【目标检测】yolox模型推理
文章目录
- 1. 源码
- 2. 相关文件
- 3. 代码
-
- 3.1 onnx_inference
- 4. yolox对视频进行推理
-
- 4.1 相关文件## 4.2 onnx_inference
1. 源码
https://github.com/Megvii-BaseDetection/YOLOX
2. 相关文件
3. 代码
3.1 onnx_inference
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright (c) Megvii, Inc. and its affiliates.
import argparse
import os
import cv2
import numpy as np
import onnxruntime
from yolox.data.data_augment import preproc as preprocess
from yolox.data.datasets import COCO_CLASSES
from yolox.utils import mkdir, multiclass_nms, demo_postprocess, vis
def make_parser():
parser = argparse.ArgumentParser("onnxruntime inference sample")
parser.add_argument(
"-m",
"--model",
type=str,
default="yolox_l.onnx",
help="Input your onnx model.",
)
parser.add_argument(
"-i",
"--image_path",
type=str,
default='1.jpg',
help="Path to your input image.",
)
parser.add_argument(
"-o",
"--output_dir",
type=str,
default='demo_output',
help="Path to your output directory.",
)
parser.add_argument(
"-s",
"--score_thr",
type=float,
default=0.3,
help="Score threshould to filter the result.",
)
parser.add_argument(
"--input_shape",
type=str,
default="640,640",
help="Specify an input shape for inference.",
)
parser.add_argument(
"--with_p6",
action="store_true",
help="Whether your model uses p6 in FPN/PAN.",
)
return parser
if __name__ == '__main__':
args = make_parser().parse_args()
input_shape = tuple(map(int, args.input_shape.split(',')))
origin_img = cv2.imread(args.image_path)
img, ratio = preprocess(origin_img, input_shape)
session = onnxruntime.InferenceSession(args.model)
ort_inputs = {session.get_inputs()[0].name: img[None, :, :, :]}
output = session.run(None, ort_inputs)
predictions = demo_postprocess(output[0], input_shape, p6=args.with_p6)[0]
boxes = predictions[:, :4]
scores = predictions[:, 4:5] * predictions[:, 5:]
boxes_xyxy = np.ones_like(boxes)
boxes_xyxy[:, 0] = boxes[:, 0] - boxes[:, 2]/2.
boxes_xyxy[:, 1] = boxes[:, 1] - boxes[:, 3]/2.
boxes_xyxy[:, 2] = boxes[:, 0] + boxes[:, 2]/2.
boxes_xyxy[:, 3] = boxes[:, 1] + boxes[:, 3]/2.
boxes_xyxy /= ratio
dets = multiclass_nms(boxes_xyxy, scores, nms_thr=0.45, score_thr=0.1)
if dets is not None:
final_boxes, final_scores, final_cls_inds = dets[:, :4], dets[:, 4], dets[:, 5]
origin_img = vis(origin_img, final_boxes, final_scores, final_cls_inds,
conf=args.score_thr, class_names=COCO_CLASSES)
mkdir(args.output_dir)
output_path = os.path.join(args.output_dir, os.path.basename(args.image_path))
cv2.imwrite(output_path, origin_img)
4. yolox对视频进行推理
4.1 相关文件
## 4.2 onnx_inference
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright (c) Megvii, Inc. and its affiliates.
import argparse
import os
import cv2
import numpy as np
import onnxruntime
from yolox.data.data_augment import preproc as preprocess
from yolox.data.datasets import COCO_CLASSES
from yolox.utils import mkdir, multiclass_nms, demo_postprocess, vis
def make_parser():
parser = argparse.ArgumentParser("onnxruntime inference sample")
parser.add_argument(
"-m",
"--model",
type=str,
default="yolox_l.onnx",
help="Input your onnx model.",
)
parser.add_argument(
"-i",
"--image_path",
type=str,
default='1.jpg',
help="Path to your input image.",
)
parser.add_argument(
"-o",
"--output_dir",
type=str,
default='demo_output',
help="Path to your output directory.",
)
parser.add_argument(
"-s",
"--score_thr",
type=float,
default=0.3,
help="Score threshould to filter the result.",
)
parser.add_argument(
"--input_shape",
type=str,
default="640,640",
help="Specify an input shape for inference.",
)
parser.add_argument(
"--with_p6",
action="store_true",
help="Whether your model uses p6 in FPN/PAN.",
)
return parser
if __name__ == '__main__':
args = make_parser().parse_args()
input_shape = tuple(map(int, args.input_shape.split(',')))
# origin_img = cv2.imread(args.image_path)
videopath = r'./test.mp4'
path_filename = './yolox-video'
videodata = cv2.VideoCapture(videopath)
# fourcc = cv2.VideoWriter_fourcc(*'XVID')
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
fps = videodata.get(cv2.CAP_PROP_FPS)
success, frame = videodata.read()
if (success):
height = frame.shape[0]
width = frame.shape[1]
size = (int(width), int(height))
out = cv2.VideoWriter(path_filename + '/' + 'yolox-l' + '.mp4', fourcc, fps, size)
sum = 0
timef = 1 # 隔timef帧获取一帧
for i in range(int(videodata.get(cv2.CAP_PROP_FRAME_COUNT))):
sum += 1
success, frame = videodata.read()
if success == True and (sum % timef == 0):
img, ratio = preprocess(frame, input_shape)
session = onnxruntime.InferenceSession(args.model)
ort_inputs = {session.get_inputs()[0].name: img[None, :, :, :]}
output = session.run(None, ort_inputs)
predictions = demo_postprocess(output[0], input_shape, p6=args.with_p6)[0]
boxes = predictions[:, :4]
scores = predictions[:, 4:5] * predictions[:, 5:]
boxes_xyxy = np.ones_like(boxes)
boxes_xyxy[:, 0] = boxes[:, 0] - boxes[:, 2] / 2.
boxes_xyxy[:, 1] = boxes[:, 1] - boxes[:, 3] / 2.
boxes_xyxy[:, 2] = boxes[:, 0] + boxes[:, 2] / 2.
boxes_xyxy[:, 3] = boxes[:, 1] + boxes[:, 3] / 2.
boxes_xyxy /= ratio
dets = multiclass_nms(boxes_xyxy, scores, nms_thr=0.45, score_thr=0.1)
if dets is not None:
final_boxes, final_scores, final_cls_inds = dets[:, :4], dets[:, 4], dets[:, 5]
out_frame = vis(frame, final_boxes, final_scores, final_cls_inds,
conf=args.score_thr, class_names=COCO_CLASSES)
out.write(out_frame)