Crop final stitched panorama image

I am using OpenCV to stitch images incrementally (Left to Right).
After the stitching process is complete I want to crop the result for final panorama.

Take this example panorama image:
enter image description here

How do I crop the image to remove the repeating part shown inside RED box on right ?

asked Jan 1 at 13:15

Manmohan Bishnoi

416828

1

Cropping would seem easy, just take the ROI that contains the non-repeating part. Perhaps you're looking for a method to detect that repetition, so you can do the cropping?

– Dan Mašek
Jan 1 at 19:19

1

yes, detect the repeating part and then crop so it forms a perfect cylindrical panorama image. Any idea how to achieve this?

– Manmohan Bishnoi
Jan 2 at 6:01

add a comment |

I am using OpenCV to stitch images incrementally (Left to Right).
After the stitching process is complete I want to crop the result for final panorama.

Take this example panorama image:
enter image description here

How do I crop the image to remove the repeating part shown inside RED box on right ?

asked Jan 1 at 13:15

Manmohan Bishnoi

416828

1

Cropping would seem easy, just take the ROI that contains the non-repeating part. Perhaps you're looking for a method to detect that repetition, so you can do the cropping?

– Dan Mašek
Jan 1 at 19:19

1

yes, detect the repeating part and then crop so it forms a perfect cylindrical panorama image. Any idea how to achieve this?

– Manmohan Bishnoi
Jan 2 at 6:01

add a comment |

I am using OpenCV to stitch images incrementally (Left to Right).
After the stitching process is complete I want to crop the result for final panorama.

Take this example panorama image:
enter image description here

How do I crop the image to remove the repeating part shown inside RED box on right ?

asked Jan 1 at 13:15

Manmohan Bishnoi

416828

I am using OpenCV to stitch images incrementally (Left to Right).
After the stitching process is complete I want to crop the result for final panorama.

Take this example panorama image:
enter image description here

How do I crop the image to remove the repeating part shown inside RED box on right ?

opencv image-processing image-stitching

asked Jan 1 at 13:15

Manmohan Bishnoi

416828

asked Jan 1 at 13:15

Manmohan Bishnoi

416828

asked Jan 1 at 13:15

Manmohan Bishnoi

416828

asked Jan 1 at 13:15

Manmohan Bishnoi

416828

asked Jan 1 at 13:15

Manmohan Bishnoi

416828

1

Cropping would seem easy, just take the ROI that contains the non-repeating part. Perhaps you're looking for a method to detect that repetition, so you can do the cropping?

– Dan Mašek
Jan 1 at 19:19

1

yes, detect the repeating part and then crop so it forms a perfect cylindrical panorama image. Any idea how to achieve this?

– Manmohan Bishnoi
Jan 2 at 6:01

add a comment |

1

Cropping would seem easy, just take the ROI that contains the non-repeating part. Perhaps you're looking for a method to detect that repetition, so you can do the cropping?

– Dan Mašek
Jan 1 at 19:19

1

yes, detect the repeating part and then crop so it forms a perfect cylindrical panorama image. Any idea how to achieve this?

– Manmohan Bishnoi
Jan 2 at 6:01

Cropping would seem easy, just take the ROI that contains the non-repeating part. Perhaps you're looking for a method to detect that repetition, so you can do the cropping?

– Dan Mašek
Jan 1 at 19:19

yes, detect the repeating part and then crop so it forms a perfect cylindrical panorama image. Any idea how to achieve this?

