I am trying to plot multiple images in a figure using matplotlib.
Basically, I read the images using PIl library, convert it to numpy array and do some operation on it (setting the elements in a row to zero). Everything works fine till this point. But when I try to save the results using matplotlib, I get inconsistent results.
Please have a look at my code.
Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import PIL.Image as PI
Loading the file
fileName = 'n01978287_43.jpg'
img = PI.open(fileName)
size = 224
img = img.resize((size, size))
img = np.asarray(img, dtype=np.uint8).astype(np.float32)
img = img/255
Result 1
temp_img = np.copy(img)
temp_img[51, :, :] = 0*temp_img[51, :, :]
fig = plt.figure()
ax1 = plt.subplot(1, 6, 1)
ax1.imshow(img, interpolation='none')
ax2 = plt.subplot(1, 6, 2)
ax2.imshow(temp_img, interpolation='none')
plt.savefig('test_516.png')
plt.close(fig)
Result 2
temp_img = np.copy(img)
temp_img[52, :, :] = 0*temp_img[52, :, :]
fig = plt.figure()
ax1 = plt.subplot(1, 6, 1)
ax1.imshow(img, interpolation='none')
ax2 = plt.subplot(1, 6, 2)
ax2.imshow(temp_img, interpolation='none')
plt.savefig('test_526.png')
plt.close(fig)
Result 3
temp_img = np.copy(img)
temp_img[51, :, :] = 0*temp_img[51, :, :]
fig = plt.figure()
ax1 = plt.subplot(1, 2, 1)
ax1.imshow(img, interpolation='none')
ax2 = plt.subplot(1, 2, 2)
ax2.imshow(temp_img, interpolation='none')
plt.savefig('test_512.png')
plt.close(fig)
Result 4
temp_img = np.copy(img)
temp_img[56, :, :] = 0*temp_img[56, :, :]
fig = plt.figure()
ax1 = plt.subplot(1, 2, 1)
ax1.imshow(img, interpolation='none')
ax2 = plt.subplot(1, 2, 2)
ax2.imshow(temp_img, interpolation='none')
plt.savefig('test_562.png')
plt.close(fig)
Now, if you look at the results, you would notice the inconsistency.
- Firstly, for first two images (figure with 6 axes), you see the black line only in one of the image. (There is a pattern to this if you zero out all the rows (one at a time) and then try to save the results).
- In the last two images, black line gets thicker. (I didn't find any pattern in this case).
System Setup - Python3, Matplotlib3, PIL, Numpy
Update:
After looking for ways to save a figure with the desired resolution (224*224 in this case), I wrote the following code (using multiple resources from web).
Importing libraries and loading the image file
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image, ImageDraw, ImageFont
fileName = 'n01978287_43.jpg'
img = Image.open(fileName)
size = 224
img = img.resize((size, size))
img = np.asarray(img, dtype=np.uint8).astype(np.float32)
img = img/255
Function to plot the grid of images
def plt_save(grid, idx, name):
nRows = len(grid)
nCols = len(grid[0])
print('Clearing figure')
plt.rcParams.update({'font.size': 8})
wFig = (nCols+2) # Figure width (two more than nCols because I want to add ylabels on the very left and very right of figure)
hFig = (nRows+1) # Figure height (one more than nRows becasue I want to add xlabels to the top of figure)
fig = plt.figure(figsize=( wFig, hFig ))
fig.subplots_adjust(left=0, bottom=0, right=1, top=1, wspace=0, hspace=0)
fig.patch.set_facecolor('grey')
for r in range(nRows):
for c in range(nCols):
ax = plt.subplot2grid( shape=[hFig, wFig], loc=[r+1, c+1] )
im= ax.imshow(grid[r][c], interpolation='none')
ax.spines['bottom'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.set_xticks([])
ax.set_yticks([])
#fig.colorbar(im, ax=ax)
#ax.set_aspect('auto')
if not r:
ax.set_title('Image',
rotation=22.5,
horizontalalignment='left',
verticalalignment='bottom')
if not c:
ax.set_ylabel('leftLabel',
rotation=0,
horizontalalignment='right',
verticalalignment='center')
if c == wFig-3:
ax2 = ax.twinx()
#ax2.axis('off')
ax2.set_xticks([])
ax2.set_yticks([])
ax2.spines['top'].set_visible(False)
ax2.spines['right'].set_visible(False)
ax2.spines['bottom'].set_visible(False)
ax2.spines['left'].set_visible(False)
ax2.set_ylabel( 'rightLabel',
rotation=0,
verticalalignment='center',
horizontalalignment='left' )
print('Saving file')
plt.savefig( ( str(idx) + '_' + name + '_' + fileName.split('.')[0] + '.png'),
orientation='landscape',
#bbox_inches='tight',
facecolor = fig.get_facecolor(),
dpi=224, # DPI is 224 becasue the axis size is 1x1 inch and I want 224x224 pixels in each axis
transparent=True,
frameon=False )
plt.close(fig)
Loop to zero out the rows of the image (one at a time)
for i in range(0, 224):
temp_img = np.copy(img)
temp_img[i, :, :] = 0*temp_img[i, :, :]
# 1*4 Grid of images (can vary based on the requirement)
grid = [img, temp_img, img, temp_img]
grid = [grid, grid] #2*4 grid of images
plt_save(grid, i, 'PLT_')
Here is how one of the 224 images looks like.
The thing is that it works perfectly as long as I stick with this kind of plot. But the moment I try to make some changes (like adding a colorbar, having some spaces between each axis etc), the image resolution changes. If I use bbox_inches = 'tight' while saving my figure, it adjusts everything but changes the original resolution while keeping the figure size constant.
Is there any other way similar to bbox_inches='tight' such that it can keep the axis resolution fixed while adjusting the figure size accordingly. Or if there is no such thing in matplotlib, could you suggest me any other way to incorporate colorbar (small spaces between axis, ylabel for each axis etc) while keeping the image resolution fixed.