一 環境搭配

環境安裝請看上一篇部落格：傳送門

二 人臉識別與關鍵點檢測

以下來源於Openvino官方model ，在win10 和ubuntu大體步驟相似，跑demo：想轉ubuntu 或者win10 方法一樣，我下面分別用win10 和ubuntu跑幾個demo，大家可以試著做一下。效果展示

一、 準備流程：

在python環境中載入openvino

開啟openvino安裝目錄如：C:\\Intel\\openvino\\python\\python3.6

把目錄下的openvino資料夾複製到

系統的python環境安裝目錄下如： C:\\Python36\\Lib\\site-packages2. 編譯

C:\\Intel\\openvino\\deployment_tools\\inference_engine\\samples 路徑下執行：

build_samples_msvc2017.bat

執行完後在

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

可以看到生成的

inference_engine_samples_build_2017 檔案目錄

在build目錄中也可以找到cpu_extension：

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

下載模型，記錄路徑

face-detection-adas-0001

landmarks-regression-retail-0009

記錄xml地址

model_xml = “”model_bin = “”

二、 引數說明

人臉檢測基於MobileNet v1版本輸入格式：[1x3x384x672] = BCHW輸出格式：[1，1，N，7] = [image_id, label, conf, x_min, y_min, x_max, y_max]landmark提取landmark提取 - 基於卷積神經網路，提取5個點輸入 [1x3x48x48] = BCHW輸出 [1X10X1X1] = 五個點座標(x0,y0,x1,y1…x4,y4)python版本的api介紹同步呼叫，執行輸入Im_exec_net.infer(inputs={“0”:face_roi})獲取輸出landmark_res = Im_exec_net.request[0].outputs[Im_output_blob]landmark_res = np.reshape(landmark_res,(5,2))

三、 附錄程式碼：

import sysimport cv2import numpy as npimport timeimport logging as logfrom openvino.inference_engine import IENetwork, IEPluginmodel_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 pluginif __name__ == '__main__': sys.exit(face_landmark_demo() or 0)

三 行人檢測

1.測試環境：

ubuntu版本：18.04.1LTS

openvino版本：2020.1.023

模型文件連結：https://docs.openvinotoolkit.org/latest/_demos_crossroad_camera_demo_README.html2.下載模型

進入open_model_zoo路徑

cd /home/kang/open_model_zoo/tools/downloader

在模型列表中找到要下載的模型並下載：

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

記錄xml檔案下載路徑：

/home/kang/open_model_zoo/tools/downloader/intel/person-vehicle-bike-detection-crossroad-0078/FP32/person-vehicle-bike-detection-crossroad-0078.xml3.編譯

執行下列命令

進入crossroad_camera_demo路徑，執行make

3.執行

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.png123

得到結果和影象資訊。

同樣也可以將xml進行python執行。

四 人臉識別與表情檢測

效果展示

一、 準備流程：

在python環境中載入openvino

開啟openvino安裝目錄如：C:\\Intel\\openvino\\python\\python3.6

把目錄下的openvino資料夾複製到

系統的python環境安裝目錄下如： C:\\Python36\\Lib\\site-packages2. 編譯

C:\\Intel\\openvino\\deployment_tools\\inference_engine\\samples 路徑下執行：

build_samples_msvc2017.bat

執行完後在

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

可以看到生成的

inference_engine_samples_build_2017 檔案目錄

在build目錄中也可以找到cpu_extension：

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

下載模型，記錄路徑

face-detection-adas-0001

emotions-recognition-retail-0003

model_xml = “”model_bin = “”

二、 引數介紹：

emotions提取基於MobileNet v1版本· 輸入格式：[1x3x384x672]= BCHW· 輸出格式：[1, 1, N, 7] = [image_id, label, conf, x_min, y_min, x_max, y_max]表情識別網路 – 輸入-[1x3x64x64]=BCHW· 輸出格式- [1, 5, 1, 1]· 檢測五種表情 (‘neutral’, ‘happy’, ‘sad’, ‘surprise’, ‘anger’)python版本的api介紹同步呼叫，執行輸入landmark_res = exec_emotions_net.infer(inputs={input_blob: [face_roi]})獲取輸出landmark_res = landmark_res[‘prob_emotion’]landmark_res = np.reshape(landmark_res, (5))landmark_res = labels[np.argmax(landmark_res)]

程式碼：

import sysimport cv2import numpy as npimport timeimport logging as logfrom openvino.inference_engine import IENetwork, IEPluginplugin_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-0001model_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-0003emotions_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...") # 生成可執行網路,非同步執行 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] # 輸出格式：[1,1,N,7] 從N行人臉中找到7個值 = [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)) # 解析結果 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 pluginif __name__ == '__main__': sys.exit(face_emotions_demo() or 0)