打包完成

.gitignore

+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*~
+# C extensions
+*.so
+src/
+# Distribution / packaging
+.Python
+.vscode
+index/
+env/
+bin/
+build/
+develop-eggs/
+dist/
+eggs/
+lib/
+lib64/
+sdist/
+var/
+.idea/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.cache
+nosetests.xml
+coverage.xml
+
+# Translations
+*.mo
+
+# Mr Developer
+.mr.developer.cfg
+.project
+.pydevproject
+
+# Rope
+.ropeproject
+
+# Django stuff:
+*.log
+*.pot
+
+# Sphinx documentation
+docs/_build/
+
+
+# config
+fabfile.py
+settings.online.py
+/gaia/settings.py
+settings_local.py
+media/
+log/
+crawldata/
+conf/
+/static
+.vagrant/
+Vagrantfile
+
+*.DS_Store
+dump.rdb
+# .gitignore for yangchuncheng
+api/management/commands/ycc*
+settings_override*
+.script/
+.tmp.sql
+.env
+*.pem
+/gaia/hospital_list_settings.py
+coverage_html/
+gaia/rpcd.json
+*.swp
+.python-version

onnx_infer/color.py

-from onnx2kera import onnxinfere
+from onnx_infer.onnx2kera import onnxinfere
 import numpy as np
 import cv2
 from copy import deepcopy

onnx_infer/croppic.py

 # crop Image  with threshold
 import cv2
-
 def cropImage(image, threshold_index):
     return image[int(threshold_index[0]): int(threshold_index[2]),
            int(threshold_index[1]): int(threshold_index[3])]
@@ -9,7 +8,7 @@ def saveCrop(image, savePath):
     cv2.imwrite(savePath, image)
 
 if __name__ == '__main__':
-    from yolodata import path2arr
+    from onnx_infer.yolodata import path2arr
     path = '/Users/apple/Desktop/8.jpg'
     threshold_index = [95.55165, 184.0512,  250.18225, 333.9927]
     image = path2arr(path)

onnx_infer/main.py

-#*******************
+# *******************
 """
 this is the main which contain color and yolo
 the color input need abs path of color.onnx and one image arr which  generate from the croppic.py。
@@ -6,19 +6,20 @@ the result is the color of the picture。
 the yolo  is more complex, need, the path of anchor.txt, label.txt, and the image path， the yolo.onnx path
 the result of this is the category and bounding box which is the max score.
 """
-#*******************
+# *******************
 
-from yolodata import path2Img, letterbox_image, path2arr
-from onnx2kera import onnxinfere
-from supression import Supress
+from onnx_infer.yolodata import path2Img, letterbox_image, path2arr
+from onnx_infer.onnx2kera import onnxinfere
+from onnx_infer.supression import Supress
 import tensorflow as tf
-from color import COLORS, featureTransform, resize, get_color
-from croppic import cropImage
+from onnx_infer.color import COLORS, featureTransform, resize, get_color
+from onnx_infer.croppic import cropImage
 
-#global
+# global
 categorys = ['long sleeve dress', 'vest dress', 'vest', 'long sleeve outwear', 'long sleeve top',
              'trousers', 'short sleeve top', 'sling dress', 'skirt', 'short sleeve dress', 'shorts']
 
+
 class Main:
     def __init__(self, colorOnnx_path, yoloOnnx_path, image_path):
         self.colorOnnx_path = colorOnnx_path
@@ -28,7 +29,6 @@ class Main:
         self.iou = 0.05
         self.picSize = (416, 416)
 
-
     def bboxAndcategory(self):
         bbox = []
         category = []
@@ -54,23 +54,26 @@ class Main:
         color, ratio = get_color(self.colorOnnx_path, tmp, COLORS)
         return color, bbox, category
 
+
 # main test
+colorOnnx_path = 'color.onnx'
+yoloOnnx_path = 'yolo3.onnx'
+
 
-def get_result(colorOnnx_path, yoloOnnx_path, image_path):
+def get_result(image_path):
     m = Main(colorOnnx_path, yoloOnnx_path, image_path)
     color, bbox, category = m.colorAndbboxAndcategory()
     return color, bbox, category
 
