def compute(self, *args):
if self.images is not None and len(self.images) > 0:
f = self.getFocalLength()
if f <= 0:
return
k1 = self.getK1()
k2 = self.getK2()
processedImages = None
if self.motionModelVar.get() == alignment.eTranslate:
processedImages = [
warp.warpSpherical(i, f, k1, k2)
for i in self.images
]
else:
processedImages = self.images
t = np.eye(3)
ipv = []
for i in range(0, len(processedImages) - 1):
self.set_status(
'Computing mapping from {0} to {1}'.format(i, i+1)
)
ipv.append(
blend.ImageInfo('', processedImages[i], np.linalg.inv(t))
)
t = self.computeMapping(
processedImages[i], processedImages[i+1]
).dot(t)
ipv.append(blend.ImageInfo(
'', processedImages[len(processedImages)-1], np.linalg.inv(t))
)
t = self.computeMapping(
processedImages[len(processedImages)-1],
processedImages[0]
).dot(t)
if self.is360Var.get():
ipv.append(blend.ImageInfo(
'', processedImages[0], np.linalg.inv(t))
)
self.set_status('Blending Images')
self.setImage(blend.blendImages(
ipv, int(self.blendWidthSlider.get()),
self.is360Var.get() == 1
))
self.set_status('Panorama generated')
else:
uiutils.error(
'Select a folder with images before creating the panorama!'
)
def pano(set_n, use_algorithm):
name_of_set = "ep" + str(set_n)
name_of_path0 = "data/" + name_of_set #+ "SIFT" + "_set_Lunch/"
if use_algorithm == "SIFT":
scale_percent = 30
elif use_algorithm == "SURF":
scale_percent = 20
elif use_algorithm == "ORB":
scale_percent = 10
scale_percent = 20
w_p = 200
name_of_final = use_algorithm + "_" + name_of_set
focalLength = 2500
k1 = -0.0484573
k2 = 0.0100024
f = focalLength
start_time = timeit.default_timer()
name_of_path = name_of_path0
iter = 1
processedImages = None
point_time_1 = timeit.default_timer()
files = os.listdir(name_of_path0)
images = [cv2.imread(os.path.join(name_of_path0, i)) for i in files]
images_crop = []
for i in images:
# calculate the 50 percent of original dimensions
width = int(i.shape[1] * scale_percent / 100)
height = int(i.shape[0] * scale_percent / 100)
# dsize
dsize = (width, height)
# resize image
images_crop.append(cv2.resize(i, dsize))
#images_crop.append(i)
point_time_2 = timeit.default_timer()
processedImages = [warp.warpSpherical(i, f, k1, k2) for i in images_crop]
#processedImages = images_crop
t = np.eye(3)
ipv = []
for i in range(0, len(processedImages) - 1):
ipv.append(blend.ImageInfo('', processedImages[i], np.linalg.inv(t)))
t = computeMapping(processedImages[i], processedImages[i + 1]).dot(t)
ipv.append(
blend.ImageInfo('', processedImages[len(processedImages) - 1],
np.linalg.inv(t)))
t = computeMapping(processedImages[len(processedImages) - 1],
processedImages[0]).dot(t)
result = blend.blendImages(ipv, int(w_p), False)
#cv2.imwrite("result_" + name_of_final + ".jpg", result)
stop_time = timeit.default_timer()
height = result.shape[0]
width = result.shape[1]
filtr_res = contraharmonic_mean(result, (height, width), 0.5)
#cv2.imwrite("result_" + name_of_final + "_f.jpg", result)
# Create our shapening kernel, it must equal to one eventually
kernel_sharpening = np.array([[-1, -1, -1], [-1, 9, -1], [-1, -1, -1]])
# applying the sharpening kernel to the input image & displaying it.
sharpened = cv2.filter2D(result, -1, kernel_sharpening)
#cv2.imwrite("result_" + name_of_final + "_s.jpg", sharpened)
median = result #cv2.medianBlur(result, 3)
#median = cv2.medianBlur(median, 3)
#cv2.imwrite("result_" + name_of_final + "_m.jpg", median)
sharpenedmedian = cv2.filter2D(median, -1, kernel_sharpening)
cv2.imwrite("result_" + name_of_final + "_SM.jpg", sharpenedmedian)
#seam_carving.main_seam("result_" + name_of_final + "_SM.jpg", "result_" + name_of_final + "_SM_SEAM.jpg")
resave = cv2.imread("result_" + name_of_final + "_SM.jpg")
cv2.imwrite("it3/result_" + name_of_final + ".jpg", resave)
print('Execution time ' + name_of_set + ' ' + use_algorithm + ': ',
stop_time - start_time)
:return: warpped images list
"""
return [warp_spherical(img, f, k1, k2) for img in images]
imgs = load_images('panorama')
warpped = warp_images(imgs)
t = np.eye(3)
info = []
T = []
for i in range(len(warpped) - 1):
print('Computing mapping from {0} to {1}'.format(i, i + 1))
info.append(ImageInfo('', warpped[i], np.linalg.inv(t)))
tmp = get_mapping(warpped[i], warpped[i + 1])
print(tmp)
t = tmp.dot(t)
T.append(tmp)
info.append(ImageInfo('', warpped[len(warpped) - 1], np.linalg.inv(t)))
print('Computing mapping from {0} to {1}'.format(len(warpped) - 1, 0))
tmp = get_mapping(warpped[len(warpped) - 1], warpped[0])
print(tmp)
t = tmp.dot(t)
info.append(ImageInfo('', warpped[0], np.linalg.inv(t)))
# json.dump(T, fp=open('transform_output.json','w'), indent=4)
print('Blending Images.')
panorama = blendImages(info, blendWidth=50, is360=True)
cv2.imwrite('Panorama.jpg', panorama)
