Cara menggunakan plot tiff image python

You can try plotting tiff images using ipyplot package

import ipyplot

ipyplot.plot_images(
   ['https://file-examples.com/wp-content/uploads/2017/10/file_example_TIFF_1MB.tiff',
      'https://file-examples.com/wp-content/uploads/2017/10/file_example_TIFF_1MB.tiff',
      'https://file-examples.com/wp-content/uploads/2017/10/file_example_TIFF_1MB.tiff'
   ],
   img_width = 250)

whcih comes to conclusion we need PIL library that I also tried and fails.

from PIL
import Image
resp2 = req.get('tiffFielurl')
resp2.raw.decode_content = True
im = Image.open(resp2.raw)
im

Gives Output:

<PIL.TiffImagePlugin.TiffImageFile image mode=I;16BS size=4800x4800 at 0x11CB7C50>

and converting the PIL object to numpy array or even getting data or pixels from PIL object gives error of unrecognized mode error.

im.getdata()
im.getpixels((0, 0))
numpy.array(im)

Suggestion : 2

You can try plotting tiff images using anycodings_jupyter-notebook ipyplot package,Next using xml.etree.ElementTree I extracted anycodings_httprequest the URL. Next what I need is to dsiplay that anycodings_httprequest tiff image (or any other image) on the anycodings_httprequest Jupyter Notebook without downloading (as one anycodings_httprequest image can be more than 50 or 100 MB anycodings_httprequest sometimes),How to check if a large list of users are friends with a small dictionary of target users on Twitter using Twitter API Python,It comes out that PIL even don't support anycodings_jupyter-notebook 16bit Signed integer pixel that is anycodings_jupyter-notebook defined in Tiff object of PIL above.

I am working with web services using anycodings_httprequest requests to get an image based on parameters anycodings_httprequest passed. The first response I get is a XML anycodings_httprequest schema with file reference URL.

<?xml version="1.0"?>
<Coverages schemaLocation="http://localhost/server/schemas/wcs/1.1.1/wcsCoverages.xsd">
   <Coverage>
      <Title>Filename</Title>
      <Abstract />
      <Identifier>Filename</Identifier>
      <Reference href="http://localhost/server/temp/filename.tif" />
   </Coverage>
</Coverages>

Currently I am downloading and plotting the anycodings_httprequest file after reading and converting file data anycodings_httprequest into array ( as pyplot plot image anycodings_httprequest array/matrix ).

import requests as req # request wcs urls
import xml.etree.ElementTree as ET # used
for xml parsing
import matplotlib.pyplot as plt # display image
import gdal

# Download the File to local directory using Chunks
chunk_size = 1024
local_filename = url.split('/')[-1] # Filename from url
r = req.get(url, stream = True)
with open(local_filename, 'wb') as f:
   for chunk in r.iter_content(chunk_size):
   if chunk:
   f.write(chunk)

# Read File Raster Data as Array using Gdal
gtif = gdal.Open(local_filename)
georaster = gtif.ReadAsArray()

# Plot image using matplotlib
plt.imshow(georaster)
plt.title(local_filename)
plt.show()

The raw response from get request for tiff anycodings_httprequest file is below

resp2 = req.get('tiffFielurl')
rawdata = resp2.content
rawdata[0: 10]

Output: b 'MM\x00*\x00\x00\x00\x08\x00\x10'

You can try plotting tiff images using anycodings_jupyter-notebook ipyplot package

import ipyplot

ipyplot.plot_images(
   ['https://file-examples.com/wp-content/uploads/2017/10/file_example_TIFF_1MB.tiff',
      'https://file-examples.com/wp-content/uploads/2017/10/file_example_TIFF_1MB.tiff',
      'https://file-examples.com/wp-content/uploads/2017/10/file_example_TIFF_1MB.tiff'
   ],
   img_width = 250)

whcih comes to conclusion we need PIL anycodings_jupyter-notebook library that I also tried and fails.

from PIL
import Image
resp2 = req.get('tiffFielurl')
resp2.raw.decode_content = True
im = Image.open(resp2.raw)
im

Gives Output:

<PIL.TiffImagePlugin.TiffImageFile image mode=I;16BS size=4800x4800 at 0x11CB7C50>

and converting the PIL object to numpy anycodings_jupyter-notebook array or even getting data or pixels anycodings_jupyter-notebook from PIL object gives error of anycodings_jupyter-notebook unrecognized mode error.

im.getdata()
im.getpixels((0, 0))
numpy.array(im)

Suggestion : 3

Last Updated : 12 Nov, 2020

To install this module run this command into your terminal. 

