OpenCV is an open-source computer vision and machine learning software library. Various image processing operations such as manipulating images and applying tons of filters can be done with the help of it. It is broadly used in Object detection, Face Detection, and other Image processing tasks.
Let’s see how to draw rectangular shape on image and extract the objects using OpenCV.
# Python program to extract rectangular# Shape using OpenCV in Python3import cv2import numpy as np drawing = False # true if mouse is pressedmode = True # if True, draw rectangle.ix, iy = -1, -1 # mouse callback functiondef draw_circle(event, x, y, flags, param): global ix, iy, drawing, mode if event == cv2.EVENT_LBUTTONDOWN: drawing = True ix, iy = x, y elif event == cv2.EVENT_MOUSEMOVE: if drawing == True: if mode == True: cv2.rectangle(img, (ix, iy), (x, y), (0, 255, 0), 3) a = x b = y if a != x | b != y: cv2.rectangle(img, (ix, iy), (x, y), (0, 0, 0), -1) else: cv2.circle(img, (x, y), 5, (0, 0, 255), -1) elif event == cv2.EVENT_LBUTTONUP: drawing = False if mode == True: cv2.rectangle(img, (ix, iy), (x, y), (0, 255, 0), 2) else: cv2.circle(img, (x, y), 5, (0, 0, 255), -1) img = np.zeros((512, 512, 3), np.uint8)cv2.namedWindow('image')cv2.setMouseCallback('image', draw_circle) while(1): cv2.imshow('image', img) k = cv2.waitKey(1) & 0xFF if k == ord('m'): mode = not mode elif k == 27: break cv2.destroyAllWindows() |
Output:
Above piece of code will work with only black background image. But rectangles can be drawn to any images. We can write a program which allows us to select desired portion in an image and extract that selected portion as well. The task includes following things –
- draw shape on any image
- re-select the extract portion for in case bad selection
- extract particular object from the image
# Write Python code here# import the necessary packagesimport cv2import argparse # now let's initialize the list of reference pointref_point = []crop = False def shape_selection(event, x, y, flags, param): # grab references to the global variables global ref_point, crop # if the left mouse button was clicked, record the starting # (x, y) coordinates and indicate that cropping is being performed if event == cv2.EVENT_LBUTTONDOWN: ref_point = [(x, y)] # check to see if the left mouse button was released elif event == cv2.EVENT_LBUTTONUP: # record the ending (x, y) coordinates and indicate that # the cropping operation is finished ref_point.append((x, y)) # draw a rectangle around the region of interest cv2.rectangle(image, ref_point[0], ref_point[1], (0, 255, 0), 2) cv2.imshow("image", image) # construct the argument parser and parse the argumentsap = argparse.ArgumentParser()ap.add_argument("-i", "--image", required = True, help ="Path to the image")args = vars(ap.parse_args()) # load the image, clone it, and setup the mouse callback functionimage = cv2.imread(args["image"])clone = image.copy()cv2.namedWindow("image")cv2.setMouseCallback("image", shape_selection) # keep looping until the 'q' key is pressedwhile True: # display the image and wait for a keypress cv2.imshow("image", image) key = cv2.waitKey(1) & 0xFF # press 'r' to reset the window if key == ord("r"): image = clone.copy() # if the 'c' key is pressed, break from the loop elif key == ord("c"): break if len(ref_point) == 2: crop_img = clone[ref_point[0][1]:ref_point[1][1], ref_point[0][0]: ref_point[1][0]] cv2.imshow("crop_img", crop_img) cv2.waitKey(0) # close all open windowscv2.destroyAllWindows() |
Run : Save the file as capture_events.py and for testing select a demo picture which is located in the same directory. Now, execute the following command –
python capture_events.py --image demo.jpg
Output: First select the desired portion from the image. In addition, we can remove bad selection by pressing ‘r’ as programmed for making a new proper selection.
Fig: Selected Portion
Now after selecting a proper selection like above, just press ‘c’ to extract, as programmed.
Fig: Cut Portion
