Reputation: 51
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.
Upvotes: 0
Views: 10454
Reputation: 461
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)
Upvotes: 1
Reputation: 46779
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.
Upvotes: 0