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import cv2
import numpy as np
import os
# import natsort
import pandas as pd
from skimage.morphology import skeletonize, erosion, square,dilation
from AV.Tools.BinaryPostProcessing import binaryPostProcessing3
from PIL import Image
from scipy.signal import convolve2d
from collections import OrderedDict
import time
#########################################
def Skeleton(a_or_v, a_and_v):
th = np.uint8(a_and_v)
# Distance transform for maximum diameter
vessels = th.copy()
dist = cv2.distanceTransform(a_or_v, cv2.DIST_L2, 3)
thinned = np.uint8(skeletonize((vessels / 255))) * 255
return thinned, dist
def cal_crosspoint(vessel):
# Removing bifurcation points by using specially designed kernels
# Can be optimized further! (not the best implementation)
thinned1, dist = Skeleton(vessel, vessel)
thh = thinned1.copy()
thh = thh / 255
kernel1 = np.array([[1, 1, 1], [1, 10, 1], [1, 1, 1]])
th = convolve2d(thh, kernel1, mode="same")
for u in range(th.shape[0]):
for j in range(th.shape[1]):
if th[u, j] >= 13.0:
cv2.circle(vessel, (j, u), 2 * int(dist[u, j]), (0, 0, 0), -1)
# thi = cv2.cvtColor(thi, cv2.COLOR_BGR2GRAY)
return vessel
def AVclassifiation(out_path, PredAll1, PredAll2, VesselPredAll, DataSet=0, image_basename=''):
"""
predAll1: predition results of artery
predAll2: predition results of vein
VesselPredAll: predition results of vessel
DataSet: the length of dataset
image_basename: the name of saved mask
"""
ImgN = DataSet
for ImgNumber in range(ImgN):
height, width = PredAll1.shape[2:4]
VesselProb = VesselPredAll[ImgNumber, 0, :, :]
ArteryProb = PredAll1[ImgNumber, 0, :, :]
VeinProb = PredAll2[ImgNumber, 0, :, :]
VesselSeg = (VesselProb >= 0.1) & ((ArteryProb >0.2) | (VeinProb > 0.2))
# VesselSeg = (VesselProb >= 0.5) & ((ArteryProb >= 0.5) | (VeinProb >= 0.5))
crossSeg = (VesselProb >= 0.1) & ((ArteryProb >= 0.6) & (VeinProb >= 0.6))
VesselSeg = binaryPostProcessing3(VesselSeg, removeArea=100, fillArea=20)
vesselPixels = np.where(VesselSeg > 0)
ArteryProb2 = np.zeros((height, width))
VeinProb2 = np.zeros((height, width))
crossProb2 = np.zeros((height, width))
image_color = np.zeros((3, height, width), dtype=np.uint8)
for i in range(len(vesselPixels[0])):
row = vesselPixels[0][i]
col = vesselPixels[1][i]
probA = ArteryProb[row, col]
probV = VeinProb[row, col]
#probA,probV = softmax([probA,probV])
ArteryProb2[row, col] = probA
VeinProb2[row, col] = probV
test_use_vessel = np.zeros((height, width), np.uint8)
ArteryPred2 = ((ArteryProb2 >= 0.2) & (ArteryProb2 >= VeinProb2))
VeinPred2 = ((VeinProb2 >= 0.2) & (VeinProb2 >= ArteryProb2))
ArteryPred2 = binaryPostProcessing3(ArteryPred2, removeArea=100, fillArea=20)
VeinPred2 = binaryPostProcessing3(VeinPred2, removeArea=100, fillArea=20)
image_color[0, :, :] = ArteryPred2 * 255
image_color[2, :, :] = VeinPred2 * 255
image_color = image_color.transpose((1, 2, 0))
#Image.fromarray(image_color).save(os.path.join(out_path, f'{image_basename[ImgNumber].split(".")[0]}_ori.png'))
imgBin_vessel = ArteryPred2 + VeinPred2
imgBin_vessel[imgBin_vessel[:, :] == 2] = 1
test_use_vessel = imgBin_vessel.copy() * 255
vessel = cal_crosspoint(test_use_vessel)
contours_vessel, hierarchy_c = cv2.findContours(vessel, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
# inter continuity
for vessel_seg in range(len(contours_vessel)):
C_vessel = np.zeros(vessel.shape, np.uint8)
C_vessel = cv2.drawContours(C_vessel, contours_vessel, vessel_seg, (255, 255, 255), cv2.FILLED)
cli = np.mean(VeinProb2[C_vessel == 255]) / np.mean(ArteryProb2[C_vessel == 255])
if cli < 1:
image_color[
(C_vessel[:, :] == 255) & (test_use_vessel[:, :] == 255)] = [255, 0, 0]
else:
image_color[
(C_vessel[:, :] == 255) & (test_use_vessel[:, :] == 255)] = [0, 0, 255]
loop=0
while loop<2:
# out vein continuity
vein = image_color[:, :, 2]
