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contour_multiple.py
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import numpy as np
import os
from os import listdir
from os.path import isfile, join
from copy import deepcopy
from scipy.linalg import norm
from scipy.spatial.distance import cdist
import matplotlib.pyplot as plt
import scipy.io
import cv2
import skfuzzy as fuzz
import math
from skimage.filters import gaussian
from skimage.segmentation import active_contour
from skimage.measure import label, regionprops
from scipy.spatial import ConvexHull
from skimage.segmentation import (morphological_chan_vese,
checkerboard_level_set)
from skimage.filters import threshold_multiotsu
IMG_PATH = '/Users/raghuveerbhat/Downloads/cv/FinalAssignment/Data'
files = [f for f in listdir(IMG_PATH) if isfile(join(IMG_PATH, f))] # read all files in IMG_PATH
def store_evolution_in(lst):
"""Returns a callback function to store the evolution of the level sets in
the given list.
"""
def _store(x):
lst.append(np.copy(x))
return _store
#colors for visualization
class_0 = np.array([0,0,102])
class_1 = np.array([75,75,255])
class_2 = np.array([102,255,255])
class_3 = np.array([255,255,0])
class_4 = np.array([255,75,75])
class_5 = np.array([125,0,0])
for file in files:
target_img_path = os.path.join(IMG_PATH,file)
if(target_img_path.endswith('.png')):
image = cv2.imread(target_img_path, cv2.IMREAD_GRAYSCALE)
backup_img = deepcopy(image)
init_ls = checkerboard_level_set(image.shape, 2)
# List with intermediate results for plotting the evolution
evolution = []
callback = store_evolution_in(evolution)
ls = morphological_chan_vese(image, num_iter=70, init_level_set=init_ls,
smoothing=11, iter_callback=callback)
evolution = np.array(evolution)
# print(np.max(ls))
ls =np.uint8(ls*255)
# plt.imshow(ls)
# plt.show()
# exit()
ret, im = cv2.threshold(ls, 127, 255, cv2.THRESH_BINARY_INV)
contours, hierarchy = cv2.findContours(ls, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
# print(contours.shape)
# print(hierarchy.shape)
res_img = ls
cv2.drawContours(image, contours, 1, (255,0,0), 3)
seg1contour = contours[0:3:2]
segement1 = np.zeros((362,434))
cv2.fillPoly(segement1,pts=seg1contour,color=255)
print(np.max(segement1))
seg2contour = contours[0:3]
segement2 = np.zeros((362,434))
cv2.fillPoly(segement2,pts=seg2contour,color=255)
seg3contour = contours[0:2]
segement3 = np.zeros((362,434))
cv2.fillPoly(segement3,pts=seg3contour,color=255)
seg4contour = contours[1]
segement4 = np.zeros((362,434))
cv2.drawContours(segement4, contours, 1, color=255, thickness=cv2.FILLED)
plt.imshow(segement4)
plt.show()
needtoseg2_1 = segement2
for i in range(0,362):
for j in range(0,434):
if segement3[i][j] == 255.0:
needtoseg2_1[i][j] = 0
#Brain region
needtoseg2 = np.zeros((362,434))
for i in range(0,362):
for j in range(0,434):
if needtoseg2_1[i][j] == 255.0:
needtoseg2[i][j] = backup_img[i][j]
#Outer region
needtoseg1 = np.full((362,434), 245)
# needtoseg1 = np.zeros((362,434))
for i in range(0,362):
for j in range(0,434):
if segement1[i][j] == 255.0:
needtoseg1[i][j] = backup_img[i][j]
needtoseg3 = np.full((362,434), 250)
for i in range(0,362):
for j in range(0,434):
if segement4[i][j] == 255.0:
needtoseg3[i][j] = backup_img[i][j]
plt.imshow(needtoseg3)
plt.show()
vectorized1 = needtoseg1.reshape((1,-1))
vectorized2 = needtoseg2.reshape((1,-1))
vectorized3 = needtoseg3.reshape((1,-1))
vectorized4 = backup_img.reshape((1,-1))
