import numpy as np
from PIL import Image
from computeH import computeH
import matplotlib.pyplot as plt
from warpImage import warpImage
t1 = np.load('cc1.npy').transpose()
t2 = np.load ('cc2.npy').transpose()
H = computeH(t1, t2)
inputIm = np.array(Image.open('crop1.jpg'))
refIm = np.array(Image.open('crop2.jpg'))
warpIm, mergeIm = warpImage(inputIm, refIm, H)
plt.figure(1)
plt.imshow(mergeIm)
plt.figure(2)
plt.imshow(warpIm)
#%%
import scipy.io as sio
#t1 = sio.loadmat('points1.mat').transpose()
#t2 = sio.loadmat('points2.mat').transpose()
#t1 = [[90.16377295896746, 206.3963834707542, 156.9975240032448, 119.2219255869141, 239.3289564490938, 214.14522417154, 246.1091920622813, 323.5975990701392, 338.1266753841126, 369.1220381872556, 278.0731599530227, 355.5615669608804],
# [178.0639945273812, 117.0418740086932, 160.6291029506132, 204.2163318925333, 137.3825808482559, 191.6244657537563, 286.5477643383822, 227.4628539948906, 167.4093385638008, 244.8977455716586, 82.17209085515719, 131.5709503226665]]
#t2 = [1,2]
l1 =[[
for point in new_point_list:
x = point[0] / point[2]
y = point[1] / point[2]
x = x / 2 * image2.shape[0]
y = y / 2 * image2.shape[1]
resulting_points.append((x, y))
#display the points
crops = image2.copy()
for i in range(len(t2_copy)):
cv2.circle(crops, (int(t2_copy[i, 0]), int(t2_copy[i, 1])), 10,
(255, 0, 0), 2)
for point in resulting_points:
cv2.circle(crops, (int(point[1][0]), int(point[0][0])), 3, (0, 0, 255), -1)
cv2.imshow("crop 2", crops)
cv2.waitKey()
cv2.destroyAllWindows()
cv2.imwrite("Kendal_1_verify_homography.jpg", crops)
#================================================================================================================
#test warp image
#test the warp image function
from warpImage import warpImage
image1 = cv2.imread('kendal1.jpg')
image2 = cv2.imread('kendal2.jpg')
result, merged = warpImage(image1, image2, h_2)
cv2.imwrite("kendal_result.jpg", result)
cv2.imwrite("merged_kendal.jpg", merged)
new_point = np.dot(h_2,point)
new_point_list.append(new_point)
#convert from h result to x,y and return to correct scale
resulting_points = []
for point in new_point_list:
x = point[0]/point[2]
y = point[1]/point[2]
x = x/2*image2.shape[0]
y = y/2*image2.shape[1]
resulting_points.append((x,y))
#display the points
crops = image2.copy()
for i in range(len(cc2)):
cv2.circle(crops,(int(cc2_copy[i,0]),int(cc2_copy[i,1])),10,(255,0,0),2)
for point in resulting_points:
cv2.circle(crops,(int(point[1][0]),int(point[0][0])),3,(0,0,255),-1)
cv2.imshow("crop 2", crops)
cv2.waitKey()
cv2.destroyAllWindows()
#================================================================================================================
#test warp image
#test the warp image function
from warpImage import warpImage
result,failures = warpImage(image1,image2,h_2)
cv2.imwrite("crop_result_1.jpg", result)
cv2.imwrite("merged_crops.jpg", failures)
for point in new_point_list:
x = point[0]/point[2]
y = point[1]/point[2]
x = x/2*image2.shape[0]
y = y/2*image2.shape[1]
resulting_points.append((x,y))
#display the points
crops = image2.copy()
for i in range(len(cc2)):
cv2.circle(crops,(int(cc2_copy[i,0]),int(cc2_copy[i,1])),10,(255,0,0),2)
for point in resulting_points:
cv2.circle(crops,(int(point[1][0]),int(point[0][0])),3,(0,0,255),-1)
cv2.imshow("crop 2", crops)
cv2.waitKey()
cv2.destroyAllWindows()
cv2.imwrite("ORB_wdc_verify.jpg",crops)
#================================================================================================================
#test warp image
#test the warp image function
from warpImage import warpImage
image1 = cv2.imread('wdc1.jpg')
image2 = cv2.imread('wdc2.jpg')
result,large = warpImage(image1,image2,h_2)
cv2.imwrite("wdc_result_ORB_Features.jpg", result)
cv2.imwrite("wdc_merged_ORB_features.jpg", large)
points1 = np.load('./cc1.npy')
points2 = np.load('./cc2.npy')
H = computeH(points1.transpose(), points2.transpose())
