如何转换使用彩虹色图的图形，例如图形1，以便使用不同的颜色图（例如感知统一的顺序图）显示相同的数据？
假设生成原始图像的基础数据不可访问，并且图像本身必须仅使用图像中的信息重新着色。
背景信息：彩虹色图往往会产生视觉伪影。看到 z = -1.15 m 附近的青色线了吗？那里似乎有锋利的边缘。但是看看彩条本身！即使是彩条也有边缘。黄色带中还有另一个假边，垂直靠近 R = 1.45 m。水平黄色条纹可能是基础数据中的真实边缘，尽管很难将这种情况与彩虹伪影区分开来。
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【问题讨论】：
标签： python python-2.7 matplotlib
这是迄今为止我最好的解决方案：
import numpy as np
import scipy
import os
import matplotlib
import copy
import matplotlib.pyplot as plt
from matplotlib.pyplot import imread, imsave
def_colorbar_loc = [[909, 22], [953 - 20, 959]]
def_working_loc = [[95, 189], [857, 708]]
def recolor_image(
filename='image.png',
colorbar_loc=def_colorbar_loc,
working_loc=def_working_loc,
colorbar_orientation='auto',
colorbar_direction=-1,
new_cmap='viridis',
normalize_before_compare=False,
max_rgb='auto',
threshold=0.4,
saturation_threshold=0.25,
compare_hue=True,
show_plot=True,
debug=False,
):
"""
This script reads in an image file (like .png), reads the image's color bar (you have to tell it where), interprets
the color map used in the image to convert colors to values, then recolors those values with a new color map and
regenerates the figure. Useful for fixing figures that were made with rainbow color maps.
Parameters
-----------
:param filename: Full path and filename of the image file.
:param colorbar_loc: Location of color bar, which will be used to analyze the image and convert colors into values.
Pixels w/ 0,0 at top left corner: [[left, top], [right, bottom]]
:param working_loc: Location of the area to recolor. You don't have to recolor the whole image.
Pixels w/ 0,0 at top left corner: [[left, top], [right, bottom]], set to [[0, 0], [-1, -1]] to do everything.
:param colorbar_orientation: Set to 'x', 'y', or 'auto' to specify whether color map is horizontal, vertical,
or should be determined based on the dimensions of the colorbar_loc
:param colorbar_direction: Controls direction of ascending value
+1: colorbar goes from top to bottom or left to right.
-1: colorbar goes from bottom to top or right to left.
:param new_cmap: String describing the new color map to use in the recolored image.
:param normalize_before_compare: Divide r, g, and b each by (r+g+b) before comparing.
:param max_rgb: Do the values of r, g, and b range from 0 to 1 or from 0 to 255? Set to 1, 255, or 'auto'.
:param threshold: Sum of absolute differences in r, g, b values must be less than threshold to be valid
(0 = perfect, 3 = impossibly bad). Higher numbers = less chance of missing pixels but more chance of recoloring
plot axes, etc.
:param saturation_threshold: Minimum color saturation below which no replacement will take place
:param compare_hue: Use differences in HSV instead of RGB to determine with which index each pixel should be
associated.
:param show_plot: T/F: Open a plot to explain what is going on. Also helpful for checking your aim on the colorbar
coordinates and debugging.
:param debug: T/F: Print debugging information.
"""
def printd(string_in):
"""
Prints debugging statements
:param string_in: String to print only if debug is on.
:return: None
"""
if debug:
print(string_in)
return
print('Recoloring image: {:} ...'.format(filename))
# Determine tag name and load original file into the tree
fn1 = filename.split(os.sep)[-1] # Filename without path
fn2 = fn1.split(os.extsep)[0] # Filename without extension (so new filename can be built later)
ext = fn1.split(os.extsep)[-1] # File extension
path = os.sep.join(filename.split(os.sep)[0:-1]) # Path; used later to save results.
