update frame

applications/face_bioassay_local_app.py

+#-*-coding:utf-8-*-
+'''
+DpCas-Light
+||||      |||||        ||||         ||       |||||||
+||  ||    ||   ||    ||    ||      ||||     ||     ||
+||    ||  ||    ||  ||      ||    ||  ||     ||
+||    ||  ||   ||   ||           ||====||     ||||||
+||    ||  |||||     ||      ||  ||======||         ||
+||  ||    ||         ||    ||  ||        ||  ||     ||
+||||      ||           ||||   ||          ||  |||||||
+
+/--------------------- Face Bioassay ---------------------/
+'''
+# date:2021-04-18
+# Author: Eric.Lee
+# function: Face Bioassay "基于人脸动作的活体检测"
+
+import os
+import cv2
+import time
+
+import numpy as np
+import random
+import time
+import shutil
+
+# 加载模型组件库
+from face_detect.yolo_v3_face import yolo_v3_face_model
+from face_multi_task.face_multi_task_component import FaceMuitiTask_Model
+from face_euler_angle.face_euler_angle_component import FaceAngle_Model
+# 加载工具库
+import sys
+sys.path.append("./lib/face_bioassay_lib/")
+from cores.face_fuction import get_faces_batch_attribute
+from utils.utils import parse_data_cfg
+
+def main_face_bioassay(video_path,cfg_file):
+    config = parse_data_cfg(cfg_file)
+    print("\n/************** Face Bioassay *****************/")
+    print("/********************************************************/\n")
+    # pose_model = light_pose_model()
+    face_detect_model = yolo_v3_face_model(conf_thres=float(config["detect_conf_thres"]),nms_thres=float(config["detect_nms_thres"]),
+        model_arch = config["detect_model_arch"],model_path = config["detect_model_path"],yolo_anchor_scale = float(config["yolo_anchor_scale"]),
+        img_size = float(config["detect_input_size"]),
+        )
+    face_multitask_model = FaceMuitiTask_Model(model_path = config["face_multitask_model_path"], model_arch = config["face_multitask_model_arch"])
+
+    face_euler_model = FaceAngle_Model(model_path = config["face_euler_model_path"])
+
+    cap = cv2.VideoCapture(video_path)
+    frame_idx = 0
+    while cap.isOpened():
+        ret,img = cap.read()
+        if ret:
+            frame_idx += 1
+            video_time = cap.get(cv2.CAP_PROP_POS_MSEC)
+
+            faces_bboxes =face_detect_model.predict(img,vis = False) # 检测手，获取手的边界框
+
+            faces_message = get_faces_batch_attribute(
+                face_multitask_model,face_euler_model,faces_bboxes,img,use_cuda = True,vis = True)
+            if faces_message is not None:
+                print("faces_message : {} \n".format(faces_message))
+
+            cv2.namedWindow("DriverFatigueMonitor",0)
+            cv2.imshow("DriverFatigueMonitor",img)
+            if cv2.waitKey(1)==27:
+                break
+        else:
+            break
+
+    cv2.destroyAllWindows()

lib/face_bioassay_lib/cfg/face_bioassay.cfg

+detect_model_path=./wyw2s_models/face_yolo_416-20210418.pt
+detect_model_arch=yolo
+detect_input_size = 416
+yolo_anchor_scale=1.
+detect_conf_thres=0.4
+detect_nms_thres=0.45
+
+#face_multitask_model_path=./wyw2s_models/face_multitask-resnet_50_imgsize-256-20210411.pth
+#face_multitask_model_arch=resnet50
+
+face_multitask_model_path=./wyw2s_models/face_multitask-resnet_34_imgsize-256-20210425.pth
+face_multitask_model_arch=resnet34
+
+face_euler_model_path=./wyw2s_models/euler_angle-resnet_18_imgsize_256.pth

lib/face_bioassay_lib/cores/face_fuction.py

+#-*-coding:utf-8-*-
+'''
+DpCas-Light
+||||      ||||       ||||         ||        ||||
+||  ||    ||  ||   ||    ||      ||||     ||    ||
+||    ||  ||   || ||      ||    ||  ||     ||
+||    ||  ||  ||  ||           ||====||      ||||
+||    ||  ||||    ||      ||  ||======||         ||
+||  ||    ||       ||    ||  ||        ||  ||    ||
+||||      ||         ||||   ||          ||   ||||
+
+/------------------ Who You Want 2 See ------------------/
+'''
+# date:2021-04-17
+# Author: Eric.Lee
+# function: pipline
+
+import os
+import numpy as np
+import cv2
+import torch
+from PIL import Image
+
+def compute_iou(rec1, rec2):
+    """
+    computing IoU
+    :param rec1: (y0, x0, y1, x1), which reflects
+            (top, left, bottom, right)
+    :param rec2: (y0, x0, y1, x1)
+    :return: scala value of IoU
+    """
+    # computing area of each rectangles
+    S_rec1 = (rec1[2] - rec1[0]) * (rec1[3] - rec1[1])
+    S_rec2 = (rec2[2] - rec2[0]) * (rec2[3] - rec2[1])
+
+    # computing the sum_area
+    sum_area = S_rec1 + S_rec2
+
+    # find the each edge of intersect rectangle
+    left_line = max(rec1[1], rec2[1])
+    right_line = min(rec1[3], rec2[3])
+    top_line = max(rec1[0], rec2[0])
+    bottom_line = min(rec1[2], rec2[2])
+
+    # judge if there is an intersect
+    if left_line >= right_line or top_line >= bottom_line:
+        return 0
+    else:
+        intersect = (right_line - left_line) * (bottom_line - top_line)
+        #return (intersect / (sum_area - intersect))*1.0
+        return (intersect / (S_rec1 + 1e-6))*1.0
+
+def draw_landmarks(img,output,face_w,face_h,x0,y0,vis = False):
+    img_width = img.shape[1]
+    img_height = img.shape[0]
+    dict_landmarks = {}
+    eyes_center = []
+    x_list = []
+    y_list = []
+    for i in range(int(output.shape[0]/2)):
+        x = output[i*2+0]*float(face_w) + x0
+        y = output[i*2+1]*float(face_h) + y0
+
+        x_list.append(x)
+        y_list.append(y)
+
+        if 41>= i >=33:
+            if 'left_eyebrow' not in dict_landmarks.keys():
+                dict_landmarks['left_eyebrow'] = []
+            dict_landmarks['left_eyebrow'].append([int(x),int(y),(0,255,0)])
+            if vis:
+                cv2.circle(img, (int(x),int(y)), 2, (0,255,0),-1)
+        elif 50>= i >=42:
+            if 'right_eyebrow' not in dict_landmarks.keys():
+                dict_landmarks['right_eyebrow'] = []
+            dict_landmarks['right_eyebrow'].append([int(x),int(y),(0,255,0)])
+            if vis:
+                cv2.circle(img, (int(x),int(y)), 2, (0,255,0),-1)
+        elif 67>= i >=60:
+            if 'left_eye' not in dict_landmarks.keys():
+                dict_landmarks['left_eye'] = []
+            dict_landmarks['left_eye'].append([int(x),int(y),(255,55,255)])
+            if vis:
+                cv2.circle(img, (int(x),int(y)), 2, (255,0,255),-1)
+        elif 75>= i >=68:
+            if 'right_eye' not in dict_landmarks.keys():
+                dict_landmarks['right_eye'] = []
+            dict_landmarks['right_eye'].append([int(x),int(y),(255,55,255)])
+            if vis:
+                cv2.circle(img, (int(x),int(y)), 2, (255,0,255),-1)
+        elif 97>= i >=96:
+            eyes_center.append((x,y))
+            if vis:
+                cv2.circle(img, (int(x),int(y)), 2, (0,0,255),-1)
+        elif 54>= i >=51:
+            if 'bridge_nose' not in dict_landmarks.keys():
+                dict_landmarks['bridge_nose'] = []
+            dict_landmarks['bridge_nose'].append([int(x),int(y),(0,170,255)])
+            if vis:
+                cv2.circle(img, (int(x),int(y)), 2, (0,170,255),-1)
+        elif 32>= i >=0:
+            if 'basin' not in dict_landmarks.keys():
+                dict_landmarks['basin'] = []
+            dict_landmarks['basin'].append([int(x),int(y),(255,30,30)])
+            if vis:
+                cv2.circle(img, (int(x),int(y)), 2, (255,30,30),-1)
+        elif 59>= i >=55:
+            if 'wing_nose' not in dict_landmarks.keys():
+                dict_landmarks['wing_nose'] = []
+            dict_landmarks['wing_nose'].append([int(x),int(y),(0,255,255)])
+            if vis:
+                cv2.circle(img, (int(x),int(y)), 2, (0,255,255),-1)
+        elif 87>= i >=76:
+            if 'out_lip' not in dict_landmarks.keys():
+                dict_landmarks['out_lip'] = []
+            dict_landmarks['out_lip'].append([int(x),int(y),(255,255,0)])
+            if vis:
+                cv2.circle(img, (int(x),int(y)), 2, (255,255,0),-1)
+        elif 95>= i >=88:
