In [30]:
plt.imshow(src.read(1))
Out[30]:
<matplotlib.image.AxesImage at 0x28d012e1b08>
Now we'll do a brief overview of visualizations--although I will admit that integrating these techniques with Matplotlib can be a very deep dive... this is just a primer.
Imports:
import rasterio as rio
from rasterio.plot import show
from matplotlib import pyplot as plt
from rasterio.plot import adjust_band
import numpy as np
import os
Important: only run this once or you'll wind up in the wrong directory.
os.chdir('../')
os.getcwd()
os.chdir('../')
os.getcwd()
Open the Flatirons DEM raster:
src = rio.open('workshopdata/dem.tif')
src = rio.open('workshopdata/dem.tif')
The matplotlib way... What we're doing here is reading it as an array and having matplotlib plot the array as an image:
plt.imshow(src.read(1))
plt.imshow(src.read(1))
<matplotlib.image.AxesImage at 0x28d012e1b08>
OR, as we've used already, the built in Rasterio method:
show(src)
show(src)
<AxesSubplot:>
Note that this one is in the proper units...
If you use show() to plot the single band, you lose the units...
show(src.read(1))
show(src.read(1))
<AxesSubplot:>
But, you can provide the transform and it will provide the proper units... this may seem pointless, but it is actually a useful method, as you'll soon see...
show(src.read(1), transform = src.transform)
show(src.read(1),transform=src.transform)
<AxesSubplot:>
Let's make it good ol' "DEM gray"...
show(src.read(1), transform = src.transform, cmap = 'gray')
show(src.read(1), transform=src.transform, cmap='gray')
<AxesSubplot:>
Here are Matplotlib's colormaps: https://matplotlib.org/stable/tutorials/colors/colormaps.html
Let's have some fun with this:
show(src.read(1), transform = src.transform, cmap = 'Reds')
show(src.read(1), transform = src.transform, cmap = 'Reds')
<AxesSubplot:>
Tired of looking at these tiny plots? We can use the Matplotlib conventions...
fig, ax = plt.subplots(1, figsize=(12,12))
show(src.read(1), transform = src.transform, cmap = 'gray', ax=ax)
fig, ax = plt.subplots(1,figsize=(12,12))
show(src.read(1), transform = src.transform, cmap='gray', ax=ax)
<AxesSubplot:>
It's quite easy to create contours from a DEM:
fig, ax = plt.subplots(1, figsize=(12,12))
show(src.read(1), transform = src.transform,contour=True, ax=ax)
fig, ax = plt.subplots(figsize=(12,12))
show(src.read(1), transform = src.transform, contour=True, ax=ax)
<AxesSubplot:>
Or do both Contours and a DEM together:
fig, ax = plt.subplots(1, figsize=(12,12))
show(src.read(1), transform = src.transform, cmap = 'gray', ax=ax)
show(src.read(1), transform = src.transform,contour=True, ax=ax)
fig, ax = plt.subplots(figsize=(12,12))
show(src.read(1), transform = src.transform, cmap='gray', ax=ax)
show(src.read(1), transform = src.transform,contour=True,ax=ax)
<AxesSubplot:>
Save some memory...
del src
We'll use our cropped NAIP image of campus:
naip = rio.open('NAIP_swQuad.tif')
naip = rio.open('NAIP_swQuad.tif')
show(naip)
show(naip)
<AxesSubplot:>
You can also specify particular bands:
show((naip,2))
show((naip,2))
<AxesSubplot:>
Near infrared:
show((naip,4))
show((naip,4))
<AxesSubplot:>
Let's work on displaying the true color version...
To display a true color RGB image, you need to normalize the bands....
Start by reading the image into a Numpy array. This will create an n-dimensional array--essentially the four different bands stacked.
image = naip.read()
image = naip.read()
Take a quick look...
show(image)
show(image)
<AxesSubplot:>
But, to create the true color visualization, we really just need the rgb bands--that is, the first 3 bands. So we'll use Numpy to grab just those three:
rgb = image[0:3]
rgb = image[0:3]
show(rgb)
show(rgb)
<AxesSubplot:>
Now, finally we can plot our rgb image properly...
Might crush memory!!!
fig, ax = plt.subplots(1, figsize=(12,12))
show(rgb_norm, transform = naip.transform, ax=ax, title = 'Aerial Image of CU Campus')
fig, ax = plt.subplots(figsize=(12,12))
show(rgb, transform = naip.transform, ax=ax, title='Aerial Image of CU Campus')
<AxesSubplot:title={'center':'Aerial Image of CU Campus'}>
