任务描述： 对栅格影像进行可视化并保存图片
数据准备： 文件夹(工作空间)内含有需要批量可视化的tif影像
工具准备： Python环境
操作： 替换代码中的路径、根据需要调整可视化图片格式
# -*- coding: utf-8 -*-
import os
import numpy as np
from osgeo import gdal
import earthpy_plot_revised as ep
# 文件目录
root_path = r"D:\DATA\04_T"
out_path = r"D:\DATA\05_Reclassify"
# 输入路径
for filepath, dirnames, filenames in os.walk(root_path):
for filename in filenames:
if filename.endswith(".tif"):
tif_file_path = os.path.join(filepath, filename)
print(tif_file_path)
# 输出路径
output_file_path_im = os.path.join(out_path,
f"{filename[0:4]}_T.jpg")
plot_title = f"{filename[0:4]}_LST"
# 打开图像，并读取为数组
t_raster = gdal.Open(tif_file_path)
raster_arr = t_raster.ReadAsArray()
# 将图像的背景值设置为nan
raster_arr[raster_arr < -16] = 'nan'
# 忽略nan值求最大和最小值 nanmin nanmax
min_DN = np.nanmin(raster_arr)
max_DN = np.nanmax(raster_arr)
print("min_DN:", min_DN, "\n", "max_DN:", max_DN)
# 栅格数据可视化
ep.plot_bands(raster_arr,
title=plot_title,
title_set=[25, 'bold'],
cmap="seismic",
cols=3,
figsize=(12, 12),
extent=None,
cbar=True,
scale=False,
vmin=-16,
vmax=45,
ax=None,
alpha=1,
norm=None,
save_or_not=True,
save_path=output_file_path_im,
dpi_out=300,
bbox_inches_out="tight",
pad_inches_out=0.1,
text_or_not=True,
text_set=[0.75, 0.95, "T(°C)", 20, 'bold'],
colorbar_label_set=True,
label_size=20,
cbar_len=2,
)
# 蓝到红的渐变 三种 seismic bwr coolwarm
# rainbow jet RdBu_r RdYIBu_r
# x_pos, y_pos 距离左上角的距离; text_content
# text_set=[0.98,0.98,"°C",15]
# x, y, text_content, fontsize=15, fontweight='normal'
注意： 需要将以下代码保存到earthpy_plot_revised.py文件中，并与上述.py文件放置在同个文件夹路径下。
# -*- coding: utf-8 -*-
"""
earthpy.plot
============
Functionality around spatial plotting.
"""
import numpy as np
import numpy.ma as ma
import matplotlib.pyplot as plt
from matplotlib import patches as mpatches
from matplotlib.colors import ListedColormap
from mpl_toolkits.axes_grid1 import make_axes_locatable
from skimage import exposure
import earthpy.spatial as es
def colorbar(mapobj, size="3%", pad=0.09):
"""Adjust colorbar height to match the matplotlib axis height.
NOTE: This function requires matplotlib v 3.0.1 or greater or v 2.9 or
lower to run properly.
Parameters
----------
mapobj : matplotlib axis object
The image that the colorbar will be representing as a matplotlib axis
object.
size : char (default = "3%")
The percent width of the colorbar relative to the plot.
pad : int (default = 0.09)
The space between the plot and the color bar.
Returns
-------
matplotlib.pyplot.colorbar
Matplotlib color bar object with the correct width that matches the
y-axis height.
Examples
--------
.. plot::
>>> import matplotlib.pyplot as plt
>>> import rasterio as rio
>>> import earthpy.plot as ep
>>> from earthpy.io import path_to_example
>>> with rio.open(path_to_example('rmnp-dem.tif')) as src:
... dem = src.read()
... fig, ax = plt.subplots(figsize = (10, 5))
>>> im = ax.imshow(dem.squeeze())
>>> ep.colorbar(im)
<matplotlib.colorbar.Colorbar object at 0x...>
>>> ax.set(title="Rocky Mountain National Park DEM")
[Text(...'Rocky Mountain National Park DEM')]
>>> ax.set_axis_off()
>>> plt.show()
"""
try:
ax = mapobj.axes
except AttributeError:
raise AttributeError(
"The colorbar function requires a matplotlib axis object. "
"You have provided a {}.".format(type(mapobj))
)
fig = ax.figure
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size=size, pad=pad)
return fig.colorbar(mapobj, cax=cax)
def _plot_image(
arr_im,
cmap="Greys_r",
title=None,
title_set=None,
extent=None,
cbar=True,
scale=False,
vmin=None,
vmax=None,
ax=None,
alpha=1,
norm=None,
colorbar_label_set=None,
label_size=None,
cbar_len=None,
):
"""
Create a matplotlib figure with an image axis and associated extent.
