def verify_hybrid_image_np(image1, image2, kernel, hybrid_image) -> bool:
"""
Interactive test to be used in IPython notebook, that will print out
test result, and return value can also be queried for success (true).
Args:
- image1
- image2
- kernel
- hybrid_image
Returns:
- Boolean indicating success.
"""
gt_image1 = load_image(f'{ROOT}/data/1a_dog.bmp')
if not np.allclose(image1, gt_image1):
print(
'Please pass in the dog image `1a_dog.bmp` as the `image1` argument.'
)
return False
gt_image2 = load_image(f'{ROOT}/data/1b_cat.bmp')
if not np.allclose(image2, gt_image2):
print(
'Please pass in the cat image `1b_cat.bmp` as the `image2` argument.'
)
return False
img_h, img_w, _ = image2.shape
k_h, k_w = kernel.shape
# Exclude the border pixels.
hybrid_interior = hybrid_image[k_h:img_h - k_h, k_w:img_w - k_w]
correct_sum = np.allclose(158339.52, hybrid_interior.sum())
# ground truth values
gt_hybrid_crop = np.array([[[0.5429589, 0.55373234, 0.5452099],
[0.5290553, 0.5485607, 0.545738]],
[[0.55020595, 0.55713284, 0.5457024],
[0.5368045, 0.5603536, 0.5505791]]],
dtype=np.float32)
# H,W,C order in Numpy
correct_crop = np.allclose(hybrid_image[100:102, 100:102, :],
gt_hybrid_crop,
atol=1e-3)
if correct_sum and correct_crop:
print('Success! Hybrid image values are correct.')
return True
else:
print(158339.52, hybrid_interior.sum())
print(hybrid_image[100:102, 100:102, :], gt_hybrid_crop)
print(
'Hybrid image values are not correct, please double check your implementation.'
)
return False
def verify_high_freq_sq_kernel_torch_manual(image2, kernel,
high_frequencies) -> bool:
"""
Interactive test to be used in IPython notebook, that will print out
test result, and return value can also be queried for success (true).
Args:
- image2: Array representing the cat image (1b_cat.bmp)
- kernel: Low pass kernel (2d Gaussian)
- high_frequencies: High frequencies of image2 (output of high-pass filter)
Returns:
- retval: Boolean indicating success.
"""
gt_image2 = load_image(f'{ROOT}/data/1b_cat.bmp')
if not np.allclose(image2, gt_image2):
print(
'Please pass in the cat image `1b_cat.bmp` as the `image2` argument.'
)
return False
img_h, img_w, _ = image2.shape
k_h, k_w = kernel.shape
# Exclude the border pixels.
high_freq_interior = high_frequencies[k_h:img_h - k_h, k_w:img_w - k_w]
correct_sum = np.allclose(12.029784, high_freq_interior.sum(), atol=0.06)
# ground truth values
gt_high_freq_crop = np.array(
[[[7.9535842e-03, 2.9861331e-02, 3.0958146e-02],
[-7.6553226e-03, 2.2351682e-02, 2.7430430e-02]],
[[1.5485287e-02, 3.3503681e-02, 3.0706093e-02],
[-6.8724155e-05, 3.3921897e-02, 3.1234175e-02]]],
dtype=np.float32)
# H,W,C order in Numpy
correct_crop = np.allclose(high_frequencies[100:102, 100:102, :],
gt_high_freq_crop,
atol=1e-3)
if correct_sum and correct_crop:
print('Success! High frequencies values are correct.')
return True
else:
print(
'High frequencies values are not correct, please double check your implementation.'
)
return False
def verify_low_freq_sq_kernel_torch_manual(image1, kernel,
low_frequencies) -> bool:
"""
Interactive test to be used in IPython notebook, that will print out
test result, and return value can also be queried for success (true).
Args:
- image1
- kernel
- low_frequencies
Returns:
- Boolean indicating success.
"""
gt_image1 = load_image(f'{ROOT}/data/1a_dog.bmp')
if not np.allclose(image1, gt_image1):
print(
'Please pass in the dog image `1a_dog.bmp` as the `image1` argument.'
)
return False
img_h, img_w, _ = image1.shape
k_h, k_w = kernel.shape
# Exclude the border pixels.
low_freq_interior = low_frequencies[k_h:img_h - k_h, k_w:img_w - k_w]
correct_sum = np.allclose(158332.02, low_freq_interior.sum())
# ground truth values
gt_low_freq_crop = np.array([[[0.53500533, 0.523871, 0.5142517],
[0.5367106, 0.526209, 0.51830757]],
[[0.53472066, 0.5236291, 0.5149963],
[0.5368732, 0.5264317, 0.5193449]]],
dtype=np.float32)
# H,W,C order in Numpy
correct_crop = np.allclose(low_frequencies[100:102, 100:102, :],
gt_low_freq_crop,
atol=1e-3)
if correct_sum and correct_crop:
print('Success! Low frequencies values are correct.')
return True
else:
print(
'Low frequencies values are not correct, please double check your implementation.'
)
return False
def get_dog_img():
"""
"""
dog_img_fpath = f'{ROOT}/data/1a_dog.bmp'
dog_img = load_image(dog_img_fpath)
return dog_img
