本文介绍将水表数字表盘分割模型贡献到PaddleHub的方法。先安装必要库，复现模型：准备数据集，配置GPU，定义图像预处理流程和数据集，用DeepLabv3p训练模型并导出。接着转换模型为PaddleHub模型，补充代码实现旋转剪裁等功能，最后测试安装与调用，实现水表数字表盘分割。
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【PaddleHub模型贡献】一行代码实现水表的数字表盘分割
一、安装必要的库
In [3]
!pip install paddlex -i https://mirror.baidu.com/pypi/simple !pip install --upgrade paddlepaddle-gpu -i https://pypi.tuna.tsinghua.edu.cn/simple !pip install --upgrade paddlehub==2.0.1 -i https://pypi.tuna.tsinghua.edu.cn/simple
二、模型训练
项目作者使用PaddleX做的语义分割，因为作者没有直接公开训练好的模型，所以这里我们先按照作者的思路复现模型。
1.准备表盘数据集
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!unzip -oq /home/aistudio/data/data73852/water.zip
2. 模型训练
2.1 配置GPU
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# 设置使用0号GPU卡（如无GPU，执行此代码后仍然会使用CPU训练模型）import matplotlib
matplotlib.use('Agg')
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '0'import paddlex as pdx
2.2 定义图像预处理流程transforms
定义数据处理流程，其中训练和测试需分别定义，训练过程包括了部分测试过程中不需要的数据增强操作，如在本示例中，训练过程使用了RandomHorizontalFlip和RandomPaddingCrop两种数据增强方式，更多图像预处理流程transforms的使用可参见paddlex.seg.transforms。
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from paddlex.seg import transforms
train_transforms = transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.Resize(target_size=512),
transforms.RandomPaddingCrop(crop_size=500),
transforms.Normalize()
])
eval_transforms = transforms.Compose([
transforms.Resize(512),
transforms.Normalize()
])
2.3 定义数据集Dataset
实例分割使用SegDataset格式的数据集，因此采用pdx.datasets.SegDataset来加载数据集，该接口的介绍可参见文档pdx.datasets.SegDataset。
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train_dataset = pdx.datasets.SegDataset(
data_dir='water',
file_list='water/train.txt',
label_list='water/class_names.txt',
transforms=train_transforms,
shuffle=True)
eval_dataset = pdx.datasets.SegDataset(
data_dir='water',
file_list='water/val.txt',
label_list='water/class_names.txt',
transforms=eval_transforms)
2021-03-11 14:54:48 [INFO] 150 samples in file water/train.txt 2021-03-11 14:54:48 [INFO] 11 samples in file water/val.txt
2.4 模型开始训练
使用本数据集在P40上训练，如有GPU，模型的训练过程预估为13分钟左右；如无GPU，则预估为5小时左右。更多训练模型的参数可参见文档paddlex.seg.DeepLabv3p。模型训练过程每间隔save_interval_epochs轮会保存一次模型在save_dir目录下，同时在保存的过程中也会在验证数据集上计算相关指标，具体相关日志参见文档。
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num_classes = len(train_dataset.labels)
model = pdx.seg.DeepLabv3p(num_classes=num_classes)
model.train(
num_epochs=40,
train_dataset=train_dataset,
train_batch_size=4,
eval_dataset=eval_dataset,
learning_rate=0.01,
save_interval_epochs=1, # pretrain_weights='output/deeplab4/best_model',
save_dir='output/water')
最后一轮的输出如下所示：
2021-03-11 15:02:56 [INFO] [TRAIN] Epoch=40/40, Step=1/37, loss=0.010831, lr=0.000362, time_each_step=0.18s, eta=0:0:10 2021-03-11 15:02:56 [INFO] [TRAIN] Epoch=40/40, Step=3/37, loss=0.010944, lr=0.000344, time_each_step=0.2s, eta=0:0:10 2021-03-11 15:02:57 [INFO] [TRAIN] Epoch=40/40, Step=5/37, loss=0.009099, lr=0.000326, time_each_step=0.22s, eta=0:0:10 2021-03-11 15:02:57 [INFO] [TRAIN] Epoch=40/40, Step=7/37, loss=0.011186, lr=0.000308, time_each_step=0.24s, eta=0:0:10 2021-03-11 