我们从Python开源项目中，提取了以下14个代码示例，用于说明如何使用cv2.BORDER_TRANSPARENT。
def warp_image(img, tM, shape): out = np.zeros(shape, dtype=img.dtype) # cv2.warpAffine(img, # tM[:2], # (shape[1], shape[0]), # dst=out, # borderMode=cv2.BORDER_TRANSPARENT, # flags=cv2.WARP_INVERSE_MAP) cv2.warpPerspective(img, tM, (shape[1], shape[0]), dst=out, borderMode=cv2.BORDER_TRANSPARENT, flags=cv2.WARP_INVERSE_MAP) return out # TODO: Modify this method to get a better face contour mask
def warp_im(self,im, M, dshape): ''' ???????? ''' output_im = np.zeros(dshape, dtype=im.dtype) cv2.warpAffine(im, M[:2], (dshape[1], dshape[0]), dst=output_im, borderMode=cv2.BORDER_TRANSPARENT, flags=cv2.WARP_INVERSE_MAP) return output_im
def warp_im(im, M, dshape): output_im = np.zeros(dshape, dtype=im.dtype) cv2.warpAffine(im, M[:2], (dshape[1], dshape[0]), dst=output_im, borderMode=cv2.BORDER_TRANSPARENT, flags=cv2.WARP_INVERSE_MAP) return output_im
def warp_im(im, M, dshape): output_im = np.ones(dshape, dtype=im.dtype)*0 cv2.warpAffine(im, M[:2], (dshape[1], dshape[0]), dst=output_im, borderMode=cv2.BORDER_TRANSPARENT, flags=cv2.WARP_INVERSE_MAP) return output_im
def warp_im(im, M, dshape): output_im = np.zeros(dshape, dtype=im.dtype) cv2.warpAffine(im,M[:2],(dshape[1], dshape[0]),dst=output_im,borderMode=cv2.BORDER_TRANSPARENT,flags=cv2.WARP_INVERSE_MAP) return output_im
def warp_im(im, M, dshape): output_im = numpy.zeros(dshape, dtype=im.dtype) cv2.warpAffine(im, M[:2], (dshape[1], dshape[0]), dst=output_im, borderMode=cv2.BORDER_TRANSPARENT, flags=cv2.WARP_INVERSE_MAP) return output_im
def warp_im(im, M, dshape): output_im = np.ones(dshape, dtype=im.dtype)*255 cv2.warpAffine(im, M[:2], (dshape[1], dshape[0]), dst=output_im, borderMode=cv2.BORDER_TRANSPARENT, flags=cv2.WARP_INVERSE_MAP) return output_im
def warp_im(im, M, dshape): """ Affine transformation with matrix M to dshape. """ output_im = numpy.zeros(dshape, dtype=im.dtype) # zero matrix cv2.warpAffine(im, M[:2], # shape of M (dshape[1], dshape[0]), dst = output_im, borderMode = cv2.BORDER_TRANSPARENT, flags = cv2.WARP_INVERSE_MAP) return output_im
def __next__(self): # THE IMAGED OBJECT WILL BE AVERAGED FROM ALL # INDIVITUAL IMAGES SHOWING THIS OBJECT FROM DIFFERENT POSITIONS: obj = MaskedMovingAverage(shape=self.obj_shape) with np.errstate(divide='ignore', invalid='ignore'): for f, h in zip(self.fits, self.Hinvs): warpedflatField = cv2.warpPerspective(self.flatField, h, (f.shape[1], f.shape[0])) obj.update(f / warpedflatField, warpedflatField != 0) self.object = obj.avg # THE NEW flatField WILL BE OBTAINED FROM THE WARPED DIVIDENT # BETWEEN ALL IMAGES THE THE ESTIMATED IMAGE OOBJECT sh = self.flatField.shape s = MaskedMovingAverage(shape=sh) for f, mask, h in zip(self.fits, self._fit_masks, self.Hs): div = f / self.object # ->do not interpolate between background and image border div[mask] = np.nan div = cv2.warpPerspective(div, h, (sh[1], sh[0]), # borderMode=cv2.BORDER_TRANSPARENT ) div = np.nan_to_num(div) s.update(div, div != 0) new_flatField = s.avg # STOP ITERATION? # RMSE excluding NaNs: dev = np.nanmean((new_flatField[::10, ::10] - self.flatField[::10, ::10])**2)**0.5 print('residuum: %s' % dev) if self.n >= self.maxIter or (self._last_dev and ( (self.n > 4 and dev > self._last_dev) or dev < self.maxDev)): raise StopIteration # remove erroneous values: self.flatField = np.clip(new_flatField, 0, 1) self.n += 1 self._last_dev = dev return self.n
