Spaces:
Sleeping
Sleeping
File size: 4,027 Bytes
5f5f544 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import os
from pathlib import Path
import cv2
import numpy as np
import torch
from sapiens.registry import VISUALIZERS
from torch import nn
@VISUALIZERS.register_module()
class NormalVisualizer(nn.Module):
def __init__(
self,
output_dir: str,
vis_interval: int = 100,
vis_max_samples: int = 4,
vis_image_width: int = 384,
vis_image_height: int = 512,
):
super().__init__()
self.output_dir = Path(output_dir)
self.output_dir.mkdir(parents=True, exist_ok=True)
self.vis_max_samples = vis_max_samples
self.vis_interval = vis_interval
self.vis_image_width = vis_image_width
self.vis_image_height = vis_image_height
def vis_normal(self, normal_map, mask=None):
normal_map[mask == 0] = np.nan
normal_map_vis = (((normal_map + 1) / 2) * 255).astype(np.uint8)
## bgr to rgb
normal_map_vis = normal_map_vis[:, :, ::-1]
return normal_map_vis
def add_batch(self, data_batch: dict, logs: dict, step: int):
pred_normals = logs["outputs"]
pred_normals = pred_normals.detach().cpu() # B x 3 x H x W
gt_normals = (
data_batch["data_samples"]["gt_normal"].detach().cpu()
) # B x 3 x H x W
masks = data_batch["data_samples"]["mask"].detach().cpu() # B x 1 x H x
inputs = data_batch["inputs"].detach().cpu() # B x 3 x H x W
if pred_normals.dtype == torch.bfloat16:
inputs = inputs.float()
pred_normals = pred_normals.float()
pred_normals = pred_normals.cpu().detach().numpy() ## B x 3 x H x W
pred_normals = pred_normals.transpose((0, 2, 3, 1)) ## B x H x W x 3
batch_size = min(len(inputs), self.vis_max_samples)
inputs = inputs[:batch_size]
pred_normals = pred_normals[:batch_size] ## B x 3 x H x W
gt_normals = gt_normals[:batch_size] ## B x 3 x H x W
masks = masks[:batch_size] ## B x 1 x H x W
prefix = os.path.join(self.output_dir, "train")
suffix = str(step).zfill(6)
suffix += "_" + data_batch["data_samples"]["meta"]["img_path"][0].split("/")[
-1
].replace(".png", "")
vis_images = []
for i, (input, gt_normal, mask, pred_normal) in enumerate(
zip(inputs, gt_normals, masks, pred_normals)
):
image = input.permute(1, 2, 0).cpu().numpy() ## bgr image
image = np.ascontiguousarray(image.copy())
gt_normal = gt_normal.numpy() ## 3 x H x W
gt_normal = gt_normal.transpose((1, 2, 0)) ## H x W x 3
mask = mask[0].numpy() > 0 ## H x W
if (
pred_normal.shape[0] != image.shape[0]
or pred_normal.shape[1] != image.shape[1]
):
image = cv2.resize(
image,
(pred_normal.shape[1], pred_normal.shape[0]),
interpolation=cv2.INTER_LINEAR,
)
vis_gt_normal = self.vis_normal(gt_normal, mask)
vis_pred_normal = self.vis_normal(pred_normal, mask)
vis_image = np.concatenate(
[
image,
vis_gt_normal,
vis_pred_normal,
],
axis=1,
)
vis_image = cv2.resize(
vis_image,
(3 * self.vis_image_width, self.vis_image_height),
interpolation=cv2.INTER_AREA,
)
vis_images.append(vis_image)
grid_image = np.concatenate(vis_images, axis=0)
# Save the grid image to a file
grid_out_file = "{}_{}.jpg".format(prefix, suffix)
cv2.imwrite(grid_out_file, grid_image)
return
|