– Manmohan Bishnoi
Jan 2 at 6:01

add a comment |

1 Answer
1

active

oldest

votes

I misunderstood your question. Regarding to your problem, take a 30-width-pixel window to the most left of the image as reference image, then slide over the x-axis of the image from left to right with a window of 30 pixels and then compare it with the reference image by the Mean Squared Error (MSE), the smaller the MSE is, the more similar the 2 images are. Look at the code for more details.

import matplotlib.pyplot as plt

import numpy as np

import cv2



img = cv2.imread('1.png')

# img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 

h=img.shape[0]

w=img.shape[1]



window_length = 30 # bigger length means more accurate and slower computing.



def mse(imageA, imageB):

    # the 'Mean Squared Error' between the two images is the

    # sum of the squared difference between the two images;

    # NOTE: the two images must have the same dimension

    err = np.sum((imageA.astype("float") - imageB.astype("float")) ** 2)

    err /= float(imageA.shape[0] * imageA.shape[1])



    # return the MSE, the lower the error, the more "similar"

    # the two images are

    return err



reference_img = img[:,0:window_length]



mse_values = 



for i in range(window_length,w-window_length):

    slide_image = img[:,i:i+window_length]

    m = mse(reference_img,slide_image)

    mse_values.append(m)    



#find the min MSE. Its index is where the image starts repeating

min_mse = min(mse_values)

index = mse_values.index(min_mse)+window_length



print(min_mse)

print(index)



repetition = img[:,index:index+window_length]   



# setup the figure

fig = plt.figure("compare")

plt.suptitle("MSE: %.2f"% (min_mse))



# show first image

ax = fig.add_subplot(1, 2, 1)

plt.imshow(reference_img, cmap = plt.cm.gray)

plt.axis("off")



# show the second image

ax = fig.add_subplot(1, 2, 2)

plt.imshow(repetition, cmap = plt.cm.gray)

plt.axis("off")



cropped_img = img[:,0:index]



cv2.imshow("img", img)    

cv2.imshow("cropped_img", cropped_img)    

# show the plot

plt.show()        



cv2.waitKey()

cv2.destroyAllWindows()

enter image description here

The idea of comparing 2 images comes from this post: https://www.pyimagesearch.com/2014/09/15/python-compare-two-images/

edited Jan 2 at 10:18

answered Jan 2 at 1:48

Ha Bom

1,3032519

Not a simple crop. I need to crop where the image starts repeating. That would include process to detect repeating pattern and then getting width on where to crop

– Manmohan Bishnoi
Jan 2 at 6:06

@ManmohanBishnoi Yes, I misunderstood your OP. I edited my answer.

– Ha Bom
Jan 2 at 9:50

add a comment |

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1 Answer
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active

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1 Answer
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oldest

votes

import matplotlib.pyplot as plt

import numpy as np

import cv2



img = cv2.imread('1.png')

# img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 

h=img.shape[0]

w=img.shape[1]



window_length = 30 # bigger length means more accurate and slower computing.



def mse(imageA, imageB):

    # the 'Mean Squared Error' between the two images is the

    # sum of the squared difference between the two images;

    # NOTE: the two images must have the same dimension

    err = np.sum((imageA.astype("float") - imageB.astype("float")) ** 2)

    err /= float(imageA.shape[0] * imageA.shape[1])



    # return the MSE, the lower the error, the more "similar"

    # the two images are

    return err



reference_img = img[:,0:window_length]



mse_values = 



for i in range(window_length,w-window_length):

    slide_image = img[:,i:i+window_length]

    m = mse(reference_img,slide_image)

    mse_values.append(m)    



#find the min MSE. Its index is where the image starts repeating

min_mse = min(mse_values)

index = mse_values.index(min_mse)+window_length



print(min_mse)

print(index)



repetition = img[:,index:index+window_length]   



# setup the figure

fig = plt.figure("compare")

plt.suptitle("MSE: %.2f"% (min_mse))



# show first image

ax = fig.add_subplot(1, 2, 1)

plt.imshow(reference_img, cmap = plt.cm.gray)

plt.axis("off")



# show the second image

ax = fig.add_subplot(1, 2, 2)

plt.imshow(repetition, cmap = plt.cm.gray)

plt.axis("off")



cropped_img = img[:,0:index]



cv2.imshow("img", img)    

cv2.imshow("cropped_img", cropped_img)    

# show the plot

plt.show()        



cv2.waitKey()

cv2.destroyAllWindows()

enter image description here

The idea of comparing 2 images comes from this post: https://www.pyimagesearch.com/2014/09/15/python-compare-two-images/

edited Jan 2 at 10:18

answered Jan 2 at 1:48

Ha Bom

1,3032519

Not a simple crop. I need to crop where the image starts repeating. That would include process to detect repeating pattern and then getting width on where to crop

– Manmohan Bishnoi
Jan 2 at 6:06

@ManmohanBishnoi Yes, I misunderstood your OP. I edited my answer.

