def safe_batch_deep_image(input_image, warning=True, debug=True):
'''
return a batch image-like deep numpy no matter what format the input is,
the shape of input should be N3HW or 3HW,
make sure the output numpy image is a copy of the input image
note:
an image-like numpy array is an array with image-like shape, but might contain arbitrary value
parameters:
input_image: image-like numpy array, N3HW or 3HW, float or uint
outputs:
np_image: N3HW numpy image, with the same datatype as the input
isnan: return True if any nan value exists
'''
if debug: assert isnparray(input_image), 'the input image should be a numpy array'
np_image = input_image.copy()
# if np_image.ndim == 2: # expand HW gradscale image to CHW image with one channel
# np_image = np.expand_dims(np_image, axis=0)
if np_image.ndim == 3: # expand CHW to NCHW batch images with batch of 1
if debug: assert np_image.shape[0] == 3, 'the image should be a color image'
np_image = np.expand_dims(np_image, axis=0)
elif np_image.ndim == 4:
if debug: assert np_image.shape[1] == 3, 'the image should be a color image'
else: assert False, 'only color images are supported'
isnan = isnannparray(np_image)
if warning and isnan: print('nan exists in the image data')
return np_image, isnan
def safe_image_like(input_image, warning=True, debug=True):
'''
return an image-like numpy no matter what format the input is, the numpy has the image shape, but value may not be in [0, 1] for float image
make sure the output numpy image is a copy of the input image
note:
an image-like numpy array is an array with image-like shape, but might contain arbitrary value
parameters:
input_image: pil image or image-like array, color or gray, float or uint
outputs:
np_image: numpy image, with the same color and datatype as the input
isnan: return True if any nan value exists
'''
if ispilimage(input_image): np_image = np.array(input_image)
elif isnparray(input_image):
np_image = input_image.copy()
assert isimage_dimension(np_image), 'the input is not an image-like numpy array'
else: assert False, 'only pil and numpy image-like data are supported'
isnan = isnannparray(np_image)
if warning and isnan: print('nan exists in the image data')
return np_image, isnan
def safe_batch_image(input_image, warning=True, debug=True):
'''
return a numpy image no matter what format the input is
make sure the output numpy image is a copy of the input image
parameters:
input_image: a numpy image, NHWC, float or uint
outputs:
np_image: NHWC numpy image, with the same datatype as the input
isnan: return True if any nan value exists
'''
if debug: assert isnparray(input_image), 'the input image should be a numpy array'
np_image = input_image.copy()
if np_image.ndim == 2: np_image = np.expand_dims(np_image, axis=0) # compatible with grayscale image
if np_image.ndim == 3: # expand HWC to NHWC batch images with batch of 1
if debug: assert isnpimage(np_image), 'the image should be a numpy image'
np_image = np.expand_dims(np_image, axis=0)
elif np_image.ndim == 4:
if debug:
assert np_image.shape[-1] == 3 or np_image.shape[-1] == 1, 'the image shape is not good'
for image_index in range(np_image.shape[0]):
assert isnpimage(np_image[image_index]), 'each individual image should be a numpy image'
else: assert False, 'only color images are supported'
isnan = isnannparray(np_image)
if warning and isnan: print('nan exists in the image data')
return np_image, isnan
def safe_image(input_image, warning=True, debug=True):
'''
return a numpy image no matter what format the input is
make sure the output numpy image is a copy of the input image
parameters:
input_image: pil or numpy image, color or gray, float or uint
outputs:
np_image: numpy image, with the same color and datatype as the input
isnan: return True if any nan value exists
'''
if ispilimage(input_image): np_image = np.array(input_image)
elif isnpimage(input_image): np_image = input_image.copy()
else: assert False, 'only pil and numpy images are supported, might be the case the image is float but has range of [0, 255], or might because the data is float64'
isnan = isnannparray(np_image)
if warning and isnan: print('nan exists in the image data')
return np_image, isnan