Here is the Python code to crop the black or white background region of panorama images:
import cv2 as cv
import numpy as np
import imutils
import glob
from numba import jit
def crop_stitched_image (stitched, canvas_color = 'black'):
# Print cropping
print("[INFO] cropping...")
# Initilize the variables
w = stitched.shape[1]
h = stitched.shape[0]
# Convert the stitched image to grayscale and threshold it
# such that all pixels greater than zero are set to 255
# (foreground) while all others remain 0 (background)
gray = cv.cvtColor(stitched, cv.COLOR_BGR2GRAY)
if canvas_color == 'black':
thresh = cv.threshold(gray, 5, 255, cv.THRESH_BINARY)[1]
else:
thresh = cv.threshold(gray, 254, 255, cv.THRESH_BINARY_INV)[1]
# Find all external contours in the threshold image then find
# the *largest* contour which will be the contour/outline of
# the stitched image
cnts = cv.findContours(thresh.copy(), cv.RETR_EXTERNAL, cv.CHAIN_APPROX_NONE)
cnts = imutils.grab_contours(cnts)
c = max(cnts, key=cv.contourArea)
# Mask or stencil
stencil_inner = np.zeros(stitched.shape, dtype=np.uint8)
cv.fillPoly(stencil_inner, pts =[c], color=(255,255,255))
stencil_outer = ~stencil_inner
# Canvas outer indices
canvas_outer_indices = np.where(stencil_outer == [255])
# Normalize the image to -1 and others
stitched = np.asarray(stitched, dtype=np.float32)
stitched[canvas_outer_indices] = -255
stitched = stitched / 255.0
@jit(nopython=True)
def bulkRun():
maxarea = 0
height = np.zeros((w)).astype(np.int32)
left = np.zeros((w)).astype(np.int32)
right= np.zeros((w)).astype(np.int32)
ll = 0
rr = 0
hh = 0
nl = 0
for line in range(h):
for k in range(w):
p = stitched[line][k]
m = max(max(p[0], p[1]), p[2])
height[k] = 0 if m < 0 else height[k] + 1 #find Color::NO
for k in range(w):
left[k] = k;
while ((left[k] > 0) and (height[k] <= height[left[k] - 1])):
left[k] = left[left[k] - 1]
for k in range(w - 1, -1, -1):
right[k] = k
while ((right[k] < w - 1) and (height[k] <= height[right[k] + 1])):
right[k] = right[right[k] + 1]
for k in range(w):
val = (right[k] - left[k] + 1) * height[k]
if(maxarea < val):
maxarea = val
ll = left[k]
rr = right[k]
hh = height[k]
nl = line
return ll, rr, hh, nl
ll, rr, hh, nl = bulkRun()
cropH = hh + 1
cropW = rr - ll + 1
offsetx = ll
offsety = nl - hh + 1
stitched *= 255
return stitched[offsety : offsety + cropH, offsetx : offsetx + cropW].astype(np.uint8)
Below is the code usage:
def main():
# Read stitched image
image = cv.imread('input_image.jpg')
# write the output stitched and cropped image to disk
# stitched_cropped = crop_stitched_image (image, 'white')
stitched_cropped = crop_stitched_image (image, 'black')
cv.imwrite('cropped.jpg', stitched_cropped)
print("Done")
if __name__ == '__main__':
main()
cv.destroyAllWindows()
I have also uploaded Python and MATLAB versions in [GitHub][1] and [MATLAB File Exchange][2], respectively.
[1]: https://github.com/preethamam/panoramaCropper
[2]: https://www.mathworks.com/matlabcentral/fileexchange/102554-panorama-image-cropper
Here is the Python code to crop the black or white background region of panorama images:
import cv2 as cv
import numpy as np
import imutils
import glob
from numba import jit
def crop_stitched_image (stitched, canvas_color = 'black'):
# Print cropping
print("[INFO] cropping...")
# Initilize the variables
w = stitched.shape[1]
h = stitched.shape[0]
# Convert the stitched image to grayscale and threshold it
# such that all pixels greater than zero are set to 255
# (foreground) while all others remain 0 (background)
gray = cv.cvtColor(stitched, cv.COLOR_BGR2GRAY)
if canvas_color == 'black':
thresh = cv.threshold(gray, 5, 255, cv.THRESH_BINARY)[1]
else:
thresh = cv.threshold(gray, 254, 255, cv.THRESH_BINARY_INV)[1]
# Find all external contours in the threshold image then find
# the *largest* contour which will be the contour/outline of
# the stitched image
cnts = cv.findContours(thresh.copy(), cv.RETR_EXTERNAL, cv.CHAIN_APPROX_NONE)
cnts = imutils.grab_contours(cnts)
c = max(cnts, key=cv.contourArea)
# Mask or stencil
stencil_inner = np.zeros(stitched.shape, dtype=np.uint8)
cv.fillPoly(stencil_inner, pts =[c], color=(255,255,255))
stencil_outer = ~stencil_inner
# Canvas outer indices
canvas_outer_indices = np.where(stencil_outer == [255])
# Normalize the image to -1 and others
stitched = np.asarray(stitched, dtype=np.float32)
stitched[canvas_outer_indices] = -255
stitched = stitched / 255.0
@jit(nopython=True)
def bulkRun():
maxarea = 0
height = np.zeros((w)).astype(np.int32)
left = np.zeros((w)).astype(np.int32)
right= np.zeros((w)).astype(np.int32)
ll = 0
rr = 0
hh = 0
nl = 0
for line in range(h):
for k in range(w):
p = stitched[line][k]
m = max(max(p[0], p[1]), p[2])
height[k] = 0 if m < 0 else height[k] + 1 #find Color::NO
for k in range(w):
left[k] = k;
while ((left[k] > 0) and (height[k] <= height[left[k] - 1])):
left[k] = left[left[k] - 1]
for k in range(w - 1, -1, -1):
right[k] = k
while ((right[k] < w - 1) and (height[k] <= height[right[k] + 1])):
right[k] = right[right[k] + 1]
for k in range(w):
val = (right[k] - left[k] + 1) * height[k]
if(maxarea < val):
maxarea = val
ll = left[k]
rr = right[k]
hh = height[k]
nl = line
return ll, rr, hh, nl
ll, rr, hh, nl = bulkRun()
cropH = hh + 1
cropW = rr - ll + 1
offsetx = ll
offsety = nl - hh + 1
stitched *= 255
return stitched[offsety : offsety + cropH, offsetx : offsetx + cropW].astype(np.uint8)
Below is the code usage:
def main():
# Read stitched image
image = cv.imread('result_26.jpg')
# write the output stitched and cropped image to disk
# stitched_cropped = crop_stitched_image (image, 'white')
stitched_cropped = crop_stitched_image (image, 'black')
cv.imwrite('cropped.jpg', stitched_cropped)
print("Done")
if __name__ == '__main__':
main()
cv.destroyAllWindows()
I have also uploaded Python and MATLAB versions in [GitHub][1] and [MATLAB File Exchange][2], respectively.
[1]: https://github.com/preethamam/panoramaCropper
[2]: https://www.mathworks.com/matlabcentral/fileexchange/102554-panorama-image-cropper
