# Use python realize warpPerspective with interpolation method bilinear

i try to use python realize warpPerspective with interpolation method bilinear
but got mse 0.12 with the cv2.warpPerspective

how can i get the same result , some one can help me ,i will be very grateful

``````def pective_transformation(img,warpMatrix,size,flag =0):
height,weight,channel=img.shape
#source=np.ones((3,1))
cv2_invert = cv2.invert(warpMatrix)[1]
print(cv2_invert)
new_img=np.zeros((size[1],size[0],channel))#创建透视变换output图像(矩阵)
M = warpMatrix.copy().reshape((1,-1))
warpMatrix = np.mat(warpMatrix)
warpMatrix = warpMatrix.reshape((3, 3))

#print(M.shape)
#print(M[0])
D = M[0][0]*M[0][4] - M[0][1]*M[0][3]
if( D != 0):
D =1./D
A11 = M[0][4]*D ; A22 = M[0][0]*D
M[0][0] = A11   ; M[0][1] *= -D
M[0][3] *= -D   ; M[0][4] = A22
b1 = -M[0][0]*M[0][2] - M[0][1]*M[0][5]
b2 = -M[0][3]*M[0][2] - M[0][4]*M[0][5]
M[0][2] = b1 ; M[0][5] = b2

M = M.reshape((3,3))
print(M)
#print(warpMatrix.I)

cv_m = np.mat([ [1.341463406454491, 0.513128077358975, -93.90243845181439],
[0, 1, 0],
[0, -0, 1]])

XY = np.zeros((size[1],size[0],2))

#Y = np.zeros((size[1],size[0]))

for i in range(size[0]):#遍历新图像的列
for j in range(size[1]):#遍历新图像的行
#从新图像的左上角到右下角依次插值
goal = [i,j,1]#目标点坐标(X,Y,Z)
goal = np.mat(goal).astype(np.float64)
goal = goal.reshape((3,1))
#print(goal1)
img_point = M * goal.astype(np.float64)#利用目标点反推原图像点坐标,得到的坐标为(x,y,z),shape=(3,1)   float64
# print(img_point.dtype)  # float64

#print("warpMatrix.I:",warpMatrix.I)
img_point = img_point.tolist()
#其中img_point[0][0]代表原图像x坐标
# 其中img_point[1][0]代表原图像y坐标
# 其中img_point[2][0]代表原图像z坐标
#实际使用中z会无限趋近于1,但不会等于1,在这里相当于一个修正参数,修正x和y坐标

if flag == 0 :

x = int( round(img_point[0][0] / img_point[2][0] ) )#计算原图像x坐标
y = int( round(img_point[1][0] / img_point[2][0] ))#计算原图像y坐标

XY[j,i] = (img_point[0][0] / img_point[2][0],img_point[1][0] / img_point[2][0])

# 用边界补齐
'''
if y>=img.shape[0]:#防止溢出原图像边界
y=img.shape[0]-1
if x>=img.shape[1]:
x=img.shape[1]-1
if y<0:
y=0
if x<0:
x=0
'''
# 留黑

if y>=img.shape[0]:#防止溢出原图像边界
continue
if x>=img.shape[1]:
continue
if y<0:
continue
if x<0:
continue
new_img[j,i] = img[y,x]

if flag == 1:
x = img_point[0][0] / img_point[2][0] #计算原图像x坐标
y = img_point[1][0] / img_point[2][0] #计算原图像y坐标
'''
int_x = int(np.floor(x)) ; s_x = x - int_x
int_y = int(np.floor(y)) ; s_y = y - int_y
'''
data_x = round( (img_point[0][0] / img_point[2][0]) * (2**5) ) #计算原图像x坐标
data_y = round( (img_point[1][0] / img_point[2][0]) * (2**5) )  #计算原图像y坐标