 
-if __name__ == '__main__':
-    colorOnnx_path = '/Users/apple/Desktop/color.onnx'
-    image_path = '/Users/apple/Desktop/8.jpg'
-    yoloOnnx_path = '/Users/apple/Desktop/yolo3.onnx'
-    m = Main(colorOnnx_path, yoloOnnx_path, image_path)
-    color, bbox, category = m.colorAndbboxAndcategory()
-    print('its color is : {}, category is : {} '.format(color, category))
-    print('the bounding box is : {}'.format(bbox))
+# if __name__ == '__main__':
+#     colorOnnx_path = '/Users/apple/Desktop/color.onnx'
+#     image_path = '/Users/apple/Desktop/8.jpg'
+#     yoloOnnx_path = '/Users/apple/Desktop/yolo3.onnx'
+#     m = Main(colorOnnx_path, yoloOnnx_path, image_path)
+#     color, bbox, category = m.colorAndbboxAndcategory()
+#     print('its color is : {}, category is : {} '.format(color, category))
+#     print('the bounding box is : {}'.format(bbox))
 #    print(color)
 #    print(bbox)
 #    print(category)
-

onnx_infer/onnx2kera.py

@@ -6,7 +6,7 @@ import cv2
 import numpy as np
 import keras.backend as k
 import tensorflow as tf
-from yolodata import path2Img, letterbox_image
+from onnx_infer.yolodata import path2Img, letterbox_image
 
 def hf2onnx(h5_path, save_path):
     model = load_model(h5_path)
@@ -23,7 +23,7 @@ def onnxinfere(onnx_path, input):
 
 
 if __name__ == '__main__':
-    from supression import Supress
+    from onnx_infer.supression import Supress
 #    path = '/Users/apple/Public/keras-yolo3/model_data/our1_yolo.h5'
     save_path = '/Users/apple/Desktop/yolo3.onnx'
     anncorPath = 'model_data/yolo_anchors.txt'

onnx_infer/supression.py

 import numpy as np
 from keras import backend as k
-from yolo3.model import yolo_eval
+from onnx_infer.yolo3.model import yolo_eval
 import os
 
 class Supress:

onnx_infer/yolo3/model.py

@@ -9,7 +9,7 @@ from keras.layers.normalization import BatchNormalization
 from keras.models import Model
 from keras.regularizers import l2
 
-from yolo3.utils import compose
+from onnx_infer.yolo3.utils import compose
 
 
 @wraps(Conv2D)

requirements.txt

+onnxmltools
+keras
+opencv-python
+tensorflow
+numpy
+onnxruntime
+pillow
+matplotlib
\ No newline at end of file

setup.py

-#!/user/bin/python
-
 from setuptools import setup, find_packages
+from codecs import open
+from os import path
 
-setup(
-    name = 'onnx_infer',
-    version = '0.1.0',
-    description = 'model inference',
-    author = 'wjl',
-    url = 'http://git.wanmeizhensuo.com/wujilong/onnx_model.git',
-    author_email = 'wujilong@igengmei.com',
-    license = 'MIT',
-    packages = ['onnx_infer', 'onnx_infer.yolo3'],
+__version__ = '0.1.0'
+
+here = path.abspath(path.dirname(__file__))
 
-    )
+# get the dependencies and installs
+with open(path.join(here, 'requirements.txt'), encoding='utf-8') as f:
+    all_reqs = f.read().split('\n')
 
+install_requires = [x.strip() for x in all_reqs if 'git+' not in x]
+dependency_links = [x.strip().replace('git+', '') for x in all_reqs if x.startswith('git+')]
+
+setup(
+    name='onnx_infer',
+    version=__version__,
+    description='A short description of the project',
+    url='https://github.com/Your github username/mxnet_insight',
+    download_url='https://github.com/Your github username/mxnet_insight/tarball/' + __version__,
+    license='BSD',
+    classifiers=[
+      'Development Status :: 3 - Alpha',
+      'Intended Audience :: Developers',
+      'Programming Language :: Python :: 3',
+    ],
+    keywords='',
+    packages=['onnx_infer'],
+    include_package_data=True,
+    author='Your name',
+    install_requires=install_requires,
+    dependency_links=dependency_links,
+    author_email='Your address email (eq. you@example.com)'
+)