Creating Images with NumPy: Random Grayscale, Color, Gradient, and Colormap Visualizations

NumPy can be used for image creation and visualization, allowing quick generation of grayscale, color, gradient, and colormap‑enhanced pictures with just a few lines of Python code.

1. Import Modules

Only NumPy is required for the drawing process; PIL's Image module is used to display or save the result, and Matplotlib's colormap can be optionally used for richer colors.

<code>import numpy as np
from PIL import Image
from matplotlib import cm as mplcm
</code>

2. Basic Drawing Process

Using Image.fromarray() , a NumPy array is converted to a PIL image. The array must be of type np.uint8 . The example creates a 100 × 300 random grayscale image and shows it.

<code>im = np.random.randint(0, 255, (100,300), dtype=np.uint8)
im = Image.fromarray(im)
im.show()  # or im.save(r'd:\gray_300_100.jpg')
</code>

3. Generate Random Color Image

If the random array has three channels, a color image is produced.

<code>>> im = np.random.randint(0, 255, (100,300,3), dtype=np.uint8)
>>> Image.fromarray(im, mode='RGB').show()
</code>

4. Generate Gradient Image

Using np.linspace() and np.tile() to build separate R, G, B channels, then stacking them with np.dstack() yields a smooth gradient picture.

<code>>> r = np.tile(np.linspace(192,255, 300, dtype=np.uint8), (600,1)).T
>>> g = np.tile(np.linspace(192,255, 600, dtype=np.uint8), (300,1))
>>> b = np.ones((300,600), dtype=np.uint8)*224
>>> im = np.dstack((r,g,b))
>>> Image.fromarray(im, mode='RGB').show()
</code>

5. Draw Curve on Gradient Background

By locating specific rows and columns in the image array and modifying their color, a sinusoidal curve is drawn on the gradient.

<code>>> r = np.tile(np.linspace(192,255, 300, dtype=np.uint8), (600,1)).T
>>> g = np.tile(np.linspace(192,255, 600, dtype=np.uint8), (300,1))
>>> b = np.ones((300,600), dtype=np.uint8)*224
>>> im = np.dstack((r,g,b))
>>> x = np.arange(600)
>>> y = np.sin(np.linspace(0, 2*np.pi, 600))
>>> y = np.int32((y+1)*0.9*300/2 + 0.05*300)
>>> for i in range(0, 150, 6):
...     im[y[:-i], (x+i)[:-i]] = np.array([255,0,255])
>>> Image.fromarray(im, mode='RGB').show()
</code>

6. Use Color Maps (ColorMap)

Matplotlib provides 82 colormaps grouped into categories such as visual‑uniform, sequential, diverging, cyclic, and miscellaneous. The example shows how to retrieve a colormap and query its colors.

<code>>> cm1 = mplcm.get_cmap('jet')   # custom class
>>> cm1.N                         # 256 colors
>>> cm1(0)                        # (0.0, 0.0, 0.5, 1.0)
>>> cm1(128)                      # (0.4901960784313725, 1.0, 0.4775458570524984, 1.0)
>>> cm1(255)                      # (0.5, 0.0, 0.0, 1.0)
>>> cm2 = mplcm.get_cmap('Paired') # segmented class
>>> cm2.N                         # 12 colors
>>> cm2(0)                        # (0.6509803921568628, 0.807843137254902, 0.8901960784313725, 1.0)
>>> cm2(11)                       # (0.6941176470588235, 0.34901960784313724, 0.1568627450980392, 1.0)
</code>

7. Showcase NumPy’s Power

The final function draw_picture builds coordinate grids, computes distances, applies two colormaps (jet for the whole image, Paired for a selected region), and displays the result.

<code>def draw_picture(w, h, cm1='jet', cm2='Paired'):
    cm1, cm2 = mplcm.get_cmap(cm1), mplcm.get_cmap(cm2)
    colormap1 = np.array([cm1(k) for k in range(cm1.N)])
    colormap2 = np.array([cm2(k) for k in range(cm2.N)])
    i, j = np.repeat(np.arange(h), w).reshape(h,w)-h//2, np.tile(np.arange(w), (h,1))-w//2
    d = np.hypot(i, j)
    e = d[(j*j/10) < i]
    d = np.int32((cm1.N-1)*(d-d.min())/(d.max()-d.min()))
    d = np.uint8(255*colormap1[d])
    e = np.int32((cm2.N-1)*(e-e.min())/(e.max()-e.min()))
    d[(j*j/10) < i] = np.uint8(255*colormap2[e])
    Image.fromarray(d).show()

draw_picture(1200, 900, cm1='jet', cm2='Paired')
</code>

Running this code reproduces the opening illustration, demonstrating how NumPy can serve as a compact yet powerful tool for image creation and visual data exploration.