Spaces:
Runtime error
Runtime error
File size: 10,002 Bytes
583456e ee2b9bc 583456e ee2b9bc 583456e ee2b9bc 583456e ee2b9bc 583456e ee2b9bc 8c62972 ba09e2c ee2b9bc f9b1bcf ee2b9bc f9b1bcf 85fdfea ee2b9bc 0e0710e ee2b9bc 8c62972 ee2b9bc f9b1bcf e9b7645 ee2b9bc 8c62972 ee2b9bc 304ea95 ee2b9bc e9b7645 ee2b9bc f9b1bcf ee2b9bc |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 |
# Copyright (c) Facebook, Inc. and its affiliates.
# Copyright (c) Meta Platforms, Inc. All Rights Reserved
import numpy as np
import torch
from torch.nn import functional as F
import cv2
from detectron2.data import MetadataCatalog
from detectron2.structures import BitMasks
from detectron2.engine.defaults import DefaultPredictor
from detectron2.utils.visualizer import ColorMode, Visualizer
from detectron2.modeling.postprocessing import sem_seg_postprocess
import open_clip
from segment_anything import SamAutomaticMaskGenerator, sam_model_registry
from open_vocab_seg.modeling.clip_adapter.adapter import PIXEL_MEAN, PIXEL_STD
from open_vocab_seg.modeling.clip_adapter.utils import crop_with_mask
class OVSegPredictor(DefaultPredictor):
def __init__(self, cfg):
super().__init__(cfg)
def __call__(self, original_image, class_names):
"""
Args:
original_image (np.ndarray): an image of shape (H, W, C) (in BGR order).
Returns:
predictions (dict):
the output of the model for one image only.
See :doc:`/tutorials/models` for details about the format.
"""
with torch.no_grad(): # https://github.com/sphinx-doc/sphinx/issues/4258
# Apply pre-processing to image.
if self.input_format == "RGB":
# whether the model expects BGR inputs or RGB
original_image = original_image[:, :, ::-1]
height, width = original_image.shape[:2]
image = self.aug.get_transform(original_image).apply_image(original_image)
image = torch.as_tensor(image.astype("float32").transpose(2, 0, 1))
inputs = {"image": image, "height": height, "width": width, "class_names": class_names}
predictions = self.model([inputs])[0]
return predictions
class OVSegVisualizer(Visualizer):
def __init__(self, img_rgb, metadata=None, scale=1.0, instance_mode=ColorMode.IMAGE, class_names=None):
super().__init__(img_rgb, metadata, scale, instance_mode)
self.class_names = class_names
def draw_sem_seg(self, sem_seg, area_threshold=None, alpha=0.8):
"""
Draw semantic segmentation predictions/labels.
Args:
sem_seg (Tensor or ndarray): the segmentation of shape (H, W).
Each value is the integer label of the pixel.
area_threshold (int): segments with less than `area_threshold` are not drawn.
alpha (float): the larger it is, the more opaque the segmentations are.
Returns:
output (VisImage): image object with visualizations.
"""
if isinstance(sem_seg, torch.Tensor):
sem_seg = sem_seg.numpy()
labels, areas = np.unique(sem_seg, return_counts=True)
sorted_idxs = np.argsort(-areas).tolist()
labels = labels[sorted_idxs]
class_names = self.class_names if self.class_names is not None else self.metadata.stuff_classes
for label in filter(lambda l: l < len(class_names), labels):
try:
mask_color = [x / 255 for x in self.metadata.stuff_colors[label]]
except (AttributeError, IndexError):
mask_color = None
binary_mask = (sem_seg == label).astype(np.uint8)
text = class_names[label]
self.draw_binary_mask(
binary_mask,
color=mask_color,
edge_color=(1.0, 1.0, 240.0 / 255),
text=text,
alpha=alpha,
area_threshold=area_threshold,
)
return self.output
class VisualizationDemo(object):
def __init__(self, cfg, instance_mode=ColorMode.IMAGE, parallel=False):
"""
Args:
cfg (CfgNode):
instance_mode (ColorMode):
parallel (bool): whether to run the model in different processes from visualization.
Useful since the visualization logic can be slow.
"""
self.metadata = MetadataCatalog.get(
cfg.DATASETS.TEST[0] if len(cfg.DATASETS.TEST) else "__unused"
)
self.cpu_device = torch.device("cpu")
self.instance_mode = instance_mode
self.parallel = parallel
if parallel:
raise NotImplementedError
else:
self.predictor = OVSegPredictor(cfg)
def run_on_image(self, image, class_names):
"""
Args:
image (np.ndarray): an image of shape (H, W, C) (in BGR order).
This is the format used by OpenCV.
Returns:
predictions (dict): the output of the model.
vis_output (VisImage): the visualized image output.
