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ba23d94 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 | # 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
from ..datasets import DOME_CLASSES_29
@VISUALIZERS.register_module()
class SegVisualizer(nn.Module):
def __init__(
self,
output_dir: str = None,
vis_interval: int = 100,
vis_max_samples: int = 4,
vis_image_width: int = 384,
vis_image_height: int = 512,
class_palette_type="dome29",
overlay_opacity: float = 0.5, # 0..1; 1 = only mask colors
with_labels: bool = True,
):
super().__init__()
if output_dir is not None:
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.class_palette_type = class_palette_type
self.overlay_opacity = float(np.clip(overlay_opacity, 0.0, 1.0))
self.with_labels = with_labels
self.class_palette = None
self.class_names = {}
if self.class_palette_type == "dome29":
self.class_palette = self._build_palette_from_dict(DOME_CLASSES_29)
self.class_names = {
cid: meta.get("name", f"class_{cid}")
for cid, meta in DOME_CLASSES_29.items()
}
else:
self.class_palette = (np.random.rand(256, 3) * 255).astype(np.uint8)
def _build_palette_from_dict(self, class_dict) -> np.ndarray:
max_id = max(int(k) for k in class_dict.keys())
pal = np.zeros((max(max_id + 1, 256), 3), dtype=np.uint8)
for cid, meta in class_dict.items():
col = meta.get("color", [0, 0, 0])
pal[int(cid)] = np.array(col, dtype=np.uint8)
return pal # RGB format
def _get_center_loc(self, mask: np.ndarray):
"""
Finds the center of the largest contour in a binary mask.
This is a robust method using moments.
"""
contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
if not contours:
return None
largest_contour = max(contours, key=cv2.contourArea)
M = cv2.moments(largest_contour)
if M["m00"] == 0:
return None
cx = int(M["m10"] / M["m00"])
cy = int(M["m01"] / M["m00"])
return (cx, cy)
def _draw_labels(self, image: np.ndarray, label_map: np.ndarray) -> np.ndarray:
"""Draws class labels on the image at the center of each segment."""
unique_labels = np.unique(label_map)
for class_id in unique_labels:
if class_id == 0 or class_id not in self.class_names:
continue # Skip background or unknown classes
class_name = self.class_names[class_id]
mask = (label_map == class_id).astype(np.uint8)
loc = self._get_center_loc(mask)
if loc is None:
continue
font = cv2.FONT_HERSHEY_SIMPLEX
scale = 0.05
fontScale = min(image.shape[0], image.shape[1]) / (75 / scale)
fontColor = (255, 255, 255) # White text
thickness = 1
rectangleThickness = 1
(label_width, label_height), baseline = cv2.getTextSize(
class_name, font, fontScale, thickness
)
x, y = loc
x = max(x - label_width // 2, 0)
y_text = y + label_height // 2
rect_start_pt = (x, y - label_height // 2 - baseline)
rect_end_pt = (x + label_width, y + label_height // 2 + baseline)
class_color_rgb = self.class_palette[class_id]
class_color_bgr = tuple(int(c) for c in class_color_rgb[::-1])
cv2.rectangle(image, rect_start_pt, rect_end_pt, class_color_bgr, -1)
cv2.rectangle(
image, rect_start_pt, rect_end_pt, (0, 0, 0), rectangleThickness
)
cv2.putText(
image, class_name, (x, y_text), font, fontScale, fontColor, thickness
)
return image
def _visualize_segmentation(
self, image_bgr: np.ndarray, label_map: np.ndarray
) -> np.ndarray:
if image_bgr.dtype != np.uint8:
raise ValueError("Input image must be uint8 for visualization.")
palette_bgr = self.class_palette[:, ::-1]
color_mask = palette_bgr[label_map]
if self.with_labels:
color_mask = self._draw_labels(color_mask, label_map)
overlay = cv2.addWeighted(
image_bgr,
1 - self.overlay_opacity,
color_mask,
self.overlay_opacity,
0,
)
return overlay
def add_batch(self, data_batch: dict, logs: dict, step: int):
inputs = data_batch["inputs"].detach().cpu() # B x 3 x H x W
pred_logits = logs["outputs"].detach().cpu() # B x num_classes x H x W
gt_labels = (data_batch["data_samples"]["gt_seg"].detach().cpu()).squeeze(
dim=1
) ## B x H x W;
if pred_logits.dtype == torch.bfloat16:
inputs = inputs.float()
pred_logits = pred_logits.float()
pred_labels = pred_logits.argmax(dim=1) ## B x H x W
batch_size = min(len(inputs), self.vis_max_samples)
inputs = inputs[:batch_size]
gt_labels = gt_labels[:batch_size] ## B x 1 x H x W
pred_labels = pred_labels[:batch_size] ## B x H x W
prefix = os.path.join(self.output_dir, "train")
suffix = str(step).zfill(6)
vis_images = []
for i, (input, gt_label, pred_label) in enumerate(
zip(inputs, gt_labels, pred_labels)
):
image = input.permute(1, 2, 0).cpu().numpy() ## bgr image
image = np.ascontiguousarray(image.copy()).astype(np.uint8)
gt_label = gt_label.numpy().astype(np.uint8) ## H x W
pred_label = pred_label.numpy().astype(np.uint8) ## H x W
vis_gt_seg = self._visualize_segmentation(image, gt_label)
vis_pred_seg = self._visualize_segmentation(image, pred_label)
vis_image = np.concatenate(
[
image,
vis_gt_seg,
vis_pred_seg,
],
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)
grid_out_file = "{}_{}.jpg".format(prefix, suffix)
cv2.imwrite(grid_out_file, grid_image)
return
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