Book Image

OpenCV 3 Computer Vision with Python Cookbook

By : Aleksei Spizhevoi, Aleksandr Rybnikov
Book Image

OpenCV 3 Computer Vision with Python Cookbook

By: Aleksei Spizhevoi, Aleksandr Rybnikov

Overview of this book

OpenCV 3 is a native cross-platform library for computer vision, machine learning, and image processing. OpenCV's convenient high-level APIs hide very powerful internals designed for computational efficiency that can take advantage of multicore and GPU processing. This book will help you tackle increasingly challenging computer vision problems by providing a number of recipes that you can use to improve your applications. In this book, you will learn how to process an image by manipulating pixels and analyze an image using histograms. Then, we'll show you how to apply image filters to enhance image content and exploit the image geometry in order to relay different views of a pictured scene. We’ll explore techniques to achieve camera calibration and perform a multiple-view analysis. Later, you’ll work on reconstructing a 3D scene from images, converting low-level pixel information to high-level concepts for applications such as object detection and recognition. You’ll also discover how to process video from files or cameras and how to detect and track moving objects. Finally, you'll get acquainted with recent approaches in deep learning and neural networks. By the end of the book, you’ll be able to apply your skills in OpenCV to create computer vision applications in various domains.
Table of Contents (11 chapters)

Making your app interactive through handling user input from a mouse

In this recipe, we will learn how to enable the handling of mouse input in your OpenCV application. An instance that gets events from a mouse is the window, so we need to use cv2.imshow. But we also need to add our handlers for mouse events. Let's see, in detail, how to do it by implementing crop functionality through selecting image regions by mouse.

Getting ready

You need to have OpenCV 3.x installed with Python API support.

How to do it...

The steps for this recipe are as follows:

  1. First, load an image and make its copy:
import cv2, numpy as np

image = cv2.imread('../data/Lena.png')
image_to_show = np.copy(image)
  1. Now, define some variables to store the mouse state:
mouse_pressed = False
s_x = s_y = e_x = e_y = -1
  1. Let's implement a handler for mouse events. This should be a function that takes four arguments, as follows:
def mouse_callback(event, x, y, flags, param):
global image_to_show, s_x, s_y, e_x, e_y, mouse_pressed

if event == cv2.EVENT_LBUTTONDOWN:
mouse_pressed = True
s_x, s_y = x, y
image_to_show = np.copy(image)

elif event == cv2.EVENT_MOUSEMOVE:
if mouse_pressed:
image_to_show = np.copy(image)
cv2.rectangle(image_to_show, (s_x, s_y),
(x, y), (0, 255, 0), 1)

elif event == cv2.EVENT_LBUTTONUP:
mouse_pressed = False
e_x, e_y = x, y
  1. Let's create the window instance that will be capturing mouse events and translating them into the handler function we defined earlier:
cv2.namedWindow('image')
cv2.setMouseCallback('image', mouse_callback)
  1. Now, let's implement the remaining part of our application, which should be reacting to buttons pushes and cropping the original image:
while True:
cv2.imshow('image', image_to_show)
k = cv2.waitKey(1)

if k == ord('c'):
if s_y > e_y:
s_y, e_y = e_y, s_y
if s_x > e_x:
s_x, e_x = e_x, s_x

if e_y - s_y > 1 and e_x - s_x > 0:
image = image[s_y:e_y, s_x:e_x]
image_to_show = np.copy(image)
elif k == 27:
break

cv2.destroyAllWindows()

How it works...

In cv2.setMouseCallback , we assigned our mouse events handler, mouse_callback , to the window named image.

After launching, we will be able to select a region by pushing the left mouse button somewhere in the image, dragging the mouse to the end point, and releasing the mouse button to confirm that our selection is finished. We can repeat the process by clicking in a new place—the previous selection disappears:

By hitting the C button on the keyboard, we can cut an area inside the selected region, as follows: