def upload_file():
if request.method == 'POST':
# check if the post request has the files part
if 'files[]' not in request.files:
flash('No file part')
return redirect(request.url)
files = request.files.getlist('files[]')
for file in files:
if file and allowed_file(file.filename):
filename = secure_filename(file.filename)
file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))
flash('File(s) successfully uploaded')
print("[INFO] loading images...")
images = []
for imagePath in files:
print(type(imagePath))
print(imagePath)
for imagePath in files:
image = cv2.imread(imagePath)
cv2.imshow("Image", image)
# image=cv2.resize(image, (64,64))
images.append(image)
print("[INFO] stitching images...")
stitcher = cv2.createStitcher() if imutils.is_cv3(
) else cv2.Stitcher_create()
(status, stitched) = stitcher.stitch(images)
if status == 0:
# cv2.imwrite(args["output"], stitched)
cv2.imshow("Stitched", stitched)
cv2.waitKey(0)
else:
print("[INFO] image stitching failed ({})".format(status))
return redirect('/')
def stitch(image_path, output_path, output_path1, crop):
print("[INFO] loading images...")
imagePaths = sorted(list(paths.list_images(image_path)))
images = []
# loop over the image paths, load each one, and add them to our
# images to stitch 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 status == 0:
if crop:
print("cropping")
cropped = crop(stitched)
cv2.imwrite(output_path, stitched)
cv2.imwrite(output_path1, cropped)
else:
cv2.imwrite(output_path, stitched)
print('done')
else:
print('not enough features')
def stitch(data, use_kaze=False):
""""this works ONLY WITH the modified opencv c++ code stitcher.stitch() takes vl images as first param and then
creates a panorama using the images from the second param. So if you want a vl pano, do
stitcher.stitch(vl_images, vl_images) and if you want an ir pano do this: stitcher.stitch(vl_images, ir_images)"""
print("stitching...")
stitcher = cv2.Stitcher_create()
if use_kaze:
stitcher.setFeaturesFinder(
cv2.KAZE.create()
) # sometimes does a better job, but can take longer. Alternative is ORB
stitcher.setPanoConfidenceThresh(1.0)
match_mask = np.zeros((len(data[0][1]), len(data[0][1])), np.uint8)
for i in range(len(data[0][1]) - 1):
match_mask[i, i + 1] = 1
stitcher.setMatchingMask(match_mask)
print("vl...")
status, stitched_vl = stitcher.stitch(data[0][1], data[0][1])
if status == 0:
print("SIZE:", stitched_vl.shape)
cv2.imwrite(data[0][0], stitched_vl)
print("ir...")
status, stitched_ir = stitcher.composePanorama(data[1][1])
if status == 0:
cv2.imwrite(data[1][0], stitched_ir)
print("mx...")
status, stitched_mx = stitcher.composePanorama(data[2][1])
if status == 0:
cv2.imwrite(data[2][0], stitched_mx)
def stitch(images):
stitcher = cv2.createStitcher() if imutils.is_cv3(
) else cv2.Stitcher_create()
status, stitched = stitcher.stitch(images)
# 四周填充黑色像素，再得到阈值图
stitched = cv2.copyMakeBorder(stitched, 10, 10, 10, 10,
cv2.BORDER_CONSTANT, (0, 0, 0))
gray = cv2.cvtColor(stitched, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY)
cnts, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnt = max(cnts, key=cv2.contourArea)
mask = np.zeros(thresh.shape, dtype="uint8")
x, y, w, h = cv2.boundingRect(cnt)
cv2.rectangle(mask, (x, y), (x + w, y + h), 255, -1)
minRect = mask.copy()
sub = mask.copy()
# 开始while循环，直到sub中不再有前景像素
while cv2.countNonZero(sub) > 0:
minRect = cv2.erode(minRect, None)
sub = cv2.subtract(minRect, thresh)
cnts, hierarchy = cv2.findContours(minRect.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnt = max(cnts, key=cv2.contourArea)
x, y, w, h = cv2.boundingRect(cnt)
# 使用边界框坐标提取最终的全景图
return stitched[y:y + h, x:x + w]
def main(data_path):
# avm = ArgoverseMap()
# argoverse_tracker_loader = ArgoverseTrackingLoader('argoverse-tracking/') #simply change to your local path of the data
# argoverse_forecasting_loader = ArgoverseForecastingLoader('argoverse-forecasting/')
# argoVerseDataset = ArgoVerseDataset()
# argoVerseDataset.readCameraParameters()
argoverse_tracker_loader = ArgoverseTrackingLoader(data_path)
camera1_name = 'ring_front_center'
camera2_name = 'ring_front_right'
camera3_name = 'ring_front_left'
camera1_images = argoverse_tracker_loader.image_list[camera1_name]
camera2_images = argoverse_tracker_loader.image_list[camera2_name]
camera3_images = argoverse_tracker_loader.image_list[camera3_name]
camera1_calib = argoverse_tracker_loader.calib[camera1_name]
camera2_calib = argoverse_tracker_loader.calib[camera2_name]
camera3_calib = argoverse_tracker_loader.calib[camera3_name]
cv2.namedWindow('img1', cv2.WINDOW_NORMAL)
cv2.resizeWindow('img1', 600,400)
cv2.namedWindow('img2', cv2.WINDOW_NORMAL)
cv2.resizeWindow('img2', 600,400)
cv2.namedWindow('img3', cv2.WINDOW_NORMAL)
cv2.resizeWindow('img3', 600,400)
cv2.namedWindow('Stiched Img', cv2.WINDOW_NORMAL)
cv2.resizeWindow('Stiched Img', 1200,800)
for i in range(len(camera1_images)):
im1 = cv2.imread(camera1_images[i])
im2 = cv2.imread(camera2_images[i])
im3 = cv2.imread(camera3_images[i])
#Merge Imgs into single
sticker = cv2.Stitcher_create()
status, stichedImg = sticker.stitch([im1, im2, im3])
# cv2.imshow('img2', im2)
# cv2.waitKey()
if status == 0:
cv2.imshow('Stiched Img', stichedImg)
cv2.imshow('img1', im1)
cv2.imshow('img2', im2)
cv2.imshow('img3', im3)
# cv2.waitKey()
if cv2.waitKey(1) & 0xFF == ord('q'):
break
def stitch(update, context):
logger.info('Stitching for %d' % update.message.chat_id)
imgs = []
for fid in r.lrange(update.message.chat_id, 0, -1):
raw = update.message.bot.get_file(
fid.decode('ascii')).download_as_bytearray()
imgs.append(
cv2.imdecode(np.fromstring(bytes(raw), np.uint8),
cv2.IMREAD_COLOR))
stitcher = cv2.Stitcher_create(cv2.Stitcher_SCANS)
return_code, res = stitcher.stitch(tuple(imgs))
del imgs
if return_code == cv2.Stitcher_OK:
status, raw_res = cv2.imencode('.png', res)
now = dt.datetime.now()
update.message.reply_document(io.BytesIO(raw_res.tobytes()),
filename='stitched%s.png' %
now.strftime('%Y%m%d%H%H%S'))
elif return_code == cv2.Stitcher_ERR_NEED_MORE_IMGS:
update.message.reply_text(
'Not enough images. Plz try screenshots with more overlap. /help')
logger.info('Stitching failed for %d, not enough images' %
update.message.chat_id)
else:
update.message.reply_text(
'Unknown error while stitching. Plz try again. /help')
logger.warning('Stitching failed for %d, error code: %d' %
(update.message.chat_id, return_code))
r.delete(update.message.chat_id)
def stitch(images_path, output_path):
print("[INFO] loading images...")
imagePaths = sorted(list(paths.list_images(images_path)))
images = []
# loop over the image paths, load each one, and add them to our
# images to stitch list
for imagePath in imagePaths:
image = cv2.imread(imagePath)
images.append(image)
# initialize OpenCV's image stitcher 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:
# write the output stitched image to disk
cv2.imwrite(output_path, stitched)
return True
# otherwise the stitching failed, likely due to not enough keypoints)
# being detected
else:
print("[INFO] image stitching failed ({})".format(status))
return False
def stitch(self):
images = []
for i in range(constants.STITCH_COUNT):
print("=== Report === Loading " + str(i) + ".png")
image = cv2.imread(constants.STITCH_IMAGE_COMMON_PATH_PREFIX +
str(i) + ".png")
images.append(image)
print("=== Report === Loading complete")
print("=== Report === Start images stiching")
stitcher = cv2.Stitcher_create()
status, stitched = stitcher.stitch(images)
if status == 0:
print("=== Report === Successfully stitched images")
cv2.imwrite(constants.STITCH_IMAGE_COMMON_PATH_PREFIX + "pano.png",
stitched)
if self.DEBUG:
cv2.imshow("StitchedImage", stitched)
cv2.waitKey(0)
return True, stitched
else:
statusMeaning = [
'OK', 'ERR_NEED_MORE_IMGS', 'ERR_HOMOGRAPHY_EST_FAIL',
'ERR_CAMERA_PARAMS_ADJUST_FAIL'
]
print("=== Report === Faild to stitch images, status code = " +
str(status) + " " + statusMeaning[status])
return False, None
def stitch(self, path):
print("loading images")
images_paths = sorted(list(paths.list_images(path)))
images = []
for i in images_paths:
print(i)
im = cv2.imread(i)
images.append(im)
cv_stitcher = cv2.Stitcher_create()
# cv_stitcher.setRegistrationResol(-1);
# cv_stitcher.setSeamEstimationResol(-1);
# cv_stitcher.setCompositingResol(-1);
# cv_stitcher.setPanoConfidenceThresh(-1);
# cv_stitcher.setWaveCorrection(True);
# cv_stitcher.setWaveCorrectKind(detail::WAVE_CORRECT_HORIZ);
(stitched_check, stitched_image) = cv_stitcher.stitch(images)
if stitched_check == 0:
# cv2.imwrite("Stitched_Image.png", stitched_image)
cv2.imshow("Stitched_Image", stitched_image)
cv2.waitKey(0)
else:
print("Stitching no good")
def stitch(data, use_kaze=False):
""""THIS WORKS!!!! with the modified opencv c++ code stitcher.stitch() takes vl images as first param and then
creates a panorama using the images from the second param. So if you want a vl pano, do
stitcher.stitch(vl_images, vl_images) and if you want an ir pano do this: stitcher.stitch(vl_images, ir_images)"""
# stitcher = cv2.createStitcher(False)
print("stitching...")
stitcher = cv2.Stitcher_create()
if use_kaze:
stitcher.setFeaturesFinder(cv2.KAZE.create(
)) # sometimes does a better job, but can take longer
print("vl...")
status, stitched_vl = stitcher.stitch(data[0][1], data[0][1])
if status == 0:
print("SIZE:", stitched_vl.shape)
cv2.imwrite(data[0][0], stitched_vl)
print("ir...")
status, stitched_ir = stitcher.composePanorama(data[1][1])
if status == 0:
cv2.imwrite(data[1][0], stitched_ir)
print("mx...")
status, stitched_mx = stitcher.composePanorama(data[2][1])
if status == 0:
cv2.imwrite(data[2][0], stitched_mx)
def __init__(self, title, x, y, width, height):
super(MainView, self).__init__(title, x, y, width, height)
self.filenames = [
"../images/Lake2.png", "../images/Lake1.png", "../images/Blank.png"
]
self.grid = ZGridLayouter(self)
self.image_views = [None, None, None]
flags = cv2.IMREAD_COLOR
# 1 Create first imageview.
self.image_views[self.FIRST] = ZOpenCVImageView(
self.grid, self.filenames[self.FIRST], flags)
self.image_views[self.SECOND] = ZOpenCVImageView(
self.grid, self.filenames[self.SECOND], flags)
self.image_views[self.THIRD] = ZOpenCVImageView(
self.grid, self.filenames[self.THIRD], flags)
self.grid.add(self.image_views[self.FIRST], 0, 0)
self.grid.add(self.image_views[self.SECOND], 0, 1)
self.grid.add(self.image_views[self.THIRD], 1, 0, 1, 2)
self.mode = False #cv2.cv.PANORAMA
#self.stitcher = cv2.createStitcher(self.mode)
self.stitcher = cv2.Stitcher_create(self.mode)
self.stitch()
self.show()
def stitch_images(self, directory, output_path):
# grab the paths to the input images and initialize our images list
print("[INFO] loading images...")
imagePaths = sorted(list(paths.list_images(directory)))
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:
# write the output stitched image to disk
cv2.imwrite(output_path, 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))
def stich_images(img_dir="./shelf"):
"""
stitch images in a given directory
"""
print("[INFO] loading images...")
