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GPU
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@Keep-Passion
Keep-Passion committed Jun 17, 2018
1 parent 74f3d4b commit 3b07c97
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115 changes: 51 additions & 64 deletions ImageFusion.py
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Original file line number Diff line number Diff line change
Expand Up @@ -36,7 +36,7 @@ def fuseByMinimum(self, images):
(imageA, imageB) = images
fuseRegion = np.minimum(imageA, imageB)
return fuseRegion

def getWeightsMatrix(self, images):
'''
获取权值矩阵
Expand All @@ -55,22 +55,21 @@ def getWeightsMatrix(self, images):
compareList.append(np.count_nonzero(imageA[row // 2: row, 0: col // 2] > 0))
compareList.append(np.count_nonzero(imageA[row // 2: row, col // 2: col] > 0))
compareList.append(np.count_nonzero(imageA[0: row // 2, col // 2: col] > 0))
self.printAndWrite(compareList)
# self.printAndWrite(" compareList:" + str(compareList))
index = compareList.index(min(compareList))
if index == 2:
# 重合区域在imageA的上左部分
self.printAndWrite("上左")
rowIndex = 0;
colIndex = 0;
# self.printAndWrite("上左")
rowIndex = 0; colIndex = 0;
for j in range(1, col):
for i in range(row - 1, -1, -1):
if imageA[i, col - j] != 0:
if imageA[i, col - j] != -1:
rowIndex = i + 1
break
if rowIndex != 0:
break
for i in range(col - 1, -1, -1):
if imageA[rowIndex, i] != 0:
if imageA[rowIndex, i] != -1:
colIndex = i + 1
break
# 赋值
Expand All @@ -84,21 +83,20 @@ def getWeightsMatrix(self, images):
weightMatB_2[:, colIndex - i] = (colIndex - i) * 1 / colIndex
weightMatB = weightMatB_1 * weightMatB_2
weightMatA = 1 - weightMatB
# elif leftCenter != 0 and bottomCenter != 0 and upCenter == 0 and rightCenter == 0:
#elif leftCenter != 0 and bottomCenter != 0 and upCenter == 0 and rightCenter == 0:
elif index == 3:
# 重合区域在imageA的下左部分
self.printAndWrite("下左")
rowIndex = 0;
colIndex = 0;
# self.printAndWrite("下左")
rowIndex = 0; colIndex = 0;
for j in range(1, col):
for i in range(row):
if imageA[i, col - j] != 0:
if imageA[i, col - j] != -1:
rowIndex = i - 1
break
if rowIndex != 0:
break
for i in range(col - 1, -1, -1):
if imageA[rowIndex, i] != 0:
if imageA[rowIndex, i] != -1:
colIndex = i + 1
break
# 赋值
Expand All @@ -115,18 +113,18 @@ def getWeightsMatrix(self, images):
# elif rightCenter != 0 and bottomCenter != 0 and upCenter == 0 and leftCenter == 0:
elif index == 0:
# 重合区域在imageA的下右部分
self.printAndWrite("下右")
# self.printAndWrite("下右")
rowIndex = 0;
colIndex = 0;
for j in range(0, col):
for i in range(row):
if imageA[i, j] != 0:
if imageA[i, j] != -1:
rowIndex = i - 1
break
if rowIndex != 0:
break
for i in range(col):
if imageA[rowIndex, i] != 0:
if imageA[rowIndex, i] != -1:
colIndex = i - 1
break
# 赋值
Expand All @@ -143,18 +141,17 @@ def getWeightsMatrix(self, images):
# elif upCenter != 0 and rightCenter != 0 and leftCenter == 0 and bottomCenter == 0:
elif index == 1:
# 重合区域在imageA的上右部分
self.printAndWrite("上右")
rowIndex = 0;
colIndex = 0;
# self.printAndWrite("上右")
rowIndex = 0; colIndex = 0;
for j in range(0, col):
for i in range(row - 1, -1, -1):
if imageA[i, j] != 0:
if imageA[i, j] != -1:
rowIndex = i + 1
break
if rowIndex != 0:
break
for i in range(col):
if imageA[rowIndex, i] != 0:
if imageA[rowIndex, i] != -1:
colIndex = i - 1
break
for i in range(rowIndex + 1):
Expand Down Expand Up @@ -182,12 +179,12 @@ def fuseByFadeInAndFadeOut(self, images, dx, dy):
row, col = imageA.shape[:2]
weightMatA = np.ones(imageA.shape, dtype=np.float32)
weightMatB = np.ones(imageA.shape, dtype=np.float32)
self.printAndWrite("ratio: " + str(np.count_nonzero(imageA > 0) / imageA.size))
if np.count_nonzero(imageA > 0) / imageA.size > 0.65:
# self.printAndWrite(" ratio: " + str(np.count_nonzero(imageA > -1) / imageA.size))
if np.count_nonzero(imageA > -1) / imageA.size > 0.65:
# 如果对于imageA中，非0值占比例比较大，则认为是普通融合
# 根据区域的行列大小来判断，如果行数大于列数，是水平方向
if col <= row:
self.printAndWrite("普通融合-水平方向")
# self.printAndWrite("普通融合-水平方向")
for i in range(0, col):
if dy <= 0:
weightMatA[:, i] = weightMatA[:, i] * i * 1.0 / col
Expand All @@ -197,7 +194,7 @@ def fuseByFadeInAndFadeOut(self, images, dx, dy):
weightMatB[:, col - i - 1] = weightMatB[:, col - i - 1] * (col - i) * 1.0 / col
# 根据区域的行列大小来判断，如果列数大于行数，是竖直方向
elif row < col:
self.printAndWrite("普通融合-竖直方向")
# self.printAndWrite("普通融合-竖直方向")
for i in range(0, row):
if dx <= 0:
weightMatA[i, :] = weightMatA[i, :] * i * 1.0 / row
Expand All @@ -207,13 +204,11 @@ def fuseByFadeInAndFadeOut(self, images, dx, dy):
weightMatB[row - i - 1, :] = weightMatB[row - i - 1, :] * (row - i) * 1.0 / row
else:
# 如果对于imageA中，非0值占比例比较小，则认为是拐角融合
self.printAndWrite("拐角融合")
# self.printAndWrite("拐角融合")
weightMatA, weightMatB = self.getWeightsMatrix(images)
imageA[imageA == -1] = 0;
imageB[imageB == -1] = 0;
imageA[imageA == -1] = 0; imageB[imageB == -1] =0;
result = weightMatA * imageA.astype(np.int) + weightMatB * imageB.astype(np.int)
result[result < 0] = 0;
result[result > 255] = 255
result[result < 0] = 0; result[result > 255] = 255
fuseRegion = np.uint8(result)
return fuseRegion

