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salimage.py
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salimage.py
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import numpy as np
import scipy.ndimage.morphology as smorph
from skimage.transform import hough_line
import cv2
import skimage.morphology as skm
################################################################################
# RGB To Gray (also exists in skimage.color.rgb2gray
################################################################################
def rgb2gray(inIm): # also available in skimage
outIm = 0.2126 * inIm[:,:,0] + 0.7152 * inIm[:,:,1] + 0.0722 * inIm[:,:,2]
return(outIm)
################################################################################
# Gray To Binary With Threshold
################################################################################
def gray2bw(inData,thr=0.5):
if (thr < 1):
#normalize the user threshold
thr = thr* np.max(np.max(inData))
# normalize the data
outData = np.array((inData > thr) * 255,dtype =np.uint8)
return outData
################################################################################
# BRESENHAM LINE ALGORITHM
################################################################################
def bresenham(start, end):
# Setup initial conditions
x1, y1 = start
x2, y2 = end
dx = x2 - x1
dy = y2 - y1
# Determine how steep the line is
is_steep = abs(dy) > abs(dx)
# Rotate line
if is_steep:
x1, y1 = y1, x1
x2, y2 = y2, x2
# Swap start and end points if necessary and store swap state
swapped = False
if x1 > x2:
x1, x2 = x2, x1
y1, y2 = y2, y1
swapped = True
# Recalculate differentials
dx = x2 - x1
dy = y2 - y1
# Calculate error
error = int(dx / 2.0)
ystep = 1 if y1 < y2 else -1
# Iterate over bounding box generating points between start and end
y = y1
points = []
for x in range(x1, x2 + 1):
coord = (y, x) if is_steep else (x, y)
points.append(coord)
error -= abs(dy)
if error < 0:
y += ystep
error += dx
# Reverse the list if the coordinates were swapped
if swapped:
points.reverse()
return points
###################################################################################
# IMPLEMENTING MATLAB BWMORPH MISSING PIECES
###################################################################################
class bwmorph:
@staticmethod
def diag(imIn):
strl = np.array([
[[0,1,0],[1,0,0],[0,0,0]],
[[0,1,0],[0,0,1],[0,0,0]],
[[0,0,0],[1,0,0],[0,1,0]],
[[0,0,0],[0,0,1],[0,1,0]],
[[0,1,0],[1,0,0],[0,1,0]],
[[0,1,0],[1,0,1],[0,0,0]],
[[0,1,0],[0,0,1],[0,1,0]],
[[0,0,0],[1,0,1],[0,1,0]]
],dtype=np.uint8)
bwIm = np.zeros(imIn.shape,dtype=int)
imIn = np.array(imIn)
imIn = imIn/np.max(np.max(imIn)) #normalizing to be added later
for i in range(7):
bwIm = bwIm + smorph.binary_hit_or_miss(imIn,strl[i,:,:])
bwIm = ((bwIm>0) + imIn)>0
return bwIm # out put is boolean
@staticmethod
def clean(imIn):
print("This is to be implemented in the future")
return None
@staticmethod
def endpoints(imIn):
print("This is to be implemented in the future")
return None
@staticmethod
def hbreak(imIn):
print("This is to be implemented in the future")
return None
@staticmethod
def spur(imIn):
print("This is to be implemented in the future")
return None
###################################################################################
# IMPLEMENTING MATLAB BWMORPH MISSING PIECES
###################################################################################