– Ha Bom
Jan 2 at 9:50

add a comment |

import matplotlib.pyplot as plt

import numpy as np

import cv2



img = cv2.imread('1.png')

# img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 

h=img.shape[0]

w=img.shape[1]



window_length = 30 # bigger length means more accurate and slower computing.



def mse(imageA, imageB):

    # the 'Mean Squared Error' between the two images is the

    # sum of the squared difference between the two images;

    # NOTE: the two images must have the same dimension

    err = np.sum((imageA.astype("float") - imageB.astype("float")) ** 2)

    err /= float(imageA.shape[0] * imageA.shape[1])



    # return the MSE, the lower the error, the more "similar"

    # the two images are

    return err



reference_img = img[:,0:window_length]



mse_values = 



for i in range(window_length,w-window_length):

    slide_image = img[:,i:i+window_length]

    m = mse(reference_img,slide_image)

    mse_values.append(m)    



#find the min MSE. Its index is where the image starts repeating

min_mse = min(mse_values)

index = mse_values.index(min_mse)+window_length



print(min_mse)

print(index)



repetition = img[:,index:index+window_length]   



# setup the figure

fig = plt.figure("compare")

plt.suptitle("MSE: %.2f"% (min_mse))



# show first image

ax = fig.add_subplot(1, 2, 1)

plt.imshow(reference_img, cmap = plt.cm.gray)

plt.axis("off")



# show the second image

ax = fig.add_subplot(1, 2, 2)

plt.imshow(repetition, cmap = plt.cm.gray)

plt.axis("off")



cropped_img = img[:,0:index]



cv2.imshow("img", img)    

cv2.imshow("cropped_img", cropped_img)    

# show the plot

plt.show()        



cv2.waitKey()

cv2.destroyAllWindows()

enter image description here

The idea of comparing 2 images comes from this post: https://www.pyimagesearch.com/2014/09/15/python-compare-two-images/

edited Jan 2 at 10:18

answered Jan 2 at 1:48

Ha Bom

1,3032519

Not a simple crop. I need to crop where the image starts repeating. That would include process to detect repeating pattern and then getting width on where to crop

– Manmohan Bishnoi
Jan 2 at 6:06

@ManmohanBishnoi Yes, I misunderstood your OP. I edited my answer.

– Ha Bom
Jan 2 at 9:50

add a comment |

import matplotlib.pyplot as plt

import numpy as np

import cv2



img = cv2.imread('1.png')

# img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 

h=img.shape[0]

w=img.shape[1]



window_length = 30 # bigger length means more accurate and slower computing.



def mse(imageA, imageB):

    # the 'Mean Squared Error' between the two images is the

    # sum of the squared difference between the two images;

    # NOTE: the two images must have the same dimension

    err = np.sum((imageA.astype("float") - imageB.astype("float")) ** 2)

    err /= float(imageA.shape[0] * imageA.shape[1])



    # return the MSE, the lower the error, the more "similar"

    # the two images are

    return err



reference_img = img[:,0:window_length]



mse_values = 



for i in range(window_length,w-window_length):

    slide_image = img[:,i:i+window_length]

    m = mse(reference_img,slide_image)

    mse_values.append(m)    



#find the min MSE. Its index is where the image starts repeating

min_mse = min(mse_values)

index = mse_values.index(min_mse)+window_length



print(min_mse)

print(index)



repetition = img[:,index:index+window_length]   



# setup the figure

fig = plt.figure("compare")

plt.suptitle("MSE: %.2f"% (min_mse))