Expand All @@ -229,12 +224,12 @@ def fuseByTrigonometric(self, images, dx, dy):
row, col = imageA.shape[:2]
weightMatA = np.ones(imageA.shape, dtype=np.float64)
weightMatB = np.ones(imageA.shape, dtype=np.float64)
self.printAndWrite("ratio: " + str(np.count_nonzero(imageA > 0) / imageA.size))
if np.count_nonzero(imageA > 0) / imageA.size > 0.65:
# self.printAndWrite(" ratio: " + str(np.count_nonzero(imageA > -1) / imageA.size))
if np.count_nonzero(imageA > -1) / imageA.size > 0.65:
# 如果对于imageA中，非0值占比例比较大，则认为是普通融合
# 根据区域的行列大小来判断，如果行数大于列数，是水平方向
if col <= row:
self.printAndWrite("普通融合-水平方向")
# self.printAndWrite("普通融合-水平方向")
for i in range(0, col):
if dy <= 0:
weightMatA[:, i] = weightMatA[:, i] * i * 1.0 / col
Expand All @@ -244,7 +239,7 @@ def fuseByTrigonometric(self, images, dx, dy):
weightMatB[:, col - i - 1] = weightMatB[:, col - i - 1] * (col - i) * 1.0 / col
# 根据区域的行列大小来判断，如果列数大于行数，是竖直方向
elif row < col:
self.printAndWrite("普通融合-竖直方向")
# self.printAndWrite("普通融合-竖直方向")
for i in range(0, row):
if dx <= 0:
weightMatA[i, :] = weightMatA[i, :] * i * 1.0 / row
Expand All @@ -254,17 +249,15 @@ def fuseByTrigonometric(self, images, dx, dy):
weightMatB[row - i - 1, :] = weightMatB[row - i - 1, :] * (row - i) * 1.0 / row
else:
# 如果对于imageA中，非0值占比例比较小，则认为是拐角融合
self.printAndWrite("拐角融合")
# self.printAndWrite("拐角融合")
weightMatA, weightMatB = self.getWeightsMatrix(images)

