113 lines
3.5 KiB
Python
113 lines
3.5 KiB
Python
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# -*- coding: utf-8 -*-
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"""
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Created on Mon Jun 4 13:31:41 2018
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@author: Armando
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"""
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import ReadIM
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import matplotlib.pyplot as plt
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import numpy as np
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from skimage import data, color, img_as_ubyte
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from skimage.feature import canny
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from skimage.transform import hough_ellipse
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from skimage.draw import ellipse_perimeter
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from PIL import Image
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# Load picture, convert to grayscale and detect edges
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image_rgb = Image.open("sphx_glr_plot_circular_elliptical_hough_transform_002.png")
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image_gray = image_rgb.convert('L')
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modif_array = np.array(image_gray,dtype = float)
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edges = canny(modif_array, sigma=2.0,
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low_threshold=0.55, high_threshold=0.8)
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# Perform a Hough Transform
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# The accuracy corresponds to the bin size of a major axis.
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# The value is chosen in order to get a single high accumulator.
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# The threshold eliminates low accumulators
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result = hough_ellipse(edges, accuracy=20, threshold=250,
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min_size=100, max_size=120)
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result.sort(order='accumulator')
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# Estimated parameters for the ellipse
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best = list(result[-1])
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yc, xc, a, b = [int(round(x)) for x in best[1:5]]
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orientation = best[5]
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# Draw the ellipse on the original image
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cy, cx = ellipse_perimeter(yc, xc, a, b, orientation)
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image_rgb[cy, cx] = (0, 0, 255)
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# Draw the edge (white) and the resulting ellipse (red)
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edges = color.gray2rgb(img_as_ubyte(edges))
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edges[cy, cx] = (250, 0, 0)
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fig2, (ax1, ax2) = plt.subplots(ncols=2, nrows=1, figsize=(8, 4),
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sharex=True, sharey=True)
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ax1.set_title('Original picture')
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ax1.imshow(image_rgb)
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ax2.set_title('Edge (white) and result (red)')
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ax2.imshow(edges)
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plt.show()
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'''
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vbuff, vatts = ReadIM.extra.get_Buffer_andAttributeList(working_dir+ image_to_process)
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v_array, vbuff = ReadIM.extra.buffer_as_array(vbuff)
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original_array = v_array[0][cut_image_y0:len(v_array[0])-cut_image_yf,cut_image_x0:len(v_array[0][0])-cut_image_xf]
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original_array = np.array(original_array,dtype = float)
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modif_array = np.array(original_array,dtype = float)
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for y in range(len(original_array)-1):
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for x in range(len(original_array[0])-1):
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if original_array[y][x] >= y_max:
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modif_array[y][x] = 12
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elif original_array[y][x] <= y_min:
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modif_array[y][x] = 4
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else:
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modif_array[y][x] = inverse_tanhfit(original_array[y][x])
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modif_array = 207608319.9386*np.power(modif_array,-9.7399)
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modif_array[modif_array<0.1] = 0
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modif_array[modif_array>8.0] = 0
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'''
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#%%
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colormap = plt.get_cmap('Greys')
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interpolation = 'bicubic'
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modif_array = np.array(image_gray,dtype = float)
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minima = modif_array.max()
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maxima = modif_array.max()
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fig = plt.figure()
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a1 = fig.add_subplot(1, 2, 1)
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#Plotting of the image
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im = plt.imshow(image_gray, interpolation=interpolation, cmap=colormap,origin='upper',vmin=minima, vmax=maxima)
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a1.set_title('Test image')
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plt.colorbar(im, orientation='horizontal')
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#norm = colors.LogNorm(vmin=minima, vmax=maxima)
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a2 = fig.add_subplot(1, 2, 2)
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histo = plt.hist(modif_array, bins=10,range = (minima,maxima), fc='k', ec='k' )
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#
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#**np.linspace(np.log10(minima), np.log10(maxima), 10)
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a2.set_title('Compare and contrast')
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'''
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a3 = fig.add_subplot(1, 2, 2)
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im2 = plt.imshow(modif_array, interpolation='bicubic', cmap=plt.get_cmap('viridis'), \
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origin='upper',vmin=0.4, vmax=10)
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a3.set_title('No interpolation')
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plt.colorbar(im2, orientation='horizontal')
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'''
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plt.tight_layout()
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plt.show()
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