def linear_adjustment(a_img: ImageImpl) -> ImageImpl:
"""
Given a matrix image representation apply, if necessary, linear transformation
to bring values in the pixel value range (0, 255).
:param a_img: numpy array of 2 or 3 dimensions.
:return: np.ndarray of same shape with values in range
"""
min_value = a_img.min_value()
max_value = a_img.max_value()
if MAX_PIXEL_VALUE >= max_value and MIN_PIXEL_VALUE <= min_value:
# values are in range
return a_img # pixels should be ints no floats
# if values are out of range, adjust based on current values
if max_value == min_value:
# a transformation should only shift values in this case
slope = 0
else:
slope = (MAX_PIXEL_VALUE - MIN_PIXEL_VALUE) / (max_value - min_value)
if max_value == min_value:
if max_value > MAX_PIXEL_VALUE:
constant = MAX_PIXEL_VALUE
elif min_value < MIN_PIXEL_VALUE:
constant = MIN_PIXEL_VALUE
else:
constant = max_value
else:
# as we want the tranformation to map MIN_PIXEL_VALUE to the min_value found
# we just solve y = mx + b for known x, y and m
constant = MIN_PIXEL_VALUE - slope * min_value
return a_img.mul_scalar(slope).add_scalar(constant)
def negative_img_fun(a_img: ImageImpl) -> ImageImpl:
"""
Given an image matrix representation, invert pixel values.
Following the function:
F: PixelDomain -> PixelDomain/
F(r) = -r + Max_Pixel_value
:param a_img: matrix image representation
:return: transformed matrix
"""
return a_img.mul_scalar(-1).add_scalar(MAX_PIXEL_VALUE)
def susan_detection(a_img: ImageImpl, threshold: int, low_filter: float,
high_filter: float, color: int) -> ImageImpl:
"""
:param a_img:
:param kernel_size:
:param threshold:
:return:
"""
# circ_kernel = circular_kernel(7)
circ_kernel = np.array([
[0, 0, 1, 1, 1, 0, 0],
[0, 1, 1, 1, 1, 1, 0],
[1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1],
[0, 1, 1, 1, 1, 1, 0],
[0, 0, 1, 1, 1, 0, 0],
])
original = ImageImpl(a_img.get_array())
a_img.apply_filter(
circ_kernel,
lambda m: _calculate_c_for_susan(m, circ_kernel, threshold))
# a_img.array = np.uint8(a_img.array > 0.75)
border_img = np.uint8(a_img.array > low_filter)
b_img = border_img - np.uint8(a_img.array > high_filter)
border_img = np.zeros((border_img.shape[0], border_img.shape[1], 3))
# because we only have 3 channels
color = color % 3
border_img[:, :, color] = b_img[:, :, 0]
border_img = ImageImpl(border_img)
result = border_img.mul_scalar(255)
original = original.to_rgb()
# result.add_image(original)
return original.add(result)