In today’s digital age, images have become integral to our lives. From social media platforms to scientific research, images provide valuable insights and information. However, understanding the content and extracting meaningful data from images can be complex. That’s where image analysis with Python comes into play. You can also perform it through Yandex reverse image.

Image analysis through Yandex reverse image API is extracting information, patterns, and features from digital images. It involves various techniques and algorithms to understand an image’s content, structure, and characteristics. This analysis can be used in various fields, including healthcare, surveillance, marketing, and more.

In this blog, we will explore how to perform image analysis using Python, a powerful programming language widely used in data analysis and scientific computing. Python offers a rich ecosystem of libraries and tools for image processing and analysis, making it an ideal choice for beginners and professionals.

We will learn how to observe the basic properties of an image, split layers, convert to greyscale, use logical operators to process pixel values, apply masking techniques, and perform various image processing operations. Furthermore, we will explore the possibility of using APIs for image analysis in Python.

Specifically, we will examine how to leverage the Zenserp API for performing image analysis tasks seamlessly. Let’s begin our journey!

man explaining image analysis with python on an image file through data science

Why Is Image Analysis Performed?

Image analysis is performed for various reasons. It allows us to extract meaningful information from images, such as object recognition, pattern detection, and image classification. It is used in healthcare, surveillance, quality control, and scientific research. Hence, enabling better decision-making and a deeper understanding of visual data.

Can We Perform Image Analysis In Python?

Yes, image analysis can be performed in Python. Python offers a variety of libraries and tools, such as PIL (Python Imaging Library), OpenCV, and scikit-image, that provide functionalities for image processing, manipulation, and analysis. These libraries make it possible to extract valuable insights from digital images using Python programming.

How To Analyse Images By Using Python Imaging Library?

First of all, we will load an image in Python. Here is the Python code:

import imageio
import matplotlib.pyplot as plt
picture = imageio.imread('images/me.jpg')
plt.figure(figsize=(5, 5))

Observe the Basic Properties of the Image

The next step is to observe the basic properties of an image by using the following code:

image_type = type(pic)
image_shape = pic.shape
image_height = pic.shape[0]
image_width = pic.shape[1]
image_dimension = pic.ndim

print('Type of the image:', image_type)
print('Shape of the image:', image_shape)
print('Image Height:', image_height)
print('Image Width:', image_width)
print('Dimension of Image:', image_dimension)

Output Results:

Type of the image :  <class 'imageio.core.util.Array'> 
Shape of the image : (728, 720, 3) 
Image Hight 728 
Image Width 720 
Dimension of Image 3

Calculating The Size Of an RGB Image

print('Image size: {}'.format(pic.size))
print('Maximum RGB value in the image: {}'.format(pic.max()))
print('Minimum RGB value in the image: {}'.format(pic.min()))

Output Results

Image size 1572480 
Maximum RGB value in this image 255 
Minimum RGB value in this image 0
# A specific pixel located at Row : 100 ; Column : 50  
# Each channel's value of it, gradually R , G , B  
print('Value of only R channel {}'.format(pic[ 100, 50, 0])) print('Value of only G channel {}'.format(pic[ 100, 50, 1])) print('Value of only B channel {}'.format(pic[ 100, 50, 2]))

Splitting Layers

You must know that three different integers represent an image using three layers. To split the layers, you can use the following code:

import numpy as np 
pic = imageio.imread('images/me.jpg') 
fig, ax = plt.subplots(nrows = 1, ncols=3, figsize=(15,5))  
for c, ax in zip(range(3), ax):     
     # create zero matrix        
     split_img = np.zeros(pic.shape, dtype="uint8") 
     # 'dtype' by default: 'numpy.float64'  # assing each channel      
     split_img[ :, :, c] = pic[ :, :, c] # display each channel     


You must know that the black and white images exist in two forms. These are binary images and greyscale images. Greyscaling is an image processing technique that refers to a process in which you convert a color image to grey shades.

Greyscaling is commonly used in various image processing and analysis tasks. It simplifies the image by removing color distractions and focusing on the structural and textural details.

Moreover, it makes performing tasks such as image enhancement, segmentation, feature extraction, and recognition easier.

It also reduces the image’s memory footprint as it requires only a single channel to store the intensity values instead of three channels for color images.

