/
opencv_mat.pyx
130 lines (92 loc) · 3.53 KB
/
opencv_mat.pyx
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import numpy as np
cimport numpy as np # for np.ndarray
from libc.string cimport memcpy
from opencv_mat cimport *
# inspired and adapted from http://makerwannabe.blogspot.ch/2013/09/calling-opencv-functions-via-cython.html
cdef Mat np2Mat3D(np.ndarray ary):
assert ary.ndim==3 and ary.shape[2]==3, "ASSERT::3channel RGB only!!"
ary = np.dstack((ary[...,2], ary[...,1], ary[...,0])) #RGB -> BGR
cdef np.ndarray[np.uint8_t, ndim=3, mode ='c'] np_buff = np.ascontiguousarray(ary, dtype=np.uint8)
cdef unsigned int* im_buff = <unsigned int*> np_buff.data
cdef int r = ary.shape[0]
cdef int c = ary.shape[1]
cdef Mat m
m.create(r, c, CV_8UC3)
memcpy(m.data, im_buff, r*c*3)
return m
cdef Mat np2Mat2D(np.ndarray ary):
assert ary.ndim==2, "ASSERT::1 channel grayscale only!!"
cdef np.ndarray[np.uint8_t, ndim=2, mode ='c'] np_buff = np.ascontiguousarray(ary, dtype=np.uint8)
cdef unsigned int* im_buff = <unsigned int*> np_buff.data
cdef int r = ary.shape[0]
cdef int c = ary.shape[1]
cdef Mat m
m.create(r, c, CV_8UC1)
memcpy(m.data, im_buff, r*c)
return m
cdef Mat np2Mat(np.ndarray ary):
if ary.ndim == 2:
return np2Mat2D(ary)
elif ary.ndim == 3:
return np2Mat3D(ary)
cdef object Mat2np(Mat m):
# Create buffer to transfer data from m.data
cdef Py_buffer buf_info
# Define the size / len of data
cdef size_t len = m.rows*m.cols*m.channels()*sizeof(CV_8UC3)
# Fill buffer
PyBuffer_FillInfo(&buf_info, NULL, m.data, len, 1, PyBUF_FULL_RO)
# Get Pyobject from buffer data
Pydata = PyMemoryView_FromBuffer(&buf_info)
# Create ndarray with data
shape_array = (m.rows, m.cols, m.channels())
ary = np.ndarray(shape=shape_array, buffer=Pydata, order='c', dtype=np.uint8)
# BGR -> RGB
if ary.ndim == 3 and ary.shape[2] == 3:
ary = np.dstack((ary[...,2], ary[...,1], ary[...,0]))
# Convert to numpy array
pyarr = np.asarray(ary)
if pyarr.ndim == 3 and pyarr.shape[2] == 1:
pyarr = np.reshape(pyarr, pyarr.shape[:-1])
return pyarr
def np2Mat2np(nparray):
cdef Mat m
# Convert numpy array to cv::Mat
m = np2Mat(nparray)
# Convert cv::Mat to numpy array
pyarr = Mat2np(m)
return pyarr
def expansion_of_known_regions(img_arr, trimap_arr, niter):
cdef Mat img_m, trimap_m
cdef int niter_c = niter
# Convert numpy array to cv::Mat
img_m = np2Mat(img_arr)
trimap_m = np2Mat(trimap_arr)
expansionOfKnownRegionsWrapper(img_m, trimap_m, niter_c)
# Convert cv::Mat to numpy array
img_arr = Mat2np(img_m)
trimap_arr = Mat2np(trimap_m)
return img_arr, trimap_arr
def global_matting(img_arr, trimap_arr):
cdef Mat img_m, trimap_m, foreground_m, alpha_m
alpha_arr = np.zeros(trimap_arr.shape)
img_m = np2Mat(img_arr)
trimap_m = np2Mat(trimap_arr)
alpha_m = np2Mat(alpha_arr)
globalMattingWrapper(img_m, trimap_m, foreground_m, alpha_m)
alpha_arr = Mat2np(alpha_m)
return alpha_arr.copy()
def guided_filter(img_arr, trimap_arr, alpha_arr, r, eps, depth=-1):
cdef Mat img_m, trimap_m, alpha_m, outp_m
img_m = np2Mat(img_arr)
trimap_m = np2Mat(trimap_arr)
alpha_m = np2Mat(alpha_arr)
outp_m = guidedFilterWrapper(img_m, alpha_m, trimap_m, r, eps, depth)
outp_arr = Mat2np(outp_m)
return outp_arr.copy()
cdef class PyMat:
cdef Mat mat
def __cinit__(self, np_mat):
self.mat = np2Mat(np_mat)
def get_mat(self):
return Mat2np(self.mat)