# verify homography matrix
# blue dot: original points on image
# red dot: transformed points from another image
plt.imshow(ref)
plt.plot(points2.transpose()[0], points2.transpose()[1], "bo")
homogeneous = np.concatenate(
(points1.transpose(), [np.ones(points1.shape[0])]))
transformed = np.matmul(H, homogeneous)
plt.plot(transformed[0] / transformed[2], transformed[1] / transformed[2],
"ro")
plt.show()
warpIm, mergeIm = warpImage(input, ref, H)
plt.imshow(warpIm)
plt.show()
plt.imshow(mergeIm)
plt.show()
# wdc
input = mpimg.imread('./wdc1.jpg')
ref = mpimg.imread('./wdc2.jpg')
points = np.load('./points.npy')
points1 = points[:, 0]
points2 = points[:, 1]
# selected points
plt.imshow(input)
plt.plot(points1[0], points1[1], "bo")
plt.show()
for i in range(len(cc1)):
cc1_rev[i, 0] = cc1[i, 1]
cc1_rev[i, 1] = cc1[i, 0]
return cc1_rev
t1 = np.load('points1.npy')
t2 = np.load('points2.npy')
img1 = cv2.imread('wdc1.jpg')
img2 = cv2.imread('wdc2.jpg')
t1, t2 = scale_down_2(t1, t2)
t1 = scale_down(t1.T, img1)
t2 = scale_down(t2.T, img2)
t1 = reverse(t1.T)
t2 = reverse(t2.T)
t1 = t1.T
t2 = t2.T
h = computeH(t1, t2)
final, merged = warpImage(img1, img2, h)
cv2.imshow("final", final)
cv2.waitKey()
cv2.imshow("merged", merged)
cv2.waitKey()
cv2.destroyAllWindows()
from computeH import computeH
from warpImage import warpImage
import numpy as np
import matplotlib.pyplot as plt
import PIL
fig, axis = plt.subplots(4, 2, figsize=(12, 12))
fig.suptitle('Images')
images = []
Hcrop = computeH("cc1.npy", "cc2.npy")
warpImageCrop, mergedImageCrop = warpImage("crop1.jpg", "crop2.jpg", Hcrop)
images.append(axis[0, 0].imshow(warpImageCrop))
axis[0, 0].set_title('Warped Crop Circle')
images.append(axis[0, 1].imshow(mergedImageCrop))
axis[0, 1].set_title('Merged Crop Circle')
Hbuilding = computeH("wdc1.npy", "wdc2.npy")
warpImageBuilding, mergedImageBuilding = warpImage("wdc1.jpg", "wdc2.jpg",
Hbuilding)
images.append(axis[1, 0].imshow(warpImageBuilding))
axis[1, 0].set_title('Warped WDC')
images.append(axis[1, 1].imshow(mergedImageBuilding))
axis[1, 1].set_title('Merged WDC')
Hmine = computeH("left.npy", "right.npy")
warpImageMine, mergedImageMine = warpImage("left.jpg", "right.jpg", Hmine)
images.append(axis[2, 0].imshow(warpImageMine))
axis[2, 0].set_title('Warped My Graphic')
for i in range(len(t2[0])):
cv2.circle(crops, (t2[1][i], t2[0][i]), 10, (255, 0, 0), 2)
for point in resulting_points:
cv2.circle(crops, (int(point[1][0]), int(point[0][0])), 3, (0, 0, 255), -1)
cv2.imshow("crop 2", crops)
cv2.waitKey()
cv2.destroyAllWindows()
#=============================================================================================================
#test the warp image function
from warpImage import warpImage
inputimg = cv2.imread('crop1.jpg')
refImg = cv2.imread('crop2.jpg')
result, failures = warpImage(inputimg, refImg, h)
import cv2
cv2.imwrite("crop_result.jpg", result)
#=============================================================================================================
#test the warp image function
from warpImage2 import warpImage2
inputimg = cv2.imread('crop1.jpg')
refImg = cv2.imread('crop2.jpg')
result = warpImage2(inputimg, refImg, h)
#========================================================
#troubleshooting
#start with 4 points
inputIm = inputimg
H = h
output = refImg
points1 = points[0][:][:].T
points2 = points[1][:][:].T
except:
onclick.listControl = True
cursor = Cursor(ax[0],
horizOn=True,
vertOn=True,
useblit=False,
color='white',
linewidth=1)
cursor1 = Cursor(ax[1],
horizOn=True,
vertOn=True,
useblit=False,
color='white',
linewidth=1)
cid = fig.canvas.mpl_connect('button_release_event', onclick)
plt.show()
points = np.asarray([points1.T, points2.T])
np.save(sys.argv[1] + sys.argv[4], points)
if len(points1) == len(points2) and len(points1) > 3:
H = cp.computeH(points1, points2)
(warpIm, mergeIm) = wi.warpImage(img1, img2, H)
plt.imsave(sys.argv[1] + 'warped.jpg', warpIm)
plt.imsave(sys.argv[1] + 'merged.jpg', mergeIm)
ax[0].imshow(warpIm)
ax[1].imshow(mergeIm)
plt.show()