Parameters
----------
arr_im : numpy array
An n-dimensional numpy array to plot.
cmap : str (default = "Greys_r")
Colormap name for plots.
title : str or list (optional)
Title of one band or list of titles with one title per band.
extent : tuple (optional)
Bounding box that the data will fill: (minx, miny, maxx, maxy).
cbar : Boolean (default = True)
Turn off colorbar if needed.
scale : Boolean (Default = False)
Turn off bytescale scaling if needed.
vmin : Int (Optional)
Specify the vmin to scale imshow() plots.
vmax : Int (Optional)
Specify the vmax to scale imshow() plots.
ax : Matplotlib axes object (Optional)
Matplotlib axis object to plot image.
alpha : float (optional)
The alpha value for the plot. This will help adjust the transparency of
the plot to the desired level.
norm : matplotlib Normalize object (Optional)
The normalized boundaries for custom values coloring. NOTE: For this
argument to work, the scale argument MUST be set to false. Otherwise,
the values will be scaled from 0-255.
Returns
----------
ax : matplotlib.axes object
The axes object(s) associated with the plot.
"""
if scale:
arr_im = es.bytescale(arr_im)
im = ax.imshow(
arr_im,
cmap=cmap,
vmin=vmin,
vmax=vmax,
extent=extent,
alpha=alpha,
norm=norm,
)
if title:
if title_set:
ax.set_title(label=title, fontsize=title_set[0], fontweight=title_set[1])
else:
ax.set(title=title)
if cbar:
## 设置色带的刻度长度和label大小
if colorbar_label_set:
cbar = colorbar(im)
cbar.ax.tick_params(length=cbar_len, labelsize=label_size)
else:
colorbar(im)
ax.set(xticks=[], yticks=[])
return ax
def plot_bands(
arr,
cmap="Greys_r",
figsize=(12, 12),
cols=3,
title=None,
title_set=None,
extent=None,
cbar=True,
scale=False,
vmin=None,
vmax=None,
ax=None,
alpha=1,
norm=None,
## add 5 variables related to saving image
save_or_not=False,
save_path=None,
dpi_out=None,
bbox_inches_out=None,
pad_inches_out=None,
text_or_not=None,
text_set=None,
colorbar_label_set=None,
label_size=None,
cbar_len=None,
):
"""Plot each band in a numpy array in its own axis.
Assumes band order (band, row, col).
Parameters
----------
arr : numpy array
An n-dimensional numpy array to plot.
cmap : str (default = "Greys_r")
Colormap name for plots.
figsize : tuple (default = (12, 12))
Figure size in inches.
cols : int (default = 3)
Number of columns for plot grid.
title : str or list (optional)
Title of one band or list of titles with one title per band.
extent : tuple (optional)
Bounding box that the data will fill: (minx, miny, maxx, maxy).
cbar : Boolean (default = True)
Turn off colorbar if needed.
scale : Boolean (Default = False)
Turn off bytescale scaling if needed.
vmin : Int (Optional)
Specify the vmin to scale imshow() plots.
vmax : Int (Optional)
Specify the vmax to scale imshow() plots.
alpha : float (optional)
The alpha value for the plot. This will help adjust the transparency
of the plot to the desired level.
norm : matplotlib Normalize object (Optional)
The normalized boundaries for custom values coloring. NOTE: For this
argument to work, the scale argument MUST be set to false. Because
of this, the function will automatically set scale to false,
even if the user manually sets scale to true.
Returns
----------
ax or axs : matplotlib.axes._subplots.AxesSubplot object(s)
The axes object(s) associated with the plot.
