Meta Onnx Toolkit
Project description
metaonnx
metaonnx部署通用框架
1、安装最新版 meta-cv
pip install meta-cv
2、安装最新版 meta-onnx
pip install meta-onnx
3、图像分类示例(参考classification_demo.py代码)
import cv2, platform
import metaonnx as m
Classification = m.Classification
y = Classification(model_path='models/mixnet_xl_bs24_4.onnx',
input_width=224,
input_height=224,
use_preprocess=True,
normal=True,
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
swap=(2, 0, 1),
class_names=classnames)
batch_size = 24
img = cv2.imread('models/bottle.jpg')
img_list = [img[:, :, ::-1]] * batch_size if batch_size > 1 else img[:, :, ::-1]
_dets, _scores, _labels = y.predict(img_list)
# 显示
y.show(img, _dets, _scores, _labels)
cv2.imwrite("models/bottle.png", img)
4、目标检测示例(参考detection_demo.py代码)
import platform, cv2
import metaonnx as m
Detection = mo.Detection
y = Detection(model_path='models/yolov8m.onnx',
input_width=640,
input_height=480,
use_preprocess=True,
pad=True,
normal=True,
swap=(2, 0, 1),
confidence_thresh=0.5,
nms_thresh=0.3,
class_names=classnames)
batch_size = 1
img = cv2.imread('models/bus.jpg')
img_list = [img[:, :, ::-1]] * batch_size if batch_size > 1 else img[:, :, ::-1]
_dets, _scores, _labels = y.predict(img_list)
# 显示
y.show(img, _dets[-1], _scores[-1], _labels[-1])
cv2.imwrite("models/bus.png", img)
5、实例分割示例(参考segment_demo.py代码)
import cv2
import metaonnx as m
Segment = mo.Segment
y = Segment(model_path='models/yolov8m-seg.onnx',
input_width=640,
input_height=480,
use_preprocess=True,
pad=True,
normal=True,
swap=(2, 0, 1),
confidence_thresh=0.5,
nms_thresh=0.3,
class_names=classnames)
batch_size = 1
img = cv2.imread('models/bus.jpg')
img_list = [img[:, :, ::-1]] * batch_size if batch_size > 1 else img[:, :, ::-1]
_dets, _scores, _labels = y.predict(img_list)
# 显示
y.show(img, _dets[-1], _scores[-1], _labels[-1])
cv2.imwrite("models/bus.png", img)
6、人脸检测示例(参考face_detection_demo.py代码)
import cv2
import metaonnx as m
FaceDetection = m.FaceDetection
y = FaceDetection(model_path='models/face_detection.onnx',
input_width=480,
input_height=640,
use_preprocess=True,
pad=True,
normal=True,
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
swap=(2, 0, 1),
confidence_thresh=0.5,
nms_thresh=0.4,
class_names=['face'])
batch_size = 1
img = cv2.imread('models/face.jpg')
img_list = [img[:, :, ::-1]] * batch_size if batch_size > 1 else img[:, :, ::-1]
_dets, _scores, _labels = y.predict(img_list)
y.show(img, _dets[-1], _scores[-1], _labels[-1])
cv2.imwrite("models/face.png", img)
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