def imread(path, is_grayscale = False):
if '.npy' in path:
return np.load(path)
elif (is_grayscale):
return _imread(path, flatten=True).astype(np.float)
else:
return _imread(path, mode='RGB').astype(np.float)
def imread(filename, *, normalize=False, flatten=False):
'''\
imread(filename, flatten=False)
Loads an image as a numerical matrix of either 2 or 3 dimensions.
Parameters
----------
filename: string
The path of the image to load
normalize: boolean, optional
If set to true, the return value will be an array with float values between 0 and 1.
If set to false, the reurn value will be an array with uint8 values between 0 and 255.
default value is False.
flatten: boolean, optional
If set to true, the return value is a 2 dimensional ndarray with the grayscale
representation of the loaded image.
Returns
-------
image: ndarray
matrix representation of the loaded image, either 2 or 3 dimensional depending on the
file and the `flatten` parameter.
Examples
--------
(TODO)
'''
img = _imread(filename)
if flatten:
img = img @ [0.299, 0.587, 0.114]
if normalize:
return (img / 255)
return img.astype(_np.uint8)
def imread(filename, *, normalize=False, flatten=False):
img = _imread(filename)
if flatten:
img = img @ [0.299, 0.587, 0.114]
if normalize:
return (img / 255)
return img.astype(_np.uint8)
def imread(filename, flatten=0):
"""Read an image file into a numpy array
using imageio.imread
Parameters
----------
filename : string
the absolute path of the image file
flatten : bool
True if the image is RGB color or False (default) if greyscale
Returns
-------
frame : np.ndarray
a numpy array with grey levels
Examples
--------
>>> image = openpiv.tools.imread( 'image.bmp' )
>>> print image.shape
(1280, 1024)
"""
im = _imread(filename)
if np.ndim(im) > 2:
im = rgb2gray(im)
return im
def imread(path, is_grayscale = False):
if (is_grayscale):
return _imread(path, flatten=True).astype(np.float)
else:
image = cv2.imread(path, cv2.IMREAD_UNCHANGED)
image[..., :3] = cv2.cvtColor(image[..., :3], cv2.COLOR_BGR2RGB)
if image.shape[2] == 4:
image_fill = np.ones((image.shape[0], image.shape[1], 3)) * 255
image_new = image[..., :3] * image[..., 3:] + image_fill * (1 - image[..., 3:])
cv2.imwrite('img.png', cv2.cvtColor(image_new.astype(np.uint8), cv2.COLOR_RGB2BGR))
return image_new
#_imread(path, mode='RGB').astype(np.float)
return image.astype(np.float32)
def find_bg(list_file=None, list_img=None):
"""finds the background for image list or file list, similar to mark_background2 function but
with minor differences in handling the images and image intensities"""
if list_img == None:
list_img = []
for I in range(len(list_file)):
list_img.append(_imread(list_file[I]))
background = np.zeros(list_img[0].shape, dtype=np.int32)
for I in range(background.shape[0]):
for J in range(background.shape[1]):
min_1 = list_img[0][I,J]
for K in range(len(list_img)):
if min_1 > list_img[K][I,J]:
min_1 = list_img[K][I,J]
background[I,J]=min_1
return background
def imread(path, is_grayscale=True):
if (is_grayscale):
return _imread(path, flatten=True).astype(np.float)
else:
return _imread(path, mode='RGB').astype(np.float)
def imread(path, is_grayscale=False):
return _imread(path).astype(np.float)
def __init__(self, specs):
super().__init__()
self.path = specs
mask = _imread(specs).astype(bool)
self.ypos, self.xpos = _np.where(mask)
def imread(path, is_grayscale = False):
if (is_grayscale):
return _imread(path, flatten=True).astype(np.float)
else:
return _imread(path, pilmode="RGB").astype(np.float)
def imread(path, is_grayscale = False):
if (is_grayscale):
return _imread(path, flatten=True).astype(np.float)
else:
return _imread(path, mode='RGB').astype(np.float)
def imread(path, is_grayscale=False):
if is_grayscale:
return _imread(path, as_gray=True).astype(np.float)
else:
return _imread(path, mode='RGB').astype(np.float)