Example
-------
.. plot::
>>> import matplotlib.pyplot as plt
>>> import earthpy.plot as ep
>>> from earthpy.io import path_to_example
>>> import rasterio as rio
>>> titles = ['Red', 'Green', 'Blue']
>>> with rio.open(path_to_example('rmnp-rgb.tif')) as src:
... ep.plot_bands(src.read(),
... title=titles,
... figsize=(8, 3))
array([<AxesSubplot:title={'center':'Red'}>...
"""
show = False
try:
arr.ndim
except AttributeError:
raise AttributeError("Input arr should be a numpy array")
if norm:
scale = False
if title:
if isinstance(title, str):
title = [title]
# A 2-dim array should only be passed one title
if arr.ndim == 2 and len(title) > 1:
raise ValueError(
"plot_bands expects one title for a single "
"band array. You have provided more than one title."
)
# A 3 dim array should have the same number of titles as dims
if arr.ndim > 2:
if len(title) != arr.shape[0]:
raise ValueError(
"plot_bands expects the number of plot titles "
"to equal the number of array raster layers."
)
# If the array is 3 dimensional setup grid plotting
if arr.ndim > 2 and arr.shape[0] > 1:
# Calculate the total rows that will be required to plot each band
plot_rows = int(np.ceil(arr.shape[0] / cols))
total_layers = arr.shape[0]
# Plot all bands
fig, axs = plt.subplots(plot_rows, cols, figsize=figsize)
axs_ravel = axs.ravel()
for ax, i in zip(axs_ravel, range(total_layers)):
band = i + 1
arr_im = arr[i]
if title:
the_title = title[i]
else:
the_title = "Band {}".format(band)
_plot_image(
arr_im,
cmap=cmap,
cbar=cbar,
scale=scale,
vmin=vmin,
vmax=vmax,
extent=extent,
title=the_title,
title_set=title_set,
ax=ax,
alpha=alpha,
norm=norm,
colorbar_label_set=colorbar_label_set,
label_size=label_size,
cbar_len=cbar_len,
)
# This loop clears out the plots for axes which are empty
# A matplotlib axis grid is always uniform with x cols and x rows
# eg: an 8 band plot with 3 cols will always be 3 x 3
for ax in axs_ravel[total_layers:]:
ax.set_axis_off()
ax.set(xticks=[], yticks=[])
plt.tight_layout()
if text_or_not:
plt.text(text_set[0], text_set[1], text_set[2], ha='center', va='center', fontsize=text_set[3],
fontweight='bold', transform=ax.transAxes)
# save figure
if save_or_not:
plt.savefig(save_path, dpi=dpi_out, bbox_inches=bbox_inches_out, pad_inches=pad_inches_out)
plt.show()
return axs
elif arr.ndim == 2 or arr.shape[0] == 1:
# If it's a 2 dimensional array with a 3rd dimension
arr = np.squeeze(arr)
if ax is None:
fig, ax = plt.subplots(figsize=figsize)
show = True
if title:
title = title[0]
_plot_image(
arr,
cmap=cmap,
scale=scale,
cbar=cbar,
vmin=vmin,
vmax=vmax,
extent=extent,
title=title,
title_set=title_set,
ax=ax,
alpha=alpha,
norm=norm,
colorbar_label_set=colorbar_label_set,
label_size=label_size,
cbar_len=cbar_len,
)
if text_or_not:
plt.text(text_set[0], text_set[1], text_set[2], ha='center', va='center', fontsize=text_set[3],
fontweight=text_set[4], transform=ax.transAxes)
if show:
# save figure
if save_or_not:
plt.savefig(save_path, dpi=dpi_out, bbox_inches=bbox_inches_out, pad_inches=pad_inches_out)
plt.show()
return ax
def _stretch_im(arr, str_clip):
"""Stretch an image in numpy ndarray format using a specified clip value.
