meta-onnx 0.1.2

Meta Onnx Toolkit

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)