def stitch_images(imageA, imageB):
''' Stitches 2 images together
Args:
imageA: First image
imageB: Second image
Returns:
result: the images stitched together
'''
# load the two images and resize them to have a width of 400 pixels
# (for faster processing)
imageA = imutils.resize(imageA, width=400, height=400)
imageB = imutils.resize(imageB, width=400, height=400)
# stitch the images together to create a panorama
stitcher = Stitcher()
(result, vis) = stitcher.stitch([imageA, imageB], showMatches=True)
# show the images (commented out for using pi through ssh)
#cv2.imshow("Image A", imageA)
#cv2.imshow("Image B", imageB)
#cv2.imshow("Keypoint Matches", vis)
#cv2.imshow("Result", result)
#cv2.waitKey(0)
return result
from matplotlib import pyplot as plt
import cv2
# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-f", "--first", required=True,
help="path to the first image")
ap.add_argument("-s", "--second", required=True,
help="path to the second image")
args = vars(ap.parse_args())
# load the two images and resize them to have a width of 400 pixels
# (for faster processing)
imageA = cv2.imread(args["first"])
imageB = cv2.imread(args["second"])
#imageA = imutils.resize(imageA, width=4000)
#imageB = imutils.resize(imageB, width=4000)
# stitch the images together to create a panorama
stitcher = Stitcher()
(result, vis) = stitcher.stitch([imageA, imageB], showMatches=True)
# show the images
cv2.imshow("Image A", imageA)
cv2.imshow("Image B", imageB)
cv2.imshow("Keypoint Matches", vis)
cv2.imshow("Result", result)
cv2.waitKey(0)
cv2.imwrite('mosaic.jpg', result)
import numpy as np
import datetime
import imutils
import time
import cv2
# initialize the video streams and allow them to warmup
print("[INFO] starting cameras...")
leftStream = VideoStream(src=1).start()
#rightStream = VideoStream(usePiCamera=True).start()
rightStream = VideoStream(src=0).start()
time.sleep(2.0)
# initialize the image stitcher, motion detector, and total
# number of frames read
stitcher = Stitcher()
motion = BasicMotionDetector(minArea=500)
total = 0
# loop over frames from the video streams
while True:
# grab the frames from their respective video streams
left = leftStream.read()
right = rightStream.read()
# resize the frames
left = imutils.resize(left, width=400)
right = imutils.resize(right, width=400)
# stitch the frames together to form the panorama
# IMPORTANT: you might have to change this line of code
ap.add_argument("-f", "--first", required=True,
help="path to the first image")
ap.add_argument("-s", "--second", required=True,
help="path to the second image")
args = vars(ap.parse_args())
# load the two images and resize them to have a width of 400 pixels
# (for faster processing)
imageA = cv2.imread(args["first"])
imageB = cv2.imread(args["second"])
#imageA = cv2.resize(imageA, (imageA.shape[0],imageA.shape[1]//2,))
#imageB = cv2.resize(imageB, (imageB.shape[0],imageB.shape[1]//2))
#imageA = imutils.resize(imageA, width=600)
#imageB = imutils.resize(imageB, width=600)
# stitch the images together to create a panorama
stitcher = Stitcher()
(result, vis) = stitcher.stitch([imageA, imageB], showMatches=True,ratio=0.75, reprojThresh=4.0)
# show the images
cv2.imshow("Image A", imutils.resize(imageA, width=800))
cv2.imshow("Image B", imutils.resize(imageB, width=800))
cv2.imshow("Keypoint Matches", imutils.resize(vis, width=1280))
cv2.imshow("Result", imutils.resize(result, width=1280))
cv2.waitKey(0)
cv2.destroyAllWindows()
outdir = os.path.split(args["first"])[0]
filename = os.path.split(args["first"])[-1]
cv2.imwrite(os.path.sep.join([outdir, "stitched.png"]),result)
from imutils.video import VideoStream
import numpy as np
import datetime
import imutils
import time
import cv2
# initialize the video streams and allow them to warmup
print("[INFO] starting cameras...")
