def test_preprocess_image_accepts_array_input():
inp_x = Image.open(PATH_SINGLE_IMAGE)
inp_x = np.array(inp_x)
target_size = (2, 2)
ret_array = preprocess_image(inp_x, target_size=target_size, grayscale=True)
assert isinstance(ret_array, np.ndarray)
assert ret_array.shape == target_size
def encode_image(
self,
image_file: Optional[Union[PurePath, str]] = None,
image_array: Optional[np.ndarray] = None,
) -> np.ndarray:
"""
Generate CNN encoding for a single image.
Args:
image_file: Path to the image file.
image_array: Optional, used instead of image_file. Image typecast to numpy array.
Returns:
encoding: Encodings for the image in the form of numpy array.
Example:
```
from imagededup.methods import CNN
myencoder = CNN()
encoding = myencoder.encode_image(image_file='path/to/image.jpg')
OR
encoding = myencoder.encode_image(image_array=<numpy array of image>)
```
"""
if isinstance(image_file, str):
image_file = Path(image_file)
if isinstance(image_file, PurePath):
if not image_file.is_file():
raise ValueError(
'Please provide either image file path or image array!'
)
image_pp = load_image(
image_file=image_file, target_size=self.target_size, grayscale=False
)
elif isinstance(image_array, np.ndarray):
image_array = expand_image_array_cnn(
image_array
) # Add 3rd dimension if array is grayscale, do sanity checks
image_pp = preprocess_image(
image=image_array, target_size=self.target_size, grayscale=False
)
else:
raise ValueError('Please provide either image file path or image array!')
return (
self._get_cnn_features_single(image_pp)
if isinstance(image_pp, np.ndarray)
else None
)
def encode_image(self,
image_file=None,
image_array: Optional[np.ndarray] = None) -> str:
"""
Generate hash for a single image.
Args:
image_file: Path to the image file.
image_array: Optional, used instead of image_file. Image typecast to numpy array.
Returns:
hash: A 16 character hexadecimal string hash for the image.
Example:
```
from imagededup.methods import <hash-method>
myencoder = <hash-method>()
myhash = myencoder.encode_image(image_file='path/to/image.jpg')
OR
myhash = myencoder.encode_image(image_array=<numpy array of image>)
```
"""
try:
if image_file and os.path.exists(image_file):
image_file = Path(image_file)
image_pp = load_image(image_file=image_file,
target_size=self.target_size,
grayscale=True)
elif isinstance(image_array, np.ndarray):
check_image_array_hash(
image_array) # Do sanity checks on array
image_pp = preprocess_image(image=image_array,
target_size=self.target_size,
grayscale=True)
else:
raise ValueError
except (ValueError, TypeError):
raise ValueError(
'Please provide either image file path or image array!')
return self._hash_func(image_pp) if isinstance(image_pp,
np.ndarray) else None
def test_preprocess_image_grayscale_false():
inp_x = Image.open(PATH_SINGLE_IMAGE)
target_size = (2, 2)
ret_array = preprocess_image(inp_x, target_size=target_size, grayscale=False)
assert isinstance(ret_array, np.ndarray)
assert ret_array.shape == target_size + (3,) # 3 for RGB
def test_preprocess_image_wrong_input():
inp = 'test_string'
with pytest.raises(ValueError):
preprocess_image(inp, target_size=(2, 2))
def get_image_array(pil_img):
return preprocess_image(pil_img, target_size=(32, 32), grayscale=False)
def get_image_phash(pil_img):
array = preprocess_image(pil_img, target_size=TARGET_SIZE, grayscale=False)
hash_mat = _hash_algo(array)
hsh = _array_to_hash(hash_mat)
return hsh
