def image_clahe(input_image,
clip_limit=2.0,
grid_size=8,
warning=True,
debug=True):
'''
do contrast limited adative histogram equalization for an image: could be a color image or gray image
the color space used for histogram equalization is LAB
parameters:
input_image: a pil or numpy image
outputs:
clahe_image: an uint8 numpy image (rgb or gray)
'''
np_image, _ = safe_image(input_image, warning=warning, debug=debug)
if isfloatimage(np_image):
np_image = (np_image.astype('float32') * 255.).astype('uint8')
if debug:
assert isuintimage(np_image), 'the input image should be a uint8 image'
clahe = cv2.createCLAHE(clipLimit=clip_limit,
tileGridSize=(grid_size, grid_size))
if iscolorimage_dimension(np_image):
lab_image = rgb2lab(np_image, warning=warning, debug=debug)
input_data = lab_image[:, :, 0] # extract the value channel
clahe_lab_image = clahe.apply(input_data)
lab_image[:, :, 0] = clahe_lab_image
clahe_image = lab2rgb(lab_image, warning=warning, debug=debug)
elif isgrayimage_dimension(np_image):
clahe_image = clahe.apply(np_image)
else:
assert False, 'the input image is neither a color image or a grayscale image'
return clahe_image
def convolve(self, input_image):
'''
convolve the kernel with the input image, whatever the input image format is. If the input image
is a color image, the filter is expanded to a 3D shape
parameters:
input_image: an pil or numpy, gray or color image
outputs:
filtered_image: a float32 numpy image, shape is same as before
'''
np_image, _ = safe_image(input_image,
warning=self.warning,
debug=self.debug)
if isuintimage(np_image): np_image = np_image.astype('float32') / 255.
if self.debug:
assert isfloatimage(
np_image), 'the input image should be a float image'
self.weights is not None, 'the kernel is not defined yet'
if iscolorimage_dimension(np_image):
self.weights = self.__expand_3d(
) # expand the filter to 3D for color image
elif isgrayimage_dimension(np_image):
np_image = np_image.reshape(
np_image.shape[0],
np_image.shape[1]) # squeeze the image dimension to 2
else:
assert False, 'the dimension of the image is not correct'
filtered_image = ndimage.filters.convolve(np_image, self.weights)
return filtered_image
def image_hist_equalization_hsv(input_image, warning=True, debug=True):
'''
do histogram equalization for an image: could be a color image or gray image
the color space used for histogram equalization is HSV
parameters:
input_image: a pil or numpy image
outputs:
equalized_image: an uint8 numpy image (rgb or gray)
'''
np_image, _ = safe_image(input_image, warning=warning, debug=debug)
if isuintimage(np_image): np_image = np_image.astype('float32') / 255.
if debug:
assert isfloatimage(
np_image), 'the input image should be a float image'
if iscolorimage_dimension(np_image):
hsv_image = rgb2hsv(np_image, warning=warning, debug=debug)
input_data = hsv_image[:, :, 2] # extract the value channel
equalized_hsv_image = (
hist_equalization(input_data, num_bins=256, debug=debug) *
255.).astype('uint8')
hsv_image[:, :, 2] = equalized_hsv_image
equalized_image = hsv2rgb(hsv_image, warning=warning, debug=debug)
elif isgrayimage_dimension(np_image):
equalized_image = (
hist_equalization(np_image, num_bins=256, debug=debug) *
255.).astype('uint8')
else:
assert False, 'the input image is neither a color image or a grayscale image'
return equalized_image
def image_rgb2bgr(input_image, warning=True, debug=True):
'''
this function converts a rgb image to a bgr image
parameters:
input_image: a pil or numpy rgb image
outputs:
np_image: a numpy bgr image
'''
np_image, _ = safe_image(input_image, warning=warning, debug=debug)
if debug:
assert iscolorimage_dimension(
np_image), 'the input image is not a color image'
np_image = np_image[:, :, ::-1] # convert RGB to BGR
return np_image
def rgb2hsv(input_image, warning=True, debug=True):
'''
convert a rgb image to a hsv image using opencv package
parameters:
input_image: an pil or numpy image
output:
hsv_image: an uint8 hsv numpy image
'''
np_image, _ = safe_image(input_image, warning=warning, debug=debug)
if isfloatimage(np_image): np_image = (np_image * 255.).astype('uint8')
if debug:
assert iscolorimage_dimension(
np_image), 'the input image should be a rgb image'
assert isuintimage(
np_image
), 'the input numpy image should be uint8 image in order to use opencv'
hsv_img = cv2.cvtColor(np_image, cv2.COLOR_RGB2HSV)
return hsv_img
def rgb2gray(input_image, warning=True, debug=True):
'''
convert a color image to a grayscale image (1-channel)
parameters:
input_image: an pil or numpy image
output:
gray_image: an uint8 HW gray numpy image
'''
np_image, _ = safe_image(input_image, warning=warning, debug=debug)
if isfloatimage(np_image): np_image = (np_image * 255.).astype('uint8')
if debug:
assert iscolorimage_dimension(
np_image), 'the input numpy image is not correct: {}'.format(
np_image.shape)
assert isuintimage(
np_image
), 'the input numpy image should be uint8 image in order to use opencv'
gray_image = cv2.cvtColor(np_image, cv2.COLOR_RGB2GRAY)
return gray_image
def rgb2hsv_v2(input_image, warning=True, debug=True):
'''
convert a rgb image to a hsv image, using PIL package, not compatible with opencv package
parameters:
input_image: an pil or numpy image
output:
hsv_image: an uint8 hsv numpy image
'''
np_image, _ = safe_image(input_image, warning=warning, debug=debug)
if isfloatimage(np_image): np_image = (np_image * 255.).astype('uint8')
if debug:
assert iscolorimage_dimension(
np_image), 'the input image should be a rgb image'
assert isuintimage(
np_image
), 'the input numpy image should be uint8 image in order to use PIL'
pil_rgb_img = Image.fromarray(np_image)
pil_hsv_img = pil_rgb_img.convert('HSV')
hsv_img = np.array(pil_hsv_img)
return hsv_img
