文章目录

• 一 环境搭配
• 二 人脸识别与关键点检测
• 三 行人检测
• 四 人脸识别与表情检测
• 四 深度学习算法交流

一 环境搭配

环境安装请看上一篇博客：传送门

二 人脸识别与关键点检测

以下来源于Openvino官方model ，在win10 和ubuntu大体步骤相似，跑demo：
想转ubuntu 或者win10 方法一样，我下面分别用win10 和ubuntu跑几个demo，大家可以试着做一下。
效果展示

一、 准备流程：

1. 在python环境中加载openvino

打开openvino安装目录如：
C:\Intel\openvino\python\python3.6

把目录下的openvino文件夹复制到

系统的python环境安装目录下如： C:\Python36\Lib\site-packages
2. 编译

C:\Intel\openvino\deployment_tools\inference_engine\samples 路径下执行：

build_samples_msvc2017.bat

执行完后在

C:\Users\kang\Documents\Intel\OpenVINO 目录

可以看到生成的

inference_engine_samples_build_2017 文件目录

在build目录中也可以找到cpu_extension：

cpu_extension = “C:\Users\kang\Documents\Intel\OpenVINO\inference_engine_samples_build_2017\intel64\Release\cpu_extension.dll”

1. 下载模型，记录路径

face-detection-adas-0001

landmarks-regression-retail-0009

记录xml地址

model_xml = “”
model_bin = “”

二、 参数说明

1. 人脸检测

   基于MobileNet v1版本
   输入格式：[1x3x384x672] = BCHW
   输出格式：[1，1，N，7] = [image_id, label, conf, x_min, y_min, x_max, y_max]

2. landmark提取

   landmark提取 - 基于卷积神经网络，提取5个点
   输入 [1x3x48x48] = BCHW
   输出 [1X10X1X1] = 五个点坐标(x0,y0,x1,y1…x4,y4)

3. python版本的api介绍

   同步调用，执行输入
   Im_exec_net.infer(inputs={“0”:face_roi})

4. 获取输出

   landmark_res = Im_exec_net.request[0].outputs[Im_output_blob]
   landmark_res = np.reshape(landmark_res,(5,2))

三、 附录代码：

import sys
import cv2
import numpy as np
import time
import logging as log
from openvino.inference_engine import IENetwork, IEPlugin
model_xml = "C:/Users/kang/Downloads/open_model_zoo-2019/model_downloader/Transportation/object_detection/face/pruned_mobilenet_reduced_ssd_shared_weights/dldt/face-detection-adas-0001.xml"
model_bin = "C:/Users/kang/Downloads/open_model_zoo-2019/model_downloader/Transportation/object_detection/face/pruned_mobilenet_reduced_ssd_shared_weights/dldt/face-detection-adas-0001.bin"
plugin_dir = "C:/Intel/openvino/deployment_tools/inference_engine/bin/intel64/Release"
cpu_extension = "C:/Users/kang/Documents/Intel/OpenVINO/inference_engine_samples_build_2017/intel64/Release/cpu_extension.dll"

landmark_xml = "C:/Users/kang/Downloads/open_model_zoo-2019/model_downloader/Retail/object_attributes/landmarks_regression/0009/dldt/landmarks-regression-retail-0009.xml"
landmark_bin = "C:/Users/kang/Downloads/open_model_zoo-2019/model_downloader/Retail/object_attributes/landmarks_regression/0009/dldt/landmarks-regression-retail-0009.bin"


def face_landmark_demo():
    log.basicConfig(format="[ %(levelname)s ] %(message)s",
                    level=log.INFO,
                    stream=sys.stdout)
    # Plugin initialization for specified device and load extensions library if specified
    log.info("Initializing plugin for {} device...".format("CPU"))
    plugin = IEPlugin(device="CPU", plugin_dirs=plugin_dir)
    plugin.add_cpu_extension(cpu_extension)

    # lut
    lut = []
    lut.append((0, 0, 255))
    lut.append((255, 0, 0))
    lut.append((0, 255, 0))
    lut.append((0, 255, 255))
    lut.append((255, 0, 255))

