def register_image(
img,
ref=None
): #img must be 3 channels, ref could be None which is random noise wiht a margin, a string with the file name and or path, or the word 'solid' which is a solid color with a margin
"""pystackreg library"""
transformations = []
if ref == None:
ref = generate_template(img) #default color
elif ref == 'solid': #random color between 5 - 250
ref = generate_template(img, color=random_color())
elif ref == 'gray':
ref = generate_template(img, color=[125, 125, 125])
else:
ref = cv2.imread(ref)
print("\nshowing original image...\n")
plt.imshow(img)
plt.show()
print("\nshowing reference image...\n")
plt.imshow(ref)
plt.show()
#Translational transformation
sr = StackReg(StackReg.TRANSLATION)
min_shape = [
min(ref.shape[0], img.shape[0]),
min(ref.shape[1], img.shape[1])
]
img = cv2.resize(img, (min_shape[1], min_shape[0]))
ref = cv2.resize(ref, (min_shape[1], min_shape[0]))
out_tra = sr.register_transform(ref[:, :, 0], img[:, :, 0])
transformations.append(out_tra)
#Rigid Body transformation
sr = StackReg(StackReg.RIGID_BODY)
out_rot = sr.register_transform(ref[:, :, 0], img[:, :, 0])
transformations.append(out_rot)
#Scaled Rotation transformation
sr = StackReg(StackReg.SCALED_ROTATION)
out_sca = sr.register_transform(ref[:, :, 0], img[:, :, 0])
transformations.append(out_sca)
#Affine transformation
sr = StackReg(StackReg.AFFINE)
out_aff = sr.register_transform(ref[:, :, 0], img[:, :, 0])
transformations.append(out_aff)
#Bilinear transformation
sr = StackReg(StackReg.BILINEAR)
out_bil = sr.register_transform(ref[:, :, 0], img[:, :, 0])
transformations.append(out_bil)
return transformations, ref
def registration(img1, img2, method='phase_cross_correlation'):
"""This function is used to register 2 images.
Args:
:param img1: the first image.
:param img2: the second image.
:param method: which method is used for registration.
List methods:
A Pyramid Approach to Subpixel Registration Based on Intensity (http://bigwww.epfl.ch/thevenaz/stackreg/#Related)
- translation
- rigid (translation + rotation)
- scaled rotation (translation + rotation + scaling)
- affine (translation + rotation + scaling + shearing)
- bilinear (non-linear transformation; does not preserve straight lines)
- phase_cross_correlation
"""
corrected_img2 = None
if method == 'translation':
sr = StackReg(StackReg.TRANSLATION)
corrected_img2 = sr.register_transform(img1, img2)
elif method == 'rigid':
sr = StackReg(StackReg.RIGID_BODY)
corrected_img2 = sr.register_transform(img1, img2)
elif method == 'scaled_rotation':
sr = StackReg(StackReg.SCALED_ROTATION)
corrected_img2 = sr.register_transform(img1, img2)
elif method == 'affine':
sr = StackReg(StackReg.AFFINE)
corrected_img2 = sr.register_transform(img1, img2)
elif method == 'bilinear':
sr = StackReg(StackReg.BILINEAR)
corrected_img2 = sr.register_transform(img1, img2)
elif method == 'phase_cross_correlation':
shift_dis, _, _ = phase_cross_correlation(img1, img2)
corrected_img2 = shift(img2, shift=shift_dis, mode='constant')
return corrected_img2
"""
from pystackreg import StackReg
from skimage import io
from matplotlib import pyplot as plt
#The following example opens two different files and registers them
#using all different possible transformations
#load reference and "moved" image
ref_img = io.imread('images/for_alignment/shale_for_alignment00.tif')
offset_img = io.imread('images/for_alignment/shale_for_alignment01.tif')
#Translational transformation
sr = StackReg(StackReg.TRANSLATION)
