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# 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 matplotlib.pyplot as plt
import numpy as np
import torch
from sapiens.registry import VISUALIZERS
from torch import nn
@VISUALIZERS.register_module()
class PointmapVisualizer(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
self.cmap = plt.get_cmap("turbo")
self.error_cmap = plt.get_cmap("hot")
def vis_point_map(self, point_map, mask=None):
depth_map = point_map[:, :, 2] ### x,y,z. z is the depth
img = self.vis_depth_map(depth_map, mask=mask)
return img
def vis_depth_map(self, depth, mask=None, background_color=100):
if mask is None:
inverse_depth = 1 / depth
inverse_depth_normalized = (inverse_depth - inverse_depth.min()) / (
inverse_depth.max() - inverse_depth.min()
)
color_depth = (self.cmap(inverse_depth_normalized)[..., :3] * 255).astype(
np.uint8
)
## convert RGB to BGR to save with cv2
color_depth = color_depth[..., ::-1]
return color_depth
depth_foreground = depth[mask > 0]
processed_depth = np.full(
(mask.shape[0], mask.shape[1], 3), background_color, dtype=np.uint8
)
if len(depth_foreground) == 0:
return processed_depth
inverse_depth_foreground = 1 / depth_foreground
# Visualize inverse depth instead of depth, clipped to [0.1m;250m] range for better visualization.
max_inverse_depth = min(inverse_depth_foreground.max(), 1 / 0.1)
min_inverse_depth = max(1 / 250, inverse_depth_foreground.min())
inverse_depth_foreground_normalized = (
inverse_depth_foreground - min_inverse_depth
) / (max_inverse_depth - min_inverse_depth)
color_depth = (
self.cmap(inverse_depth_foreground_normalized)[..., :3] * 255
).astype(np.uint8)
processed_depth[mask] = color_depth
## convert RGB to BGR to save with cv2
processed_depth = processed_depth[..., ::-1]
return processed_depth
def vis_normal_from_point_map(self, point_map, mask=None, kernel_size=7):
depth_map = point_map[:, :, 2] ### x,y,z. z is the depth
if mask.sum() == 0:
return np.full((mask.shape[0], mask.shape[1], 3), 100, dtype=np.uint8)
depth_foreground = depth_map[mask > 0]
min_val, max_val = np.min(depth_foreground), np.max(depth_foreground)
depth_normalized = np.full(mask.shape, np.inf)
depth_normalized[mask > 0] = 1 - (
(depth_map[mask > 0] - min_val) / (max_val - min_val)
)
grad_x = cv2.Sobel(
depth_normalized.astype(np.float32), cv2.CV_32F, 1, 0, ksize=kernel_size
)
grad_y = cv2.Sobel(
depth_normalized.astype(np.float32), cv2.CV_32F, 0, 1, ksize=kernel_size
)
normals = np.dstack((-grad_x, -grad_y, np.full(grad_x.shape, -1)))
normals_mag = np.linalg.norm(normals, axis=2, keepdims=True)
normals_normalized = normals / (normals_mag + 1e-5)
normal_vis = ((normals_normalized + 1) / 2 * 255).astype(np.uint8)
return normal_vis[:, :, ::-1]
def vis_l1_error(self, gt_pointmap, pred_pointmap, mask=None, background_color=100):
"""Visualize L1 error between ground truth and predicted pointmaps."""
if mask is None:
mask = np.ones_like(gt_pointmap[:, :, 0], dtype=bool)
error_map = np.full(
(mask.shape[0], mask.shape[1], 3), background_color, dtype=np.uint8
)
# Calculate L1 error for valid points
l1_error = np.abs(gt_pointmap - pred_pointmap) # H x W x 3
l1_error = np.mean(l1_error, axis=2) # Average across XYZ dimensions, H x W
if np.sum(mask) > 0:
error_foreground = l1_error[mask]
# Normalize error for visualization
error_normalized = (error_foreground - error_foreground.min()) / (
error_foreground.max() - error_foreground.min() + 1e-6
)
# Convert to color using hot colormap
error_colored = (self.error_cmap(error_normalized)[..., :3] * 255).astype(
np.uint8
)
error_map[mask] = error_colored
# Convert to BGR for OpenCV
error_map = error_map[..., ::-1]
return error_map
def add_batch(self, data_batch: dict, logs: dict, step: int):
(pred_pointmaps, _) = logs["outputs"]
pred_pointmaps = pred_pointmaps.detach().cpu() # B x 3 x H x W
gt_pointmaps = (
data_batch["data_samples"]["gt_pointmap"].detach().cpu()
) # B x 3 x H x
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_pointmaps.dtype == torch.bfloat16:
inputs = inputs.float()
pred_pointmaps = pred_pointmaps.float()
pred_pointmaps = pred_pointmaps.cpu().detach().numpy() ## B x 3 x H x W
pred_pointmaps = pred_pointmaps.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_pointmaps = pred_pointmaps[:batch_size] ## B x 3 x H x W
gt_pointmaps = gt_pointmaps[: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_pointmap, mask, pred_pointmap) in enumerate(
zip(inputs, gt_pointmaps, masks, pred_pointmaps)
):
image = input.permute(1, 2, 0).cpu().numpy() ## bgr image
image = np.ascontiguousarray(image.copy())
gt_pointmap = gt_pointmap.numpy() ## 3 x H x W
gt_pointmap = gt_pointmap.transpose((1, 2, 0)) ## H x W x 3
mask = mask[0].numpy() > 0 ## H x W
## resize predpoint to image size
if (
pred_pointmap.shape[0] != image.shape[0]
or pred_pointmap.shape[1] != image.shape[1]
):
image = cv2.resize(
image,
(pred_pointmap.shape[1], pred_pointmap.shape[0]),
interpolation=cv2.INTER_LINEAR,
)
vis_gt_pointmap = self.vis_point_map(gt_pointmap, mask)
vis_pred_pointmap = self.vis_point_map(pred_pointmap, mask)
vis_gt_normal = self.vis_normal_from_point_map(gt_pointmap, mask)
vis_pred_normal = self.vis_normal_from_point_map(pred_pointmap, mask)
vis_error = self.vis_l1_error(gt_pointmap, pred_pointmap, mask)
vis_image = np.concatenate(
[
image,
vis_gt_pointmap,
vis_gt_normal,
vis_pred_pointmap,
vis_pred_normal,
vis_error,
],
axis=1,
)
vis_image = cv2.resize(
vis_image,
(6 * 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
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