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Functions.py
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Functions.py
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# -*- coding: utf-8 -*-
# -*- coding: utf-8 -*-
"""
Created on Wed Nov 13 22:50:08 2019
@author: TEJAS
"""
import numpy as np
import matplotlib.pyplot as plt
from skimage import segmentation as seg
import cv2
from skimage import data, io, segmentation, color
from skimage.future import graph
from skimage.measure import regionprops
from skimage import draw
import PIL.Image
from scipy.ndimage.filters import generic_filter
from itertools import product, chain
import scipy.stats as st
from sklearn.feature_extraction import image
from skimage.feature import greycomatrix,greycoprops
import multiprocessing
from joblib import Parallel, delayed
from sklearn.cluster import KMeans,SpectralClustering
from skimage.filters import rank
from skimage.morphology import watershed, disk
from scipy import ndimage as ndi
from plot_rag_merge import merge_mean_color,_weight_mean_color
from functools import reduce
class computeLibs:
def __sharpen(self,img):
# Create our shapening kernel, it must equal to one eventually
kernel_sharpening = np.array([[-1,-1,-1],
[-1, 9,-1],
[-1,-1,-1]])
# applying the sharpening kernel to the input image & displaying it.
sharpened = cv2.filter2D(img, -1, kernel_sharpening)
return sharpened
def preprocess(self,img,sharpen=False,cgray=True,resiz=True,plot=True):
original_img = img
if sharpen:
img = self.__sharpen(img)
if resiz:
img = cv2.resize(img,(512,512))
color_img = cv2.resize(original_img,(512,512))
if plot:
plt.figure(figsize=(10,10))
plt.title('Pre-Processed Image')
plt.imshow(img,cmap='gray')
return img,color_img
def __MSE(self,Im1, Im2):
# computes error
Diff_Im = Im2-Im1
Diff_Im = np.power(Diff_Im, 2)
Diff_Im = np.sum(Diff_Im, axis=2)
Diff_Im = np.sqrt(Diff_Im)
sum_diff = np.sum(np.sum(Diff_Im))
avg_error = sum_diff / float(Im1.shape[0]*Im2.shape[1])
return avg_error
def __KmeansHelper(self,img,no_of_clusters):
Kmean = KMeans(n_clusters=no_of_clusters)
Kmean.fit(img)
kmean_clusters = np.asarray(Kmean.cluster_centers_,dtype=np.float32)
reconstructedImg = kmean_clusters[Kmean.labels_,:].reshape((512,512,-1))
loss = self.__MSE(img.reshape((512,512,-1)),reconstructedImg)
labels = Kmean.labels_.reshape((512,512,-1))
return labels,loss,reconstructedImg
def kmeans(self,img,no_of_clusters=6,bruteforceRange=(0,12),bruteforce=False):
labels = None
if bruteforce:
l,h = bruteforceRange
loss = []
reconstructedImg = []
for i in range(l,h):
print('Starting Clustering with'+str(i)+'centers')
_,l,reconstImg = self.__KmeansHelper(img,i)
loss.append(l)
reconstructedImg.append(reconstImg)
else:
labels,loss,reconstructedImg = self.__KmeansHelper(img,no_of_clusters)
return labels.reshape(512,512),no_of_clusters,loss,reconstructedImg
def watershed(self,img,smoothen = 5,extrasmoothen=2,con_grad=10,plot=True):
smoothImg = rank.median(img, disk(smoothen))
markers = rank.gradient(smoothImg, disk(smoothen)) < con_grad
markers = ndi.label(markers)[0]
gradient = rank.gradient(img, disk(extrasmoothen))
labels = watershed(gradient, markers)
if plot==True:
fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(8, 8),
sharex=True, sharey=True)
ax = axes.ravel()
ax[0].imshow(img, cmap=plt.cm.gray)
ax[0].set_title("Original")
ax[1].imshow(gradient, cmap=plt.cm.nipy_spectral)
ax[1].set_title("Local Gradient")
ax[2].imshow(markers, cmap=plt.cm.nipy_spectral)
ax[2].set_title("Markers")
ax[3].imshow(img, cmap=plt.cm.gray)
ax[3].imshow(labels, cmap=plt.cm.nipy_spectral, alpha=.7)
ax[3].set_title("Segmented")
for a in ax:
a.axis('off')
fig.tight_layout()
plt.show()
return labels
def slic(self,slicParam,img,plot=True,save=0):
colorspace = slicParam['colorSpace_s']
slic_img = seg.slic(img, n_segments=slicParam['n_segments'], compactness=slicParam['compactness'], max_iter=slicParam['max_iter'])
