Spaces:
Runtime error
Runtime error
File size: 7,827 Bytes
f5fdf51 |
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 |
# Copyright (c) Tencent Inc. All rights reserved.
import os
import cv2
import argparse
import os.path as osp
import torch
from mmengine.config import Config, DictAction
from mmengine.runner.amp import autocast
from mmengine.dataset import Compose
from mmengine.utils import ProgressBar
from mmdet.apis import init_detector
from mmdet.utils import get_test_pipeline_cfg
import supervision as sv
BOUNDING_BOX_ANNOTATOR = sv.BoundingBoxAnnotator(thickness=1)
MASK_ANNOTATOR = sv.MaskAnnotator()
class LabelAnnotator(sv.LabelAnnotator):
@staticmethod
def resolve_text_background_xyxy(
center_coordinates,
text_wh,
position,
):
center_x, center_y = center_coordinates
text_w, text_h = text_wh
return center_x, center_y, center_x + text_w, center_y + text_h
LABEL_ANNOTATOR = LabelAnnotator(text_padding=4,
text_scale=0.5,
text_thickness=1)
def parse_args():
parser = argparse.ArgumentParser(description='YOLO-World Demo')
parser.add_argument('config', help='test config file path')
parser.add_argument('checkpoint', help='checkpoint file')
parser.add_argument('image', help='image path, include image file or dir.')
parser.add_argument(
'text',
help=
'text prompts, including categories separated by a comma or a txt file with each line as a prompt.'
)
parser.add_argument('--topk',
default=100,
type=int,
help='keep topk predictions.')
parser.add_argument('--threshold',
default=0.1,
type=float,
help='confidence score threshold for predictions.')
parser.add_argument('--device',
default='cuda:0',
help='device used for inference.')
parser.add_argument('--show',
action='store_true',
help='show the detection results.')
parser.add_argument(
'--annotation',
action='store_true',
help='save the annotated detection results as yolo text format.')
parser.add_argument('--amp',
action='store_true',
help='use mixed precision for inference.')
parser.add_argument('--output-dir',
default='demo_outputs',
help='the directory to save outputs')
parser.add_argument(
'--cfg-options',
nargs='+',
action=DictAction,
help='override some settings in the used config, the key-value pair '
'in xxx=yyy format will be merged into config file. If the value to '
'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
'Note that the quotation marks are necessary and that no white space '
'is allowed.')
args = parser.parse_args()
return args
def inference_detector(model,
image,
texts,
test_pipeline,
max_dets=100,
score_thr=0.3,
output_dir='./work_dir',
use_amp=False,
show=False,
annotation=False):
data_info = dict(img_id=0, img_path=image, texts=texts)
data_info = test_pipeline(data_info)
data_batch = dict(inputs=data_info['inputs'].unsqueeze(0),
data_samples=[data_info['data_samples']])
with autocast(enabled=use_amp), torch.no_grad():
output = model.test_step(data_batch)[0]
pred_instances = output.pred_instances
pred_instances = pred_instances[pred_instances.scores.float() >
score_thr]
if len(pred_instances.scores) > max_dets:
indices = pred_instances.scores.float().topk(max_dets)[1]
pred_instances = pred_instances[indices]
pred_instances = pred_instances.cpu().numpy()
if 'masks' in pred_instances:
masks = pred_instances['masks']
else:
masks = None
detections = sv.Detections(xyxy=pred_instances['bboxes'],
class_id=pred_instances['labels'],
confidence=pred_instances['scores'],
mask=masks)
labels = [
f"{texts[class_id][0]} {confidence:0.2f}" for class_id, confidence in
zip(detections.class_id, detections.confidence)
]
# label images
image = cv2.imread(image_path)
anno_image = image.copy()
image = BOUNDING_BOX_ANNOTATOR.annotate(image, detections)
image = LABEL_ANNOTATOR.annotate(image, detections, labels=labels)
if masks is not None:
image = MASK_ANNOTATOR.annotate(image, detections)
cv2.imwrite(osp.join(output_dir, osp.basename(image_path)), image)
if annotation:
images_dict = {}
annotations_dict = {}
images_dict[osp.basename(image_path)] = anno_image
annotations_dict[osp.basename(image_path)] = detections
ANNOTATIONS_DIRECTORY = os.makedirs(r"./annotations", exist_ok=True)
MIN_IMAGE_AREA_PERCENTAGE = 0.002
MAX_IMAGE_AREA_PERCENTAGE = 0.80
APPROXIMATION_PERCENTAGE = 0.75
sv.DetectionDataset(
classes=texts, images=images_dict,
annotations=annotations_dict).as_yolo(
annotations_directory_path=ANNOTATIONS_DIRECTORY,
min_image_area_percentage=MIN_IMAGE_AREA_PERCENTAGE,
max_image_area_percentage=MAX_IMAGE_AREA_PERCENTAGE,
approximation_percentage=APPROXIMATION_PERCENTAGE)
if show:
cv2.imshow('Image', image) # Provide window name
k = cv2.waitKey(0)
if k == 27:
# wait for ESC key to exit
cv2.destroyAllWindows()
if __name__ == '__main__':
args = parse_args()
# load config
cfg = Config.fromfile(args.config)
if args.cfg_options is not None:
cfg.merge_from_dict(args.cfg_options)
cfg.work_dir = osp.join('./work_dirs',
osp.splitext(osp.basename(args.config))[0])
# init model
cfg.load_from = args.checkpoint
model = init_detector(cfg, checkpoint=args.checkpoint, device=args.device)
# init test pipeline
test_pipeline_cfg = get_test_pipeline_cfg(cfg=cfg)
# test_pipeline[0].type = 'mmdet.LoadImageFromNDArray'
test_pipeline = Compose(test_pipeline_cfg)
if args.text.endswith('.txt'):
with open(args.text) as f:
lines = f.readlines()
texts = [[t.rstrip('\r\n')] for t in lines] + [[' ']]
else:
texts = [[t.strip()] for t in args.text.split(',')] + [[' ']]
output_dir = args.output_dir
if not osp.exists(output_dir):
os.mkdir(output_dir)
# load images
if not osp.isfile(args.image):
images = [
osp.join(args.image, img) for img in os.listdir(args.image)
if img.endswith('.png') or img.endswith('.jpg')
]
else:
images = [args.image]
# reparameterize texts
model.reparameterize(texts)
progress_bar = ProgressBar(len(images))
for image_path in images:
inference_detector(model,
image_path,
texts,
test_pipeline,
args.topk,
args.threshold,
output_dir=output_dir,
use_amp=args.amp,
show=args.show,
annotation=args.annotation)
progress_bar.update()
|