test_utils_bluring.py

import pytest
import numpy as np
import tensorflow as tf
from NN.utils_bluring import \
  gaussian_kernel, applyBluring, extractBlured, extractBluredOne
from NN.utils import extractInterpolated

# extractBlured same as extractInterpolated after applying gaussians
# TODO: find out why the results are so numerically instable
def test_extractBlured_same():
  H = 10
  img = np.random.rand(H, H, 3).astype(np.float32)
  points = np.array([
    [0.5, 0.5], [0.1, 0.1], [0.9, 0.9], [0.1, 0.9], [0.9, 0.1], [0.7, 0.2]
  ]).astype(np.float32)
  R = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).astype(np.float32)
  imgBlured = applyBluring(img[None], gaussian_kernel(H, R))
  assert imgBlured.shape == (6, H, H, 3)

  blur = extractBlured(R)
  blured = blur(img, points, ptR=np.full((6, 1), 1.0))

  extracted = extractInterpolated(imgBlured[0, None], points[None])[0]
  assert extracted.shape == blured.shape
  for i, a, b in zip(range(extracted.shape[0]), extracted, blured):
    diff = np.abs(a - b).max()
    assert diff < 5e-2, '%d | %s != %s' % (i, a, b)
    continue
  return

def test_extractBluredOne_eq_extractBlured():
  img = np.random.uniform(size=(10, 10, 3)).astype(np.float32)
  points = np.random.uniform(size=(10, 2)).astype(np.float32)

  R = np.array([1.0]).astype(np.float32)
  blur = extractBlured(R)
  blured = blur(img, points, ptR=np.full((10, 1), 1.0))

  blurOne = extractBluredOne()
  bluredOne = blurOne(img, points, R=R)

  assert blured.shape == bluredOne.shape, '%s != %s' % (blured.shape, bluredOne.shape)
  for i, a, b in zip(range(blured.shape[0]), blured, bluredOne):
    diff = np.abs(a - b).max()
    assert diff < 5e-5, '%d | %s != %s' % (i, a, b)
    continue
  return