imagePaths = sorted(list(paths.list_images(img_dir)))
images = []
# loop over the image paths, load each one, and add them to our
# images to stitch list
for imagePath in imagePaths:
image = cv2.imread(imagePath)
images.append(image)
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:
# write the output stitched image to disk
cv2.imwrite("./pano.jpg", 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))
def work(self):
imagePaths = sorted(list(paths.list_images('images')))
ip = [] # 用于存放按次序的图片，因为拼接对次序敏感
tmp = imagePaths[0]
for i in range(len(imagePaths)):
ip.append(tmp[:12] + str(i) + tmp[13:])
images = []
for i, imagePath in enumerate(ip[:]):
if i % 1 == 0: # 2为隔一张，不需要隔则设置为1即可
print(imagePath)
image = cv2.imread(imagePath)
image = cv2.rotate(image, 2) # 横向旋转，因为拼接对方向敏感
images.append(image)
import time
a = time.time()
print('stitching images...')
stitcher = cv2.createStitcher() if imutils.is_cv3(
) else cv2.Stitcher_create()
(status, stitched) = stitcher.stitch(images)
print(time.time() - a)
if status == 0:
cv2.imwrite('res.png', stitched)
return stitched
else:
print("got an error ({})".format(status))
def main():
ap = argparse.ArgumentParser()
ap.add_argument("-i",
"--images",
default='images/building',
help="path to input directory of images to stitch")
ap.add_argument("-o",
"--output",
default='images/building/stitched.png',
help="path to the output image")
args = vars(ap.parse_args())
imagePaths = sorted(list(paths.list_images(args["images"])))
images = []
for imagePath in imagePaths:
image = cv2.imread(imagePath)
images.append(image)
stitcher = cv2.Stitcher_create()
(status, stitched) = stitcher.stitch(images)
if status == 0:
cv2.imwrite(args["output"], stitched)
cv2.imshow("Stitched", stitched)
cv2.waitKey(0)
else:
print("[INFO] image stitching failed ({})".format(status))
def create_stitich():
in_path = os.path.join('..', 'data', 'interim', 'frames',
'2019-08-13-1700') # 4Dec18 Ubir Geese
out_path = os.path.join('..', 'data', 'interim', 'stitched')
image_paths = sorted(list(paths.list_images(in_path)))
print(image_paths)
images = []
for image_path in image_paths:
#if 'DJI_0455' in image_path or 'DJI_0456' in image_path:
image = cv2.imread(image_path)
images.append(image)
stitcher = cv2.createStitcher() if imutils.is_cv3(
) else cv2.Stitcher_create()
(status, stitched) = stitcher.stitch(images)
file_name = '{0}.jpg'.format(in_path.split(os.sep)[-1])
out_path = os.path.join(out_path, file_name)
if status == 0:
cv2.imwrite(out_path, stitched)
cv2.imshow("Stitched", stitched)
cv2.waitKey(0)
else:
print("[INFO] image stitching failed ({})".format(status))
def image_stitch(images, crop=True):
start = time.clock()
stitcher = cv.Stitcher_create()
(status, stitched) = stitcher.stitch(images)
if status == 0:
if crop:
stitched = cv.copyMakeBorder(stitched, 2, 2, 2, 2, cv.BORDER_CONSTANT, (0, 0, 0))
gray = cv.cvtColor(stitched, cv.COLOR_BGR2GRAY)
thresh = cv.threshold(gray, 0, 255, cv.THRESH_BINARY)[1]
contours = cv.findContours(thresh.copy(), cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
contours = imutils.grab_contours(contours)
cnt = max(contours, key=cv.contourArea)
mask = np.zeros(thresh.shape, dtype="uint8")
(x, y, w, h) = cv.boundingRect(cnt)
cv.rectangle(mask, (x, y), (x + w, y + h), 255, -1)
minRect = mask.copy()
sub = mask.copy()
while cv.countNonZero(sub) > 0:
minRect = cv.erode(minRect, None)
sub = cv.subtract(minRect, thresh)
contours = cv.findContours(minRect.copy(), cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
contours = imutils.grab_contours(contours)
cnt = max(contours, key=cv.contourArea)
(x, y, w, h) = cv.boundingRect(cnt)
stitched = stitched[y:y + h, x:x + w]
print(f"time taken:{time.clock() - start}")
return stitched
else:
return print(f"image stitching failed {status}")
def main():
# 加载参数
args = parser_args()
print(args.images)
# 获取图像列表
imagePaths = sorted(list(paths.list_images(args.images)))
images = []
# 加载图像
for imagePath in imagePaths:
image = cv2.imread(imagePath)
images.append(image)
# 拼接
print("[INFO] stitching images...")
