dd = np.array([0, p1, p2]) # principal point principal_point = np.array([xp, yp]) fiducialsCam = 'no fiducials' # create camera instance cam1 = cam(focal, principal_point, rd1, dd, fiducialsCam) # create SingleImage instances IMG_2003 = SingleImage(cam1) IMG_2004 = SingleImage(cam1) IMG_2005 = SingleImage(cam1) # points in images IMG_2003_points = rd.ReadSampleFile('IMG_2003.json') IMG_2004_points = rd.ReadSampleFile('IMG_2004.json') IMG_2005_points = rd.ReadSampleFile('IMG_2005.json') # Compute Inner Orientation inner_param_2003 = IMG_2003.ComputeInnerOrientation([]) inner_param_2004 = IMG_2004.ComputeInnerOrientation([]) inner_param_2005 = IMG_2005.ComputeInnerOrientation([]) # image to camera cam_2003_points = IMG_2003.ImageToCamera(IMG_2003_points) cam_2004_points = IMG_2004.ImageToCamera(IMG_2004_points) cam_2005_points = IMG_2004.ImageToCamera(IMG_2005_points) # create ImagePair instances img_pair1 = ImagePair(IMG_2003, IMG_2004)
# bin_gauss = build_gaussian_pyramid(mask) # comb_laplac = combine_pyramids(George_laplac,ref_laplac,bin_gauss) # refrence_image[520*2:680*2,490*2:570*2] = build_from_pyramid(comb_laplac) # # refrence_image[520*2:680*2,490*2:570*2] = masked # plt.imshow(cv2.cvtColor(refrence_image, cv2.COLOR_BGR2RGB)) # plt.xticks([]), plt.yticks([]) # plt.show() # importing sampled points data sampled_points_files = [] sampled_points_files = glob.glob( r'panorama\*.json' ) # reading the names of the json files in the folder images_points = [] for filename in sampled_points_files: im_points = rd.ReadSampleFile(filename) images_points.append(im_points * (scale_percent / 100)) refrence_image_points = images_points[ 3] # points of the image that is the refrence plain # calculating transformation matrices trans_matrices = [] for im in images_points[:4]: trans_matrices.append( transformation_parameters(refrence_image_points[:len(im)], im)) trans_matrices.append( transformation_parameters( refrence_image_points[np.hstack( [np.arange(2, 6), np.arange(7, 20)])], images_points[4])) trans_matrices.append( transformation_parameters(refrence_image_points[np.arange(8, 20)],
from ImagePair import ImagePair import pandas as pd import copy from ImageTriple import ImageTriple # Setting Parameters y_size = 3648 # size of y axis of a picture pix_s = 2.4e-3 #single pixel size (micron should be e-6) focal = 4239.655*pix_s ppoint = np.array([2746.295,1837.450])*pix_s radial_d = np.array([0.0473,-0.414,])*pix_s dece_d = np.array([-0.0014,-0.0028,0])*pix_s # Reading and Creating list of Homological points list_of_raw_points = [] list_of_raw_points.append(rd.ReadSampleFile(r'colorbase/IMG_1793.json')) list_of_raw_points.append(rd.ReadSampleFile(r'colorbase/IMG_1794.json')) list_of_raw_points.append(rd.ReadSampleFile(r'colorbase/IMG_1795.json')) # Fixing camera axis list_of_y_fixed_points = copy.deepcopy(list_of_raw_points) for m in range(len(list_of_raw_points)): for row in range(len(list_of_raw_points[m]-1)): list_of_y_fixed_points[m][row,1] = y_size - list_of_raw_points[m][row,1] # Fixing camera center list_of_y_center_fixed_points = copy.deepcopy(list_of_y_fixed_points) for m in range(len(list_of_y_fixed_points)): for row in range(len(list_of_y_fixed_points[m]-1)): list_of_y_center_fixed_points[m][row,:] =\ list_of_y_fixed_points[m][row,:] - ppoint/pix_s