.histogram()

In Pillow, the .histogram() method is used to compute the histogram of an image. The histogram is a representation of the distribution of pixel values in the image, which can be useful for various image processing tasks, such as contrast adjustment and thresholding.

Syntax

Image.histogram(self, mask=None)

Parameters:

• self: The image object for which the histogram is to be computed.
• mask (Optional): A mask image that specifies which pixels to include in the histogram calculation. If not provided, all pixels are included.

Return Value:

Returns a list of integers representing the histogram of the image. The length of the list depends on the mode of the image:

• For grayscale images, the histogram will have 256 values (0-255).
• For RGB images, it will have 768 values (256 for each channel: red, green, and blue).
• For RGBA images, it will have 1024 values (256 for each channel plus an additional 256 for the alpha channel).
• For other modes, the histogram will have a different number of values depending on the number of channels in the image.

Examples

The image file used in all the examples is:

Example 1: Plotting a Grayscale Image Histogram

This example demonstrates how to convert an image to grayscale and plot its pixel value distribution using .histogram():

from PIL import Image
import matplotlib.pyplot as plt

# Open the image file
image = Image.open('town.jpg')

# Convert the image to grayscale
grayscale_image = image.convert('L')

# Calculate the histogram
histogram = grayscale_image.histogram()

# Plot the histogram
plt.plot(histogram)
plt.title('Grayscale Histogram')
plt.xlabel('Pixel Value')
plt.ylabel('Count')
plt.show()

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In this example, an image file is opened and converted to grayscale using the .convert() method. The grayscale image’s histogram is then computed using the .histogram() method and visualized with Matplotlib. The x-axis of the plot shows the pixel intensity values, while the y-axis represents the frequency of each intensity level:

Example 2: Visualizing Histogram Data for RGB Images

This example shows how to compute and plot the pixel distribution of an RGB image using .histogram():

from PIL import Image
import matplotlib.pyplot as plt

# Open the image file
image = Image.open('town.jpg')

# Calculate the histogram
histogram = image.histogram()

# Plot the histogram
plt.plot(histogram)
plt.title('RGB Histogram')
plt.xlabel('Pixel Value')
plt.ylabel('Count')
plt.show()

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In this example, an RGB image is opened, and its histogram is generated. The resulting histogram contains 768 values — 256 for each color channel: red, green, and blue. The plot displays the distribution of pixel values across all three channels:

Example 3: Using .histogram() with Transparency (RGBA Images)

This example illustrates how to generate a histogram for an RGBA image, including the alpha (transparency) channel:

from PIL import Image
import matplotlib.pyplot as plt

# Open the image file
image = Image.open('town.jpg')

# Calculate the histogram
histogram = image.histogram()

# Plot the histogram
plt.plot(histogram)
plt.title('RGBA Histogram')
plt.xlabel('Pixel Value')
plt.ylabel('Count')
plt.show()

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In this example, the histogram will have 1024 values (256 for each of the four channels: red, green, blue, and alpha). The plot will show the distribution of pixel values across all four channels: