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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)}")
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