stitcher = cv2.createStitcher() if imutils.is_cv3(
) else cv2.Stitcher_create()
(status, stitched) = stitcher.stitch(images)
if status == 0:
# status=0时，表示拼接成功
if args.crop > 0:
# 边界填充
stitched = cv2.copyMakeBorder(stitched, 10, 10, 10, 10,
cv2.BORDER_CONSTANT, (0, 0, 0))
# 转为灰度图进行并二值化
gray = cv2.cvtColor(stitched, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY)[1]
# 获取轮廓
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
c = max(cnts, key=cv2.contourArea)
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)
minRect = mask.copy()
sub = mask.copy()
while cv2.countNonZero(sub) > 0:
minRect = cv2.erode(minRect, None)
sub = cv2.subtract(minRect, thresh)
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)
# 取出图像区域
stitched = stitched[y:y + h, x:x + w]
cv2.imwrite(args.output, stitched)
else:
print("[INFO] image stitching failed ({})".format(status))
def makePanorama(self, root, dest):
if root.replace(' ', '') == '' or dest.replace(' ', '') == '':
buttonReply = QMessageBox.warning(self, 'Warning',
'Please enter the folder path',
QMessageBox.Cancel)
else:
self.btnFlag = True
img_list = []
imgs = []
for file in glob.iglob(root + '\\**', recursive=True):
if os.path.splitext(file)[1] not in ['.jpg', '.png']:
continue
img_list.append(file)
print(file)
img_list = sorted(img_list)
try:
for i, img_path in enumerate(img_list):
img = cv2.imread(img_path)
imgs.append(img)
stitcher = cv2.Stitcher_create(cv2.STITCHER_PANORAMA)
status, stitched = stitcher.stitch(imgs)
# if status == 0:
# plt.imshow(cv2.cvtColor(stitched, cv2.COLOR_BGR2RGB))
# plt.show()
# else:
# print('failed... %s' % status)
gray = cv2.cvtColor(stitched, cv2.COLOR_BGR2GRAY)
thresh = cv2.bitwise_not(
cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY)[1])
thresh = cv2.medianBlur(thresh, 5)
stitched_copy = stitched.copy()
thresh_copy = thresh.copy()
while np.sum(thresh_copy) > 0:
thresh_copy = thresh_copy[1:-1, 1:-1]
stitched_copy = stitched_copy[1:-1, 1:-1]
cv2.imwrite(os.path.join(dest, 'result_crop.jpg'), stitched_copy)
buttonReply = QMessageBox.information(self, 'Succeess',
'Make Panorama Succeed',
QMessageBox.Ok)
self.btnFlag = False
# plt.imshow(cv2.cvtColor(stitched_copy, cv2.COLOR_BGR2RGB))
# plt.show()
except Exception as e:
buttonReply = QMessageBox.critical(
self, 'Error',
"It's not a similar image or can't find anything in common between the images.",
QMessageBox.Ok)
self.btnFlag = False
print(e)
def stitch_debug(imgs):
window2 = cv2.imread("window2.jpg")
stitcher = cv2.Stitcher_create(mode=cv2.STITCHER_PANORAMA)
status, stitched = stitcher.stitch([window2, imgs[4]])
cv2.imshow("s", stitched)
cv2.waitKey()
cv2.destroyAllWindows()
print(status)
def img_stitching(images):
"""
图片拼接
"""
stitcher = cv2.Stitcher_create()
status, stitch_img = stitcher.stitch(images)
if status != cv2.Stitcher_OK:
print(f"合拼图片失败，status = {status}")
return stitch_img
def stitch(img_pair):
stitcher = cv2.createStitcher() if imutils.is_cv3(
) else cv2.Stitcher_create()
imgs = [img_pair[0], img_pair[1]]
(status, stitched) = stitcher.stitch(imgs)
if status == 0:
return stitched
else:
return None
def stitch_images(self):
if self.flags["image_loaded"]:
# call the cv2 stitcher class and use
stitcher = cv2.createStitcher() if imutils.is_cv3(
) else cv2.Stitcher_create()
(status, self.stitched) = stitcher.stitch(self.images)
if status == 0:
self.flags["image_stitched"] = True
def stitchImages(images):
stitcher = cv2.Stitcher_create()
(status, stitched) = stitcher.stitch(images) #using opencv stitch function
stitched, mask, thresh = createMask(stitched) #creating mask for bordered area
stitched = cropPanorama(stitched, mask, thresh) #determining bounding box and cropping image
return stitched
def stitch(files):
imgs = []
for file in files:
imgs.append(cv2.imread(DIR + '/' + file))
stitcher = cv2.Stitcher_create()
status, pano = stitcher.stitch(imgs)
if status == 0:
return pano
else:
return None
def stitch(self, images):
print("Stitching images")
stitcher = cv2.Stitcher_create(cv2.Stitcher_SCANS)
status, scan = stitcher.stitch(images)
if status != cv2.Stitcher_OK:
print("Stitching Successful.")