out_tra = sr.register_transform(ref_img, offset_img)
plt.imshow(out_tra, cmap='gray')
#Rigid Body transformation
sr = StackReg(StackReg.RIGID_BODY)
out_rot = sr.register_transform(ref_img, offset_img)
#Scaled Rotation transformation
#sr = StackReg(StackReg.SCALED_ROTATION)
#out_sca = sr.register_transform(ref_img, offset_img)
#Affine transformation
sr = StackReg(StackReg.AFFINE)
out_aff = sr.register_transform(ref_img, offset_img)
#Bilinear transformation
num_rois = len(rois)
if num_rois <= shown_rois:
for index in range(num_rois, shown_rois):
if index > 0:
cv2.destroyWindow('roi_' + str(index))
#print('number of detected ROIs: ', num_rois)
shown_rois = num_rois
#cell_counter +=1
if len(rois) > 0:
t0 = time.time()
curr = rois[0]
out = sr.register_transform(prev, curr)
#out = out.astype('uint8')
#print (time.time()-t0)
#mip = np.maximum(mip,rois[0])
#print(np.amax(out))
mip = np.maximum(mip, out)
prev = out
mip_rescaled = np.clip(mip.astype('float') * 3, 20, 255).astype(
'uint8'
) #multiplication with 3 is to increase the contrast only
#mip_rescaled = np.clip((mip/np.amax(mip)*255),20,255).astype('uint8')
mip_resized = cv2.resize(mip_rescaled,
dim,
from pystackreg import StackReg
from skimage import io
from matplotlib import pyplot as plt
#The following example opens two different files and registers them
#using all different possible transformations
#load reference and "moved" image
ref_img = io.imread(
'images/for_alignment/translated/shale_for_alignment00.tif')
offset_img = io.imread(
'images/for_alignment/translated/shale_for_alignment01.tif')
#Translational transformation
sr = StackReg(StackReg.TRANSLATION) #Create an operator first
out_tra = sr.register_transform(ref_img, offset_img) #Apply the operator
plt.imshow(out_tra, cmap='gray')
#Rigid Body transformation
sr = StackReg(StackReg.RIGID_BODY)
out_rot = sr.register_transform(ref_img, offset_img)
#Scaled Rotation transformation
#sr = StackReg(StackReg.SCALED_ROTATION)
#out_sca = sr.register_transform(ref_img, offset_img)
#Affine transformation
sr = StackReg(StackReg.AFFINE)
out_aff = sr.register_transform(ref_img, offset_img)
#Plotting a few outputs
from pystackreg import StackReg
from skimage import io
import cv2
import matplotlib.pyplot as plt
#load reference and "moved" image
path = 'C:\\Users\\Andrea Bassi\\Documents\\Data\\PROCHIP\\Throughput_video\\registration_test\\'
#filename = 'dual_color_stack'
filename = 'PSF'
ref = io.imread(path + 'im0.tif')
mov = io.imread(path + 'im1.tif')
#Translational transformation
sr = StackReg(StackReg.RIGID_BODY)
out_tra = sr.register_transform(ref, mov)
imgplot = plt.imshow(out_tra)
plt.show()
#import cv2
#cv2.imshow("Out", out_tra/256)
#cv2.waitKey(0)
plt.show() plt.imshow(template) plt.show() gray_test = cv2.cvtColor(template, cv2.COLOR_BGR2GRAY) plt.imshow(gray_test) plt.show() #%% #load reference and "moved" image ref = template mov = img_rotated #Translational transformation sr = StackReg(StackReg.TRANSLATION) out_tra = sr.register_transform(ref[:, :, 0], mov[:, :, 0]) #Rigid Body transformation sr = StackReg(StackReg.RIGID_BODY) out_rot = sr.register_transform(ref[:, :, 0], mov[:, :, 0]) #Scaled Rotation transformation sr = StackReg(StackReg.SCALED_ROTATION) out_sca = sr.register_transform(ref[:, :, 0], mov[:, :, 0]) #Affine transformation sr = StackReg(StackReg.AFFINE) out_aff = sr.register_transform(ref[:, :, 0], mov[:, :, 0]) #Bilinear transformation sr = StackReg(StackReg.BILINEAR)