You can use the following formula in Python to get a grayscale image through an input image:

Y' = 0.299 R + 0.587 G + 0.114 B

Example code to get grayscale images through a digital image:

pic = imageio.imread('images/me.jpg') 
gray = lambda rgb :[... , :3] , [0.299 , 0.587, 0.114])   gray = gray(pic) plt.figure( figsize = (5,5))  
plt.imshow(gray, cmap = plt.get_cmap(name = 'gray'))

Use Logical Operator To Process Pixel Values

Suppose you want to filter out all the values below 20 for image formats. You can do it by using the logical operators as under:

low_pixel = pic < 20  
# to ensure of it let's check if all values in low_pixel are True or not 
if low_pixel.any() == True:     

The output of the above code is:

(743, 911, 3)


Image masking is used in image processing to selectively apply operations or modifications to specific regions while preserving the rest of the image. It involves creating a binary mask that defines which pixels or regions should be affected by the applied operation.

The mask is typically a binary image with the same dimensions as the original image, where each pixel has a value of either 1 (foreground) or 0 (background). The pixels with a value of 1 in the mask represent the region of interest that will be affected by the operation, while the pixels with a value of 0 remain unchanged.

Here is an example code to perform image masking in Python:

# Load the image 
pic = imageio.imread('images/logic_op_pic.JPG')  
# seperate the row and column values  
total_row , total_col , layers = pic.shape  
'''     Create vector.     Ogrid is a compact method of creating a multidimensional     ndarray operations in single lines.     
for ex:     
>>> ogrid[0:5,0:5]     
output: [array([[0],
         array([[0, 1, 2, 3, 4]])]  
x , y = np.ogrid[:total_row , :total_col]  
# get the center values of the image 
cen_x , cen_y = total_row/2 , total_col/2  
 Measure distance value from center to each border pixel.     To make it easy, we can think it's like, we draw a line from center-     to each edge pixel value --> s**2 = (Y-y)**2 + (X-x)**2  
distance_from_the_center = np.sqrt((x-cen_x)**2 + (y-cen_y)**2)  
# Select convenient radius value 
radius = (total_row/2)  
# Using logical operator '>'  
logical operator to do this task which will return as a value  of True for all the index according to the given condition 
circular_pic = distance_from_the_center > radius  
let assign value zero for all pixel value that outside the cirular disc. All the pixel value outside the circular disc, will be black now. 
pic[circular_pic] = 0 
plt.figure(figsize = (5,5)) 

Can We Use APIs for Image Analysis In Python Code?

APIs like Zenserp can be used for image analysis in Python code such as image segmentation. Zenserp provides an API that allows you to extract information and perform analysis on search engine result pages (SERPs), including image search results.

By leveraging the Zenserp API in your Python code, you can programmatically retrieve image search results and perform various image analysis tasks. This includes extracting image URLs, metadata, dimensions, and other relevant information about the images.

You can incorporate the Zenserp API into your image analysis workflow to gather data for tasks such as image classification, object recognition, content moderation, or any other analysis that requires access to image search results.

Using Python, you can request HTTP to the Zenserp API, retrieve the response in JSON format, and then process the data per your specific image analysis requirements.

By combining the power of Python and the capabilities of the Zenserp API, you can efficiently perform image analysis tasks and gain valuable insights from search engine image results.

How To Perform Image Analysis By Using Zenserp API?

You can use the Zenserp reverse image search API to perform such functionalities. Here is an example code in Python:

import requests

headers = { 
  "apikey": "YOUR-APIKEY"}

params = (

response = requests.get('', headers=headers, params=params);

The response may look similar to the below example:

               "title":"The Pied Piper of Hamelin • Report »",
               "destination":" › story › weserbergland › the-...",
               "description":"The legend of the Pied Piper. Photo: Weserbergland Tourismus e.V.. In the year 1284, the town of Hamelin suffered from a terrible rat infestation ...",
               "dimensions":"900 × 450 · Dec 18, 2018"


Image processing in Python goes beyond reverse image search, offering vast possibilities for extracting insights and information from digital images. With libraries like PIL, OpenCV, and scikit-image, Python provides a robust toolkit for image processing and analysis. We explored image analysis basics, from observing image properties to performing operations like greyscaling and masking.

Additionally, we discovered the potential of APIs, such as Zenserp, for accessing image search results programmatically. By leveraging Python’s power and these tools, we can unlock the hidden potential of visual data, enabling applications in various fields like healthcare, marketing, and more. Embracing image analysis with Python opens up a new dimension of understanding and leveraging the visual content around us.


Can I Do Image Analysis in Python?

Yes, image analysis can be done in Python using libraries such as PIL, OpenCV, and scikit-image.

How Is Python Used in Image Processing?

Python is used in image processing for tasks like image manipulation, filtering, segmentation, feature extraction, and analysis algorithms. It uses image processing libraries.

How to Read Data From Images in Python?

To read data from images in Python, you can use libraries such as PIL or OpenCV to load and access pixel values.

Can Python Work With Images?

Yes, Python can work with images by using an image processing library and other image processing tools.

Ready to unlock the power of image analysis? Try Zenserp API today!