def _get_image_blob(im):
 im_orig = im.astype(np.float32, copy=True)
 im_orig -= cfg.PIXEL_MEANS
 im_shape = im_orig.shape
 im_size_min = np.min(im_shape[0:2])
 im_size_max = np.max(im_shape[0:2])
 processed_ims = []
 im_scale_factors = []
 for target_size in cfg.TEST.SCALES:
  im_scale = (float(target_size) / float(im_size_min))
  if (np.round((im_scale * im_size_max)) > cfg.TEST.MAX_SIZE):
   im_scale = (float(cfg.TEST.MAX_SIZE) / float(im_size_max))
  im = cv2.resize(im_orig, None, None, fx=im_scale, fy=im_scale, interpolation=cv2.INTER_LINEAR)
  im_scale_factors.append(im_scale)
  processed_ims.append(im)
 blob = im_list_to_blob(processed_ims)
 return (blob, np.array(im_scale_factors))

def _get_image_blob ( im ) : im_orig = im . astype ( np . float32 , copy = True ) im_orig -= cfg . PIXEL_MEANS im_shape = im_orig . shape im_size_min = np . min ( im_shape [ 0 : 2 ] ) im_size_max = np . max ( im_shape [ 0 : 2 ] ) processed_ims = [ ] im_scale_factors = [ ] for target_size in cfg . TEST . SCALES : im_scale = ( float ( target_size ) / float ( im_size_min ) ) if ( np . round ( ( im_scale * im_size_max ) ) > cfg . TEST . MAX_SIZE ) : im_scale = ( float ( cfg . TEST . MAX_SIZE ) / float ( im_size_max ) ) im = cv2 . resize ( im_orig , None , None , fx = im_scale , fy = im_scale , interpolation = cv2 . INTER_LINEAR ) im_scale_factors . append ( im_scale ) processed_ims . append ( im ) blob = im_list_to_blob ( processed_ims ) return ( blob , np . array ( im_scale_factors ) )
converts an image into a network input .