    # Read IR
    log.info("Reading IR...")
    net = IENetwork(model=model_xml, weights=model_bin)
    landmark_net = IENetwork(model=landmark_xml, weights=landmark_bin)

    if plugin.device == "CPU":
        supported_layers = plugin.get_supported_layers(net)
        not_supported_layers = [
            l for l in net.layers.keys() if l not in supported_layers
        ]
        if len(not_supported_layers) != 0:
            log.error(
                "Following layers are not supported by the plugin for specified device {}:\n {}"
                .format(plugin.device, ', '.join(not_supported_layers)))
            log.error(
                "Please try to specify cpu extensions library path in demo's command line parameters using -l "
                "or --cpu_extension command line argument")
            sys.exit(1)
    assert len(
        net.inputs.keys()) == 1, "Demo supports only single input topologies"
    assert len(net.outputs) == 1, "Demo supports only single output topologies"

    input_blob = next(iter(net.inputs))
    out_blob = next(iter(net.outputs))

    lm_input_blob = next(iter(landmark_net.inputs))
    lm_out_blob = next(iter(landmark_net.outputs))

    log.info("Loading IR to the plugin...")
    exec_net = plugin.load(network=net, num_requests=2)
    lm_exec_net = plugin.load(network=landmark_net)

    # Read and pre-process input image
    n, c, h, w = net.inputs[input_blob].shape
    nm, cm, hm, wm = landmark_net.inputs[lm_input_blob].shape

    del net
    del landmark_net

    cap = cv2.VideoCapture("C:/Users/kang/Downloads/material/av77002671.mp4")

    cur_request_id = 0
    next_request_id = 1

    log.info("Starting inference in async mode...")
    log.info("To switch between sync and async modes press Tab button")
    log.info("To stop the demo execution press Esc button")
    is_async_mode = True
    render_time = 0
    ret, frame = cap.read()

    print(
        "To close the application, press 'CTRL+C' or any key with focus on the output window"
    )
    while cap.isOpened():
        if is_async_mode:
            ret, next_frame = cap.read()
        else:
            ret, frame = cap.read()
        if not ret:
            break
        initial_w = cap.get(3)
        initial_h = cap.get(4)
        inf_start = time.time()
        if is_async_mode:
            in_frame = cv2.resize(next_frame, (w, h))
            in_frame = in_frame.transpose(
                (2, 0, 1))  # Change data layout from HWC to CHW
            in_frame = in_frame.reshape((n, c, h, w))
            exec_net.start_async(request_id=next_request_id,
                                inputs={input_blob: in_frame})
        else:
            in_frame = cv2.resize(frame, (w, h))
            in_frame = in_frame.transpose(
                (2, 0, 1))  # Change data layout from HWC to CHW
            in_frame = in_frame.reshape((n, c, h, w))
            exec_net.start_async(request_id=cur_request_id,
                                inputs={input_blob: in_frame})
        if exec_net.requests[cur_request_id].wait(-1) == 0:

            res = exec_net.requests[cur_request_id].outputs[out_blob]
            for obj in res[0][0]:
                if obj[2] > 0.5:
                    xmin = int(obj[3] * initial_w)
                    ymin = int(obj[4] * initial_h)
                    xmax = int(obj[5] * initial_w)
                    ymax = int(obj[6] * initial_h)
                    if xmin > 0 and ymin > 0 and (xmax < initial_w) and (
                            ymax < initial_h):
                        roi = frame[ymin:ymax, xmin:xmax, :]
                        rh, rw = roi.shape[:2]
                        face_roi = cv2.resize(roi, (wm, hm))
                        face_roi = face_roi.transpose((2, 0, 1))
                        face_roi = face_roi.reshape((nm, cm, hm, wm))
                        lm_exec_net.infer(inputs={'0': face_roi})
                        landmark_res = lm_exec_net.requests[0].outputs[
                            lm_out_blob]
                        landmark_res = np.reshape(landmark_res, (5, 2))
                        for m in range(len(landmark_res)):
                            x = landmark_res[m][0] * rw
                            y = landmark_res[m][1] * rh
                            cv2.circle(roi, (np.int32(x), np.int32(y)), 3,
                                    lut[m], 2, 8, 0)

                    cv2.rectangle(frame, (xmin, ymin), (xmax, ymax),
                                (0, 0, 255), 2, 8, 0)

            inf_end = time.time()
            det_time = inf_end - inf_start

            # Draw performance stats
            inf_time_message = "Inference time: {:.3f} ms, FPS:{:.3f}".format(
                det_time * 1000, 1000 / (det_time * 1000 + 1))
            render_time_message = "OpenCV rendering time: {:.3f} ms".format(
                render_time * 1000)
            async_mode_message = "Async mode is on. Processing request {}".format(cur_request_id) if is_async_mode else \
                "Async mode is off. Processing request {}".format(cur_request_id)