Parameters
----------
arr: numpy array
N-dimensional array in rasterio band order (bands, rows, columns)
str_clip: int
The % of clip to apply to the stretch. Default = 2 (2 and 98)
Returns
----------
arr: numpy array with values stretched to the specified clip %
"""
s_min = str_clip
s_max = 100 - str_clip
arr_rescaled = np.zeros_like(arr)
for ii, band in enumerate(arr):
lower, upper = np.nanpercentile(band, (s_min, s_max))
arr_rescaled[ii] = exposure.rescale_intensity(
band, in_range=(lower, upper)
)
return arr_rescaled.copy()
def plot_rgb(
arr,
rgb=(0, 1, 2),
figsize=(10, 10),
str_clip=2,
ax=None,
extent=None,
title="",
stretch=None,
):
"""Plot three bands in a numpy array as a composite RGB image.
Parameters
----------
arr : numpy array
An n-dimensional array in rasterio band order (bands, rows, columns)
containing the layers to plot.
rgb : list (default = (0, 1, 2))
Indices of the three bands to be plotted.
figsize : tuple (default = (10, 10)
The x and y integer dimensions of the output plot.
str_clip: int (default = 2)
The percentage of clip to apply to the stretch. Default = 2 (2 and 98).
ax : object (optional)
The axes object where the ax element should be plotted.
extent : tuple (optional)
The extent object that matplotlib expects (left, right, bottom, top).
title : string (optional)
The intended title of the plot.
stretch : Boolean (optional)
Application of a linear stretch. If set to True, a linear stretch will
be applied.
Returns
----------
ax : axes object
The axes object associated with the 3 band image.
Example
-------
.. plot::
>>> import matplotlib.pyplot as plt
>>> import rasterio as rio
>>> import earthpy.plot as ep
>>> from earthpy.io import path_to_example
>>> with rio.open(path_to_example('rmnp-rgb.tif')) as src:
... img_array = src.read()
>>> # Ensure the input array doesn't have nodata values like -9999
>>> ep.plot_rgb(img_array)
<AxesSubplot:>
"""
if len(arr.shape) != 3:
raise ValueError(
"Input needs to be 3 dimensions and in rasterio "
"order with bands first"
)
# Index bands for plotting and clean up data for matplotlib
rgb_bands = arr[rgb, :, :]
if stretch:
rgb_bands = _stretch_im(rgb_bands, str_clip)
nan_check = np.isnan(rgb_bands)
if np.any(nan_check):
rgb_bands = np.ma.masked_array(rgb_bands, nan_check)
# If type is masked array - add alpha channel for plotting
if ma.is_masked(rgb_bands):
# Build alpha channel
mask = ~(np.ma.getmask(rgb_bands[0])) * 255
# Add the mask to the array & swap the axes order from (bands,
# rows, columns) to (rows, columns, bands) for plotting
rgb_bands = np.vstack(
(es.bytescale(rgb_bands), np.expand_dims(mask, axis=0))
).transpose([1, 2, 0])
else:
# Index bands for plotting and clean up data for matplotlib
rgb_bands = es.bytescale(rgb_bands).transpose([1, 2, 0])
# Then plot. Define ax if it's undefined
show = False
if ax is None:
fig, ax = plt.subplots(figsize=figsize)
show = True
ax.imshow(rgb_bands, extent=extent)
ax.set_title(title)
ax.set(xticks=[], yticks=[])
# Multipanel won't work if plt.show is called prior to second plot def
if show:
plt.show()
return ax
def hist(
arr,
colors=["purple"],
figsize=(12, 12),
cols=2,
bins=20,
title=None,
xlabel="",
ylabel="",
hist_range=None,
alpha=1,
):
"""Plot histogram for each layer in a numpy array.
Parameters
----------
arr : numpy array
An n-dimensional numpy array from which n histograms will be plotted.
colors : list (default = ["purple"])
A list of color values that should either equal the number of bands
or be a single color.
figsize : tuple (default = (12, 12))
The x and y integer dimensions of the output plot.
cols : int (default = 2)
The number of columns for plot grid.
bins : int or list (default = 20)
The number of bins to generate for the histogram or a list of break
points for each bin following matplotlib ax.hist documentation.
title : str or list (optional)
A list of title values that should either equal the number of bands
or be empty. A string is accepted for a single dimension array.
xlabel : str (optional)
The text to print on the x axis.
ylabel : str (optional)
The text to print on the y axis.
hist_range : tuple (optional)
The lower and upper range of the bins. Lower and upper outliers are
ignored. If not provided, range is (x.min(), x.max()).
