CODEWITHSUNDEEP
pythonpython-3.xopencvimage-processingpython-imaging-library
SalGorithm
SalGorithm

Reputation: 901

make pixels of certain area of an image blank or fill that area with any color

I thought I could do it myself!

So, I've been working on a project of mine, where I need to find an/multiple object(s) in an image (Currently, I am using simple template matching).

When an object is found, I want to remove pixels of that area and make the area transparent or fill it with any color.

For example, I have this Image (where I want to find the coke bottle cane):-

image_before

After running Object detection script, I have:-

After_image

You can see the matched object inside the red rectangle!

Now, what I want to do is remove this rectangle area and make it transparent or fill it with any color!

I've tried many things, still trying but no luck. Here's what I have so far:

import numpy as np
import argparse
import imutils
import glob
import cv2
from matplotlib import pyplot as plt


ap = argparse.ArgumentParser()
ap.add_argument("-t", "--template", required=True, help="Path to template image")
ap.add_argument("-i", "--images", required=True,
    help="Path to images where template will be matched")
ap.add_argument("-v", "--visualize",
    help="Flag indicating whether or not to visualize each iteration")
args = vars(ap.parse_args())

def match_and_count(template, image):
    img_rgb = cv2.imread(image)
    img_gray = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2GRAY)
    template = cv2.imread(template,0)
    w, h = template.shape[::-1]

    res = cv2.matchTemplate(img_gray,template,cv2.TM_CCOEFF_NORMED)
    threshold = 0.8
    loc = np.where( res >= threshold)

    f = set()
    sensitivity = 100

    for pt in zip(*loc[::-1]):
        cv2.rectangle(img_rgb, pt, (pt[0] + w, pt[1] + h), (0,0,255), 2)
        # I want to make each rectangle transparent here
        f.add((round(pt[0]/sensitivity), round(pt[1]/sensitivity)))

    cv2.imwrite('multiple_objects.jpg',img_rgb)

    print("Occurence of Object: %s" % len(f))

match_and_count(args["template"], args["images"])

If anyone could give a hint or a piece of code, that does the same. I'll be glad, thank you.

Upvotes: 4

Views: 1711

Answers (1)

ZdaR
ZdaR

Reputation: 22954

You can use numpy slicing syntax to crop the box and then simply replace it with new color as:

replacement_color = np.array([20, 125, 89]) # any random color

for pt in zip(*loc[::-1]):
     # cv2.rectangle(...)
     img[pt[1]:pt[1] + h, pt[0]:pt[0]+w] = replacement_color

Or alternatively, you could have also used the cv2.rectangle API to get the same results as:

replacement_color = np.array([20, 125, 89]) # any random color

for pt in zip(*loc[::-1]):
     cv2.rectangle(img_rgb, pt, (pt[0] + w, pt[1] + h), replacement_color, -1)

You just need to pass the line_width param as -1.

Upvotes: 6

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