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from __future__ import division
import cv2
import numpy as np
from skimage import morphology
np.seterr(divide='ignore', invalid='ignore')
"""This is the profiled code, very fast, takes 0.25s"""
def fakePad(Image, Mask, iterations=50):
"""
add an extra padding around the front mask
:param Image:
:param Mask:
:param iterations:
:return: DilatedImg
"""
if len(Image.shape) == 3: ##for RGB Image
"""for RGB Images"""
Mask0 = Mask.copy()
height, width = Mask0.shape[:2]
Mask0[0, :] = 0 # np.zeros(width)
Mask0[-1, :] = 0 # np.zeros(width)
Mask0[:, 0] = 0 # np.zeros(height)
Mask0[:, -1] = 0 # np.zeros(height)
# Erodes the mask to avoid weird region near the border.
structureElement1 = morphology.disk(5)
Mask0 = cv2.morphologyEx(Mask0, cv2.MORPH_ERODE, structureElement1, iterations=1)
# DilatedImg = Img_green_reverse * Mask
DilatedImg = cv2.bitwise_and(Image, Image, mask=Mask0)
OldMask = Mask0.copy()
filter = np.ones((3, 3))
filterRows, filterCols = np.where(filter > 0)
filterRows = filterRows - 1
filterCols = filterCols - 1
structureElement2 = morphology.diamond(1)
for i in range(0, iterations):
NewMask = cv2.morphologyEx(OldMask, cv2.MORPH_DILATE, structureElement2, iterations=1)
pixelIndex = np.where(NewMask - OldMask) # [rows, cols]
imgValues = np.zeros((len(pixelIndex[0]), len(filterRows), 3))
for k in range(len(filterRows)):
filterRowIndexes = pixelIndex[0] - filterRows[k]
filterColIndexes = pixelIndex[1] - filterCols[k]
selectMask0 = np.bitwise_and(np.bitwise_and(filterRowIndexes < height, filterRowIndexes >= 0),
np.bitwise_and(filterColIndexes < width, filterColIndexes >= 0))
selectMask1 = OldMask[filterRowIndexes[selectMask0], filterColIndexes[selectMask0]] > 0
selectedPositions = [filterRowIndexes[selectMask0][selectMask1],
filterColIndexes[selectMask0][selectMask1]]
imgValues[np.arange(len(pixelIndex[0]))[selectMask0][selectMask1], k, :] = DilatedImg[
selectedPositions[0],
selectedPositions[1], :]
DilatedImg[pixelIndex[0], pixelIndex[1], :] = np.sum(imgValues, axis=1) // np.sum(imgValues > 0, axis=1)
OldMask = NewMask
return DilatedImg
########################################################################
else: #for green channel only
"""for green channel only"""
Mask0 = Mask.copy()
height, width = Mask0.shape
Mask0[0, :] = 0 # np.zeros(width)
Mask0[-1, :] = 0 # np.zeros(width)
Mask0[:, 0] = 0 # np.zeros(height)
Mask0[:, -1] = 0 # np.zeros(height)
# Erodes the mask to avoid weird region near the border.
structureElement1 = morphology.disk(5)
Mask0 = cv2.morphologyEx(Mask0, cv2.MORPH_ERODE, structureElement1, iterations=1)
# DilatedImg = Img_green_reverse * Mask
DilatedImg = cv2.bitwise_and(Image, Image, mask=Mask0)
OldMask = Mask0.copy()
filter = np.ones((3, 3))
filterRows, filterCols = np.where(filter > 0)
filterRows = filterRows - 1
filterCols = filterCols - 1
structureElement2 = morphology.diamond(1)
for i in range(0, iterations):
NewMask = cv2.morphologyEx(OldMask, cv2.MORPH_DILATE, structureElement2, iterations=1)
pixelIndex = np.where(NewMask - OldMask) # [rows, cols]
imgValues = np.zeros((len(pixelIndex[0]), len(filterRows)))
for k in range(len(filterRows)):
filterRowIndexes = pixelIndex[0] - filterRows[k]
filterColIndexes = pixelIndex[1] - filterCols[k]
selectMask0 = np.bitwise_and(np.bitwise_and(filterRowIndexes < height, filterRowIndexes >= 0),
np.bitwise_and(filterColIndexes < width, filterColIndexes >= 0))
selectMask1 = OldMask[filterRowIndexes[selectMask0], filterColIndexes[selectMask0]] > 0
selectedPositions = [filterRowIndexes[selectMask0][selectMask1], filterColIndexes[selectMask0][selectMask1]]
imgValues[np.arange(len(pixelIndex[0]))[selectMask0][selectMask1], k] = DilatedImg[selectedPositions[0], selectedPositions[1]]
DilatedImg[pixelIndex[0], pixelIndex[1]] = np.sum(imgValues, axis=1) / np.sum(imgValues > 0, axis=1)
OldMask = NewMask
return DilatedImg
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