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from pathlib import Path
import random
import time
from omegaconf import DictConfig, OmegaConf
import numpy as np
import torch
import os
import glob
from PIL import Image
import argparse
import yaml
import scipy
import math
import lpips
import cv2
import matplotlib.pyplot as plt
from skimage.metrics import structural_similarity as ssim_sk
from pgnd.utils import get_root
root: Path = get_root(__file__)
def calc_psnr(img1, img2, mask):
# img1: (H, W, 3)
# img2: (H, W, 3)
# mask: (H, W)
mse = np.sum((img1 - img2) ** 2 * mask[..., None]) / np.sum(mask)
if mse == 0:
return 100
PIXEL_MAX = 255.0
return 20 * np.log10(PIXEL_MAX / np.sqrt(mse))
def calc_ssim(img1, img2, mask):
# img1: (H, W, 3)
# img2: (H, W, 3)
# mask: (H, W)
return ssim_sk(img1, img2, data_range=255, mask=mask, multichannel=True, channel_axis=2)
def mse_dist(xyz, xyz_gt):
# xyz: (N, 3)
# xyz_gt: (N, 3)
return torch.mean(torch.norm(xyz - xyz_gt, 2, dim=1)).item()
def chamfer_dist(xyz, xyz_gt):
# xyz: (N, 3)
# xyz_gt: (N, 3)
dist1 = torch.sqrt(torch.sum((xyz[:, None] - xyz_gt[None]) ** 2, dim=2))
dist2 = torch.sqrt(torch.sum((xyz_gt[:, None] - xyz[None]) ** 2, dim=2))
chamfer = torch.mean(torch.min(dist1, dim=1).values) + torch.mean(torch.min(dist2, dim=1).values)
return chamfer.item()
def em_distance(x, y):
# x: [N, D]
# y: [M, D]
cost_matrix = scipy.spatial.distance.cdist(x.cpu(), y.cpu())
try:
ind1, ind2 = scipy.optimize.linear_sum_assignment(
cost_matrix, maximize=False
)
except:
print("Error in linear sum assignment!")
ind1 = torch.tensor(ind1).to(x.device)
ind2 = torch.tensor(ind2).to(y.device)
x_new = x[ind1]
y_new = y[ind2]
emd = torch.mean(torch.norm(x_new - y_new, 2, dim=1))
return emd.item()
def seg2bmap(seg, width=None, height=None):
"""
From a segmentation, compute a binary boundary map with 1 pixel wide
boundaries. The boundary pixels are offset by 1/2 pixel towards the
origin from the actual segment boundary.
Arguments:
seg : Segments labeled from 1..k.
width : Width of desired bmap <= seg.shape[1]
height : Height of desired bmap <= seg.shape[0]
Returns:
bmap (ndarray): Binary boundary map.
David Martin <dmartin@eecs.berkeley.edu>
January 2003
"""
seg = seg.astype(bool)
seg[seg > 0] = 1
assert np.atleast_3d(seg).shape[2] == 1
width = seg.shape[1] if width is None else width
height = seg.shape[0] if height is None else height
h, w = seg.shape[:2]
ar1 = float(width) / float(height)
ar2 = float(w) / float(h)
assert not (
width > w | height > h | abs(ar1 - ar2) > 0.01
), "Can" "t convert %dx%d seg to %dx%d bmap." % (w, h, width, height)
e = np.zeros_like(seg)
s = np.zeros_like(seg)
se = np.zeros_like(seg)
e[:, :-1] = seg[:, 1:]
s[:-1, :] = seg[1:, :]
se[:-1, :-1] = seg[1:, 1:]
b = seg ^ e | seg ^ s | seg ^ se
b[-1, :] = seg[-1, :] ^ e[-1, :]
b[:, -1] = seg[:, -1] ^ s[:, -1]
b[-1, -1] = 0
if w == width and h == height:
bmap = b
else:
bmap = np.zeros((height, width))
for x in range(w):
for y in range(h):
if b[y, x]:
j = 1 + math.floor((y - 1) + height / h)
i = 1 + math.floor((x - 1) + width / h)
bmap[j, i] = 1
return bmap
def compute_f(mask, mask_gt):
# Only loaded when run to reduce minimum requirements
# from pycocotools import mask as mask_utils
from skimage.morphology import disk
import cv2
bound_th = 0.008
bound_pix = bound_th if bound_th >= 1 - np.finfo('float').eps else \
np.ceil(bound_th * np.linalg.norm(mask.shape))
# Get the pixel boundaries of both masks
fg_boundary = seg2bmap(mask)
gt_boundary = seg2bmap(mask_gt)
# fg_dil = binary_dilation(fg_boundary, disk(bound_pix))
fg_dil = cv2.dilate(fg_boundary.astype(np.uint8), disk(bound_pix).astype(np.uint8))