"""
predictions = self.predictor(image, class_names)
# Convert image from OpenCV BGR format to Matplotlib RGB format.
image = image[:, :, ::-1]
visualizer = OVSegVisualizer(image, self.metadata, instance_mode=self.instance_mode, class_names=class_names)
if "sem_seg" in predictions:
r = predictions["sem_seg"]
blank_area = (r[0] == 0)
pred_mask = r.argmax(dim=0).to('cpu')
pred_mask[blank_area] = 255
pred_mask = np.array(pred_mask, dtype=np.int)
vis_output = visualizer.draw_sem_seg(
pred_mask
)
else:
raise NotImplementedError
return predictions, vis_output
class SAMVisualizationDemo(object):
def __init__(self, cfg, granularity, sam_path, ovsegclip_path, instance_mode=ColorMode.IMAGE, parallel=False):
self.metadata = MetadataCatalog.get(
cfg.DATASETS.TEST[0] if len(cfg.DATASETS.TEST) else "__unused"
)
self.cpu_device = torch.device("cpu")
self.instance_mode = instance_mode
self.parallel = parallel
self.granularity = granularity
sam = sam_model_registry["vit_l"](checkpoint=sam_path).cuda()
self.predictor = SamAutomaticMaskGenerator(sam, points_per_batch=16)
self.clip_model, _, _ = open_clip.create_model_and_transforms('ViT-L-14', pretrained=ovsegclip_path)
def run_on_image(self, ori_image, class_names):
height, width, _ = ori_image.shape
if width > height:
new_width = 1280
new_height = int((new_width / width) * height)
else:
new_height = 1280
new_width = int((new_height / height) * width)
image = cv2.resize(ori_image, (new_width, new_height))
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
ori_image = cv2.cvtColor(ori_image, cv2.COLOR_BGR2RGB)
visualizer = OVSegVisualizer(ori_image, self.metadata, instance_mode=self.instance_mode, class_names=class_names)
with torch.no_grad(), torch.cuda.amp.autocast():
masks = self.predictor.generate(image)
pred_masks = [masks[i]['segmentation'][None,:,:] for i in range(len(masks))]
pred_masks = np.row_stack(pred_masks)
pred_masks = BitMasks(pred_masks)
bboxes = pred_masks.get_bounding_boxes()
mask_fill = [255.0 * c for c in PIXEL_MEAN]
image = torch.as_tensor(image.astype("float32").transpose(2, 0, 1))
regions = []
for bbox, mask in zip(bboxes, pred_masks):
region, _ = crop_with_mask(
image,
mask,
bbox,
fill=mask_fill,
)
regions.append(region.unsqueeze(0))
regions = [F.interpolate(r.to(torch.float), size=(224, 224), mode="bicubic") for r in regions]
pixel_mean = torch.tensor(PIXEL_MEAN).reshape(1, -1, 1, 1)
pixel_std = torch.tensor(PIXEL_STD).reshape(1, -1, 1, 1)
imgs = [(r/255.0 - pixel_mean) / pixel_std for r in regions]
imgs = torch.cat(imgs)
if len(class_names) == 1:
class_names.append('others')
txts = [f'a photo of {cls_name}' for cls_name in class_names]
text = open_clip.tokenize(txts)
img_batches = torch.split(imgs, 32, dim=0)
with torch.no_grad(), torch.cuda.amp.autocast():
self.clip_model.cuda()
text_features = self.clip_model.encode_text(text.cuda())
text_features /= text_features.norm(dim=-1, keepdim=True)
image_features = []
for img_batch in img_batches:
image_feat = self.clip_model.encode_image(img_batch.cuda().half())
image_feat /= image_feat.norm(dim=-1, keepdim=True)
image_features.append(image_feat.detach())
image_features = torch.cat(image_features, dim=0)
class_preds = (100.0 * image_features @ text_features.T).softmax(dim=-1)
select_cls = torch.zeros_like(class_preds)
max_scores, select_mask = torch.max(class_preds, dim=0)
if len(class_names) == 2 and class_names[-1] == 'others':
select_mask = select_mask[:-1]
if self.granularity < 1:
thr_scores = max_scores * self.granularity
select_mask = []
if len(class_names) == 2 and class_names[-1] == 'others':
thr_scores = thr_scores[:-1]
for i, thr in enumerate(thr_scores):
cls_pred = class_preds[:,i]
locs = torch.where(cls_pred > thr)
select_mask.extend(locs[0].tolist())
for idx in select_mask:
select_cls[idx] = class_preds[idx]
semseg = torch.einsum("qc,qhw->chw", select_cls.float(), pred_masks.tensor.float().cuda())
r = semseg
blank_area = (r[0] == 0)
pred_mask = r.argmax(dim=0).to('cpu')
pred_mask[blank_area] = 255
pred_mask = np.array(pred_mask, dtype=np.int)
pred_mask = cv2.resize(pred_mask, (width, height), interpolation=cv2.INTER_NEAREST)
vis_output = visualizer.draw_sem_seg(
pred_mask
)
return None, vis_output |