基于Python+OpenCV对多张图片进行全景图像拼接源码+项目使用说明(课程设计大作业).zip

# 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, 2, 2, 2, 2, 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))