# gt_dil = binary_dilation(gt_boundary, disk(bound_pix))
gt_dil = cv2.dilate(gt_boundary.astype(np.uint8), disk(bound_pix).astype(np.uint8))
# Get the intersection
gt_match = gt_boundary * fg_dil
fg_match = fg_boundary * gt_dil
# Area of the intersection
n_fg = np.sum(fg_boundary)
n_gt = np.sum(gt_boundary)
# % Compute precision and recall
if n_fg == 0 and n_gt > 0:
precision = 1
recall = 0
elif n_fg > 0 and n_gt == 0:
precision = 0
recall = 1
elif n_fg == 0 and n_gt == 0:
precision = 1
recall = 1
else:
precision = np.sum(fg_match) / float(n_fg)
recall = np.sum(gt_match) / float(n_gt)
# Compute F measure
if precision + recall == 0:
f_val = 0
else:
f_val = 2 * precision * recall / (precision + recall)
return f_val
def compute_j(mask, mask_gt):
iou = np.sum(mask & mask_gt) / np.sum(mask | mask_gt)
return iou
def compute_lpips(fn, im, im_gt):
im = torch.tensor(im).permute(2, 0, 1).unsqueeze(0).float()
im_gt = torch.tensor(im_gt).permute(2, 0, 1).unsqueeze(0).float()
im = im / 255.0
im_gt = im_gt / 255.0
perception = fn.forward(im, im_gt)
return perception.item()
def inverse_preprocess(cfg, p_x, xyz):
dx = cfg.sim.num_grids[-1]
R = torch.tensor(
[[1, 0, 0],
[0, 0, -1],
[0, 1, 0]]
).to(xyz.device).to(xyz.dtype)
xyz = torch.einsum('nij,jk->nik', xyz, R.T)
scale = cfg.sim.preprocess_scale
xyz = xyz * scale
if cfg.sim.preprocess_with_table:
global_translation = torch.tensor([
0.5 - (xyz[:, :, 0].max() + xyz[:, :, 0].min()) / 2,
dx * (cfg.model.clip_bound + 0.5) + 1e-5 - xyz[:, :, 1].min(),
0.5 - (xyz[:, :, 2].max() + xyz[:, :, 2].min()) / 2,
], dtype=xyz.dtype)
else:
global_translation = torch.tensor([
0.5 - (xyz[:, :, 0].max() + xyz[:, :, 0].min()) / 2,
0.5 - (xyz[:, :, 1].max() + xyz[:, :, 1].min()) / 2,
0.5 - (xyz[:, :, 2].max() + xyz[:, :, 2].min()) / 2,
], dtype=xyz.dtype)
xyz += global_translation
if not (xyz[:, :, 0].min() >= dx * (cfg.model.clip_bound + 0.5) - 1e-6 \
and xyz[:, :, 0].max() <= 1 - dx * (cfg.model.clip_bound + 0.5) + 1e-6 \
and xyz[:, :, 1].min() >= dx * (cfg.model.clip_bound + 0.5) - 1e-6 \
and xyz[:, :, 1].max() <= 1 - dx * (cfg.model.clip_bound + 0.5) + 1e-6 \
and xyz[:, :, 2].min() >= dx * (cfg.model.clip_bound + 0.5) - 1e-6 \
and xyz[:, :, 2].max() <= 1 - dx * (cfg.model.clip_bound + 0.5) + 1e-6):
print('inverse_preprocess out of bound')
xyz_max = xyz.max(dim=0).values
xyz_max_mask = (xyz_max[:, 0] > 1 - dx * (cfg.model.clip_bound + 0.5) + 1e-6) | \
(xyz_max[:, 1] > 1 - dx * (cfg.model.clip_bound + 0.5) + 1e-6) | \
(xyz_max[:, 2] > 1 - dx * (cfg.model.clip_bound + 0.5) + 1e-6)
xyz_min = xyz.min(dim=0).values
xyz_min_mask = (xyz_min[:, 0] < dx * (cfg.model.clip_bound + 0.5) - 1e-6) | \
(xyz_min[:, 1] < dx * (cfg.model.clip_bound + 0.5) - 1e-6) | \
(xyz_min[:, 2] < dx * (cfg.model.clip_bound + 0.5) - 1e-6)
xyz_mask = xyz_max_mask | xyz_min_mask
xyz = xyz[:, ~xyz_mask]
assert (xyz[:, :, 0].min() >= dx * (cfg.model.clip_bound + 0.5) - 1e-6 \
and xyz[:, :, 0].max() <= 1 - dx * (cfg.model.clip_bound + 0.5) + 1e-6 \
and xyz[:, :, 1].min() >= dx * (cfg.model.clip_bound + 0.5) - 1e-6 \
and xyz[:, :, 1].max() <= 1 - dx * (cfg.model.clip_bound + 0.5) + 1e-6 \
and xyz[:, :, 2].min() >= dx * (cfg.model.clip_bound + 0.5) - 1e-6 \
and xyz[:, :, 2].max() <= 1 - dx * (cfg.model.clip_bound + 0.5) + 1e-6)
xyz -= global_translation
p_x -= global_translation
p_x = p_x / scale
p_x = torch.einsum('nij,jk->nik', p_x, torch.linalg.inv(R).T)
# optional: recover xyz
xyz = xyz / scale
xyz = torch.einsum('nij,jk->nik', xyz, torch.linalg.inv(R).T)
return p_x, xyz
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