a = imread(filename).astype(float)
printd(f'Read image; shape = {np.shape(a)}')
if max_rgb == 'auto':
# Determine if values of R, G, and B range from 0 to 1 or from 0 to 255
if a.max() > 1:
max_rgb = 255.0
else:
max_rgb = 1.0
# Normalize a so RGB values go from 0 to 1 and are floats.
a /= max_rgb
# Extract the colorbar
x = np.array([colorbar_loc[0][0], colorbar_loc[1][0]])
y = np.array([colorbar_loc[0][1], colorbar_loc[1][1]])
cb = a[y[0]:y[1], x[0]:x[1]]
# Take just the working area, not the whole image
xw = np.array([working_loc[0][0], working_loc[1][0]])
yw = np.array([working_loc[0][1], working_loc[1][1]])
a1 = a[yw[0]:yw[1], xw[0]:xw[1]]
# Pick color bar orientation
if colorbar_orientation == 'auto':
if np.diff(x) > np.diff(y):
colorbar_orientation = 'x'
else:
colorbar_orientation = 'y'
printd('Auto selected colorbar_orientation')
printd('Colorbar orientation is {:}'.format(colorbar_orientation))
# Analyze the colorbar
if colorbar_orientation == 'y':
cb = np.nanmean(cb, axis=1)
else:
cb = np.nanmean(cb, axis=0)
if colorbar_direction < 0:
cb = cb[::-1]
# Compress colorbar to only count unique colors
# If the array gets too big, it will fill memory and crash python: https://github.com/numpy/numpy/issues/14136
dcb = np.append(1, np.sum(abs(np.diff(cb[:, 0:3], axis=0)), axis=1))
cb = cb[dcb > 0]
# Find and mask of special colors that should not be recolored
n1a = np.sum(a1[:, :, 0:3], axis=2)
replacement_mask = np.ones(np.shape(n1a), bool)
for col in [0, 3]: # Black and white will come out as 0 and 3.
mask_update = n1a != col
if mask_update.max() == 0:
print('Warning: masking to protect special colors prevented all changes to the image!')
else:
printd('Good: Special color mask {:} allowed at least some changes'.format(col))
replacement_mask *= mask_update
if replacement_mask.max() == 0:
print('Warning: replacement mask will prevent all changes to the image! '
'(Reached this point during special color protection)')
printd('Sum(replacement_mask) = {:} (after considering special color {:})'
.format(np.sum(np.atleast_1d(replacement_mask)), col))
# Also apply limits to total r+g+b
replacement_mask *= n1a > 0.75
replacement_mask *= n1a < 2.5
if replacement_mask.max() == 0:
print('Warning: replacement mask will prevent all changes to the image! '
'(Reached this point during total r+g+b+ limits)')
printd('Sum(replacement_mask) = {:} (after considering r+g+b upper threshold)'
.format(np.sum(np.atleast_1d(replacement_mask))))
if saturation_threshold > 0:
hsv1 = matplotlib.colors.rgb_to_hsv(a1[:, :, 0:3])
sat = hsv1[:, :, 1]
printd('Saturation ranges from {:} <= sat <= {:}'.format(sat.min(), sat.max()))
sat_mask = sat > saturation_threshold
if sat_mask.max() == 0:
print('Warning: saturation mask will prevent all changes to the image!')
else:
printd('Good: Saturation mask will allow at least some changes')
replacement_mask *= sat_mask
if replacement_mask.max() == 0:
print('Warning: replacement mask will prevent all changes to the image! '
'(Reached this point during saturation threshold)')
printd(f'shape(a1) = {np.shape(a)}')
printd(f'shape(cb) = {np.shape(cb)}')
# Find where on the colorbar each pixel sits
if compare_hue:
# Difference in hue
hsv1 = matplotlib.colors.rgb_to_hsv(a1[:, :, 0:3])
hsv_cb = matplotlib.colors.rgb_to_hsv(cb[:, 0:3])
d2 = abs(hsv1[:, :, :, np.newaxis] - hsv_cb.T[np.newaxis, np.newaxis, :, :])
# d2 = d2[:, :, 0, :] # Take hue only
d2 = np.sum(d2, axis=2)
printd(' shape(d2) = {:} (hue version)'.format(np.shape(d2)))
else:
# Difference in RGB
if normalize_before_compare:
# Difference of normalized RGB arrays
n1 = n1a[:, :, np.newaxis]
n2 = np.sum(cb[:, 0:3], axis=1)[:, np.newaxis]
w1 = n1 == 0
w2 = n2 == 0
n1[w1] = 1
n2[w2] = 1
d = (a1/n1)[:, :, 0:3, np.newaxis] - (cb/n2).T[np.newaxis, np.newaxis, 0:3, :]
else:
# Difference of non-normalized RGB arrays
d = (a1[:, :, 0:3, np.newaxis] - cb.T[np.newaxis, np.newaxis, 0:3, :])
printd(f'Shape(d) = {np.shape(d)}')
d2 = np.sum(np.abs(d[:, :, 0:3, :]), axis=2) # 0:3 excludes the alpha channel from this calculation
printd('Processed colorbar')
index = d2.argmin(axis=2)
md2 = d2.min(axis=2)
index_valid = md2 < threshold
if index_valid.max() == 0:
print('Warning: minimum difference is greater than threshold: all changes rejected!')
