-
Notifications
You must be signed in to change notification settings - Fork 1.4k
/
automatic_label_simple_demo.py
166 lines (133 loc) · 4.89 KB
/
automatic_label_simple_demo.py
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
import cv2
import numpy as np
import supervision as sv
from typing import List
from PIL import Image
import torch
from groundingdino.util.inference import Model
from segment_anything import sam_model_registry, SamPredictor
# Tag2Text
# from ram.models import tag2text_caption
from ram.models import ram
# from ram import inference_tag2text
from ram import inference_ram
import torchvision
import torchvision.transforms as TS
# Hyper-Params
SOURCE_IMAGE_PATH = "./assets/demo9.jpg"
DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
GROUNDING_DINO_CONFIG_PATH = "GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py"
GROUNDING_DINO_CHECKPOINT_PATH = "./groundingdino_swint_ogc.pth"
SAM_ENCODER_VERSION = "vit_h"
SAM_CHECKPOINT_PATH = "./sam_vit_h_4b8939.pth"
TAG2TEXT_CHECKPOINT_PATH = "./tag2text_swin_14m.pth"
RAM_CHECKPOINT_PATH = "./ram_swin_large_14m.pth"
TAG2TEXT_THRESHOLD = 0.64
BOX_THRESHOLD = 0.2
TEXT_THRESHOLD = 0.2
IOU_THRESHOLD = 0.5
# Building GroundingDINO inference model
grounding_dino_model = Model(model_config_path=GROUNDING_DINO_CONFIG_PATH, model_checkpoint_path=GROUNDING_DINO_CHECKPOINT_PATH)
# Building SAM Model and SAM Predictor
sam = sam_model_registry[SAM_ENCODER_VERSION](checkpoint=SAM_CHECKPOINT_PATH)
sam_predictor = SamPredictor(sam)
# Tag2Text
# initialize Tag2Text
normalize = TS.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]
)
transform = TS.Compose(
[
TS.Resize((384, 384)),
TS.ToTensor(),
normalize
]
)
DELETE_TAG_INDEX = [] # filter out attributes and action which are difficult to be grounded
for idx in range(3012, 3429):
DELETE_TAG_INDEX.append(idx)
# tag2text_model = tag2text_caption(
# pretrained=TAG2TEXT_CHECKPOINT_PATH,
# image_size=384,
# vit='swin_b',
# delete_tag_index=DELETE_TAG_INDEX
# )
# # threshold for tagging
# # we reduce the threshold to obtain more tags
# tag2text_model.threshold = TAG2TEXT_THRESHOLD
# tag2text_model.eval()
# tag2text_model = tag2text_model.to(DEVICE)
ram_model = ram(pretrained=RAM_CHECKPOINT_PATH,
image_size=384,
vit='swin_l')
ram_model.eval()
ram_model = ram_model.to(DEVICE)
# load image
image = cv2.imread(SOURCE_IMAGE_PATH) # bgr
image_pillow = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)) # rgb
image_pillow = image_pillow.resize((384, 384))
image_pillow = transform(image_pillow).unsqueeze(0).to(DEVICE)
specified_tags='None'
# res = inference_tag2text(image_pillow , tag2text_model, specified_tags)
res = inference_ram(image_pillow , ram_model)
# Currently ", " is better for detecting single tags
# while ". " is a little worse in some case
AUTOMATIC_CLASSES=res[0].split(" | ")
print(f"Tags: {res[0].replace(' |', ',')}")
# detect objects
detections = grounding_dino_model.predict_with_classes(
image=image,
classes=AUTOMATIC_CLASSES,
box_threshold=BOX_THRESHOLD,
text_threshold=BOX_THRESHOLD
)
# NMS post process
print(f"Before NMS: {len(detections.xyxy)} boxes")
nms_idx = torchvision.ops.nms(
torch.from_numpy(detections.xyxy),
torch.from_numpy(detections.confidence),
IOU_THRESHOLD
).numpy().tolist()
detections.xyxy = detections.xyxy[nms_idx]
detections.confidence = detections.confidence[nms_idx]
detections.class_id = detections.class_id[nms_idx]
print(f"After NMS: {len(detections.xyxy)} boxes")
# annotate image with detections
box_annotator = sv.BoxAnnotator()
labels = [
f"{AUTOMATIC_CLASSES[class_id]} {confidence:0.2f}"
for _, _, confidence, class_id, _, _
in detections]
annotated_frame = box_annotator.annotate(scene=image.copy(), detections=detections, labels=labels)
# save the annotated grounding dino image
cv2.imwrite("groundingdino_auto_annotated_image.jpg", annotated_frame)
# Prompting SAM with detected boxes
def segment(sam_predictor: SamPredictor, image: np.ndarray, xyxy: np.ndarray) -> np.ndarray:
sam_predictor.set_image(image)
result_masks = []
for box in xyxy:
masks, scores, logits = sam_predictor.predict(
box=box,
multimask_output=True
)
index = np.argmax(scores)
result_masks.append(masks[index])
return np.array(result_masks)
# convert detections to masks
detections.mask = segment(
sam_predictor=sam_predictor,
image=cv2.cvtColor(image, cv2.COLOR_BGR2RGB),
xyxy=detections.xyxy
)
# annotate image with detections
box_annotator = sv.BoxAnnotator()
mask_annotator = sv.MaskAnnotator()
labels = [
f"{AUTOMATIC_CLASSES[class_id]} {confidence:0.2f}"
for _, _, confidence, class_id, _, _
in detections]
annotated_image = mask_annotator.annotate(scene=image.copy(), detections=detections)
annotated_image = box_annotator.annotate(scene=annotated_image, detections=detections, labels=labels)
# save the annotated grounded-sam image
cv2.imwrite("ram_grounded_sam_auto_annotated_image.jpg", annotated_image)