weightMatA = np.power(np.sin(weightMatA * math.pi / 2), 2)
weightMatB = 1 - weightMatA

imageA[imageA == -1] = 0;
imageB[imageB == -1] = 0;
imageA[imageA == -1] = 0; imageB[imageB == -1] =0;
result = weightMatA * imageA.astype(np.int) + weightMatB * imageB.astype(np.int)
result[result < 0] = 0;
result[result > 255] = 255
result[result < 0] = 0; result[result > 255] = 255
fuseRegion = np.uint8(result)
return fuseRegion

Expand All @@ -273,7 +266,7 @@ def fuseByMultiBandBlending(self, images):
imagesReturn = np.uint8(self.BlendArbitrary2(imageA, imageB, 4))
return imagesReturn

# 带权拉普拉斯金字塔融合
#带权拉普拉斯金字塔融合
def BlendArbitrary(self, img1, img2, R, level):
# img1 and img2 have the same size
# R represents the region to be combined
Expand All @@ -287,11 +280,11 @@ def BlendArbitrary(self, img1, img2, R, level):
GRN.append(np.ones((GR[i].shape[0], GR[i].shape[1])) - GR[i])
LC = []
for i in range(level):
LC.append(LA[i] * GR[level - i - 1] + LB[i] * GRN[level - i - 1])
LC.append(LA[i] * GR[level - i -1] + LB[i] * GRN[level - i - 1])
result = self.reconstruct(LC)
return result
return result

# 均值融合
#均值融合
def BlendArbitrary2(self, img1, img2, level):
# img1 and img2 have the same size
# R represents the region to be combined
Expand All @@ -306,7 +299,7 @@ def BlendArbitrary2(self, img1, img2, level):

def LaplacianPyramid(self, img, level):
gp = self.GaussianPyramid(img, level)
lp = [gp[level - 1]]
lp = [gp[level-1]]
for i in range(level - 1, -1, -1):
GE = cv2.pyrUp(gp[i])
GE = cv2.resize(GE, (gp[i - 1].shape[1], gp[i - 1].shape[0]), interpolation=cv2.INTER_CUBIC)
Expand All @@ -318,7 +311,7 @@ def reconstruct(self, input_pyramid):
out = input_pyramid[0]
for i in range(1, len(input_pyramid)):
out = cv2.pyrUp(out)
out = cv2.resize(out, (input_pyramid[i].shape[1], input_pyramid[i].shape[0]), interpolation=cv2.INTER_CUBIC)
out = cv2.resize(out, (input_pyramid[i].shape[1],input_pyramid[i].shape[0]), interpolation = cv2.INTER_CUBIC)
out = cv2.add(out, input_pyramid[i])
return out

Expand All @@ -330,7 +323,7 @@ def GaussianPyramid(self, R, level):
gp.append(G)
return gp