#leftStream = VideoStream(src=0).start()
#rightStream = VideoStream(usePiCamera=True).start()
#time.sleep(2.0)
# initialize the image stitcher, motion detector, and total
# number of frames read
stitcher = Stitcher()
#motion = BasicMotionDetector(minArea=500)
total = 0
vidcap_left = cv2.VideoCapture('/Users/jayesh/Documents/Jayesh/left.mp4')
vidcap_right = cv2.VideoCapture('/Users/jayesh/Documents/Jayesh/right.mp4')
# loop over frames from the video streams
while True:
# grab the frames from their respective video streams
#left = leftStream.read()
#right = rightStream.read()
# resize the frames
success_left, image_left = vidcap_left.read()
success_right, image_right = vidcap_right.read()
if success_left is False or success_right is False:
print("frames are finished")
# USAGE
# python realtime_stitching.py
# import the necessary packages
from __future__ import print_function
from pyimagesearch.panorama import Stitcher
import datetime
import time
import cv2
import socket
import numpy
stitcher = Stitcher()
sock = socket.socket()
cap_1 = cv2.VideoCapture(0)
Self_IP = "192.168.0.110"
Self_PORT = 8005
Pre_IP = "192.168.0.111"
Pre_PORT = 8002
sock.connect((Pre_IP, Pre_PORT))
def recvall(sock, count):
buf = b''
while count:
newbuf = sock.recv(count)
if not newbuf: return None
# si 0 enregistrer l'image resultante # load the two images and resize them to have a width of 400 pixels # (for faster processing) if (i == 2 or (nbrVerticalImages == 2 and i == 0)): n = j - 1 first = file + "/" + str(n) + "." + formatImage imageA = cv2.imread(first) second = file + "/" + str(j) + "." + formatImage imageB = cv2.imread(second) imageA = imutils.resize(imageA, width=400) imageB = imutils.resize(imageB, width=400) # stitch the images together to create a panorama stitcher = Stitcher() (result, vis) = stitcher.stitch_horizontal([imageA, imageB], showMatches=True) elif i != 1: imageA = result second = file + "/" + str(j) + "." + formatImage imageB = cv2.imread(second) imageB = imutils.resize(imageB, width=400) # stitch the images together to create a panorama stitcher = Stitcher() (result, vis) = stitcher.stitch_horizontal([imageA, imageB], showMatches=True) if interm: # show the images
from pyimagesearch.panorama import Stitcher
import argparse
import imutils
import cv2
# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-f", "--first", required=True,
help="path to the first image")
ap.add_argument("-s", "--second", required=True,
help="path to the second image")
args = vars(ap.parse_args())
# load the two images and resize them to have a width of 400 pixels
# (for faster processing)
imageA = cv2.imread(args["first"])
imageB = cv2.imread(args["second"])
imageA = imutils.resize(imageA, width=400)
imageB = imutils.resize(imageB, width=400)
# stitch the images together to create a panorama
stitcher = Stitcher()
(result, vis) = stitcher.stitch([imageA, imageB], showMatches=True)
# show the images
cv2.imshow("Image A", imageA)
cv2.imshow("Image B", imageB)
cv2.imshow("Keypoint Matches", vis)
cv2.imshow("Result", result)
cv2.waitKey(0)
from pyimagesearch.panorama import Stitcher
import argparse
import imutils
import cv2
import numpy as np
# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-f", "--first", required=True, help="path to the first image")
ap.add_argument("-s",
"--second",
required=True,
help="path to the second image")
args = vars(ap.parse_args())
imageA = cv2.imread(args["first"])