# This requires a thinned image to be passed in
# The little openings between the connected lines can be fixed by changing the line width in
# "cv2.line(im, (x1, y1), (x2, y2), (0, 0, 0), thickness=2, lineType=8)" to "1"
# But that's only recommended if the lines are super straight.
def preciseHough(im,lineMinLen=10,lineMaxGap=5):
# performing thinking on the image to create diagonal artifacts
im = np.array(im * 255, dtype=np.uint8)
labledLines = np.zeros(im.shape)
numLines = 0
strel = np.ones((4, 4))
# Implementing Custome Hough Transform
linesCenter = np.array([]).reshape(2, 0)
lineEndCoords = np.array([]).reshape(0, 4)
while True: # Iterate untill all the walls are extracted
hspace, _, _ = hough_line(im) # Getting hspace to threshold and extract one line at a time
lines = cv2.HoughLinesP(im, 1, np.pi / 180, np.max(np.max(hspace)), lineMinLen, lineMaxGap)
if lines is None:
break
x = np.array([lines[:, 0, 0], lines[:, 0, 2]]) ; y = np.array([lines[:, 0, 1], lines[:, 0, 3]])
iterLineCent = np.array([np.mean(x, 0), np.mean(y, 0)])
linesCenter = np.hstack([linesCenter, iterLineCent])
lineEndCoords = np.vstack([lineEndCoords,lines[:,0,:]])
counter = 0
for x1, y1, x2, y2 in lines[:, 0, :]:
cv2.line(im, (x1, y1), (x2, y2), (0, 0, 0), thickness=2, lineType=8)
cv2.line(labledLines, (x1, y1), (x2, y2), (255 - (numLines+counter), 0, 0), thickness=1, lineType=8)
counter = counter + 1
# end of for
im = skm.closing(im, strel) # close image to fill holes
numLines = numLines + lines.shape[0]
else: # When there are no other lines detected by the Hough transform
pass
# end of while
return labledLines,linesCenter,lineEndCoords,numLines
################################################################################
# Heatmap Data to RGB IMAGE
################################################################################
def data2heatmap(data, dynamicRange = 'linear'):
dataShape = data.shape
# normalizing the data
data = data.reshape((-1, 1))
alpha = np.min(np.min(data))
beta = np.max(np.max(data))
gamma = beta - alpha
data = data - alpha
data = data / gamma
# Intensity Transformation
if dynamicRange.lower() == 'log':# be aware this manipulates the dynamic range
# Constarst Streching
for i in range(2):
data = 1*np.log2(1+data)
# Defining Colormap Transissions
Rpx = np.array([0, .125, .38, .62, .88, 1])
Gpx = Rpx
Bpx = Rpx
Rpy = np.array([0, 0, .00, 1, 1, .5])
Gpy = np.array([0, 0, 1, 1, 0, 0])
Bpy = np.array([.5, 1, 1, 0, 0, 0])
RGBmap3D = np.zeros((1, data.size, 3))
for i in range(data.size):
if data[i] <= Rpx[1]:
RGBmap3D[0, i, :] = [(np.diff(Rpy[0:2]) / np.diff(Rpx[0:2])) * (data[i] - Rpx[0]) + Rpy[0],
(np.diff(Gpy[0:2]) / np.diff(Gpx[0:2])) * (data[i] - Gpx[0]) + Gpy[0],
(np.diff(Bpy[0:2]) / np.diff(Bpx[0:2])) * (data[i] - Bpx[0]) + Bpy[0]]
elif data[i] <= Rpx[2]:
RGBmap3D[0, i, :] = [(np.diff(Rpy[1:3]) / np.diff(Rpx[1:3])) * (data[i] - Rpx[1]) + Rpy[1],
(np.diff(Gpy[1:3]) / np.diff(Gpx[1:3])) * (data[i] - Gpx[1]) + Gpy[1],
(np.diff(Bpy[1:3]) / np.diff(Bpx[1:3])) * (data[i] - Bpx[1]) + Bpy[1]]
elif data[i] <= Rpx[3]:
RGBmap3D[0, i, :] = [(np.diff(Rpy[2:4]) / np.diff(Rpx[2:4])) * (data[i] - Rpx[2]) + Rpy[2],
(np.diff(Gpy[2:4]) / np.diff(Gpx[2:4])) * (data[i] - Gpx[2]) + Gpy[2],
(np.diff(Bpy[2:4]) / np.diff(Bpx[2:4])) * (data[i] - Bpx[2]) + Bpy[2]]
elif data[i] <= Rpx[4]:
RGBmap3D[0, i, :] = [(np.diff(Rpy[3:5]) / np.diff(Rpx[3:5])) * (data[i] - Rpx[3]) + Rpy[3],
(np.diff(Gpy[3:5]) / np.diff(Gpx[3:5])) * (data[i] - Gpx[3]) + Gpy[3],
(np.diff(Bpy[3:5]) / np.diff(Bpx[3:5])) * (data[i] - Bpx[3]) + Bpy[3]]
elif data[i] <= Rpx[5]:
RGBmap3D[0, i, :] = [(np.diff(Rpy[4:6]) / np.diff(Rpx[4:6])) * (data[i] - Rpx[4]) + Rpy[4],
(np.diff(Gpy[4:6]) / np.diff(Gpx[4:6])) * (data[i] - Gpx[4]) + Gpy[4],
(np.diff(Bpy[4:6]) / np.diff(Bpx[4:6])) * (data[i] - Bpx[4]) + Bpy[4]]
RGBmap = np.reshape(RGBmap3D, (dataShape[0], dataShape[1], 3))
return RGBmap, RGBmap3D