If you worry about, how to read and show an image using the matplotlib library then here you will get a solution for your problem. Along with that, you will be got a bonus. The matplotlib imshow() function helps to show the image.
But plt.imshow() didn’t work without mpimg.imread() function which is belongs to matplotlib.image module. So lets start practical.
Import Libraries
| 12 | import matplotlib.pyplot as pltimport matplotlib.image as mpimg # image module for image reading |
Reading Image
Here, we use mpimg.imread() method. Which belongs to the matplotlib image module.
| 12 | img = mpimg.imread("pie_char.png") # give addres of image locationprint(img) |
Output >>>
[[[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
...,
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.]],
[[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
...,
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.]],
[[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
...,
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.]],
...,
[[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
...,
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.]],
[[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
...,
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.]],
[[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
...,
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.]]], dtype=float32)Above you can see, when we call img then got numpy array but how. because mpimg.imread() function read pie_char.png image and store in numpy array format.
Get more information about img.
| 123 | print("Data type of img > ", type(img))print("Shape of img > ", img.shape)print("Dimention of img > ",img.ndim) |
output >>>
| 12345 | Data type of img > numpy.ndarray Shape of img > (288, 432, 4) Dimention of img > 3 |
Show Image using matplotlib imshow
It’s time to show an image using a read dataset.
To show an image, use plt.imshow() function.
Syntax :plt.imshow(
X,
cmap=None,
norm=None,
aspect=None,
interpolation=None,
alpha=None,
vmin=None,
vmax=None,
origin=None,
extent=None,
shape=None,
filternorm=1,
filterrad=4.0,
imlim=None,
resample=None,
url=None,
*,
data=None,
**kwargs,
)
plt.imshow(img)plt.show() |
Output >>>
Now, removing the axis and increase figure size and then show the same image.
| 1234 | plt.figure(figsize=(16,9))plt.axis("off")plt.imshow(img)plt.show() |
Output >>>
Now, it looks great but can we add it’s a color bar. Yes, using plt.colorbar() function.
Show Image with Colorbar
| 12345 | plt.figure(figsize=(16,9))plt.axis("off")plt.imshow(img)plt.colorbar() # Show color bar of above imageplt.show() |
Output >>>
Show Image with cmap Parameter
Let’s play with plt.imshow() functions parameter. Here use cmap means color map to show a single-channel image in a different color.
| 123456 | single_channel = img[:,:,1] # get single channel data from imgplt.figure(figsize=(16,9))plt.axis("off")plt.imshow(single_channel, cmap = "hot") # show image with hot color mapplt.colorbar()plt.show() |
Output >>>
If you want to show an image using a folder path, then follow the below code.
| 123456 | img2 = mpimg.imread("D:\\Private\\Wallpapers\\Pixels\\automobile-beautiful-car-1226458.jpg")plt.figure(figsize=(16,9))plt.axis("off")plt.imshow(img2)plt.colorbar()plt.show() |
Output >>>
If, you don’t want to show color bar then remove 5’th no line.
Model image shows with hot color maps (cmap).
| 1234567 | single_channel2_img = img2[:,:,1]plt.figure(figsize=(16,9))plt.axis("off")plt.imshow(single_channel2_img, cmap="hot")plt.colorbar()plt.savefig("model_hot.png")plt.show() |
Model image shows with nipy_spectral color maps (cmap).
If we will generate an image with all cmaps then it takes more time. So for that follow the below code.
