How to perform automated detection and cropping of region of interest in an image in python?

0
votes

I want to perform operation on the region of my interest..that is central rectangular table which you can see in the image. I am able to give the co-ordinates of my region of interest manually and crop that part img = cv2.imread('test12.jpg',0) box = img[753:1915,460:1315]

but i want to crop that part automatically without giving the pixels or coordinates manually.Can anyone please help me with this?

http://picpaste.com/test12_-_Copy-BXqHMAnd.jpg this is my original image.

http://picpaste.com/boxdemo-zHz57dBM.jpg this is my cropped image. for doing this I entered the coordinates of the desired region and cropped. But , now i have to deal with many similar images where the coordinates of my region of interest will slightly vary. I want a method which will detect the table(my region of interest) and crop it. Currently I'm using this img = cv2.imread('test12.jpg',0) box = img[753:1915,460:1315] to crop my image.

2
pythonopencvautomationcrop
You will need to use OpenCV to help you detect the edges of your table. How you do this depends on the image in question. Are you able to give a picture sample (or at least a link to one until you have enough reputation). – Martin Evans
picpaste.com/test12_-_Copy-BXqHMAnd.jpg thisis the link to my image. And yes I'm using opencv . But I am a beginner in both python and image processing.. so I need to know how exactly to get my region of interest. – yash Wanth Shetty
@MartinEvans can you please help me with a piece of code..? – yash Wanth Shetty
your two links are dead. – snoob dogg

2 Answers

1
votes

You could try using the openCV Template Matching to find the coordinates of your rectangular table within the image. Template Matching

The following is a test program to find the coordinates for images I am trying to find.

from __future__ import print_function
import cv2
import numpy as np
from matplotlib import pyplot as plt

try:
    img = cv2.imread(r'new_webcam_image.jpg',0)
    template = cv2.imread(r'table_template.jpg',0)
except IOError as e:
    print("({})".format(e))
else:
    img2 = img.copy()
    w, h = template.shape[::-1]

# All the 6 methods for comparison in a list
 methods = ['cv2.TM_CCOEFF', 'cv2.TM_CCOEFF_NORMED', 'cv2.TM_CCORR',
             'cv2.TM_CCORR_NORMED', 'cv2.TM_SQDIFF', 'cv2.TM_SQDIFF_NORMED']


for meth in methods:
    img = img2.copy()
    method = eval(meth)

    # Apply template Matching
    res = cv2.matchTemplate(img,template,method)
    min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
    print("Method: %s" , meth)
    print("min_val: " , min_val)
    print("max_val: " , max_val)
    print("min_loc: " , min_loc)
    print("max_loc: " , max_loc)
    print(" ")
    # If the method is TM_SQDIFF or TM_SQDIFF_NORMED, take minimum
    if method in [cv2.TM_SQDIFF, cv2.TM_SQDIFF_NORMED]:
        top_left = min_loc
    else:
        top_left = max_loc
    bottom_right = (top_left[0] + w, top_left[1] + h)

    cv2.rectangle(img,top_left, bottom_right, 255, 2)

    plt.subplot(121),plt.imshow(res,cmap = 'gray')
    plt.title('Matching Result'), plt.xticks([]), plt.yticks([])
    plt.subplot(122),plt.imshow(img,cmap = 'gray')
    plt.title('Detected Point'), plt.xticks([]), plt.yticks([])
    plt.suptitle(meth) #; plt.legend([min_val, max_val, min_loc, max_loc], ["min_val", "max_val", "min_loc", "max_loc"])

    plt.show()
    box = img[top_left[1]:top_left[1]+h,0:bottom_right[1]+w]
    cv2.imshow("cropped", box)
    cv2.waitKey(0)
0
votes

I don't have a full solution for you. The code shown was based on some code I was using to fix output from a scanner. The template solution to me sounds like a better approach, but the following should give you something else to work with.

import cv2

imageSrc = cv2.imread("test12.jpg")

# First cut the source down slightly
h = imageSrc.shape[0]
w = imageSrc.shape[1]
cropInitial = 50
imageSrc = imageSrc[100:50+(h-cropInitial*2), 50:50+(w-cropInitial*2)]

# Threshold the image and find edges (to reduce the amount of pixels to count)
ret, imageDest = cv2.threshold(imageSrc, 220, 255, cv2.THRESH_BINARY_INV)
imageDest = cv2.Canny(imageDest, 100, 100, 3)

# Create a list of remaining pixels
points = cv2.findNonZero(imageDest)

# Calculate a bounding rectangle for these points
hull = cv2.convexHull(points)
x,y,w,h = cv2.boundingRect(hull)

# Crop the original image to the bounding rectangle
imageResult = imageSrc[y:y+h,x:x+w]
cv2.imwrite("test12 cropped.jpg", imageResult)

The output does not crop as much as you need. Playing with the various threshold parameters should improve your results.

I suggest using imshow at various points on imageThresh and imageDest so you can see what is happening at each stage in the code. Hopefully this helps you progress.