if plot:
if colorspace=='lab':
img = cv2.cvtColor(img,cv2.COLOR_Lab2BGR)
plotLibs().plotBoundaries(img,slic_img,save=save,title='Slic Segmented Image')
return slic_img
def felzenszwalb(self,felzParam,img,plot=True,save=0):
colorspace = felzParam['colorSpace_s']
felzenszwalb_img = seg.felzenszwalb(img,scale=felzParam['scale'],sigma=felzParam['sigma'],min_size=felzParam['min_size'])
if plot:
if colorspace=='lab':
img = cv2.cvtColor(img,cv2.COLOR_Lab2BGR)
plotLibs().plotBoundaries(img,felzenszwalb_img,save=save,title='F-H segmented Image')
return felzenszwalb_img
def labels2img(self,img,labels,plot=True,save=0):
labels = labels + 1 # So that no labelled region is 0 and ignored by regionprops
label_rgb = color.label2rgb(labels, img, kind='avg')
if plot:
plotLibs().plotBoundaries(label_rgb,labels,save=save,title='Label rgb Image')
return label_rgb
def graphMergeHierarchical(self,img, labels,thresh=0.15,plot=True,save=0):
g = graph.rag_mean_color(img,labels)
new_labels = graph.merge_hierarchical(labels, g, thresh, rag_copy=False,
in_place_merge=True,
merge_func=merge_mean_color,
weight_func=_weight_mean_color)
if plot:
plotLibs().dispImg(color.label2rgb(new_labels, img, kind='avg'),save=save,title='Merge Hierarchical Label rgb Image')
plotLibs().plotBoundaries(img,new_labels,save=save,title='Merge Hierarchical')
return new_labels
def graphNormalizedCuts(self,img, labels,thresh=0.5,num_cuts=100,plot=True,save=0):
g = graph.rag_mean_color(img, labels)
new_labels = graph.cut_normalized(labels, g,thresh,num_cuts)
if plot:
plotLibs().dispImg(color.label2rgb(new_labels, img, kind='avg'),save=save,title='Ncut Label rgb Image')
plotLibs().plotBoundaries(img,new_labels,save=save,title='Ncut Boundary Images')
return new_labels
def decorrstretch(self,A, tol=None):
"""
Apply decorrelation stretch to image
Arguments:
A -- image in cv2/numpy.array format
tol -- upper and lower limit of contrast stretching
"""
# save the original shape
orig_shape = A.shape
# reshape the image
# B G R
# pixel 1 .
# pixel 2 .
# . . . .
A = A.reshape((-1,3)).astype(np.float)
# covariance matrix of A
cov = np.cov(A.T)
# source and target sigma
sigma = np.diag(np.sqrt(cov.diagonal()))
# eigen decomposition of covariance matrix
eigval, V = np.linalg.eig(cov)
# stretch matrix
S = np.diag(1/np.sqrt(eigval))
# compute mean of each color
mean = np.mean(A, axis=0)
# substract the mean from image
A -= mean
# compute the transformation matrix
T = reduce(np.dot, [sigma, V, S, V.T])
# compute offset
offset = mean - np.dot(mean, T)
# transform the image
A = np.dot(A, T)
# add the mean and offset
A += mean + offset
# restore original shape
B = A.reshape(orig_shape)
# for each color...
for b in range(3):
# apply contrast stretching if requested
if tol:
# find lower and upper limit for contrast stretching
low, high = np.percentile(B[:,:,b], 100*tol), np.percentile(B[:,:,b], 100-100*tol)
B[B<low] = low
B[B>high] = high
# ...rescale the color values to 0..255
B[:,:,b] = 1 * (B[:,:,b] - B[:,:,b].min())/(B[:,:,b].max() - B[:,:,b].min())
# return it as uint8 (byte) image
return np.asarray(B,dtype='float32')
def slic0(self,slic0Param,img,plot=True,save=0):
colorspace = slic0Param['colorSpace_s']
slic0_img = seg.slic(img,n_segments=slic0Param['n_segments'],max_iter=slic0Param['max_iter'],slic_zero=True)
if plot:
if colorspace=='lab':
img = cv2.cvtColor(img,cv2.COLOR_Lab2BGR)
plotLibs().plotBoundaries(img,slic0_img,save=save,title='Slic0 segmented Image')
return slic0_img
class plotLibs:
def dispImg(self,img,save=0,title='image'):
plt.figure(figsize=(10,10))
plt.title(title)
plt.imshow(img)
plt.show()
def dispGrayImg(self,img,save=0,title='image'):
plt.figure(figsize=(10,10))
plt.title(title)
plt.imshow(img,cmap='gray')
plt.show()
def plot_3d(self,img):
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
r, g, b = cv2.split(img)
r,g,b = r.flatten(), g.flatten(), b.flatten()
fig = plt.figure()
