
path_w = -path_w
pathy = pathy[::-1]
pathx = pathx[::-1]
pathyl = np.ones(path_w) * np.nan
for j in range(num_points):
#importante sometimes the start point is nnot the lestmost
this_index = np.clip(pathx[j] - pathx[0], 0,
path_w - 1)
pathyl[this_index] = float(pathy[j] - 1)
add_3 = np.append(pathyl[::-1], pathyl,
axis=0) # cascade
add_3 = np.append(add_3, pathyl[::-1],
axis=0) # cascade
s = pd.Series(add_3)
pathyl = s.interpolate()
pathyl = pathyl[path_w:2 * path_w].to_numpy()
pathxl = np.arange(pathx[0], pathx[num_points - 1])
contoursx[iter] = pathxl
contoursy[iter] = pathyl
max_buffer[iter] = np.min(pathyl)
pass
new_index = np.argsort(max_buffer)
self.contours_x = [None] * len_list
self.contours_y = [None] * len_list
for iter in range(len_list):
self.contours_x[iter] = contoursx[new_index[iter]]
self.contours_y[iter] = contoursy[new_index[iter]]
if self.display_flag == True:
img1 = self.draw_coordinates_color(
img1, self.contours_x[iter],
self.contours_y[iter], iter)
#save this result
self.img_num = a
#self.contours_x = [path0ln, path1ln, path2ln, path3ln]
#self.saver.append_new_name_contour (self.img_num,self.contours,self.database_root)
self.saver.append_new_name_contour(self.img_num,
self.contours_x,
self.contours_y,
self.database_root)
cv2.imshow('pic', img1)
print(str(a))
cv2.waitKey(10)
class Read_read_check_ROI_label(object):
def __init__(self ):
#self.image_dir = "../../OCT/beam_scanning/Data set/pic/NORMAL-BACKSIDE-center/"
#self.roi_dir = "../../OCT/beam_scanning/Data set/seg label/NORMAL-BACKSIDE-center/"
self.database_root = "../../OCT/beam_scanning/Data Set Reorganize/NORMAL/"
self.image_dir = self.database_root + "pic/"
self.roi_dir = self.database_root + "seg label/"
self.img_num = 0
self.contours = []
self.saver = Save_Contour_pkl()
def check_one_folder (self):
for i in os.listdir(self.roi_dir):
#for i in os.listdir("E:\\estimagine\\vs_project\\PythonApplication_data_au\\pic\\"):
# 分离文件名与后缀
a, b = os.path.splitext(i)
# 如果后缀名是“.xml”就旋转related的图像
if b == ".zip"or b== ".ZIP":
img_path = self.image_dir + a + ".jpg"
img1 = cv2.imread(img_path)
if img1 is None:
print ("no_img for this zip")
else:
roi_dir = self.roi_dir + a +b
with ZipFile(roi_dir, 'r') as zipObj:
# Get list of files names in zip
#listOfiles = zipObj.namelist()
# this line of code is importanct sice the the formmer one will change the sequence
listOfiles = zipObj.infolist()
line_name0,_ = os.path.splitext(listOfiles[0].filename)
number0 = int(line_name0.split("-")[0])
line_name1,_ = os.path.splitext(listOfiles[1].filename)
number1 = int(line_name1.split("-")[0])
line_name2,_ = os.path.splitext(listOfiles[2].filename)
number2 = int(line_name2.split("-")[0])
line_name3,_ = os.path.splitext(listOfiles[3].filename)
number3 = int(line_name3.split("-")[0])
number_list= [number0,number1,number2,number3]
new_index = np.argsort( number_list)
name_list = [line_name0,line_name1,line_name2,line_name3]
line_name0 = name_list[new_index[0]]
line_name1 = name_list[new_index[1]]
line_name2 = name_list[new_index[2]]
line_name3 = name_list[new_index[3]]
rois = read_roi_zip(roi_dir)
gray = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
H,W = gray.shape
path0y = rois[line_name0]['y']
path0x = rois[line_name0]['x']
path0l = np.ones(W) * np.nan
path1y = rois[line_name1]['y']
path1x = rois[line_name1]['x']
path1l = np.ones(W) * np.nan
path2y = rois[line_name2]['y']
path2x = rois[line_name2]['x']
path2l = np.ones(W) * np.nan
path3y = rois[line_name3]['y']
path3x = rois[line_name3]['x']
path3l = np.ones(W) * np.nan
for j in range (len(path0x)):
this_index = np.clip( path0x[j]-1, 0,W-1)
path0l[this_index] = float (path0y[j] -1)
for j in range (len(path1x)):
this_index = np.clip( path1x[j]-1, 0,W-1)
path1l[this_index] = float (path1y[j] -1)
for j in range (len(path2x)):
this_index = np.clip( path2x[j]-1, 0,W-1)
path2l[this_index] = float (path2y[j] -1)
for j in range (len(path3x)):
this_index = np.clip( path3x[j]-1, 0,W-1)
path3l[this_index] = float (path3y[j] -1)
add_3 = np.append(path0l[::-1],path0l,axis=0) # cascade
add_3 = np.append(add_3,path0l[::-1],axis=0) # cascade
s = pd.Series(add_3)
path0ln = s.interpolate( )
path0ln = path0ln[W:2*W].to_numpy()
add_3 = np.append(path1l[::-1],path1l,axis=0) # cascade
add_3 = np.append(add_3,path1l[::-1],axis=0) # cascade
s = pd.Series(add_3)
path1ln = s.interpolate( )
path1ln = path1ln[W:2*W].to_numpy()
add_3 = np.append(path2l[::-1],path2l,axis=0) # cascade
add_3 = np.append(add_3,path2l[::-1],axis=0) # cascade
s = pd.Series(add_3)
path2ln = s.interpolate( )
path2ln = path2ln[W:2*W].to_numpy()
add_3 = np.append(path3l[::-1],path3l,axis=0) # cascade
add_3 = np.append(add_3,path3l[::-1],axis=0) # cascade
s = pd.Series(add_3)
path3ln = s.interpolate( )
path3ln = path3ln[W:2*W].to_numpy()
#path0 = signal.resample(path0, W)
for j in range (len(path0ln)):
#path0l[path0x[j]]
img1[int(path0ln[j])+1,j,:]=img1[int(path0ln[j])-1,j,:]=img1[int(path0ln[j]),j,:]=[254,0,0]
img1[int(path1ln[j]+1),j,:]=img1[int(path1ln[j]-1),j,:]=img1[int(path1ln[j]),j,:]=[0,254,0]
img1[int(path2ln[j]+1),j,:]=img1[int(path2ln[j]-1),j,:]=img1[int(path2ln[j]),j,:]=[0,0,254]
img1[int(path3ln[j]+1),j,:]=img1[int(path3ln[j]-1),j,:]=img1[int(path3ln[j]),j,:]=[254,254,254]
#save this result
self.img_num = a
self.contours = [path0ln, path1ln, path2ln, path3ln]
self.saver.append_new_name_contour (self.img_num,self.contours,self.database_root)
cv2.imshow('pic',img1)
print(str(a))
cv2.waitKey(10)