else:
printd('Good: Minimum difference filter is lower than threshold for at least one pixel.')
printd('Sum(index_valid) = {:} (before *= replacement_mask)'.format(np.sum(np.atleast_1d(index_valid))))
printd('Sum(replacement_mask) = {:} (final, before combining w/ index_valid)'
.format(np.sum(np.atleast_1d(replacement_mask))))
index_valid *= replacement_mask
if index_valid.max() == 0:
print('Warning: index_valid mask prevents all changes to the image after combination w/ replacement_mask.')
else:
printd('Good: Mask will allow at least one pixel to change.')
printd('Sum(index_valid) = {:}'.format(np.sum(np.atleast_1d(index_valid))))
value = index/(len(cb)-1.0)
printd('Index ranges from {:} to {:}'.format(index.min(), index.max()))
# Make a new image with replaced colors
b = matplotlib.cm.ScalarMappable(cmap=new_cmap).to_rgba(value) # Remap everything
printd('shape(b) = {:}, min(b) = {:}, max(b) = {:}'.format(np.shape(b), b.min(), b.max()))
c = copy.copy(a1) # Copy original
c[index_valid] = b[index_valid] # Transfer only pixels where color was close to colormap
# Transfer working area to full image
c2 = copy.copy(a) # Copy original full image
c2[yw[0]:yw[1], xw[0]:xw[1], :] = c # Replace working area
c2[:, :, 3] = a[:, :, 3] # Preserve original alpha channel
# Save the image in the same path as the original but with _recolored added to the filename.
new_filename = '{:}{:}{:}_recolored{:}{:}'.format(path, os.sep, fn2, os.extsep, ext)
imsave(new_filename, c2)
print('Done recoloring. Result saved to {:} .'.format(new_filename))
if show_plot:
# Setup figure for showing things to the user
f, axs = plt.subplots(2, 3)
axo = axs[0, 0] # Axes for original figure
axoc = axs[0, 1] # Axes for original color bar
axf = axs[0, 2] # Axes for final figure
axm = axs[1, 1] # Axes for mask
axre = axs[1, 2] # Axes for recolored section only (it might not be the whole figure)
axraw = axs[1, 0] # Axes for raw recoloring result before masking
for ax in axs.flatten():
ax.set_xlabel('x pixel')
ax.set_ylabel('y pixel')
axo.set_title('Original image w/ colorbar ID overlay')
axoc.set_title('Color progression from original colorbar')
axm.set_title('Mask')
axre.set_title('Recolored section')
axraw.set_title('Raw recolor result (no masking)')
axf.set_title('Final image')
axoc.set_xlabel('Index')
axoc.set_ylabel('Value')
# Show the user where they placed the color bar and working location
axo.imshow(a)
xx = x[np.array([0, 0, 1, 1, 0])]
yy = y[np.array([0, 1, 1, 0, 0])]
axo.plot(xx, yy, '+-', label='colorbar')
xxw = xw[np.array([0, 0, 1, 1, 0])]
yyw = yw[np.array([0, 1, 1, 0, 0])]
axo.plot(xxw, yyw, '+-', label='target')
tots = np.sum(cb[:, 0:3], axis=1)
if normalize_before_compare:
# Normalized version
axoc.plot(cb[:, 0] / tots, 'r', label='r/(r+g+b)', lw=2)
axoc.plot(cb[:, 1] / tots, 'g', label='g/(r+g+b)', lw=2)
axoc.plot(cb[:, 2] / tots, 'b', label='b/(r+g+b)', lw=2)
axoc.set_ylabel('Normalized value')
else:
axoc.plot(cb[:, 0], 'r', label='r', lw=2)
axoc.plot(cb[:, 1], 'g', label='g', lw=2)
axoc.plot(cb[:, 2], 'b', label='b', lw=2)
axoc.plot(cb[:, 3], color='gray', linestyle='--', label='$\\alpha$')
axoc.plot(tots, 'k', label='r+g+b')
# Display the new colors with no mask, the mask, and the recolored section
axraw.imshow(b)
axm.imshow(index_valid)
axre.imshow(c)
# Display the final result
axf.imshow(c2)
# Finishing touches on plots
axo.legend(loc=0).set_draggable(True)
axoc.legend(loc=0).set_draggable(True)
plt.show()
return
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