ax = Axes3D(fig)
ax.scatter(r, g, b)
plt.show()
def __segments(self,img,labels,center):
labels = np.array(labels,dtype='float32')
labels[labels!=center]= np.nan
labels[labels==center]=1
labels[labels==np.nan] = 0
return img*labels
def dispSegment(self,img,labels,number_of_clusters):
M,N = number_of_clusters//3,3
fig, axs = plt.subplots(M,N, figsize=(60, 60), facecolor='w', edgecolor='k',squeeze=True)
fig.subplots_adjust(hspace = 0.1, wspace=.01)
axs = axs.ravel()
for i in range(number_of_clusters):
segment = self.__segments(img,labels,i)
axs[i].imshow(segment)
axs[i].set_title('segment'+str(i))
def dispKmeansBruteImg(self,reconstructedImg,l):
M,N = len(reconstructedImg)//2,2
fig, axs = plt.subplots(M,N, figsize=(60, 60), facecolor='w', edgecolor='k',squeeze=True)
fig.subplots_adjust(hspace = 0.1, wspace=.01)
axs = axs.ravel()
for i in range(len(reconstructedImg)):
axs[i].imshow(reconstructedImg[i].reshape((512,512,-1)))
axs[i].set_title('K_'+str(i+l))
plt.show()
def plotResponse(self,response):
fig2, ax2 = plt.subplots(3, 3)
for axes, res in zip(ax2.ravel(), response):
axes.imshow(res, cmap=plt.cm.gray)
axes.set_xticks(())
axes.set_yticks(())
ax2[-1, -1].set_visible(False)
plt.show()
def plotLoss(self,Loss):
plt.plot(Loss)
plt.show()
def __display_edges(self,image, g, threshold):
"""Draw edges of a RAG on its image
Returns a modified image with the edges drawn.Edges are drawn in green
and nodes are drawn in yellow.
Parameters
----------
image : ndarray
The image to be drawn on.
g : RAG
The Region Adjacency Graph.
threshold : float
Only edges in `g` below `threshold` are drawn.
Returns:
out: ndarray
Image with the edges drawn.
"""
image = image.copy()
for edge in g.edges:
n1, n2 = edge
r1, c1 = map(int, g.node[n1]['centroid'])
r2, c2 = map(int, g.node[n2]['centroid'])
line = draw.line(r1, c1, r2, c2)
circle = draw.circle(r1,c1,2)
if g[n1][n2]['weight'] < threshold :
image[line] = 0,1,0
image[circle] = 1,1,0
return image
def plotRAG(self,img,rag,save=0,title='RAG Image'):
edges_drawn_all = self.__display_edges(img, rag, np.inf)
self.dispImg(edges_drawn_all,save,title)
def plotRagwithColorMaps(self,img,labels):
g = graph.rag_mean_color(img, labels)
fig, ax = plt.subplots(nrows=2, sharex=True, sharey=True, figsize=(6, 8))
ax[0].set_title('RAG drawn with default settings')
lc = graph.show_rag(labels, g, img, ax=ax[0])
# specify the fraction of the plot area that will be used to draw the colorbar
fig.colorbar(lc, fraction=0.03, ax=ax[0])
ax[1].set_title('RAG drawn with grayscale image and viridis colormap')
lc = graph.show_rag(labels, g, img,
img_cmap='gray', edge_cmap='viridis', ax=ax[1])
fig.colorbar(lc, fraction=0.03, ax=ax[1])
for a in ax:
a.axis('off')
plt.tight_layout()
plt.show()
def plotBoundaries(self,img,labels,save=0,title='Boundaries Image'):
Boundary_Img = seg.mark_boundaries(img, labels)
self.dispImg(Boundary_Img,save,title)
class imgLibs:
def __init__(self,imgName=None,clrSpace='rgb'):
if imgName is not None:
self.img = np.load(imgName)
self.imgShape = self.img.shape
self.clrSpace = clrSpace
def loadImg(self):
self.img[np.isnan(self.img)] = 0
if self.clrSpace =='rgb':
return self.img
elif self.clrSpace == 'hsv':
return cv2.cvtColor(self.img, cv2.COLOR_BGR2HSV)
elif self.clrSpace == 'gray':
return cv2.cvtColor(self.img,cv2.COLOR_BGR2GRAY)
elif self.clrSpace == 'lab':
return cv2.cvtColor(self.img,cv2.COLOR_BGR2Lab)
def loadImgHelper(self,fpath,colorSpace_s,plot=True):
img = np.asarray(PIL.Image.open(fpath))
if colorSpace_s=='lab':
colorSpace_img = cv2.cvtColor(img,cv2.COLOR_BGR2LAB)
if colorSpace_s=='hsv':
colorSpace_img = cv2.cvtColor(img,cv2.COLOR_BGR2HSV)
if colorSpace_s == 'rgb':
colorSpace_img = img
if plot:
plotLibs().dispImg(img,save=0,title='Input Image')
return img,colorSpace_img
def plotBoundaries(self,img,labels,save=0,title='Boundaries Image'):
Boundary_Img = seg.mark_boundaries(img, labels)
self.dispImg(Boundary_Img,save,title)