Range has no effect if bins is a sequence.
alpha : float (optional)
The alpha value for the plot. This will help adjust the transparency
of the plot to the desired level.
Returns
----------
tuple
fig : figure object
The figure object associated with the histogram.
ax or axs : ax or axes object
The axes object(s) associated with the histogram.
Example
-------
.. plot::
>>> import matplotlib.pyplot as plt
>>> import rasterio as rio
>>> import earthpy.plot as ep
>>> from earthpy.io import path_to_example
>>> with rio.open(path_to_example('rmnp-rgb.tif')) as src:
... img_array = src.read()
>>> ep.hist(img_array,
... colors=['r', 'g', 'b'],
... title=['Red', 'Green', 'Blue'],
... cols=3,
... figsize=(8, 3))
(<Figure size 800x300 with 3 Axes>, ...)
"""
if title:
if isinstance(title, str):
title = [title]
if colors:
if isinstance(colors, str):
colors = [colors]
if not hist_range:
hist_range = (np.nanmin(arr), np.nanmax(arr))
# If the arr has a single extra dim, flatten it
if arr.shape[0] == 1:
arr = arr.squeeze()
# If the array is 3 dimensional setup grid plotting
if arr.ndim > 2:
# Compress the arr if it's masked
n_layers = arr.shape[0]
if title and not len(title) == n_layers:
raise ValueError(
"The number of plot titles should be the same "
"as the number of raster layers in your array."
)
# Calculate the total rows that will be required to plot each band
plot_rows = int(np.ceil(arr.shape[0] / cols))
if np.ma.is_masked(arr):
arrlis = []
for i in range(arr.shape[0]):
# Use compressed to flatten masked arr
arrlis.append(arr[i].compressed())
arr = arrlis
fig, axs = plt.subplots(
plot_rows, cols, figsize=figsize, sharex=True, sharey=True
)
axs_ravel = axs.ravel()
for band, ax, i in zip(arr, axs.ravel(), range(n_layers)):
if len(colors) == 1:
the_color = colors[0]
else:
the_color = colors[i]
ax.hist(
band.ravel(),
bins=bins,
color=the_color,
alpha=alpha,
range=hist_range,
)
if title:
ax.set_title(title[i])
if xlabel:
ax.set(xlabel=xlabel)
if ylabel:
ax.set(ylabel=ylabel)
# Clear additional axis elements
for ax in axs_ravel[n_layers:]:
ax.set_axis_off()
return fig, axs
elif arr.ndim <= 2:
# Test that only one title is provided for a 2-dim array
if title:
if len(title) > 1:
raise ValueError(
"You have one array to plot but more than one title. "
"Please provide a single title value."
)
# Plot all bands
if np.ma.is_masked(arr):
arr_comp = arr.compressed()
else:
arr_comp = arr.ravel()
if not hist_range:
hist_range = (np.nanmin(arr_comp), np.nanmax(arr_comp))
fig, ax = plt.subplots(figsize=figsize)
ax.hist(
arr_comp,
range=hist_range,
bins=bins,
color=colors[0],
alpha=alpha,
)
if title:
ax.set(title=title[0])
if xlabel:
ax.set(xlabel=xlabel)
if ylabel:
ax.set(ylabel=ylabel)
return fig, ax
def make_col_list(unique_vals, nclasses=None, cmap=None):
"""
Convert a matplotlib named colormap into a discrete list of n-colors in
RGB format.
Parameters
----------
unique_vals : list
A list of values to make a color list from.
nclasses : int (optional)
The number of classes.
cmap : str (optional)
Colormap name used to create output list.
Returns
-------
list
A list of colors based on the given set of values in matplotlib
format.