cv2.imwrite("./output/stitched.jpg", scan)
return status, scan
def stitch_images(images: list):
print("INFO> stitching images")
stitcher = cv2.Stitcher_create(mode=0)
(status, stitched) = stitcher.stitch(images)
if status == 0:
return stitched
else:
print(f"[INFO] image stitching failed. Status: {status}")
return None
def stitching(trainImg,
queryImg,
ransacMet="fwd",
th=5,
d=70,
n=4,
k=1000,
blending=False,
blendrate=0.2,
mode=None,
override=0,
cylinderT=1):
if override == 0:
#_trainImg = cv2.resize(trainImg, None,fx=0.5, fy=0.5)
#_queryImg = cv2.resize(queryImg, None,fx=0.5, fy=0.5)
#if cylinderT:
# trainImg = cylindericlMap(trainImg)
# queryImg = cylindericlMap(queryImg)
trainImg_gray = cv2.cvtColor(trainImg, cv2.COLOR_RGB2GRAY)
queryImg_gray = cv2.cvtColor(queryImg, cv2.COLOR_RGB2GRAY)
descriptor = cv2.ORB_create()
(kpsA, featuresA) = descriptor.detectAndCompute(trainImg_gray, None)
(kpsB, featuresB) = descriptor.detectAndCompute(queryImg_gray, None)
bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
best_matches = bf.match(featuresA, featuresB)
matches = sorted(best_matches, key=lambda x: x.distance)
kpsA = np.float32([kp.pt for kp in kpsA])
kpsB = np.float32([kp.pt for kp in kpsB])
ptsA = np.float32([kpsA[m.queryIdx] for m in matches])
ptsB = np.float32([kpsB[m.trainIdx] for m in matches])
if mode is None:
model = HomoModel(th=th, d=d, n=4)
ransac = RANSAC(model, k=k)
H, inliers, _len = ransac.run([ptsA.T, ptsB.T], method=ransacMet)
else:
reprojThresh = 4
(H, status) = cv2.findHomography(ptsA, ptsB, cv2.RANSAC,
reprojThresh)
h, w, c = queryImg.shape
imgn = stitchPanorama(queryImg,
trainImg,
H=H,
blending=blending,
blendrate=blendrate)
else:
stitcher = cv2.Stitcher_create(
) #cv2.createStitcher() #v2.Stitcher_create()
(status, imgn) = stitcher.stitch([trainImg, queryImg])
return imgn
def perform_stitching(self, output_file):
stitcher = cv2.createStitcher() if imutils.is_cv3(
) else cv2.Stitcher_create()
stitched = np.zeros(
(self.imgs[0].shape[0], self.imgs[0].shape[1] * len(self.imgs)))
(status, stitched) = stitcher.stitch(self.imgs, stitched)
if status == 0:
# write the output stitched image to disk
cv2.imwrite(output_file, stitched)
return status, stitched
def stitch(self):
# print("Stitching images")
stitcher = cv2.Stitcher_create(cv2.Stitcher_SCANS)
img_list = [self.source_img] + self.reference_imgs
status, self.stitched_img = stitcher.stitch(img_list)
if status == cv2.Stitcher_OK:
print("Stitching Successful.")
cv2.imwrite("./stitched.jpg", self.stitched_img)
return status, self.stitched_img