def mean_color(self, img, order: str = 'rgb'):
""" Calculates and returns the mean/average color of an image
Params:
img: (numpy.array, PIL.image, cv2.image)
order: (RGB, BGR) input order of the colors BGR/RGB. Deafult order: RGB
Returns:
Tuple of RGB values of the mean color calculated
"""
image = helpers.format_image_to_PIL(img, order)
return ca.mean_color(image)
def trim_to_content(self, img, order: str = 'rgb'):
""" Trim/Crop an image to its content (removes uniform color spaced padding around the image)
Params:
img: (numpy.array, PIL.image, cv2.image)
order: (RGB, BGR) input order of the colors BGR/RGB. Deafult order: RGB
Returns:
numpy.array of the order specified
"""
image = helpers.format_image_to_PIL(img, order)
return tr.trim(image, order)
def ahash(self, image, hash_size: int = 8, order: str = 'rgb'):
"""
Average Hash computation
Params:
image - must be a PIL instance image or numpy array in RGB or opencv image in BGR
hash_size - (integer) default 8 for 64 bit hash
order - (string) RGB, BGR: input order of the colors BGR/RGB. Deafult order: RGB
Returns:
<ImageHash> object. To get the hash value simply use - str(<ImageHash>)
"""
image = helpers.format_image_to_PIL(image, order)
return ahash(image, hash_size)
def phash(self, image, hash_size=8, highfreq_factor=4, order: str = 'rgb'):
"""
Perceptual Hash computation.
Params:
image - must be a PIL instance image or numpy array in RGB or opencv image in BGR
hash_size - an integer specifying the hash size (hash_size * highfreq_factor should be less than number of rows or columns of the gray_image)
highfreq_factor - an integer specyfing the highfrequency factor
Returns:
<ImageHash> object. To get the hash value simply use - str(<ImageHash>)
"""
image = helpers.format_image_to_PIL(image, order)
return phash(image, hash_size, highfreq_factor)
def image_segmentation(image, rgb_list, order: str = 'rgb'):
""" reconstruct an image with only a specified list of colors
Params:
img: (numpy.array, PIL.image, cv2.image)
rgb_list: colors list - a 2 dimensional np array with shape (n,3) 3 being the channel values in order RGB, eg: [[224, 166, 147], [110, 34, 71], [195, 98, 100]]
order: (RGB, BGR) input order of the colors BGR/RGB. Deafult order: RGB
Note: The output will be a numpy.array of the same order
Returns:
numpy.array of the order specified
"""
image = helpers.format_image_to_PIL(image, order)
# create an array of pixel values from the given image
pixels = np.array(image.getdata(), dtype=np.uint8)
# convert rgb_list to a numpy array
rgb_list = np.array(rgb_list)
rgb_list = np.array([rgb_list]) if rgb_list.ndim == 1 else rgb_list
if not rgb_list.ndim == 2 or not rgb_list.shape[1] == 3:
raise ValueError(
'rgb_list must be a two dimensional array of shape (n, 3)')
# create an array of empty pixel values
new_pixels = [None] * len(image.getdata())
# assign new pixel the color closest to the original image's pixel value
for idx, pixel in enumerate(pixels):
shortest = float('Inf')
for color in rgb_list:
distance = helpers.calculate_distance(color, pixel)
if distance < shortest:
shortest = distance
nearest = color
new_pixels[idx] = nearest
_w, _h = image.size
# reconstruct the image np array with the new pixels
new_pixels = np.asarray(new_pixels)\
.astype('uint8')\
.reshape((_h, _w, 3))
return helpers.format_output_order_input_RGB(new_pixels, order)
def whash(self,
image,
hash_size=8,
image_scale=None,
mode='haar',
remove_max_haar_ll=True,
order: str = 'rgb'):
"""
Wavelet Hash computation.
Params:
image - must be a PIL instance image or numpy array in RGB or opencv image in BGR
hash_size - must be a power of 2 and less than 'image_scale'
image_scale- must be power of 2 and less than image size. By default is equal to max power of 2 for an input image.
mode (see modes in pywt library):
'haar' - Haar wavelets, by default
'db4' - Daubechies wavelets
remove_max_haar_ll - remove the lowest low level (LL) frequency using Haar wavelet.
Returns:
<ImageHash> object. To get the hash value simply use - str(<ImageHash>)
"""
image = helpers.format_image_to_PIL(image, order)
return whash(image, hash_size, image_scale, mode, remove_max_haar_ll)