I get asked all the time, “What is computer vision?”. Computer vision has been around since the late 1960s. Although the field has evolved significantly since then, the basic idea of computer vision remains the same.
Computer vision deals with how computers extract meaningful pieces of information from digital image or video pixels (i.e. “picture elements”), and then match these pieces of information to known, labeled ones in order to achieve understanding.
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.
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()
Here is the output for the csv file for the baby photo above:
Given an image as input, how do we get the corresponding histogram using OpenCV? First, let us take a look at what a histogram is, then let us take a look at how to create one given an image.
A histogram is another way of looking at an image. It is a graph that shows pixel brightness values on the x-axis (e.g. 0 [black] to 255 [white] for grayscale images) and the corresponding number (i.e. frequency) of pixels (for each brightness value) on the y-axis.
There are two links I particularly like that show how to create the image histogram given an input image.
I like these tutorials because they lead the reader through all the essentials of how to find and analyze image histograms, step-by-step. This process boils down to the following code:
# Import the required libraries
import cv2 # Open CV
from matplotlib import pyplot as plt #Matplotlib for plotting
# Read the input image
img = cv2.imread('example.jpg',0)
# Calculate the frequency of pixels in the brightness range 0 - 255
histr = cv2.calcHist([img],[0],None,[256],[0,256])
# Plot the histogram and display
plt.plot(histr)
plt.show()