# ncenters = 3
# cntr, u, u0, d, jm, p, fpc = fuzz.cluster.cmeans(
# vectorized1, ncenters, 2, error=0.005, maxiter=1000, init=None)
# print(u[0])
# cluster_membership = np.argmax(u, axis=0)
# segmented_outer = cluster_membership.reshape(needtoseg1.shape).astype('int')
# ncenters = 4
# cntr, u, u0, d, jm, p, fpc = fuzz.cluster.cmeans(
# vectorized2, ncenters, 2, error=0.005, maxiter=1000, init=None)
# print(u[0])
# cluster_membership = np.argmax(u, axis=0)
# segmented_brain = cluster_membership.reshape(needtoseg2.shape).astype('int')
# Applying multi-Otsu threshold for the default value, generating
# four classes.
thresholds_brain = threshold_multiotsu(needtoseg2,classes=4)
# Using the threshold values, we generate the three regions.
regions_brain = np.digitize(needtoseg2, bins=thresholds_brain)
thresholds_outerbrain = threshold_multiotsu(needtoseg3,classes=5)
# Using the threshold values, we generate the three regions.
regions_outerbrain = np.digitize(needtoseg3, bins=thresholds_brain)
plt.imshow(regions_outerbrain)
plt.show()
# Applying multi-Otsu threshold for the default value, generating
# four classes.
thresholds_outer = threshold_multiotsu(needtoseg1, classes=3)
# Using the threshold values, we generate the three regions.
regions_outer = np.digitize(needtoseg1, bins=thresholds_outer)
# ncenters = 5
# cntr, u, u0, d, jm, p, fpc = fuzz.cluster.cmeans(
# vectorized4, ncenters, 2, error=0.005, maxiter=1000, init=None)
# # print(u[0])
# cluster_membership = np.argmax(u, axis=0)
# segmented_full = cluster_membership.reshape(backup_img.shape).astype('int')
# plt.imshow(segmented_full)
# plt.show()
brain_final_segement = np.stack((brain_final_segement,)*3, axis=-1)
for i in range(0,362):
for j in range(0,434):
if brain_final_segement[i][j][0] == 0:
brain_final_segement[i][j] = class_0
elif brain_final_segement[i][j][0] == 1:
brain_final_segement[i][j] = class_3
elif brain_final_segement[i][j][0] == 2:
brain_final_segement[i][j] = class_5
elif brain_final_segement[i][j][0] == 3:
brain_final_segement[i][j] = class_4
# print("brain_final_segement=",brain_final_segement)
# print(type(brain_final_segement))
# plt.imshow(brain_final_segement.astype(int))
# plt.show()
# fig, ax = plt.subplots()
# ax.imshow(image, cmap=plt.cm.gray)
# regions = regionprops(regions_brain)
# for props in regions:
# y0, x0 = props.centroid
# print("y0,x0=",y0,x0)
# orientation = props.orientation
# x1 = x0 + math.cos(orientation) * 0.5 * props.axis_minor_length
# y1 = y0 - math.sin(orientation) * 0.5 * props.axis_minor_length
# x2 = x0 - math.sin(orientation) * 0.5 * props.axis_major_length
# y2 = y0 - math.cos(orientation) * 0.5 * props.axis_major_length
# ax.plot((x0, x1), (y0, y1), '-r', linewidth=2.5)
# ax.plot((x0, x2), (y0, y2), '-r', linewidth=2.5)
# ax.plot(x0, y0, '.g', markersize=15)
# minr, minc, maxr, maxc = props.bbox
# bx = (minc, maxc, maxc, minc, minc)
# by = (minr, minr, maxr, maxr, minr)
# ax.plot(bx, by, '-b', linewidth=2.5)
# ax.axis((0, 600, 600, 0))
# plt.show()
# plt.imshow(ls_brain)
# plt.show()
# cv2.namedWindow('Contours1',cv2.WINDOW_NORMAL)
# cv2.imshow('Contours1', segement3)
# if cv2.waitKey(0):
# cv2.destroyAllWindows()
# print(res_img.shape)
# plt.imshow(segmented_outer)
# plt.show()
# plt.imshow(segmented_brain)
# plt.show()
# plt.imshow(regions_brain)
# plt.show()
# plt.imshow(regions_outer)
# plt.show()