import argparse from datasets import load_dataset import open3d as o3d import pyvista as pv from PIL import Image import matplotlib import matplotlib.pyplot as plt import numpy as np import random def plot_3D_image(values, resolution, p=None, interactive_slice=False, orthogonal_slices=True): ''' Interactive plot of the 3D volume''' # Create the spatial reference grid = pv.ImageData() values = np.transpose(values, (1,2,0)) # Set the grid dimensions: shape + 1 because we want to inject our values on # the CELL data grid.dimensions = np.array(values.shape) + 1 # Edit the spatial reference # The bottom left corner of the data set origin = np.array(resolution[0]) * np.array(values.shape) * 0.5 grid.origin = origin #print(f'Scan size in meter: {origin * 2}') grid.spacing = resolution[0] # These are the cell sizes along each axis # Add the data values to the cell data grid.cell_data["values"] = values.flatten(order="F") # Flatten the array! if p is None: p = pv.Plotter() if orthogonal_slices: slices = grid.slice_orthogonal() cmap = matplotlib.colors.ListedColormap(['black', 'indianred', 'goldenrod', 'steelblue', 'ghostwhite']) p.add_mesh(slices, cmap=cmap) if interactive_slice: p.add_mesh_clip_plane(grid) return p def get_sliced_mri_png(sample): # get data mri = np.asarray(sample['mri_seg']) resolution = np.asarray(sample['resolution']) # set plotter p = pv.Plotter(shape=(1, 1), off_screen=True) p.subplot(0, 0) plotter = plot_3D_image(mri, resolution, p, interactive_slice=False, orthogonal_slices=True) plotter.view_yz() plotter.remove_scalar_bar() # store screenshot img = p.screenshot("./extras/img.png", return_img=True) # read screenshot img = Image.fromarray(img) # set plotter lateral p = pv.Plotter(shape=(1, 1), off_screen=True) p.subplot(0, 0) plotter = plot_3D_image(mri, resolution, p, interactive_slice=False, orthogonal_slices=True) plotter.remove_scalar_bar() plotter.view_xz() img_lateral = p.screenshot("./extras/img_lateral.png", return_img=True) img_lateral = Image.fromarray(img_lateral) # resize img = img.resize((512+128, 372+128)) img_lateral = img_lateral.resize((512+128, 372+128)) return img, img_lateral def vis_hit_sample(sample): """ :param sample: HIT dataset sample :return: """ # get point-cloud from sample pc = np.asarray(sample['body_cont_pc']) # get mesh and mesh-free-verts from sample mesh_verts = np.asarray(sample['smpl_dict']['verts']) mesh_verts_free = np.asarray(sample['smpl_dict']['verts_free']) mesh_faces = np.asarray(sample['smpl_dict']['faces']) # create point-cloud pcd = o3d.geometry.PointCloud() pcd.points = o3d.utility.Vector3dVector(pc) pcd.paint_uniform_color([0.6509803922, 0.2901960784, 0.2823529412]) pcd_front = pcd.__copy__() # create mesh mesh = o3d.geometry.TriangleMesh() mesh.vertices = o3d.utility.Vector3dVector(mesh_verts) mesh.triangles = o3d.utility.Vector3iVector(mesh_faces) mesh.paint_uniform_color([0.737254902, 0.7960784314, 0.8196078431]) # create mesh-free-verts mesh_free = o3d.geometry.TriangleMesh() mesh_free.vertices = o3d.utility.Vector3dVector(mesh_verts_free) mesh_free.triangles = o3d.utility.Vector3iVector(mesh_faces) mesh_free.paint_uniform_color([0.737254902, 0.7960784314, 0.8196078431]) # visualize sample vis = o3d.visualization.Visualizer() vis.create_window() # side-view xyz = (-np.pi / 2, 0, 0) R1 = o3d.geometry.get_rotation_matrix_from_xyz(xyz) # vis mesh with pointcloud vis.add_geometry(mesh.rotate(R1, center=(0, 0, 0))) vis.add_geometry(pcd.rotate(R1, center=(0, 0, 0))) # vis mesh-free-verts with pointcloud vis.add_geometry(mesh_free.translate((1.2, 0, 0))) vis.add_geometry(mesh_free.rotate(R1, center=(0, 0, 0))) vis.add_geometry(pcd_front.translate((1.2, 0, 0))) vis.add_geometry(pcd_front.rotate(R1, center=(0, 0, 0))) # render vis.get_render_option().mesh_show_wireframe = True vis.get_render_option().point_size = 2 vis.poll_events() vis.update_renderer() vis.run() return 0 if __name__ == '__main__': parser = argparse.ArgumentParser(description='HIT dataset visualization') parser.add_argument('--gender', type=str, default='male') parser.add_argument('--split', type=str, default='train') parser.add_argument('--idx', type=int, default=None) parser.add_argument('--show_skin', action='store_true') parser.add_argument('--show_tissue', action='store_true') # get args args = parser.parse_args() assert args.gender in ['male', 'female'] assert args.split in ['train', 'validation', 'test'] # load split hit_dataset = load_dataset("varora/hit", name=args.gender, split=args.split) # to load specific split, use: # male splits #male_val = load_dataset("varora/hit", "male", split="validation") #male_val = load_dataset("varora/hit", "male", split="validation") #male_test = load_dataset("varora/hit", "male", split="test") # female splits #female_train = load_dataset("varora/hit", "female", split="train") #female_val = load_dataset("varora/hit", "female", split="validation") #female_test = load_dataset("varora/hit", "female", split="test") # len of split N_dataset = hit_dataset.__len__() # get idx for sample if not args.idx: idx = random.randint(0, N_dataset) else: idx = args.idx assert idx < N_dataset, f"{idx} in {args.gender}:{args.split} is out of range for dataset of length {N_dataset}." # get sample hit_sample = hit_dataset[idx] # visualize the sample print(f"Visualizing sample no. {idx} in {args.gender}:{args.split}.") if args.show_tissue: img, img_lateral = get_sliced_mri_png(hit_sample) img.show() img_lateral.show() elif args.show_skin: vis_hit_sample(hit_sample) else: img, img_lateral = get_sliced_mri_png(hit_sample) img.show() img_lateral.show() vis_hit_sample(hit_sample)