Suggestion : 4

First, let's start IPython. It is a most excellent enhancement to the standard Python prompt, and it ties in especially well with Matplotlib. Start IPython either directly at a shell, or with the Jupyter Notebook (where IPython as a running kernel).,A short tutorial on plotting images with Matplotlib.,With IPython started, we now need to connect to a GUI event loop. This tells IPython where (and how) to display plots. To connect to a GUI loop, execute the %matplotlib magic at your IPython prompt. There's more detail on exactly what this does at IPython's documentation on GUI event loops.,If you're using Jupyter Notebook, the same commands are available, but people commonly use a specific argument to the %matplotlib magic:

In[1]: % matplotlib inline

import matplotlib.pyplot as plt
import matplotlib.image as mpimg

img = mpimg.imread('../../doc/_static/stinkbug.png')
print(img)

[
   [
      [0.40784314 0.40784314 0.40784314]
      [0.40784314 0.40784314 0.40784314]
      [0.40784314 0.40784314 0.40784314]
      ...[0.42745098 0.42745098 0.42745098]
      [0.42745098 0.42745098 0.42745098]
      [0.42745098 0.42745098 0.42745098]
   ]

   [
      [0.4117647 0.4117647 0.4117647]
      [0.4117647 0.4117647 0.4117647]
      [0.4117647 0.4117647 0.4117647]
      ...[0.42745098 0.42745098 0.42745098]
      [0.42745098 0.42745098 0.42745098]
      [0.42745098 0.42745098 0.42745098]
   ]

   [
      [0.41960785 0.41960785 0.41960785]
      [0.41568628 0.41568628 0.41568628]
      [0.41568628 0.41568628 0.41568628]
      ...[0.43137255 0.43137255 0.43137255]
      [0.43137255 0.43137255 0.43137255]
      [0.43137255 0.43137255 0.43137255]
   ]

   ...

   [
      [0.4392157 0.4392157 0.4392157]
      [0.43529412 0.43529412 0.43529412]
      [0.43137255 0.43137255 0.43137255]
      ...[0.45490196 0.45490196 0.45490196]
      [0.4509804 0.4509804 0.4509804]
      [0.4509804 0.4509804 0.4509804]
   ]

   [
      [0.44313726 0.44313726 0.44313726]
      [0.44313726 0.44313726 0.44313726]
      [0.4392157 0.4392157 0.4392157]
      ...[0.4509804 0.4509804 0.4509804]
      [0.44705883 0.44705883 0.44705883]
      [0.44705883 0.44705883 0.44705883]
   ]

   [
      [0.44313726 0.44313726 0.44313726]
      [0.4509804 0.4509804 0.4509804]
      [0.4509804 0.4509804 0.4509804]
      ...[0.44705883 0.44705883 0.44705883]
      [0.44705883 0.44705883 0.44705883]
      [0.44313726 0.44313726 0.44313726]
   ]
]

imgplot = plt.imshow(img)

lum_img = img[:, :, 0]

# This is array slicing. You can read more in the `Numpy tutorial
# <https: //numpy.org/doc/stable/user/quickstart.html>`_.

   plt.imshow(lum_img)

Suggestion : 5

store NumPy arrays in TIFF (Tagged Image File Format) files, and,read image and metadata from TIFF-like files used in bioimaging.,Tifffile can also be used to inspect TIFF structures, read image data from multi-dimensional file sequences, write fsspec ReferenceFileSystem for TIFF files and image file sequences, patch TIFF tag values, and parse many proprietary metadata formats.,Read an image stack from a series of TIFF files with a file name pattern as NumPy or Zarr arrays:

Install the tifffile package and recommended dependencies from the Python Package Index:

python - m pip install - U tifffile imagecodecs matplotlib lxml zarr fsspec

Write a NumPy array to a single-page RGB TIFF file:

>>> data = numpy.random.randint(0, 255, (256, 256, 3), 'uint8') >>>
   imwrite('temp.tif', data, photometric = 'rgb')

Read the image from the TIFF file as NumPy array:

>>> image = imread('temp.tif') >>>
   image.shape(256, 256, 3)

Write a 3-dimensional NumPy array to a multi-page, 16-bit grayscale TIFF file:

>>> data = numpy.random.randint(0, 2 ** 12, (64, 301, 219), 'uint16') >>>
   imwrite('temp.tif', data, photometric = 'minisblack')

Suggestion : 6

Explain the characteristics of the BMP, JPEG, and TIFF image formats.,Explain the characteristics of the BMP, JPEG, and TIFF image formats. ,The following table summarises the characteristics of the BMP, JPEG, and TIFF image formats:,Now that we have our libraries loaded, we will run a Jupyter Magic Command that will ensure our images display in our Jupyter document with pixel information that will help us more efficiently run commands later in the session.

""
" *
Python libraries
for learning and performing image processing.*
   ""
"
import numpy as np
import skimage.io
import skimage.viewer
import matplotlib.pyplot as plt
import ipympl

import skimage # form 1, load whole skimage library
import skimage.io # form 2, load skimage.io module only
from skimage.io
import imread # form 3, load only the imread
function
import numpy as np # form 4, load all of numpy into an object called np

image = skimage.io.imread(fname = "data/eight.tif")
plt.imshow(image)

print(image.shape)
print(image)

(5, 3)[[0. 0. 0.]
   [0. 1. 0.]
   [0. 0. 0.]
   [0. 1. 0.]
   [0. 0. 0.]]