imageB = cv2.imread(args["second"])
imageA = imutils.resize(imageA, width=500)
imageB = imutils.resize(imageB, width=500)
# stitch the images together to create a panorama
stitcher = Stitcher()
(result) = stitcher.stitch([imageA, imageB])
# show the images
#cv2.imshow("Image A", imageA)
#cv2.imshow("Image B", imageB)
cv2.imshow("Result", result)
cv2.imshow("Stacked", np.hstack((imageA, imageB)))
cv2.imwrite("Result.jpg", result)
cv2.waitKey(0)
# import the necessary packages
from pyimagesearch.panorama import Stitcher
import argparse
import imutils
import cv2
# define basic parameter
IM_NUM = 17
result = []
stitcher = Stitcher()
for i in range(IM_NUM, 14, -1):
if (i < 10):
im_name = "000" + str(i) + ".jpg"
else:
im_name = "00" + str(i) + ".jpg"
im_path = "/auto/extra/b02902015/py-faster-rcnn/video_image/Compress/" + im_name
image = cv2.imread(im_path)
cv2.imshow(im_name, image)
if (i == IM_NUM):
result = image
continue
result = stitcher.stitch([result, image], showMatches=True, diraction=0)
cv2.imshow("Result", result)
cv2.waitKey(0)
from pyimagesearch.panorama import Stitcher
import argparse
import imutils
import cv2
import numpy as np
img1 = cv2.imread("images/femfel1.png")
img2 = cv2.imread("images/femfel2.png")
stitcher = Stitcher()
(result, vis) = stitcher.stitch([img1, img2], showMatches=True)
img_diff = cv2.absdiff(result, img1)
kernel = np.ones((2, 2), np.uint8)
img_eroded = cv2.erode(img_diff, kernel, iterations=1)
gray = cv2.cvtColor(img_eroded, cv2.COLOR_BGR2GRAY)
retval, dest = cv2.threshold(gray, 40, 255, cv2.THRESH_BINARY)
image, contours, hierarchy = cv2.findContours(dest, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_NONE)
for c in contours:
(x, y, w, h) = cv2.boundingRect(c)
cv2.rectangle(img_eroded, (x, y), (x + w, y + h), (0, 0, 255), 1)
# Initiate ORB detector
orb = cv2.ORB_create()
# find the keypoints and descriptors with ORB
import time
import cv2
import rospy
from sensor_msgs.msg import Image
from cv_bridge import CvBridge, CvBridgeError
# ialize the video streams and allow them to warmup
print("[INFO] starting cameras...")
leftStream = VideoStream(src=0).start()
rightStream = VideoStream(src=1).start()
time.sleep(1.0)
# initialize the image stitcher, motion detector, and total
# number of frames read
stitcher = Stitcher()
motion = BasicMotionDetector(minArea=500)
total = 0
ImgStitcher = rospy.Publisher('ImgStitcher', Image, queue_size=10)
rospy.init_node('ImgPublisher')
rate = rospy.Rate(10) # 10hz
# loop over frames from the video streams
while not rospy.is_shutdown():
# grab the frames from their respective video streams
left = leftStream.read()
right = rightStream.read()
# resize the frames
left = imutils.resize(left, width=400)
args = vars(ap.parse_args())
# grab a reference to the video files
vs1 = cv2.VideoCapture(args["video1"])
vs2 = cv2.VideoCapture(args["video2"])
# allow the camera or video file to warm up
time.sleep(2.0)
#image = image_org.copy()
#image = imutils.resize(image_org, width=800)
outdir = os.path.split(args["video1"])[0]
# stitch the images together to create a panorama
stitcher = Stitcher()
fc1 = 0
fc2 = 0
fourcc = cv2.VideoWriter_fourcc(*"MJPG")
writer = cv2.VideoWriter(args["output"], fourcc, 30, (2560, 960), True)
while True:
# grab the current frame from left video
result1, image1 = vs1.read()
# if we are viewing a video and we did not grab a frame,
# then we have reached the end of the video
if result1 == False:
break