contours_vein, hierarchy_b = cv2.findContours(vein, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
vein_size = []
for z in range(len(contours_vein)):
vein_size.append(contours_vein[z].size)
vein_size = np.sort(np.array(vein_size))
# image_color_copy = np.uint8(image_color).copy()
for vein_seg in range(len(contours_vein)):
judge_number = min(np.mean(vein_size),500)
# cv2.putText(image_color_copy, str(vein_seg), (int(contours_vein[vein_seg][0][0][0]), int(contours_vein[vein_seg][0][0][1])), 3, 1,
# color=(255, 0, 0), thickness=2)
if contours_vein[vein_seg].size < judge_number:
C_vein = np.zeros(vessel.shape, np.uint8)
C_vein = cv2.drawContours(C_vein, contours_vein, vein_seg, (255, 255, 255), cv2.FILLED)
max_diameter = np.max(Skeleton(C_vein, C_vein)[1])
image_color_copy_vein = image_color[:, :, 2].copy()
image_color_copy_arter = image_color[:, :, 0].copy()
# a_ori = cv2.drawContours(a_ori, contours_b, k, (0, 0, 0), cv2.FILLED)
image_color_copy_vein = cv2.drawContours(image_color_copy_vein, contours_vein, vein_seg,
(0, 0, 0),
cv2.FILLED)
# image_color[(C_cross[:, :] == 255) & (image_color[:, :, 1] == 255)] = [255, 0, 0]
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (
4 * int(np.ceil(max_diameter)), 4 * int(np.ceil(max_diameter))))
C_vein_dilate = cv2.dilate(C_vein, kernel, iterations=1)
# cv2.imwrite(path_out_3, C_vein_dilate)
C_vein_dilate_judge = np.zeros(vessel.shape, np.uint8)
C_vein_dilate_judge[
(C_vein_dilate[:, :] == 255) & (image_color_copy_vein == 255)] = 1
C_arter_dilate_judge = np.zeros(vessel.shape, np.uint8)
C_arter_dilate_judge[
(C_vein_dilate[:, :] == 255) & (image_color_copy_arter == 255)] = 1
if (len(np.unique(C_vein_dilate_judge)) == 1) & (
len(np.unique(C_arter_dilate_judge)) != 1) & (np.mean(VeinProb2[C_vein == 255]) < 0.6):
image_color[
(C_vein[:, :] == 255) & (image_color[:, :, 2] == 255)] = [255, 0,
0]
# out artery continuity
arter = image_color[:, :, 0]
contours_arter, hierarchy_a = cv2.findContours(arter, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
arter_size = []
for z in range(len(contours_arter)):
arter_size.append(contours_arter[z].size)
arter_size = np.sort(np.array(arter_size))
for arter_seg in range(len(contours_arter)):
judge_number = min(np.mean(arter_size),500)
if contours_arter[arter_seg].size < judge_number:
C_arter = np.zeros(vessel.shape, np.uint8)
C_arter = cv2.drawContours(C_arter, contours_arter, arter_seg, (255, 255, 255), cv2.FILLED)
max_diameter = np.max(Skeleton(C_arter, test_use_vessel)[1])
image_color_copy_vein = image_color[:, :, 2].copy()
image_color_copy_arter = image_color[:, :, 0].copy()
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (
4 * int(np.ceil(max_diameter)), 4 * int(np.ceil(max_diameter))))
image_color_copy_arter = cv2.drawContours(image_color_copy_arter, contours_arter, arter_seg,
(0, 0, 0),
cv2.FILLED)
C_arter_dilate = cv2.dilate(C_arter, kernel, iterations=1)
# image_color[(C_cross[:, :] == 255) & (image_color[:, :, 1] == 255)] = [255, 0, 0]
C_arter_dilate_judge = np.zeros(arter.shape, np.uint8)
C_arter_dilate_judge[
(C_arter_dilate[:, :] == 255) & (image_color_copy_arter[:, :] == 255)] = 1
C_vein_dilate_judge = np.zeros(arter.shape, np.uint8)
C_vein_dilate_judge[
(C_arter_dilate[:, :] == 255) & (image_color_copy_vein[:, :] == 255)] = 1
if (len(np.unique(C_arter_dilate_judge)) == 1) & (
len(np.unique(C_vein_dilate_judge)) != 1) & (np.mean(ArteryProb2[C_arter == 255]) < 0.6):
image_color[
(C_arter[:, :] == 255) & (image_color[:, :, 0] == 255)] = [0,
0,
255]
loop=loop+1
# image_basename = os.path.basename(image_basename)
# Image.fromarray(image_color).save(os.path.join(out_path, f'{image_basename.split(".")[0]}.png'))
# Image.fromarray(np.uint8(VesselProb*255)).save(os.path.join(out_path, f'{image_basename.split(".")[0]}_vessel.png'))
return image_color
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