Below code get cmaps name as a string and split all cmap name as a single item of a list cmap_name_list
| 123 | cmap = """Accent, Accent_r, Blues, Blues_r, BrBG, BrBG_r, BuGn, BuGn_r, BuPu, BuPu_r, CMRmap, CMRmap_r, Dark2, Dark2_r, GnBu, GnBu_r, Greens, Greens_r, Greys, Greys_r, OrRd, OrRd_r, Oranges, Oranges_r, PRGn, PRGn_r, Paired, Paired_r, Pastel1, Pastel1_r, Pastel2, Pastel2_r, PiYG, PiYG_r, PuBu, PuBuGn, PuBuGn_r, PuBu_r, PuOr, PuOr_r, PuRd, PuRd_r, Purples, Purples_r, RdBu, RdBu_r, RdGy, RdGy_r, RdPu, RdPu_r, RdYlBu, RdYlBu_r, RdYlGn, RdYlGn_r, Reds, Reds_r, Set1, Set1_r, Set2, Set2_r, Set3, Set3_r, Spectral, Spectral_r, Wistia, Wistia_r, YlGn, YlGnBu, YlGnBu_r, YlGn_r, YlOrBr, YlOrBr_r, YlOrRd, YlOrRd_r, afmhot, afmhot_r, autumn, autumn_r, binary, binary_r, bone, bone_r, brg, brg_r, bwr, bwr_r, cividis, cividis_r, cool, cool_r, coolwarm, coolwarm_r, copper, copper_r, cubehelix, cubehelix_r, flag, flag_r, gist_earth, gist_earth_r, gist_gray, gist_gray_r, gist_heat, gist_heat_r, gist_ncar, gist_ncar_r, gist_rainbow, gist_rainbow_r, gist_stern, gist_stern_r, gist_yarg, gist_yarg_r, gnuplot, gnuplot2, gnuplot2_r, gnuplot_r, gray, gray_r, hot, hot_r, hsv, hsv_r, inferno, inferno_r, jet, jet_r, magma, magma_r, nipy_spectral, nipy_spectral_r, ocean, ocean_r, pink, pink_r, plasma, plasma_r, prism, prism_r, rainbow, rainbow_r, seismic, seismic_r, spring, spring_r, summer, summer_r, tab10, tab10_r, tab20, tab20_r, tab20b, tab20b_r, tab20c, tab20c_r, terrain, terrain_r, twilight, twilight_r, twilight_shifted, twilight_shifted_r, viridis, viridis_r, winter, winter_r""" cmap_name_list = cmap.split(sep = ", ") |
Output >>>
| 123456789 | ['Accent','Accent_r','Blues',...'viridis_r','winter','winter_r'] |
Using cmap name create a unique address to store or save generated image in a folder.
| 12345 | save_image_addr_name = []for i in range(len(cmap_name_list)):cmap_str = cmap_name_list[i]save_image_addr_name.append("D:\\\cmap_image\\\_"+"girl_" + cmap_name_list[i] + ".png")print(save_image_addr_name[i]) |
Output >>>
Output >>>
| 12345678910111213141516171819202122 | D:\\cmap_image\\_girl_Accent.pngD:\\cmap_image\\_girl_Accent_r.pngD:\\cmap_image\\_girl_Blues.pngD:\\cmap_image\\_girl_Blues_r.pngD:\\cmap_image\\_girl_BrBG.pngD:\\cmap_image\\_girl_BrBG_r.pngD:\\cmap_image\\_girl_BuGn.pngD:\\cmap_image\\_girl_BuGn_r.pngD:\\cmap_image\\_girl_BuPu.png.....D:\\cmap_image\\_girl_twilight.pngD:\\cmap_image\\_girl_twilight_r.pngD:\\cmap_image\\_girl_twilight_shifted.pngD:\\cmap_image\\_girl_twilight_shifted_r.pngD:\\cmap_image\\_girl_viridis.pngD:\\cmap_image\\_girl_viridis_r.pngD:\\cmap_image\\_girl_winter.pngD:\\cmap_image\\_girl_winter_r.png |
Using cmap_name_list and save_image_addr_name generate cmap image and save it define location with unique address.
| 12345678910111213 | for i in range(len(cmap_name_list)): cmap_name = cmap_name_list[i]plt.figure(figsize=(16,9))plt.axis("off") print(cmap_name) plt.imshow(single_channel2_img, cmap=cmap_name)#plt.colorbar()#save_image_name1 = "D:\\cmap_image\\"+"girl" + cmap_list[i]print(save_image_addr_name[i])plt.savefig(save_image_addr_name[i], orientation='portrate', facecolor= "k")plt.show() |
Output >>>
Conclusion
In the matplotlib imshow blog, we learn how to read, show image and colorbar with a real-time example using the mpimg.imread, plt.imshow() and plt.colorbar() function. Along with that used different method and different parameter. We suggest you make your hand dirty with each and every parameter of the above methods. This is the best coding practice. After completion of the matplotlib tutorial jump on Seaborn.
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