            cv2.putText(frame, inf_time_message, (15, 15),
                        cv2.FONT_HERSHEY_COMPLEX, 0.5, (200, 10, 10), 1)
            cv2.putText(frame, render_time_message, (15, 30),
                        cv2.FONT_HERSHEY_COMPLEX, 0.5, (10, 10, 200), 1)
            cv2.putText(frame, async_mode_message, (10, int(initial_h - 20)),
                        cv2.FONT_HERSHEY_COMPLEX, 0.5, (10, 10, 200), 1)

        render_start = time.time()
        cv2.imshow("face detection", frame)
        render_end = time.time()
        render_time = render_end - render_start

        if is_async_mode:
            cur_request_id, next_request_id = next_request_id, cur_request_id
            frame = next_frame

        key = cv2.waitKey(1)
        if key == 27:
            break

    cv2.destroyAllWindows()
    del exec_net
    del lm_exec_net
    del plugin


if __name__ == '__main__':
    sys.exit(face_landmark_demo() or 0)

三 行人检测

1.测试环境：

ubuntu版本：18.04.1LTS

openvino版本：2020.1.023

模型文档链接：https://docs.openvinotoolkit.org/latest/_demos_crossroad_camera_demo_README.html
2.下载模型

进入open_model_zoo路径

cd /home/kang/open_model_zoo/tools/downloader

在模型列表中找到要下载的模型并下载：

./downloader.py --name person-vehicle-bike-detection-crossroad-0078

记录xml文件下载路径：

/home/kang/open_model_zoo/tools/downloader/intel/person-vehicle-bike-detection-crossroad-0078/FP32/person-vehicle-bike-detection-crossroad-0078.xml
3.编译

执行下列命令

cd /opt/intel/openvino/deployment_tools/inference_engine/demos
 
./build_demos.sh

进入crossroad_camera_demo路径，执行make

cd  ~/omz_demos_build/crossroad_camera_demo
make -j4

3.运行

cd ~/omz_demos_build/intel64/Release
 
./crossroad_camera_demo -m /home/kang/open_model_zoo/tools/downloader/intel/person-vehicle-bike-detection-crossroad-0078/FP32/person-vehicle-bike-detection-crossroad-0078.xml -d CPU -i /home/kang/Downloads/test_data/pedestrian.png

得到结果和图像信息。

同样也可以将xml进行python运行。

四 人脸识别与表情检测

效果展示

一、 准备流程：

1. 在python环境中加载openvino

打开openvino安装目录如：
C:\Intel\openvino\python\python3.6

把目录下的openvino文件夹复制到

系统的python环境安装目录下如： C:\Python36\Lib\site-packages
2. 编译

C:\Intel\openvino\deployment_tools\inference_engine\samples 路径下执行：

build_samples_msvc2017.bat

执行完后在

C:\Users\kang\Documents\Intel\OpenVINO 目录

可以看到生成的

inference_engine_samples_build_2017 文件目录

在build目录中也可以找到cpu_extension：

cpu_extension = “C:\Users\kang\Documents\Intel\OpenVINO\inference_engine_samples_build_2017\intel64\Release\cpu_extension.dll”

1. 下载模型，记录路径

face-detection-adas-0001

emotions-recognition-retail-0003

model_xml = “”
model_bin = “”

二、 参数介绍：

1. emotions提取

   基于MobileNet v1版本
   · 输入格式：[1x3x384x672]= BCHW
   · 输出格式：[1, 1, N, 7] = [image_id, label, conf, x_min, y_min, x_max, y_max]
   表情识别网络 – 输入-[1x3x64x64]=BCHW
   · 输出格式- [1, 5, 1, 1]
   · 检测五种表情 (‘neutral’, ‘happy’, ‘sad’, ‘surprise’, ‘anger’)

2. python版本的api介绍

   同步调用，执行输入
   landmark_res = exec_emotions_net.infer(inputs={input_blob: [face_roi]})

3. 获取输出

   landmark_res = landmark_res[‘prob_emotion’]
   landmark_res = np.reshape(landmark_res, (5))
   landmark_res = labels[np.argmax(landmark_res)]

