Spaces:
Runtime error
Runtime error
| from pathlib import Path | |
| from OCC.Extend.DataExchange import write_stl_file | |
| from lightning_fabric import seed_everything | |
| from tqdm import tqdm | |
| from matplotlib import pyplot as plt | |
| import sys, pickle | |
| import numpy as np | |
| import trimesh | |
| from diffusion.utils import get_triangulations | |
| from eval.check_valid import check_step_valid_soild | |
| from diffusion.utils import can_be_triangularized | |
| def remove_outliers_zscore(data, threshold=3): | |
| """ | |
| Remove outliers using z-score method | |
| Args: | |
| data: 1D numpy array | |
| threshold: z-score threshold (default 3) | |
| """ | |
| mean = np.mean(data) | |
| std = np.std(data) | |
| z_scores = np.abs((data - mean) / std) | |
| mask = z_scores < threshold | |
| return data[mask], mask | |
| if __name__ == "__main__": | |
| seed_everything(0) | |
| if len(sys.argv) == 3: | |
| pkl = sys.argv[1] | |
| output_path = sys.argv[2] | |
| src_step_root = None | |
| elif len(sys.argv) == 4: | |
| pkl = sys.argv[1] | |
| output_path = sys.argv[2] | |
| src_step_root = sys.argv[3] | |
| src_folder_list, nearest_name, data_ori = pickle.load(open(pkl, "rb")) | |
| if src_step_root is not None: | |
| # check valid | |
| print("Checking Validity...") | |
| is_valid_mask = np.array([False] * len(src_folder_list)) | |
| src_step_root = Path(src_step_root) | |
| for i, folder in tqdm(enumerate(src_folder_list)): | |
| assert (src_step_root / folder / "recon_brep.step").exists() | |
| is_valid = check_step_valid_soild(str(src_step_root / folder / "recon_brep.step")) | |
| is_valid_mask[i] = is_valid | |
| src_folder_list = [src_folder_list[i] for i in range(len(src_folder_list)) if is_valid_mask[i]] | |
| nearest_name = [nearest_name[i] for i in range(len(nearest_name)) if is_valid_mask[i]] | |
| data_ori = data_ori[is_valid_mask] | |
| print(f"Valid Shapes: {len(src_folder_list)}") | |
| mean_list = [] | |
| median_list = [] | |
| percentile_75_list = [] | |
| data = data_ori.min(axis=1) | |
| for i in range(10): | |
| print("\nNo. {} Iteration".format(i)) | |
| # random sample 3000 shapes | |
| data = np.random.choice(data, 1000) | |
| data, _ = remove_outliers_zscore(data, threshold=3) | |
| his, bin_edges = np.histogram(data, bins=45, range=(0, 4500)) | |
| plt.xlim(0, 100) | |
| plt.barh(bin_edges[:-1], his, height=50) | |
| plt.title("Light Field Distance (LFD) Distribution of the Generated Shapes") | |
| plt.xlabel("Frequency") | |
| plt.ylabel("Light Field Distance (LFD)") | |
| plt.savefig(output_path, dpi=600) | |
| print("Mean: ", data.mean()) | |
| print("Max: ", data.max()) | |
| print("Min: ", data.min()) | |
| print("Std: ", data.std()) | |
| print("Median: ", np.median(data)) | |
| print("1%: ", np.percentile(data, 1)) | |
| print("10%: ", np.percentile(data, 10)) | |
| print("25%: ", np.percentile(data, 25)) | |
| print("50%: ", np.percentile(data, 50)) | |
| print("75%: ", np.percentile(data, 75)) | |
| print("90%: ", np.percentile(data, 90)) | |
| print("99%: ", np.percentile(data, 99)) | |
| median_list.append(np.median(data)) | |
| mean_list.append(np.mean(data)) | |
| percentile_75_list.append(np.percentile(data, 75)) | |
| if True: | |
| small_ids = np.where(data < 100) | |
| # data_root = Path("/mnt/d/brepgen_train") | |
| # data_root = Path("/mnt/d/deepcad_train_v6") | |
| # pkl = Path(pkl) | |
| # src_dir = pkl.parent / Path(pkl).name.replace("_lfd.pkl", "_post") | |
| # dst_file = pkl.parent / Path(pkl).name.replace("_lfd.pkl", "_lfd.ply") | |
| # mesh = trimesh.Trimesh(vertices=np.zeros((0, 3)), faces=np.zeros((0, 3))) | |
| # offset = 0 | |
| # for id in tqdm(small_ids[0]): | |
| # item = trimesh.load(str(src_dir / src_folder_list[id] / "recon_brep.stl")) | |
| # delta_x = offset % 20 * 9 | |
| # delta_y = offset // 20 * 3 | |
| # item.vertices += [delta_x, delta_y, 0] | |
| # mesh = mesh + item | |
| # item = trimesh.load(str(data_root / nearest_name[id] / "mesh.ply")) | |
| # delta_x = offset % 20 * 9 + 3 | |
| # delta_y = offset // 20 * 3 | |
| # item.vertices += [delta_x, delta_y, 0] | |
| # mesh = mesh + item | |
| # offset += 1 | |
| # mesh.export(str(dst_file)) | |
| print(f"{small_ids[0].shape[0]} shapes' LFD is less than 100") | |
| pass | |
| print("\nMean List: ", mean_list) | |
| print("Mean of Mean List: ", np.mean(mean_list)) | |
| print("\nMedian List: ", median_list) | |
| print("Mean of Median List: ", np.mean(median_list)) | |
| print("\nPercentile 75 List: ", percentile_75_list) | |
| print("Mean of Percentile 75 List: ", np.mean(percentile_75_list)) | |
| print(f"{np.mean(mean_list)} {np.mean(median_list)} {np.mean(percentile_75_list)}") | |