Example
-------
>>> import numpy as np
>>> import earthpy.plot as ep
>>> import matplotlib.pyplot as plt
>>> arr = np.array([[1, 2], [3, 4], [5, 4], [5, 5]])
>>> f, ax = plt.subplots()
>>> im = ax.imshow(arr, cmap="Blues")
>>> the_legend = ep.draw_legend(im_ax=im)
>>> # Get the array and cmap from axis object
>>> cmap_name = im.axes.get_images()[0].get_cmap().name
>>> unique_vals = list(np.unique(im.get_array().data))
>>> cmap_colors = ep.make_col_list(unique_vals, cmap=cmap_name)
"""
if not nclasses:
nclasses = len(unique_vals)
increment = 1 / (nclasses - 1)
# Create increments to grab colormap colors
col_index = [(increment * c) for c in range(nclasses - 1)]
col_index.append(1.0)
# Create cmap list of colors
cm = plt.cm.get_cmap(cmap)
return [cm(c) for c in col_index]
def draw_legend(im_ax, bbox=(1.05, 1), titles=None, cmap=None, classes=None):
"""Create a custom legend with a box for each class in a raster.
Parameters
----------
im_ax : matplotlib image object
This is the image returned from a call to imshow().
bbox : tuple (default = (1.05, 1))
This is the bbox_to_anchor argument that will place the legend
anywhere on or around your plot.
titles : list (optional)
A list of a title or category for each unique value in your raster.
This is the label that will go next to each box in your legend. If
nothing is provided, a generic "Category x" will be populated.
cmap : str (optional)
Colormap name to be used for legend items.
classes : list (optional)
A list of unique values found in the numpy array that you wish to plot.
Returns
----------
matplotlib.pyplot.legend
A matplotlib legend object to be placed on the plot.
Example
-------
.. plot::
>>> import numpy as np
>>> import earthpy.plot as ep
>>> import matplotlib.pyplot as plt
>>> im_arr = np.random.uniform(-2, 1, (15, 15))
>>> bins = [-np.Inf, -0.8, 0.8, np.Inf]
>>> im_arr_bin = np.digitize(im_arr, bins)
>>> cat_names = ["Class 1", "Class 2", "Class 3"]
>>> f, ax = plt.subplots()
>>> im = ax.imshow(im_arr_bin, cmap="gnuplot")
>>> im_ax = ax.imshow(im_arr_bin)
>>> leg_neg = ep.draw_legend(im_ax = im_ax, titles = cat_names)
>>> plt.show()
"""
try:
im_ax.axes
except AttributeError:
raise AttributeError(
"The legend function requires a matplotlib axis object to "
"run properly. You have provided a {}.".format(type(im_ax))
)
# If classes not provided, get them from the im array in the ax object
# Else use provided vals
if classes is not None:
# Get the colormap from the mpl object
cmap = im_ax.cmap.name
# If the colormap is manually generated from a list
if cmap == "from_list":
cmap = ListedColormap(im_ax.cmap.colors)
colors = make_col_list(
nclasses=len(classes), unique_vals=classes, cmap=cmap
)
# If there are more colors than classes, raise value error
if len(set(colors)) < len(classes):
raise ValueError(
"There are more classes than colors in your cmap. "
"Please provide a ListedColormap with the same number "
"of colors as classes."
)
else:
classes = list(np.unique(im_ax.axes.get_images()[0].get_array()))
# Remove masked values, could next this list comp but keeping it simple
classes = [
aclass for aclass in classes if aclass is not np.ma.core.masked
]
colors = [im_ax.cmap(im_ax.norm(aclass)) for aclass in classes]
# If titles are not provided, create filler titles
if not titles:
titles = ["Category {}".format(i + 1) for i in range(len(classes))]
if not len(classes) == len(titles):
raise ValueError(
"The number of classes should equal the number of "
"titles. You have provided {0} classes and {1} titles.".format(
len(classes), len(titles)
)
)
patches = [
mpatches.Patch(color=colors[i], label="{lab}".format(lab=titles[i]))
for i in range(len(titles))
]
# Get the axis for the legend
ax = im_ax.axes
return ax.legend(
handles=patches,
bbox_to_anchor=bbox,
loc=2,
borderaxespad=0.0,
prop={
"size": 13},
)
earthpy.plot.plot_bands源码： https://earthpy.readthedocs.io/en/latest/_modules/earthpy/plot.html#plot_bands