s_x = float( ( data_x & ( 2**5 - 1 ) ) / 2**5 )
#print(f_x)
s_y = float( ( data_y & ( 2**5 - 1 ) ) / 2**5 )
int_x =  int( round( data_x / 2**5 ) )
int_y =  int( round( data_y / 2**5 ) )

#print(x,y,int_x,int_y,s_x,s_y)
if int_y>=img.shape[0]:#防止溢出原图像边界
continue
if int_x>=img.shape[1]:
continue
if int_y+1<=0:
continue
if int_x+1<=0:
continue

int_x1 = cv2.borderInterpolate(int_x,size[0],cv2.BORDER_CONSTANT)
int_x2 = cv2.borderInterpolate(int_x+1,size[0],cv2.BORDER_CONSTANT)
int_y1 = cv2.borderInterpolate(int_y,size[1],cv2.BORDER_CONSTANT)
int_y2 = cv2.borderInterpolate(int_y+1,size[1],cv2.BORDER_CONSTANT)

'''
if y >= img.shape[0]-1 :
if x> img.shape[1]-1:
new_img[j,i] =  (1.0-s_x)*(1.0-s_y)*img[int_y,int_x]
else:
new_img[j,i] =  (1.0-s_x)*(1.0-s_y)*img[int_y,int_x] + s_x*(1.0-s_y)*img[int_y,(int_x+1)]
continue

if x >= img.shape[1]-1:
if y > img.shape[0]-1 :
new_img[j,i] =  (1.0-s_x)*(1.0-s_y)*img[int_y,int_x]
else:
#print(int_x,int_y)
new_img[j,i] =  (1.0-s_x)*(1.0-s_y)*img[int_y,int_x] + (1.0-s_x)*s_y*img[(int_y+1),int_x]
continue

if y <= 0:
s_y = 1 - s_y
if x < 0:
s_x = 1 -s_x
new_img[j,i] = s_x*s_y*img[(int_y+1),(int_x+1)]
else:
new_img[j,i] = (1.0-s_x)*s_y*img[(int_y+1),int_x] + s_x*s_y*img[(int_y+1),(int_x+1)]
continue

if x <= 0:
if y < 0:
print(s_y,s_x)
s_y = 1 - s_y
new_img[j,i] = s_x*s_y*img[(int_y+1),(int_x+1)]
else:

new_img[j,i] = s_x*(1.0-s_y)*img[int_y,(int_x+1)] + s_x*s_y*img[(int_y+1),(int_x+1)]
#print("xy < 0",s_y,s_x,new_img[j,i],i,j,int_x,int_y)
continue
'''

if int_x1 >=0  and int_y1>=0 :
v0 = img[int_y1,int_x1]
else:
v0 = np.array([0,0,0])
if int_x2 >=0  and int_y1>=0 :
v1 = img[int_y1,int_x2]
else:
v1 = np.array([0,0,0])
if int_x1 >=0  and int_y2>=0 :
v2 = img[int_y2,int_x1]
else:
v2 = np.array([0,0,0])
if int_x2 >=0  and int_y2>=0 :
v3 = img[int_y2,int_x2]
else:
v3 = np.array([0,0,0])

new_img[j,i] =  (1.0-s_x)*(1.0-s_y)*v0 +  \
(1.0-s_x)*s_y*v2 +    \
s_x*(1.0-s_y)*v1 +    \
s_x*s_y*v3

#print(new_img.reshape(6,6))
#print(new_img[0])
''''''
for i in range(new_img.shape[0]):
for j in range(new_img.shape[1]):
new_img[i][j][0] = int(round(new_img[i][j][0]) )
new_img[i][j][1] = int(round(new_img[i][j][1]) )
new_img[i][j][2] = int(round(new_img[i][j][2]) )
#print(new_img[0])
new_img = (new_img).astype(np.uint8)
#print(new_img)
#print(new)

#new_img_1 = cv2.remap(img,XY.astype(np.float32),map2.astype(np.float32),interpolation = cv2.INTER_LINEAR)
return new_img
``````