How to Display an Image Using OpenCV

In this project, I will show you how to display an image using OpenCV.

You Will Need

Python 3.7+

Directions

Let’s say you have an image like the one below. The file name is 1.jpg.

To display it using OpenCV, go to your favorite IDE or text editor and create the following Python program:

# Display a color image using OpenCV
import numpy as np
import cv2

# Load an color image in grayscale
img = cv2.imread('1.jpg',1)

cv2.imshow('image',img)
cv2.waitKey(0)
cv2.destroyAllWindows()

Save the program into the same directory as 1.jpg.

Run the file.

Watch the image display to your computer. That’s it!

Pros and Cons of Gaussian Smoothing

First, before we get into the pros and cons of Gaussian smoothing, let us take a quick look at what Gaussian smoothing is and why we use it.

What is Gaussian Smoothing?

Have you ever had a photo or portrait of either yourself or someone else and wanted to smooth out the facial imperfections, pimples, pores, or wrinkles? Gaussian smoothing (also known as Gaussian blur) is one way to do this. Gaussian smoothing uses a mathematical equation called the Gaussian function to blur an image, reducing image detail and noise.

Below is an example of an image with a small and large Gaussian blur.

Image Source: Wikimedia

Pros of Gaussian Smoothing

Reduces noise in an image

Noise reduction is one of the main use cases of Gaussian smoothing.

Easy to implement

No complicated algorithms with multiple nested for loops needed. As you can see in this MATLAB implementation, Gaussian smoothing can be done with just a single line of code.

Automatic censoring

Some use cases might require you to conceal the identity of someone or to censor images that might contain material that might be inappropriate to certain audiences. Gaussian smoothing works well in these cases.

Symmetric

Gaussian smoothing produces an image that is rotationally symmetric. It is applied the same no matter what direction you go in.

Cons of Gaussian Smoothing

Lose fine image detail and contrast

If you have a use case that requires you to examine fine detail, Gaussian smoothing might make that a lot harder. An example where you might want to examine fine detail would be in a medical image or a robot trying to grasp a specific point on an object.

Does not handle “salt and pepper noise” well

Sometimes an image might have what is known as “salt-and-pepper noise.” Salt-and-pepper noise is defined as sparsely occurring white and black pixels. Below is an image showing salt-and-pepper noise.

Image Source: Wikimedia

Median filters typically do a better job than Gaussian smoothing at handling salt-and-pepper noise.

How to Annotate Images Using OpenCV

In this project, we will learn how to annotate images using OpenCV — a popular and powerful open source library for image processing and computer vision.

OpenCV is a cross-platform library with wrappers for Python, Ruby, C#, JavaScript, and other languages designed for real-time image processing. OpenCV has methods for image I/O, filtering, motion tracking, segmentation, 3D reconstruction, as well as machine learning techniques such as boosting, support vector machines, and deep learning.

Requirements

Design a software application using Python and OpenCV that allows users to click in an image, annotate a number of points within an image, and export the annotated points into a CSV file.
- Code must be implemented in Python and using OpenCV
- The input image and output CSV files will be provided as parameters.
  - Example: python annotate_images.py cat_dog.jpg cat_dog.csv

You Will Need

Python 3.7

Input Images

Directions

To run the program, open up an Anaconda Prompt terminal.

Go to the proper directory.

Type python annotate_images.py cat_dog.jpg cat_dog.csv to run the program.

Here is the code:

import cv2 # Import the OpenCV library
import numpy as np # Import Numpy library
import pandas as pd # Import Pandas library
import sys # Enables the passing of arguments

# Project: Annotate Images Using OpenCV
# Author: Addison Sears-Collins
# Date created: 9/11/2019
# Python version: 3.7
# Description: This program allows users to click in an image, annotate a 
#   number of points within an image, and export the annotated points into
#   a CSV file.

# Define the file name of the image
INPUT_IMAGE = sys.argv[1] # "cat_dog.jpg"
IMAGE_NAME = INPUT_IMAGE[:INPUT_IMAGE.index(".")]
OUTPUT_IMAGE = IMAGE_NAME + "_annotated.jpg"
output_csv_file = sys.argv[2]

# Load the image and store into a variable
# -1 means load unchanged
image = cv2.imread(INPUT_IMAGE, -1)

# Create lists to store all x, y, and annotation values
x_vals = []
y_vals = []
annotation_vals = []

# Dictionary containing some colors
colors = {'blue': (255, 0, 0), 'green': (0, 255, 0), 'red': (0, 0, 255), 
          'yellow': (0, 255, 255),'magenta': (255, 0, 255), 
          'cyan': (255, 255, 0), 'white': (255, 255, 255), 'black': (0, 0, 0), 
          'gray': (125, 125, 125), 
          'rand': np.random.randint(0, high=256, size=(3,)).tolist(), 
          'dark_gray': (50, 50, 50), 'light_gray': (220, 220, 220)}

def draw_circle(event, x, y, flags, param):
    """
    Draws dots on double clicking of the left mouse button
    """
    # Store the height and width of the image
    height = image.shape[0]
    width = image.shape[1]

    if event == cv2.EVENT_LBUTTONDBLCLK:
        # Draw the dot
        cv2.circle(image, (x, y), 5, colors['magenta'], -1)

        # Annotate the image
        txt = input("Describe this pixel using one word (e.g. dog) and press ENTER: ")

        # Append values to the list
        x_vals.append(x)
        y_vals.append(y)
        annotation_vals.append(txt)

        # Print the coordinates and the annotation to the console
        print("x = " + str(x) + "  y = " + str(y) + "  Annotation = " + txt + "\n")

        # Set the position of the text part of the annotation
        text_x_pos = None
        text_y_pos = y

        if x < (width/2):
            text_x_pos = int(x + (width * 0.075))
        else:
            text_x_pos = int(x - (width * 0.075))
 
        # Write text on the image
        cv2.putText(image, txt, (text_x_pos,text_y_pos), cv2.FONT_HERSHEY_SIMPLEX, 1, colors['magenta'], 2)

        cv2.imwrite(OUTPUT_IMAGE, image)

        # Prompt user for another annotation
        print("Double click another pixel or press 'q' to quit...\n")

print("Welcome to the Image Annotation Program!\n")
print("Double click anywhere inside the image to annotate that point...\n")

# We create a named window where the mouse callback will be established
cv2.namedWindow('Image mouse')

# We set the mouse callback function to 'draw_circle':
cv2.setMouseCallback('Image mouse', draw_circle)

while True:
    # Show image 'Image mouse':
    cv2.imshow('Image mouse', image)

    # Continue until 'q' is pressed:
    if cv2.waitKey(20) & 0xFF == ord('q'):
        break

# Create a dictionary using lists
data = {'X':x_vals,'Y':y_vals,'Annotation':annotation_vals}

# Create the Pandas DataFrame
df = pd.DataFrame(data)
print()
print(df)
print()

# Export the dataframe to a csv file
df.to_csv(path_or_buf = output_csv_file, index = None, header=True) 

# Destroy all generated windows:
cv2.destroyAllWindows()

Output Images

CSV Output

Here is the output for the csv file for the baby photo above: