【昇腾CANN训练营第二期】第三课基于MindX SDK开发图像分类应用作业

pip3.7 install opencv-python -i http://mirrors.aliyun.com/pypi/simple --trusted-host mirrors.aliyun.com

创建一个resnet50 文件夹。然后下载resnet50模型

wget --no-check-certificate  https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/turing/resourcecenter/model/Resnet50V1.5/zh/1.1/ATC_Resnet50_V1.5_from_Tensorflow_Ascend310.zip

解压ATC_Resnet50_V1.5_from_Tensorflow_Ascend310.zip 文件

unzip ATC_Resnet50_V1.5_from_Tensorflow_Ascend310.zip

aipp文件  转模型配置文件aipp_resnet50_224_224.aippconfig

aipp_op {
    aipp_mode: static
    input_format : YUV420SP_U8
    csc_switch : true
    rbuv_swap_switch : false
    matrix_r0c0 : 256
    matrix_r0c1 : 0
    matrix_r0c2 : 359
    matrix_r1c0 : 256
    matrix_r1c1 : -88
    matrix_r1c2 : -183
    matrix_r2c0 : 256
    matrix_r2c1 : 454
    matrix_r2c2 : 0
    input_bias_0 : 0
    input_bias_1 : 128
    input_bias_2 : 128
    
    mean_chn_0 : 124
    mean_chn_1 : 117
    mean_chn_2 : 104
}

vi  aipp_resnet50_224_224.aippconfig  然后在文件里写入上面信息

转换模型

atc --model=./resnet50_v1.5.pb --framework=3 --output=resnet50_aipp_tf --output_type=FP32 --soc_version=Ascend310 --input_shape=input_tensor:1,224,224,3 --insert_op_conf=./aipp_resnet50_224_224.aippconfig

模型转换指令，自行尝试转换模型，输出的om模型要求为resnet50_aipp_tf.om，并把转换好om模型拷贝到工程的model目录下，若转换有难度，请直接下载转好的模型。链接：https://pan.baidu.com/s/1rrMC8dYz9MxP9-whmlKJtw 提取码：g095

模型转换完成

新建作业文件夹，下载好的作业代码文件通过ftp传到对应文件夹

cd /home/HwHiAiUser/MindX

cp /home/HwHiAiUser/MindX/resnet50/resnet50_aipp_tf.om ./zuoye/model/

/home/HwHiAiUser/MindX/zuoye

• 配置环境变量

export MX_SDK_HOME=/home/HwHiAiUser/MindX/MindXSDK/mxVision
export LD_LIBRARY_PATH=${MX_SDK_HOME}/lib:${MX_SDK_HOME}/opensource/lib:${MX_SDK_HOME}/opensource/lib64:/usr/local/Ascend/ascend-toolkit/latest/acllib/lib64:/usr/local/Ascend/driver/lib64/
export GST_PLUGIN_SCANNER=${MX_SDK_HOME}/opensource/libexec/gstreamer-1.0/gst-plugin-scanner
export GST_PLUGIN_PATH=${MX_SDK_HOME}/opensource/lib/gstreamer-1.0:${MX_SDK_HOME}/lib/plugins
export PYTHONPATH=${MX_SDK_HOME}/python

• 补全main.py代码

#!/usr/bin/env python
# coding=utf-8

# Copyright(C) 2021. Huawei Technologies Co.,Ltd. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import cv2
import json
import os
from StreamManagerApi import StreamManagerApi, MxDataInput

if __name__ == '__main__':
    streamManagerApi = StreamManagerApi()
    # init stream manager
    ret = streamManagerApi.InitManager()
    if ret != 0:
        print("Failed to init Stream manager, ret=%s" % str(ret))
        exit()

    path=b"./test.pipeline"
    ret = streamManagerApi.CreateMultipleStreamsFromFile(path)
    if ret != 0:
        print("Failed to create Stream, ret=%s" % str(ret))
        exit()

    # Construct the input of the stream
    dataInput = MxDataInput()
    if os.path.exists('test.jpg') != 1:
        print("The test image does not exist.")

    with open("test.jpg", 'rb') as f:
        dataInput.data = f.read()

    # Inputs data to a specified stream based on streamName.
    streamName = b'classication'
    inPluginId = 0
    uniqueId = streamManagerApi.SendDataWithUniqueId(streamName, inPluginId, dataInput)

    if uniqueId < 0:
        print("Failed to send data to stream.")
        exit()

    # Obtain the inference result by specifying streamName and uniqueId.
    infer_result = streamManagerApi.GetResultWithUniqueId(streamName, uniqueId, 3000)
    if infer_result.errorCode != 0:
        print("GetResultWithUniqueId error. errorCode=%d, errorMsg=%s" % (
            infer_result.errorCode, infer_result.data.decode()))
        exit()

    results = json.loads(infer_result.data.decode())
    print(results)
    
    #可视化代码填写处
    bboxes = []
    
    #从results取数据，获取分类的className，并利用opencv把标签名标记到图片
    img_path = "test.jpg"
    img = cv2.imread(img_path)
    bboxes = []

    for bbox in results['MxpiClass']:
        bboxes = { 'classId': int(bbox['classId']),
                   'className': bbox['className'],
                   'confidence': round(bbox['confidence'],9)}
    img = cv2.imread(img_path)
    #可视化代码填写处
    #从results取数据，获取分类的className，并利用opencv把标签名标记到图片
    #className = 'class:' +bboxes['className']
    className = '[' +bboxes['className'] + ']'
    print(className)

    cv2.putText(img, className, (50,50),cv2.FONT_HERSHEY_SIMPLEX, 1.0, (255, 255, 255), 1)
    # 图像，文字内容， 坐标 ，字体，大小，颜色，字体厚度

    cv2.imwrite("./result.jpg", img)
    # destroy streams
    streamManagerApi.DestroyAllStreams()

• 运行

python3 main.py