15:02:57 [INFO] [TRAIN] Epoch=40/40, Step=9/37, loss=0.008269, lr=0.00029, time_each_step=0.25s, eta=0:0:10 2021-03-11 15:02:58 [INFO] [TRAIN] Epoch=40/40, Step=11/37, loss=0.011792, lr=0.000272, time_each_step=0.25s, eta=0:0:10 2021-03-11 15:02:58 [INFO] [TRAIN] Epoch=40/40, Step=13/37, loss=0.010976, lr=0.000254, time_each_step=0.26s, eta=0:0:9 2021-03-11 15:02:58 [INFO] [TRAIN] Epoch=40/40, Step=15/37, loss=0.01399, lr=0.000236, time_each_step=0.26s, eta=0:0:9 2021-03-11 15:02:58 [INFO] [TRAIN] Epoch=40/40, Step=17/37, loss=0.009998, lr=0.000217, time_each_step=0.26s, eta=0:0:8 2021-03-11 15:02:58 [INFO] [TRAIN] Epoch=40/40, Step=19/37, loss=0.012266, lr=0.000198, time_each_step=0.26s, eta=0:0:8 2021-03-11 15:02:58 [INFO] [TRAIN] Epoch=40/40, Step=21/37, loss=0.011713, lr=0.00018, time_each_step=0.13s, eta=0:0:5 2021-03-11 15:02:58 [INFO] [TRAIN] Epoch=40/40, Step=23/37, loss=0.010291, lr=0.00016, time_each_step=0.11s, eta=0:0:5 2021-03-11 15:02:58 [INFO] [TRAIN] Epoch=40/40, Step=25/37, loss=0.010211, lr=0.000141, time_each_step=0.09s, eta=0:0:4 2021-03-11 15:02:59 [INFO] [TRAIN] Epoch=40/40, Step=27/37, loss=0.02097, lr=0.000121, time_each_step=0.08s, eta=0:0:4 2021-03-11 15:02:59 [INFO] [TRAIN] Epoch=40/40, Step=29/37, loss=0.008198, lr=0.000101, time_each_step=0.07s, eta=0:0:3 2021-03-11 15:02:59 [INFO] [TRAIN] Epoch=40/40, Step=31/37, loss=0.010346, lr=8.1e-05, time_each_step=0.06s, eta=0:0:3 2021-03-11 15:02:59 [INFO] [TRAIN] Epoch=40/40, Step=33/37, loss=0.009331, lr=6e-05, time_each_step=0.06s, eta=0:0:3 2021-03-11 15:02:59 [INFO] [TRAIN] Epoch=40/40, Step=35/37, loss=0.01259, lr=3.8e-05, time_each_step=0.06s, eta=0:0:3 2021-03-11 15:02:59 [INFO] [TRAIN] Epoch=40/40, Step=37/37, loss=0.013072, lr=1.4e-05, time_each_step=0.06s, eta=0:0:3 2021-03-11 15:02:59 [INFO] [TRAIN] Epoch 40 finished, loss=0.011522, lr=0.000195 . 2021-03-11 15:02:59 [INFO] Start to evaluating(total_samples=11, total_steps=3)... 100%|██████████| 3/3 [00:02<00:00, 1.00it/s] 2021-03-11 15:03:02 [INFO] [EVAL] Finished, Epoch=40, miou=0.814756, category_iou=[0.99168644 0.63782582], oacc=0.991806, category_acc=[0.99431391 0.84710874], kappa=0.774722, category_F1-score=[0.99582587 0.77886893] . 2021-03-11 15:03:03 [INFO] Model saved in output/water/epoch_40. 2021-03-11 15:03:03 [INFO] Current evaluated best model in eval_dataset is epoch_35, miou=0.8284633456567256
3.模型导出
模型训练时会自动保存模型参数，我们需要把训练模型导出成可预测模型。
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!paddlex --export_inference --model_dir=output/water/best_model --save_dir=./inference_model
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/setuptools/depends.py:2: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses import imp W0311 15:49:28.613981 782 device_context.cc:362] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 11.0, Runtime API Version: 10.1 W0311 15:49:28.618839 782 device_context.cc:372] device: 0, cuDNN Version: 7.6. 2021-03-11 15:49:32 [INFO] Model[DeepLabv3p] loaded. 2021-03-11 15:49:32 [INFO] Model for inference deploy saved in ./inference_model.
三、封装Module
下面正式开始模型转换！
1.模型转换
PaddleX模型可以快速转换成PaddleHub模型，只需要用下面这一句命令即可：
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!hub convert --model_dir inference_model \
--module_name WatermeterSegmentation \
--module_version 1.0.0 \
--output_dir outputs
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!gzip -dfq /home/aistudio/outputs/WatermeterSegmentation.tar.gz !tar -xf /home/aistudio/outputs/WatermeterSegmentation.tar
2.补充代码
刚刚转换的模型其实已经是PaddleHub的Module了，但是原项目中，作者做了一些图片的裁剪等操作，把数字提取出来了，因此，我们需要把这部分代码补充进去。
完整的module.py文件内容如下：
from __future__ import absolute_importfrom __future__ import divisionimport osimport cv2import argparseimport base64import paddlex as pdxfrom math import *import time, math, reimport numpy as npimport paddlehub as hubfrom paddlehub.module.module import moduleinfo, runnable, servingdef base64_to_cv2(b64str):
data = base64.b64decode(b64str.encode('utf8'))
data = np.fromstring(data, np.uint8)
data = cv2.imdecode(data, cv2.IMREAD_COLOR) return datadef cv2_to_base64(image):
# return base64.b64encode(image)
data = cv2.imencode('.jpg', image)[1] return base64.b64encode(data.tostring()).decode('utf8')def read_images(paths):
images = [] for path in paths:
images.append(cv2.imread(path)) return images'''旋转图像并剪裁'''def rotate(
img, # 图片
pt1, pt2, pt3, pt4,
imgOutSrc):
# print(pt1,pt2,pt3,pt4)
withRect = math.sqrt((pt4[0] - pt1[0]) ** 2 + (pt4[1] - pt1[1]) ** 2) # 矩形框的宽度
heightRect = math.sqrt((pt1[0] - pt2[0]) ** 2 + (pt1[1] - pt2[1]) **2) # print("矩形的宽度",withRect, "矩形的高度", heightRect)
angle = acos((pt4[0] - pt1[0]) / withRect) * (180 / math.pi) # 矩形框旋转角度
# print("矩形框旋转角度", angle)
if withRect > heightRect: if pt4[1]>pt1[1]: # print("顺时针旋转")
pass
else: # print("逆时针旋转")
angle=-angle else: # print("逆时针旋转")
angle=90 - angle
height = img.shape[0] # 原始图像高度
width = img.shape[1] # 原始图像宽度
rotateMat = cv2.getRotationMatrix2D((width / 2, height / 2), angle, 1) # 按angle角度旋转图像
heightNew = int(width * fabs(sin(radians(angle))) + height * fabs(cos(radians(angle))))
widthNew = int(height * fabs(sin(radians(angle))) + width * fabs(cos(radians(angle))))
rotateMat[0, 2] += (widthNew - width) / 2
rotateMat[1, 2] += (heightNew - height) / 2
imgRotation = cv2.warpAffine(img, rotateMat, (widthNew, heightNew), borderValue=(255, 255, 255)) # cv2.imwrite("imgRotation.jpg", imgRotation)
# 旋转后图像的四点坐标
[[pt1[0]], [pt1[1]]] = np.dot(rotateMat, np.array([[pt1[0]], [pt1[1]], [1]]))
[[pt3[0]], [pt3[1]]] = np.dot(rotateMat, np.array([[pt3[0]], [pt3[1]], [1]]))
[[pt2[0]], [pt2[1]]] = np.dot(rotateMat, np.array([[pt2[0]], [pt2[1]], [1]]))
[[pt4[0]], [pt4[1]]] = np.dot(rotateMat, np.array([[pt4[0]], [pt4[1]], [1]])) # 处理反转的情况
if pt2[1]>pt4[1]:
pt2[1],pt4[1]=pt4[1],pt2[1] if pt1[0]>pt3[0]:
pt1[0],pt3[0]=pt3[0],pt1[0]
imgOut = imgRotation[int(pt2[1]):int(pt4[1]), int(pt1[0]):int(pt3[0])]
cv2.imwrite(imgOutSrc, imgOut) # 裁减得到的旋转矩形框@moduleinfo(
name='WatermeterSegmentation', type='CV/semantic_segmentatio',
author='郑博培、彭兆帅',
author_email='2733821739@qq.com',
summary='Digital dial segmentation of water meter',
version='1.0.0')class MODULE(hub.Module):
def _initialize(self, **kwargs):
self.default_pretrained_model_path = os.path.join(
self.directory, 'assets')
self.model = pdx.deploy.Predictor(self.default_pretrained_model_path,
**kwargs) def predict(self,
images=None,
paths=None,
data=None,
batch_size=1,
use_gpu=False,
**kwargs):
all_data = images if images is not None else read_images(paths)
total_num = len(all_data)
loop_num = int(np.ceil(total_num / batch_size))
res = [] for iter_id in range(loop_num):
batch_data = list()
handle_id = iter_id * batch_size for image_id in range(batch_size): try:
batch_data.append(all_data[handle_id + image_id]) except IndexError: break
out = self.model.batch_predict(batch_data, **kwargs)
res.extend(out) return res def cutPic(self, picUrl):
# seg = hub.Module(name='WatermeterSegmentation')
image_name = picUrl
im = cv2.imread(image_name)
