Image stitch with OpenCV in Python!

Main

I tried very hard to find a script which was able to stitch images. Other scripts were either too old to works on python3 or the functions are out of data. I final find a one in the post made by Adrian Rosebrock, 2018. Thanks Adrian Rosebrock, this script works very well and I’d like to note and share it here.

Origin Contributor: Adrian Rosebrock, 2018

Please reading the post from the original sites. It would be a great help.

pic_stitch.py

#!/usr/bin/env python3
'''
https://www.pyimagesearch.com/2018/12/17/image-stitching-with-opencv-and-python/
'''
from imutils import paths
import numpy as np
import argparse
import imutils
import cv2
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--input", type=str, required=True,
	help="path to input directory of input to stitch")
ap.add_argument("-o", "--output", type=str, required=True,
	help="path to the output image")
ap.add_argument("-c", "--crop", type=int, default=0,
	help="whether to crop out largest rectangular region")
ap.add_argument("-t", "--type", type=str, default="d",
	help="type of the input, 'd' is directory, 'v' is video")
args = vars(ap.parse_args())
# grab the paths to the input images and initialize our images list
images = []

if args["type"] == "d":
  print("[INFO] loading images...")
  imagePaths = sorted(list(paths.list_images(args["input"])))
  # loop over the image paths, load each one, and add them to our
  # images to stich list
  for imagePath in imagePaths:
  	image = cv2.imread(imagePath)
  	images.append(image)
  # initialize OpenCV's image sticher object and then perform the image
  # stitching
  print("[INFO] stitching images...")
elif args["type"] =="v":
  cap=cv2.VideoCapture(args["input"])
  Num = 0
  while Num < cap.get(cv2.CAP_PROP_FRAME_COUNT):
     Num += 1
     if Num % 1 == 0:
       ret,frame=cap.read()
       images.append(frame)

stitcher = cv2.createStitcher() if imutils.is_cv3() else cv2.Stitcher_create()
(status, stitched) = stitcher.stitch(images)

# if the status is '0', then OpenCV successfully performed image
# stitching
if status == 0:
	# check to see if we supposed to crop out the largest rectangular
	# region from the stitched image
	if args["crop"] > 0:
		# create a 10 pixel border surrounding the stitched image
		print("[INFO] cropping...")
		stitched = cv2.copyMakeBorder(stitched, 10, 10, 10, 10,
			cv2.BORDER_CONSTANT, (0, 0, 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 = cv2.cvtColor(stitched, cv2.COLOR_BGR2GRAY)
		thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY)[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 = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
			cv2.CHAIN_APPROX_SIMPLE)
		cnts = imutils.grab_contours(cnts)
		c = max(cnts, key=cv2.contourArea)
		# allocate memory for the mask which will contain the
		# rectangular bounding box of the stitched image region
		mask = np.zeros(thresh.shape, dtype="uint8")
		(x, y, w, h) = cv2.boundingRect(c)
		cv2.rectangle(mask, (x, y), (x + w, y + h), 255, -1)
		# create two copies of the mask: one to serve as our actual
		# minimum rectangular region and another to serve as a counter
		# for how many pixels need to be removed to form the minimum
		# rectangular region
		minRect = mask.copy()
		sub = mask.copy()
		# keep looping until there are no non-zero pixels left in the
		# subtracted image
		while cv2.countNonZero(sub) > 0:
			# erode the minimum rectangular mask and then subtract
			# the thresholded image from the minimum rectangular mask
			# so we can count if there are any non-zero pixels left
			minRect = cv2.erode(minRect, None)
			sub = cv2.subtract(minRect, thresh)
		# find contours in the minimum rectangular mask and then
		# extract the bounding box (x, y)-coordinates
		cnts = cv2.findContours(minRect.copy(), cv2.RETR_EXTERNAL,
			cv2.CHAIN_APPROX_SIMPLE)
		cnts = imutils.grab_contours(cnts)
		c = max(cnts, key=cv2.contourArea)
		(x, y, w, h) = cv2.boundingRect(c)
		# use the bounding box coordinates to extract the our final
		# stitched image
		stitched = stitched[y:y + h, x:x + w]
	# write the output stitched image to disk
	cv2.imwrite(args["output"], stitched)
	# display the output stitched image to our screen
	cv2.imshow("Stitched", stitched)
	cv2.waitKey(0)
# otherwise the stitching failed, likely due to not enough keypoints)
# being detected
else:
	print("[INFO] image stitching failed ({})".format(status))

How to use it

tree test

test
├── IMG_20210421_143828.jpg
├── IMG_20210421_143832.jpg
└── IMG_20210421_143834.jpg

Python

python3  pic_stitch.py -i test -o result.png

Result:

The Simplified Script

@MoonJian 2018

import numpy as np
import cv2
from cv2 import Stitcher

if __name__ == "__main__":
    img1 = cv2.imread('/home/ken/Desktop/test/IMG_20210421_143834.jpg')
    img2 = cv2.imread('/home/ken/Desktop/IMG_20210421_143832.jpg')
    #stitcher = cv2.createStitcher(False)
    stitcher = cv2.Stitcher.create(cv2.Stitcher_PANORAMA)# , 根据不同的OpenCV版本来调用
    (_result, pano) = stitcher.stitch((img1, img2))
    cv2.imshow('pano',pano)
    cv2.waitKey(0)

Stick A video

No work so well

import numpy as np
import cv2, sys, time
from cv2 import Stitcher

def progress_bar(i):
     print("\r", end="")
     print("Progress: {}%: ".format(i), "▋" * (int(i) // 2), end="")
     sys.stdout.flush()
     time.sleep(0.05)

cap=cv2.VideoCapture("stitch.mp4")
fps_c = cap.get(cv2.CAP_PROP_FRAME_COUNT)
stitcher = cv2.Stitcher.create(cv2.Stitcher_PANORAMA)# , 根据不同的OpenCV版本来调用

Num = 0
ret,Result =cap.read()
while Num <= fps_c:
   Num += 1
   ret,frame=cap.read()
   _result = 1
   if Num % 1 == 0:
     (_result, Result_tmp) = stitcher.stitch((Result, frame))
   progress_bar(100 * Num/fps_c )
   if _result == 0:
      Result = Result_tmp
   Ratio = [len(Result[0])/1080*2,len(Result)/1920*2]
   Ratio.sort()
   Ratio = Ratio[-1]
   test = cv2.resize(Result, (int(len(Result[0])/Ratio),int(len(Result)/Ratio)), interpolation = cv2.INTER_AREA)
   cv2.imshow("Stitched", test)
   if cv2.waitKey(1) & 0xFF == ord('q'):
       cv2.destroyAllWindows()
       break