# 权值矩阵归一化
#权值矩阵归一化
def stretchImage(self, Region):
minI = Region.min()
maxI = Region.max()
Expand Down Expand Up @@ -358,11 +351,11 @@ def fuseByOptimalSeamLine(self, images, direction="horizontal"):
fuseRegion = imageA.copy()
fuseRegion[(1 - mask) == 0] = imageA[(1 - mask) == 0]
fuseRegion[(1 - mask) == 1] = imageB[(1 - mask) == 1]
drawFuseRegion = self.drawOptimalLine(1 - mask, fuseRegion)
drawFuseRegion = self.drawOptimalLine(1- mask, fuseRegion)
cv2.imwrite("optimalLine.jpg", drawFuseRegion)
cv2.imwrite("fuseRegion.jpg", np.uint8(self.BlendArbitrary(imageA, imageB, mask, 4)))
cv2.imwrite("fuseRegion.jpg", np.uint8(self.BlendArbitrary(imageA,imageB, mask, 4)))
cv2.waitKey(0)
return np.uint8(self.BlendArbitrary(imageA, imageB, mask, 4))
return np.uint8(self.BlendArbitrary(imageA,imageB, mask, 4))

def caculateVaule(self, images):
(imageA, imageB) = images
Expand All @@ -373,8 +366,8 @@ def caculateVaule(self, images):
[-1, 0, 1],
[-2, 0, 2]])
Sy = np.array([[-2, -1, -2],
[0, 0, 0],
[2, 1, 2]])
[ 0, 0, 0],
[ 2, 1, 2]])
Egeometry = np.power(cv2.filter2D(Ecolor, -1, Sx), 2) + np.power(cv2.filter2D(Ecolor, -1, Sy), 2)

diff = np.abs(imageA - imageB) / np.maximum(imageA, imageB).max()
Expand Down Expand Up @@ -412,27 +405,22 @@ def findOptimalSeamLine(self, value, direction="horizontal"):
# print(indexMatrix[i, j])
elif j == col - 1:
dpMatrix[i, j] = (np.array([dpMatrix[i - 1, j - 1], dpMatrix[i - 1, j]]) + value[i, j]).min()
indexMatrix[i, j] = (np.array([dpMatrix[i - 1, j - 1], dpMatrix[i - 1, j]]) + value[
i, j]).argmin() - 1
indexMatrix[i, j] = (np.array([dpMatrix[i - 1, j - 1], dpMatrix[i - 1, j]]) + value[i, j]).argmin() - 1
else:
dpMatrix[i, j] = (
np.array([dpMatrix[i - 1, j - 1], dpMatrix[i - 1, j], dpMatrix[i - 1, j + 1]]) + value[
i, j]).min()
indexMatrix[i, j] = (np.array(
[dpMatrix[i - 1, j - 1], dpMatrix[i - 1, j], dpMatrix[i - 1, j + 1]]) + value[
i, j]).argmin() - 1
dpMatrix[i, j] = (np.array([dpMatrix[i - 1, j - 1], dpMatrix[i - 1, j], dpMatrix[i - 1, j + 1]]) + value[i, j]).min()
indexMatrix[i, j] = (np.array([dpMatrix[i - 1, j - 1], dpMatrix[i - 1, j], dpMatrix[i - 1, j + 1]]) + value[i, j]).argmin() - 1
# print(indexMatrix)
# generate the mask
index = dpMatrix[row - 1, :].argmin()
# print("here" + str(dpMatrix[row - 1, :]))
# print(index)
for j in range(index, col):
mask[row - 1, j] = 1
mask[row-1, j] = 1
for i in range(row - 1, 1, -1):
index = indexMatrix[i, index] + index
# print(index)
for j in range(index, col):
mask[i - 1, j] = 1
mask[i-1, j] = 1
if direction == "vertical":
mask = np.transpose(mask)
return mask
Expand All @@ -448,8 +436,7 @@ def drawOptimalLine(self, mask, fuseRegion):
break
return drawing


if __name__ == "__main__":
if __name__=="__main__":
# 测试
num = 6
A_1 = np.zeros((num, num), dtype=np.uint8)
Expand All @@ -467,4 +454,4 @@ def drawOptimalLine(self, mask, fuseRegion):

A_2 = np.ones((num, num), dtype=np.uint8)
imageFusion = ImageFusion()
imageFusion.fuseByFadeInAndFadeOut([A_1, A_2])
imageFusion.fuseByFadeInAndFadeOut([A_1, A_2])
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