# USAGE
# python image_stitching_.py --images images/scottsdale --output output.png --crop 1

# import the necessary packages
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", "--images", type=str, required=True,
	help="path to input directory of images 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")
args = vars(ap.parse_args())

# grab the paths to the input images and initialize our images list
print("[INFO] loading images...")
imagePaths = sorted(list(paths.list_images(args["images"])))
images = []

# 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...")
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))