# show first image

ax = fig.add_subplot(1, 2, 1)

plt.imshow(reference_img, cmap = plt.cm.gray)

plt.axis("off")



# show the second image

ax = fig.add_subplot(1, 2, 2)

plt.imshow(repetition, cmap = plt.cm.gray)

plt.axis("off")



cropped_img = img[:,0:index]



cv2.imshow("img", img)    

cv2.imshow("cropped_img", cropped_img)    

# show the plot

plt.show()        



cv2.waitKey()

cv2.destroyAllWindows()

enter image description here

The idea of comparing 2 images comes from this post: https://www.pyimagesearch.com/2014/09/15/python-compare-two-images/

edited Jan 2 at 10:18

answered Jan 2 at 1:48

Ha Bom

1,3032519

import matplotlib.pyplot as plt

import numpy as np

import cv2



img = cv2.imread('1.png')

# img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 

h=img.shape[0]

w=img.shape[1]



window_length = 30 # bigger length means more accurate and slower computing.



def mse(imageA, imageB):

    # the 'Mean Squared Error' between the two images is the

    # sum of the squared difference between the two images;

    # NOTE: the two images must have the same dimension

    err = np.sum((imageA.astype("float") - imageB.astype("float")) ** 2)

    err /= float(imageA.shape[0] * imageA.shape[1])



    # return the MSE, the lower the error, the more "similar"

    # the two images are

    return err



reference_img = img[:,0:window_length]



mse_values = 



for i in range(window_length,w-window_length):

    slide_image = img[:,i:i+window_length]

    m = mse(reference_img,slide_image)

    mse_values.append(m)    



#find the min MSE. Its index is where the image starts repeating

min_mse = min(mse_values)

index = mse_values.index(min_mse)+window_length



print(min_mse)

print(index)



repetition = img[:,index:index+window_length]   



# setup the figure

fig = plt.figure("compare")

plt.suptitle("MSE: %.2f"% (min_mse))



# show first image

ax = fig.add_subplot(1, 2, 1)

plt.imshow(reference_img, cmap = plt.cm.gray)

plt.axis("off")



# show the second image

ax = fig.add_subplot(1, 2, 2)

plt.imshow(repetition, cmap = plt.cm.gray)

plt.axis("off")



cropped_img = img[:,0:index]



cv2.imshow("img", img)    

cv2.imshow("cropped_img", cropped_img)    

# show the plot

plt.show()        



cv2.waitKey()

cv2.destroyAllWindows()

enter image description here

The idea of comparing 2 images comes from this post: https://www.pyimagesearch.com/2014/09/15/python-compare-two-images/

edited Jan 2 at 10:18

answered Jan 2 at 1:48

Ha Bom

1,3032519

edited Jan 2 at 10:18

answered Jan 2 at 1:48

Ha Bom

1,3032519

answered Jan 2 at 1:48

Ha Bom

1,3032519

answered Jan 2 at 1:48

Ha Bom

1,3032519

Not a simple crop. I need to crop where the image starts repeating. That would include process to detect repeating pattern and then getting width on where to crop

– Manmohan Bishnoi
Jan 2 at 6:06

@ManmohanBishnoi Yes, I misunderstood your OP. I edited my answer.

– Ha Bom
Jan 2 at 9:50

add a comment |

Not a simple crop. I need to crop where the image starts repeating. That would include process to detect repeating pattern and then getting width on where to crop

– Manmohan Bishnoi
Jan 2 at 6:06

@ManmohanBishnoi Yes, I misunderstood your OP. I edited my answer.

– Ha Bom
Jan 2 at 9:50

Not a simple crop. I need to crop where the image starts repeating. That would include process to detect repeating pattern and then getting width on where to crop

– Manmohan Bishnoi
Jan 2 at 6:06

@ManmohanBishnoi Yes, I misunderstood your OP. I edited my answer.

– Ha Bom
Jan 2 at 9:50

add a comment |

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