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import os
import json
import argparse
import os.path as osp
import cv2
import numpy as np
import supervision as sv
import onnxruntime as ort
from mmengine.utils import ProgressBar
try:
import torch
from torchvision.ops import nms
except Exception as e:
print(e)
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('YOLO-World ONNX Demo')
parser.add_argument('onnx', help='onnx file')
parser.add_argument('image', help='image path, include image file or dir.')
parser.add_argument(
'text',
help=
'detecting texts (str or json), should be consistent with the ONNX model'
)
parser.add_argument('--output-dir',
default='./output',
help='directory to save output files')
parser.add_argument('--device',
default='cuda:0',
help='device used for inference')
parser.add_argument(
'--onnx-nms',
action='store_false',
help='whether ONNX model contains NMS and postprocessing')
args = parser.parse_args()
return args
def preprocess(image, size=(640, 640)):
h, w = image.shape[:2]
max_size = max(h, w)
scale_factor = size[0] / max_size
pad_h = (max_size - h) // 2
pad_w = (max_size - w) // 2
pad_image = np.zeros((max_size, max_size, 3), dtype=image.dtype)
pad_image[pad_h:h + pad_h, pad_w:w + pad_w] = image
image = cv2.resize(pad_image, size,
interpolation=cv2.INTER_LINEAR).astype('float32')
image /= 255.0
image = image[None]
return image, scale_factor, (pad_h, pad_w)
def visualize(image, bboxes, labels, scores, texts):
detections = sv.Detections(xyxy=bboxes, class_id=labels, confidence=scores)
labels = [
f"{texts[class_id][0]} {confidence:0.2f}" for class_id, confidence in
zip(detections.class_id, detections.confidence)
]
image = BOUNDING_BOX_ANNOTATOR.annotate(image, detections)
image = LABEL_ANNOTATOR.annotate(image, detections, labels=labels)
return image
def inference(ort_session,
image_path,
texts,
output_dir,
size=(640, 640),
**kwargs):
# normal export
# with NMS and postprocessing
ori_image = cv2.imread(image_path)
h, w = ori_image.shape[:2]
image, scale_factor, pad_param = preprocess(ori_image[:, :, [2, 1, 0]],
size)
input_ort = ort.OrtValue.ortvalue_from_numpy(image.transpose((0, 3, 1, 2)))
results = ort_session.run(["num_dets", "labels", "scores", "boxes"],
{"images": input_ort})
num_dets, labels, scores, bboxes = results
num_dets = num_dets[0][0]
labels = labels[0, :num_dets]
scores = scores[0, :num_dets]
bboxes = bboxes[0, :num_dets]
bboxes -= np.array(
[pad_param[1], pad_param[0], pad_param[1], pad_param[0]])
bboxes /= scale_factor
bboxes[:, 0::2] = np.clip(bboxes[:, 0::2], 0, w)
bboxes[:, 1::2] = np.clip(bboxes[:, 1::2], 0, h)
bboxes = bboxes.round().astype('int')
image_out = visualize(ori_image, bboxes, labels, scores, texts)
cv2.imwrite(osp.join(output_dir, osp.basename(image_path)), image_out)
return image_out
def inference_with_postprocessing(ort_session,
image_path,
texts,
output_dir,
size=(640, 640),
nms_thr=0.7,
score_thr=0.3,
max_dets=300):
# export with `--without-nms`
ori_image = cv2.imread(image_path)
h, w = ori_image.shape[:2]
image, scale_factor, pad_param = preprocess(ori_image[:, :, [2, 1, 0]],
size)
input_ort = ort.OrtValue.ortvalue_from_numpy(image.transpose((0, 3, 1, 2)))
results = ort_session.run(["scores", "boxes"], {"images": input_ort})
scores, bboxes = results
# move numpy array to torch
ori_scores = torch.from_numpy(scores[0]).to('cuda:0')
ori_bboxes = torch.from_numpy(bboxes[0]).to('cuda:0')
scores_list = []
labels_list = []
bboxes_list = []
# class-specific NMS
for cls_id in range(len(texts)):
cls_scores = ori_scores[:, cls_id]
labels = torch.ones(cls_scores.shape[0], dtype=torch.long) * cls_id
keep_idxs = nms(ori_bboxes, cls_scores, iou_threshold=nms_thr)
cur_bboxes = ori_bboxes[keep_idxs]
cls_scores = cls_scores[keep_idxs]
labels = labels[keep_idxs]
scores_list.append(cls_scores)
labels_list.append(labels)
bboxes_list.append(cur_bboxes)
scores = torch.cat(scores_list, dim=0)
labels = torch.cat(labels_list, dim=0)
bboxes = torch.cat(bboxes_list, dim=0)
keep_idxs = scores > score_thr
scores = scores[keep_idxs]
labels = labels[keep_idxs]
bboxes = bboxes[keep_idxs]
if len(keep_idxs) > max_dets:
_, sorted_idx = torch.sort(scores, descending=True)
keep_idxs = sorted_idx[:max_dets]
bboxes = bboxes[keep_idxs]
scores = scores[keep_idxs]
labels = labels[keep_idxs]
# Get candidate predict info by num_dets
scores = scores.cpu().numpy()
bboxes = bboxes.cpu().numpy()
labels = labels.cpu().numpy()
bboxes -= np.array(
[pad_param[1], pad_param[0], pad_param[1], pad_param[0]])
bboxes /= scale_factor
bboxes[:, 0::2] = np.clip(bboxes[:, 0::2], 0, w)
bboxes[:, 1::2] = np.clip(bboxes[:, 1::2], 0, h)
bboxes = bboxes.round().astype('int')
image_out = visualize(ori_image, bboxes, labels, scores, texts)
cv2.imwrite(osp.join(output_dir, osp.basename(image_path)), image_out)
return image_out
def main():
args = parse_args()
onnx_file = args.onnx
# init ONNX session
ort_session = ort.InferenceSession(
onnx_file, providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
print("Init ONNX Runtime session")
output_dir = "onnx_outputs"
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]
if args.text.endswith('.txt'):
with open(args.text) as f:
lines = f.readlines()
texts = [[t.rstrip('\r\n')] for t in lines]
elif args.text.endswith('.json'):
texts = json.load(open(args.text))
else:
texts = [[t.strip()] for t in args.text.split(',')]
print("Start to inference.")
progress_bar = ProgressBar(len(images))
if args.onnx_nms:
inference_func = inference
else:
inference_func = inference_with_postprocessing
for img in images:
inference_func(ort_session, img, texts, output_dir=output_dir)
progress_bar.update()
print("Finish inference")
if __name__ == "__main__":
main()
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