Looks like the image is too big and the window simply doesn’t fit the screen. Create window with the cv2.WINDOW_NORMAL flag, it will make it scalable. Then you can resize it to fit your screen like this: from __future__ import division import cv2 img = cv2.imread(‘1.jpg’) screen_res = 1280, 720 scale_width = screen_res[0] / img.shape[1] … Read more
Yes, there is! Use LogNorm. Here is a code excerpt from a utility that I wrote to display confusion matrices on a log scale. from pylab import figure, cm from matplotlib.colors import LogNorm # C = some matrix f = figure(figsize=(6.2, 5.6)) ax = f.add_axes([0.17, 0.02, 0.72, 0.79]) axcolor = f.add_axes([0.90, 0.02, 0.03, 0.79]) im … Read more
To display the multiple images use subplot() plt.figure() #subplot(r,c) provide the no. of rows and columns f, axarr = plt.subplots(4,1) # use the created array to output your multiple images. In this case I have stacked 4 images vertically axarr[0].imshow(v_slice[0]) axarr[1].imshow(v_slice[1]) axarr[2].imshow(v_slice[2]) axarr[3].imshow(v_slice[3])
You can use a ListedColormap to specify the white and red as the only colors in the color map, and the bounds determine where the transition is from one color to the next: import matplotlib.pyplot as plt from matplotlib import colors import numpy as np np.random.seed(101) zvals = np.random.rand(100, 100) * 10 # make a … Read more
The following code will load an image from a file image.png and will display it as grayscale. import numpy as np import matplotlib.pyplot as plt from PIL import Image fname=”image.png” image = Image.open(fname).convert(“L”) arr = np.asarray(image) plt.imshow(arr, cmap=’gray’, vmin=0, vmax=255) plt.show() If you want to display the inverse grayscale, switch the cmap to cmap=’gray_r’.
Hrm, it appears I can use a masked array to do this: masked_array = np.ma.array (a, mask=np.isnan(a)) cmap = matplotlib.cm.jet cmap.set_bad(‘white’,1.) ax.imshow(masked_array, interpolation=’nearest’, cmap=cmap) This should suffice, though I’m still open to suggestions. :]
Here is my approach that you may try: import numpy as np import matplotlib.pyplot as plt w = 10 h = 10 fig = plt.figure(figsize=(8, 8)) columns = 4 rows = 5 for i in range(1, columns*rows +1): img = np.random.randint(10, size=(h,w)) fig.add_subplot(rows, columns, i) plt.imshow(img) plt.show() The resulting image: (Original answer date: Oct 7 … Read more
If you are using matplotlib and want to show the image in your interactive notebook, try the following: %matplotlib inline import matplotlib.pyplot as plt import matplotlib.image as mpimg img = mpimg.imread(‘your_image.png’) imgplot = plt.imshow(img) plt.show()
You want to loop over the values in grid, and use ax.text to add the label to the plot. Fortunately, for 2D arrays, numpy has ndenumerate, which makes this quite simple: for (j,i),label in np.ndenumerate(grid): ax1.text(i,j,label,ha=”center”,va=”center”) ax2.text(i,j,label,ha=”center”,va=”center”)
Google colab crashes if you try to display image using cv2.imshow() instead import from google.colab.patches import cv2_imshow and display using cv2_imshow(<image>)