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 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 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 __stitchImages(self, firstImage, secondImage, matches, wtak, pSize,
eThresh):
# Create stitcher and stitch images
stitcher = cv2.createStitcher(True)
orb = cv2.ORB_create(WTA_K=wtak,
scaleFactor=1.1,
patchSize=pSize,
edgeThreshold=eThresh)
if (wtak == 4):
bf = cv2.BFMatcher(cv2.NORM_HAMMING2, crossCheck=True)
else:
bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
try:
status, image = stitcher.stitch([firstImage[2], secondImage[2]])
except:
return (firstImage, secondImage)
kp, desc = orb.detectAndCompute(image, None)
#print(status)
if (status == 0):
# Draw first 10 matches.
img3 = cv2.drawMatches(firstImage[2],
firstImage[0],
secondImage[2],
secondImage[0],
matches[:10],
None,
flags=2)
return (kp, desc, image)
else:
return (firstImage, secondImage)
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 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 stitch(folder):
time1 = time.time()
VideoPrep.vid2imgs(folder)
dir_path = os.path.dirname(folder)
newpath = os.path.join(dir_path, "QuadOutputFrames")
imgs = VideoPrep.findFrames(newpath)
# intimgs = VideoPrep.chooseFrame(newpath)
# imgs=[]
# print(str(len(intimgs)))
# for i in range (0, len(intimgs)):
# paus = intimgs[i]
# img = np.array(paus)
# imgs.append(img)
# for i in range(0, len(imgs)):
# print("count: " + str(i))
# plt.imshow(imgs[i])
# plt.show()
stitcher = cv2.createStitcher(False)
result = np.empty(shape=[2048, 2048])
ret, result = stitcher.stitch(imgs, result)
# cv2.imwrite("/Users/amolsingh/Documents/OhgamiLab/Videos/QuadOutputFrames/QuadStitched.jpg", result)
cv2.imwrite(os.path.join(newpath, "QuadStitched.jpg"), result)
time2 = time.time()
fintime = time2 - time1
print("Time: " + str(fintime))
def stackNstitch1vid(folder):
time1 = time.time()
VideoPrep.vid2imgs1vid(folder)
for n in range(1, 486):
stacked = StackingMain.stacker(
"/Users/amolsingh/Documents/OhgamiLab/Videos/FramesStackNStitch/OutputFrames%d"
% n)
cv2.imwrite(
"/Users/amolsingh/Documents/OhgamiLab/Videos/FramesStackNStitch/final/frame%d.jpg"
% n, stacked)
path, dirs, files = os.walk(
"/Users/amolsingh/Documents/OhgamiLab/Videos/FramesStackNStitch/final"
).__next__()
# path, dirs, files = os.walk("/Users/amolsingh/Documents/OhgamiLab/Videos/FramesStackNStitch/OutputFrames(singlebatch)").__next__()
size = len(files)
inp = []
for i in range(1, size):
a = cv2.imread(
"/Users/amolsingh/Documents/OhgamiLab/Videos/FramesStackNStitch/final/frame%d.jpg"
% i)
# a = cv2.imread("/Users/amolsingh/Documents/OhgamiLab/Videos/FramesStackNStitch/OutputFrames(singlebatch)/frame%d.jpg" %i)
inp.append(a)
result = np.empty(shape=[4000, 4000])
stitcher = cv2.createStitcher(False)
ret, result = stitcher.stitch(inp, result)
cv2.imwrite(
"/Users/amolsingh/Documents/OhgamiLab/Videos/FramesStackNStitch/final/stitched.jpg",
result)
# cv2.imwrite("/Users/amolsingh/Documents/OhgamiLab/Videos/FramesStackNStitch/final/stitched(singlebatch).jpg", result)
time2 = time.time()
fintime = time2 - time1
print("Time: " + str(fintime))
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 create_modified_stitcher():
stitcher = cv2.createStitcher(False) #don't use GPU
stitcher.setPanoConfidenceThresh(.05) # changed from .5, basically makes it a 'make a panorama at any cost, even if it's terrible' function
stitcher.setRegistrationResol(.6) # changed from .5, expands the image a little bit, might change later
stitcher.setCompositingResol(-1) # doesnt really make a difference, left at default
stitcher.setSeamEstimationResol(.1) # this is supposed to smooth out seam connections, but doesn't seem to really matter. also heavily increases computation time
return stitcher
def stitch(imgs):
stitcher = cv2.createStitcher()
ret, pano = stitcher.stitch(imgs)
if ret == cv2.STITCHER_OK:
return pano
else:
print('Error during stiching %d' % ret)
def image_stitch(imgs):
stitcher = cv2.createStitcher(False)
result = stitcher.stitch(imgs)
if result[0] != 0:
print("Image stitching is failed, please check the input images!")
