def mark_plot(image, f_poly_power = -1, plot_pts_size=50):
"""
Searches for plot axes and point marks then constructs a poly to interpolate a diagram
:param image: image to operate on
:param f_poly_power: one can specifically set power of interpolation poly
:param plot_pts_size: number of points in drawn plot
:return:
"""
draw = ImageDraw.Draw(image)
width = image.size[0]
height = image.size[1]
pix = image.load()
image_data = convert_image_to_data_buffer(pix, width, height, channels=3)
x_axis_cross, y_axis_cross, crosses_result_list = find_crosses(image_data)
#This step is crucial for further poly interpolation and diagram drawing
crosses_result_list.sort(reverse=True, key=lambda x: x[0])
x_list = [point[0] for point in crosses_result_list]
y_list = [point[1] for point in crosses_result_list]
xp, yp = prepare_poly_interpolated_plot(x_list, y_list, plot_pts_size, f_poly_power)
draw_point_plot(draw, xp, yp, plot_pts_size)
draw.point((x_axis_cross, y_axis_cross), (255, 0, 0))
for pt in crosses_result_list:
draw.point((pt[0], pt[1]), (0, 255, 0))
def apply_apply_gaussian_laplasian_to_image(pix, derivative_kernel, longitude_kernel, width, height, radius = 2, sigma = 1, channels=3, mode="image"):
gaussian_laplasian = get_gaussian_laplasian(radius, sigma, derivative_kernel, normalized=True)
gaussian_laplasian = convolve2d(longitude_kernel, gaussian_laplasian)
if mode == "image":
image_data = convert_image_to_data_buffer(pix, width, height, channels, orientation="user-wise")
result_data = convolve2d(image_data, gaussian_laplasian, mode='same')
elif mode == "buffer":
result_data = convolve2d(pix, gaussian_laplasian, mode='same')
else:
raise ValueError("ERROR: wrong type of image: {} (only 'image' and 'buffer' are possible))".format(mode))
return result_data