result = self.predict(images=[im]) # 将多边形polygon转矩形
contours, hier = cv2.findContours(result[0]['label_map'], cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
print(type(contours[0]))
n = 0
m = 0
for index,contour in enumerate(contours): if len(contour) > n:
n = len(contour)
m = index
image = cv2.imread(image_name) # 获取最小的矩形
rect = cv2.minAreaRect(contours[m])
box = np.int0(cv2.boxPoints(rect)) # 获取到矩形的四个点
tmp = cv2.drawContours(image, [box], 0, (0, 0, 255), 3)
imgOutSrc = 'result.jpg'
rotate(image, box[0], box[1], box[2], box[3], imgOutSrc)
res = []
res.append(imgOutSrc) return res @serving
def serving_method(self, images, **kwargs):
"""
Run as a service.
"""
images_decode = [base64_to_cv2(image) for image in images]
results = self.predict(images_decode, **kwargs)
res = [] for result in results: if isinstance(result, dict): # result_new = dict()
for key, value in result.items(): if isinstance(value, np.ndarray):
result[key] = cv2_to_base64(value) elif isinstance(value, np.generic):
result[key] = np.asscalar(value) elif isinstance(result, list): for index in range(len(result)): for key, value in result[index].items(): if isinstance(value, np.ndarray):
result[index][key] = cv2_to_base64(value) elif isinstance(value, np.generic):
result[index][key] = np.asscalar(value) else: raise RuntimeError('The result cannot be used in serving.')
res.append(result) return res @runnable
def run_cmd(self, argvs):
"""
Run as a command.
"""
self.parser = argparse.ArgumentParser(
description="Run the {} module.".format(self.name),
prog='hub run {}'.format(self.name),
usage='%(prog)s',
add_help=True)
self.arg_input_group = self.parser.add_argument_group(
title="Input options", description="Input data. Required")
self.arg_config_group = self.parser.add_argument_group(
title="Config options",
description= "Run configuration for controlling module behavior, not required.")
self.add_module_config_arg()
self.add_module_input_arg()
args = self.parser.parse_args(argvs)
results = self.predict(
paths=[args.input_path],
use_gpu=args.use_gpu) return results def add_module_config_arg(self):
"""
Add the command config options.
"""
self.arg_config_group.add_argument( '--use_gpu', type=bool,
default=False, help="whether use GPU or not") def add_module_input_arg(self):
"""
Add the command input options.
"""
self.arg_input_group.add_argument( '--input_path', type=str, help="path to image.")if __name__ == '__main__':
module = MODULE(directory='./new_model')
images = [cv2.imread('./cat.jpg'), cv2.imread('./cat.jpg'), cv2.imread('./cat.jpg')]
res = module.predict(images=images)
3.模型测试
首先安装我们刚刚写好的Module：
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!hub install WatermeterSegmentation
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/setuptools/depends.py:2: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses import imp /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/__init__.py:107: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working from collections import MutableMapping /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/rcsetup.py:20: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working from collections import Iterable, Mapping /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/colors.py:53: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working from collections import Sized[2021-03-11 16:42:50,225] [ INFO] - Successfully uninstalled WatermeterSegmentation[2021-03-11 16:42:50,441] [ INFO] - Successfully installed WatermeterSegmentation-1.0.0
模型调用：
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import cv2import paddlehub as hub seg = hub.Module(name='WatermeterSegmentation') res = seg.cutPic(picUrl="water/images/val/20200521105032.png")
[2021-03-11 17:13:36,113] [ WARNING] - The _initialize method in HubModule will soon be deprecated, you can use the __init__() to handle the initialization of the object
预测结果如下。
输入图片：
最终将截取的图片显示效果如下：