代码：

import sys
import cv2
import numpy as np
import time
import logging as log
from openvino.inference_engine import IENetwork, IEPlugin

plugin_dir = "C:/Intel/openvino/deployment_tools/inference_engine/bin/intel64/Release"
cpu_extension = "C:/Users/kang/Documents/Intel/OpenVINO/inference_engine_samples_build_2017/intel64/Release/cpu_extension.dll"


# face-detection-adas-0001
model_xml  = "C:/Users/kang/Downloads/openvino_sample_show/open_model_zoo/model_downloader/Transportation/object_detection/face/pruned_mobilenet_reduced_ssd_shared_weights/dldt/face-detection-adas-0001.xml"
model_bin = "C:/Users/kang/Downloads/openvino_sample_show/open_model_zoo/model_downloader/Transportation/object_detection/face/pruned_mobilenet_reduced_ssd_shared_weights/dldt/face-detection-adas-0001.bin"

# emotions-recognition-retail-0003
emotions_xml = "C:/Users/kang/Downloads/openvino_sample_show/open_model_zoo/model_downloader/Retail/object_attributes/emotions_recognition/0003/dldt/emotions-recognition-retail-0003.xml"
emotions_bin = "C:/Users/kang/Downloads/openvino_sample_show/open_model_zoo/model_downloader/Retail/object_attributes/emotions_recognition/0003/dldt/emotions-recognition-retail-0003.bin"

labels = ['neutral', 'happy', 'sad', 'surprise', 'anger']


def face_emotions_demo():
    log.basicConfig(format="[ %(levelname)s ] %(message)s",
                    level=log.INFO,
                    stream=sys.stdout)
    # Plugin initialization for specified device and load extensions library if specified
    log.info("Initializing plugin for {} device...".format("CPU"))
    plugin = IEPlugin(device="CPU", plugin_dirs=plugin_dir)
    plugin.add_cpu_extension(cpu_extension)
    # Read IR
    log.info("Reading IR...")
    net = IENetwork(model=model_xml, weights=model_bin)


    emotions_net = IENetwork(model=emotions_xml, weights=emotions_bin)

    if plugin.device == "CPU":
        supported_layers = plugin.get_supported_layers(net)
        not_supported_layers = [
            l for l in net.layers.keys() if l not in supported_layers
        ]
        if len(not_supported_layers) != 0:
            log.error(
                "Following layers are not supported by the plugin for specified device {}:\n {}"
                .format(plugin.device, ', '.join(not_supported_layers)))
            log.error(
                "Please try to specify cpu extensions library path in demo's command line parameters using -l "
                "or --cpu_extension command line argument")
            sys.exit(1)
    assert len(
        net.inputs.keys()) == 1, "Demo supports only single input topologies"
    assert len(net.outputs) == 1, "Demo supports only single output topologies"


    input_blob = next(iter(net.inputs))
    out_blob = next(iter(net.outputs))
    em_input_blob = next(iter(emotions_net.inputs))
    em_out_blob = next(iter(emotions_net.outputs))


    log.info("Loading IR to the plugin...")

    # 生成可执行网络,异步执行 num_requests=2
    exec_net = plugin.load(network=net, num_requests=2)
    exec_emotions_net = plugin.load(network=emotions_net)

    # Read and pre-process input image
    n, c, h, w = net.inputs[input_blob].shape
    en, ec, eh, ew = emotions_net.inputs[em_input_blob].shape

    del net
    del emotions_net

    cap = cv2.VideoCapture("C:/Users/kang/Downloads/openvino_sample_show/material/face_detection_demo.mp4")

    cur_request_id = 0
    next_request_id = 1

    log.info("Starting inference in async mode...")
    log.info("To switch between sync and async modes press Tab button")
    log.info("To stop the demo execution press Esc button")
    is_async_mode = True
    render_time = 0
    ret, frame = cap.read()

    print(
        "To close the application, press 'CTRL+C' or any key with focus on the output window"
    )
    while cap.isOpened():
        if is_async_mode:
            ret, next_frame = cap.read()
        else:
            ret, frame = cap.read()
        if not ret:
            break
        initial_w = cap.get(3)
        initial_h = cap.get(4)
        inf_start = time.time()
        if is_async_mode:
            in_frame = cv2.resize(next_frame, (w, h))
            in_frame = in_frame.transpose(
                (2, 0, 1))  # Change data layout from HWC to CHW
            in_frame = in_frame.reshape((n, c, h, w))
            exec_net.start_async(request_id=next_request_id,
                                inputs={input_blob: in_frame})
        else:
            in_frame = cv2.resize(frame, (w, h))
            in_frame = in_frame.transpose(
                (2, 0, 1))  # Change data layout from HWC to CHW
            in_frame = in_frame.reshape((n, c, h, w))
            exec_net.start_async(request_id=cur_request_id,
                                inputs={input_blob: in_frame})
        if exec_net.requests[cur_request_id].wait(-1) == 0:


            res = exec_net.requests[cur_request_id].outputs[out_blob]



            # 输出格式：[1,1,N,7] 从N行人脸中找到7个值 = [image_id,label,conf,x_min,y_min,x_max,y_max]
            for obj in res[0][0]:
                if obj[2] > 0.5:
                    xmin = int(obj[3] * initial_w)
                    ymin = int(obj[4] * initial_h)
                    xmax = int(obj[5] * initial_w)
                    ymax = int(obj[6] * initial_h)
                    if xmin > 0 and ymin > 0 and (xmax < initial_w) and (ymax < initial_h):
                        roi = frame[ymin:ymax,xmin:xmax,:]
                        face_roi = cv2.resize(roi,(ew,eh))
                        face_roi =face_roi.transpose((2, 0, 1)) 
                        face_roi= face_roi.reshape((en, ec, eh, ew))
                        # 解析结果
                        landmark_res = exec_emotions_net.infer(inputs={input_blob: [face_roi]})
                        landmark_res = landmark_res['prob_emotion']
                        landmark_res = np.reshape(landmark_res, (5))
                        landmark_res = labels[np.argmax(landmark_res)]
                        cv2.putText(frame, landmark_res, (np.int32(xmin), np.int32(ymin)), cv2.FONT_HERSHEY_SIMPLEX, 1.0,
                                (255, 0, 0), 2)
                        cv2.rectangle(frame, (np.int32(xmin), np.int32(ymin)), (np.int32(xmax), np.int32(ymax)),
                                    (0, 0, 255), 2, 8, 0)
                    cv2.rectangle(frame, (xmin,ymin), (xmax,ymax), (0, 0, 255), 2, 8, 0)


            inf_end = time.time()
            det_time = inf_end - inf_start

            # Draw performance stats
            inf_time_message = "Inference time: {:.3f} ms, FPS:{:.3f}".format(det_time * 1000, 1000 / (det_time*1000 + 1))
            render_time_message = "OpenCV rendering time: {:.3f} ms".format(
                render_time * 1000)
            async_mode_message = "Async mode is on. Processing request {}".format(cur_request_id) if is_async_mode else \
                "Async mode is off. Processing request {}".format(cur_request_id)

            cv2.putText(frame, inf_time_message, (15, 15),
                        cv2.FONT_HERSHEY_COMPLEX, 0.5, (200, 10, 10), 1)
            cv2.putText(frame, render_time_message, (15, 30),
                        cv2.FONT_HERSHEY_COMPLEX, 0.5, (10, 10, 200), 1)
            cv2.putText(frame, async_mode_message, (10, int(initial_h - 20)),
                        cv2.FONT_HERSHEY_COMPLEX, 0.5, (10, 10, 200), 1)


        render_start = time.time()
        cv2.imshow("face emotions demo", frame)
        render_end = time.time()
        render_time = render_end - render_start

        if is_async_mode:
            cur_request_id, next_request_id = next_request_id, cur_request_id
            frame = next_frame

        key = cv2.waitKey(1)
        if key == 27:
            break
    cv2.destroyAllWindows()

    del exec_net
    del exec_emotions_net
    del plugin


if __name__ == '__main__':
    sys.exit(face_emotions_demo() or 0) 

四 深度学习算法交流

OpenVINO是英特尔基于自身现有的硬件平台开发的一种可以加快高性能计算机视觉和深度学习视觉应用开发速度工具套件，支持各种英特尔平台的硬件加速器上进行深度学习，并且允许直接异构执行。 支持在Windows与Linux系统，Python/C++语言。优化工具包 OpenVINO 让不懂电脑视觉和深度学习原理的小白可以在很短的时间上手，不必担心如何建置开发平台、选择深度学习框架、训练及优化模型和硬体加速等问题，只需利用预先训练及优化过的语义分割模型，很快就可以做出一组看起来很专业的自驾车视觉分析系统。
大家快来一起试一试吧～