return None
return result[1]
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 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(images):
stitcher = cv2.createStitcher()
(status, stitched) = stitcher.stitch(images)
if status == 0:
return stitched
else:
raise Exception(f"Status: {status}")
def hu(img1, img2, i):
stitcher = cv2.createStitcher(False)
foo = cv2.imread(img1)
bar = cv2.imread(img2)
result = stitcher.stitch((foo, bar))
cv2.imwrite("../scene/res2/{}.jpg".format(i), result[1])
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 make_mosainic(input_files, output_file):
if sys.platform == 'win32':
result = (0, cv2.imread('cache/r.jpg'))
else:
stitcher = cv2.createStitcher(False)
result = stitcher.stitch(tuple(cv2.imread(fn) for fn in input_files))
cv2.imwrite(output_file, result[1])
return result[1].shape[:2]
def make_panorama(imgs_list, pathout, frames):
stitcher = cv2.createStitcher(False)
blurry_detect(imgs_list)
# 첫번째 초기 이미지 pop
temp_img = imgs_list.pop(0)
# result 초기화
result = stitcher.stitch([temp_img, temp_img])
# count 초기화
cnt = 1
final = result
# 모든 img 한번씩 stitch
for img in imgs_list:
# count 출력 및 증가
print(cnt)
cnt = cnt + 1
# result[0] 코드 번호
# result[1] img value
# result value 값이 있으면 result value 값과 현재 img stitch
# result value 값이 없으면 초기 이미지와 현재 img stitch
if (np.any(result[1] != None)):
result = stitcher.stitch([img, result[1]])
else:
result = stitcher.stitch([temp_img, img])
# 에러 코드 출력
if (result[0] != 0):
print("error for code", result[0])
else:
result = draw_line(result[1])
final = result
print("done")
cv2.imshow(str(frames) + " Frames Result", final[1])
'''error code
OK = 0,
ERR_NEED_MORE_IMGS = 1,
ERR_HOMOGRAPHY_EST_FAIL = 2,
ERR_CAMERA_PARAMS_ADJUST_FAIL = 3
'''
now = time.localtime()
file_name = pathout + "\\" + str(
frames) + "FPS_%04d-%02d-%02d_%02d-%02d-%02d.jpg" % (
now.tm_year, now.tm_mon, now.tm_mday, now.tm_hour, now.tm_min,
now.tm_sec)
cv2.imwrite(file_name, final[1]) # save frame as JPEG file
cv2.waitKey(0)
cv2.destroyAllWindows()
def opencvStitching(list_img):
'''create panorama using cv2.createStitcher()'''
#create panorama using cv2.createStitcher()
stitcher = cv2.createStitcher()
(status, stitched) = stitcher.stitch(list_img)
# create a 10 pixel border surrounding the stitched image
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]
return stitched
def multi_stitch(img_array):
stitcher = cv2.createStitcher(False)
ims = []
for i, img in enumerate(img_array):
ims.append(cv2.imread(img))
output = stitcher.stitch(tuple(ims))
return output
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 stitch_images(self):
stitcher = cv2.createStitcher(False)
filtered_buffer = filter(None, self.image_buffer)
output = stitcher.stitch(
map(lambda ext_img: ext_img.cv_img(), filtered_buffer))
panorama = output[1]
if panorama != None: # Successfully stitched buffer
self.publish_image(panorama)
def stitch(left, right, output_folder, use_opencv):
create_folder(output_folder)
descriptor = cv2.xfeatures2d.SIFT_create()
print(left.shape)
print(right.shape)
if not use_opencv:
ratio = 0.75
min_match = 10
print('tests')
gray_left = cv2.cvtColor(left, cv2.COLOR_BGR2GRAY)
gray_right = cv2.cvtColor(right, cv2.COLOR_BGR2GRAY)
(kps_left,
features_left) = descriptor.detectAndCompute(gray_left, None)
(kps_right,
features_right) = descriptor.detectAndCompute(gray_right, None)
# bf = cv.BFMatcher(cv.NORM_HAMMING, crossCheck=True)
# matches = bf.match(features_left,features_right)
matcher = cv2.BFMatcher()
raw_matches = matcher.knnMatch(features_left, features_right, k=2)
good_points = []
good_matches = []
for m1, m2 in raw_matches:
if m1.distance < ratio * m2.distance:
good_points.append((m1.trainIdx, m1.queryIdx))
good_matches.append([m1])
if len(good_points) > min_match:
image1_kp = np.float32([kps_left[i].pt for (_, i) in good_points])
image2_kp = np.float32([kps_right[i].pt for (i, _) in good_points])
H, status = cv2.findHomography(image2_kp, image1_kp, cv2.RANSAC,
5.0)
result = cv2.warpPerspective(
right, H, (right.shape[1] + left.shape[1], right.shape[0]))
result[0:left.shape[0], 0:left.shape[1]] = left
cv2.imwrite(os.path.join(output_folder, 'uncropped.png'), result)
# plt.imshow(result)
cropped = crop(result)
cv2.imwrite(os.path.join(output_folder, 'cropped.png'), cropped)
# plt.imshow(cropped)
else:
stitcher = cv2.createStitcher()
(status, stitched) = stitcher.stitch([left, right])
if status == 0:
cv2.imwrite(
os.path.join(output_folder, 'opencv_stitcher_uncropped.png'),
stitched)
cropped = crop(stitched)
cv2.imwrite(
os.path.join(output_folder, 'opencv_stitcher_cropped.png'),
cropped)
def test_simple(self):
img1 = self.get_sample('stitching/a1.png')
img2 = self.get_sample('stitching/a2.png')
stitcher = cv.createStitcher(False)
stitcher.estimateTransform((img1, img2))
result, _ = stitcher.composePanorama((img1, img2))
assert result == 0
def mode_changed(self, text):
new_mode = self.modes[text]
print("prev mode {}".format(self.mode))
if new_mode != self.mode:
# Need to recreate stitcher object.
self.mode = new_mode
print("Recreated new stitcher {}".format(self.mode))
self.stitcher = cv2.createStitcher(self.mode) #
self.stitch()
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(files):
# 拼接成全景图
imgs = []
for file in files:
imgs.append(cv2.imread(DIR + '/' + file))
try_use_gpu = False
stitcher = cv2.createStitcher(try_use_gpu)
status, pano = stitcher.stitch(imgs)
if status == 0:
return pano
else:
return None
def stitch_images(self):
print("Stitching images, please wait....")
self.stitcher = ocv.createStitcher(try_use_gpu=False)
(self.status, self.stitched) = self.stitcher.stitch(self.images)
self.images = []
if self.status == 0:
print("Image stitching completed")
ocv.imshow("stitched image", self.stitched)
ocv.waitKey(0)
ocv.destroyAllWindows()
else:
print("Cannot stitch images. " + " Error code " + str(self.status))
def test_simple(self):
img1 = self.get_sample('stitching/a1.png')
img2 = self.get_sample('stitching/a2.png')
stitcher = cv2.createStitcher(False)
(_result, pano) = stitcher.stitch((img1, img2))
#cv2.imshow("pano", pano)
#cv2.waitKey()
self.assertAlmostEqual(pano.shape[0], 685, delta=100, msg="rows: %r" % list(pano.shape))
self.assertAlmostEqual(pano.shape[1], 1025, delta=100, msg="cols: %r" % list(pano.shape))