+            if 'in_lip' not in dict_landmarks.keys():
+                dict_landmarks['in_lip'] = []
+            dict_landmarks['in_lip'].append([int(x),int(y),(50,220,255)])
+            if vis:
+                cv2.circle(img, (int(x),int(y)), 2, (50,220,255),-1)
+        else:
+            if vis:
+                cv2.circle(img, (int(x),int(y)), 2, (255,0,255),-1)
+    face_area = (max(x_list) - min(x_list))*(max(y_list) - min(y_list))
+    return dict_landmarks,eyes_center,face_area
+
+def draw_contour(image,dict,vis = False):
+    x0 = 0# 偏置
+    y0 = 0
+
+    for key in dict.keys():
+        # print(key)
+        _,_,color = dict[key][0]
+
+        if 'left_eye' == key:
+            eye_x = np.mean([dict[key][i][0]+x0 for i in range(len(dict[key]))])
+            eye_y = np.mean([dict[key][i][1]+y0 for i in range(len(dict[key]))])
+            if vis:
+                cv2.circle(image, (int(eye_x),int(eye_y)), 3, (255,255,55),-1)
+        if 'right_eye' == key:
+            eye_x = np.mean([dict[key][i][0]+x0 for i in range(len(dict[key]))])
+            eye_y = np.mean([dict[key][i][1]+y0 for i in range(len(dict[key]))])
+            if vis:
+                cv2.circle(image, (int(eye_x),int(eye_y)), 3, (255,215,25),-1)
+
+        if 'basin' == key or 'wing_nose' == key:
+            pts = np.array([[dict[key][i][0]+x0,dict[key][i][1]+y0] for i in range(len(dict[key]))],np.int32)
+            if vis:
+                cv2.polylines(image,[pts],False,color,thickness = 2)
+
+        else:
+            points_array = np.zeros((1,len(dict[key]),2),dtype = np.int32)
+            for i in range(len(dict[key])):
+                x,y,_ = dict[key][i]
+                points_array[0,i,0] = x+x0
+                points_array[0,i,1] = y+y0
+
+            # cv2.fillPoly(image, points_array, color)
+            if vis:
+                cv2.drawContours(image,points_array,-1,color,thickness=2)
+
+
+def plot_box(x, img, color=None, label=None, line_thickness=None):
+    # Plots one bounding box on image img
+    tl = line_thickness or round(0.002 * max(img.shape[0:2])) + 1  # line thickness
+    color = color or [random.randint(0, 255) for _ in range(3)]
+    c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))
+    cv2.rectangle(img, c1, c2, color, thickness=tl)
+    if label:
+        tf = max(tl - 1, 2)  # font thickness
+        t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]
+        c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3
+        cv2.rectangle(img, c1, c2, color, -1)  # filled
+        cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [185, 195,190], thickness=tf, lineType=cv2.LINE_AA)
+#-------------------------------------------------------------------------------
+
+def face_alignment(imgn,eye_left_n,eye_right_n,\
+desiredLeftEye=(0.34, 0.42),desiredFaceWidth=256, desiredFaceHeight=None):
+
+    if desiredFaceHeight is None:
+        desiredFaceHeight = desiredFaceWidth
+
+    leftEyeCenter = eye_left_n
+    rightEyeCenter = eye_right_n
+    # compute the angle between the eye centroids
+    dY = rightEyeCenter[1] - leftEyeCenter[1]
+    dX = rightEyeCenter[0] - leftEyeCenter[0]
+    angle = np.degrees(np.arctan2(dY, dX))
+
+
+    # compute the desired right eye x-coordinate based on the
+    # desired x-coordinate of the left eye
+    desiredRightEyeX = 1.0 - desiredLeftEye[0]
+	# determine the scale of the new resulting image by taking
+	# the ratio of the distance between eyes in the *current*
+	# image to the ratio of distance between eyes in the
+	# *desired* image
+    dist = np.sqrt((dX ** 2) + (dY ** 2))
+    desiredDist = (desiredRightEyeX - desiredLeftEye[0])
+    desiredDist *= desiredFaceWidth
+    scale = desiredDist / dist
+    # compute center (x, y)-coordinates (i.e., the median point)
+    # between the two eyes in the input image
+    eyesCenter = ((leftEyeCenter[0] + rightEyeCenter[0]) / 2,(leftEyeCenter[1] + rightEyeCenter[1]) / 2)
+    # grab the rotation matrix for rotating and scaling the face
+    M = cv2.getRotationMatrix2D(eyesCenter, angle, scale)
+    # update the translation component of the matrix
+    tX = desiredFaceWidth * 0.5
+    tY = desiredFaceHeight * desiredLeftEye[1]
+    M[0, 2] += (tX - eyesCenter[0])
+    M[1, 2] += (tY - eyesCenter[1])
+
+    M_reg = np.zeros((3,3),dtype = np.float32)
+    M_reg[0,:] = M[0,:]
+    M_reg[1,:] = M[1,:]
+    M_reg[2,:] = (0,0,1.)
+    # print(M_reg)
+    M_I = np.linalg.inv(M_reg)#矩阵求逆，从而获得，目标图到原图的关系
+    # print(M_I)
+    # apply the affine transformation
+    (w, h) = (desiredFaceWidth, desiredFaceHeight)
+    # cv_resize_model = [cv2.INTER_LINEAR,cv2.INTER_CUBIC,cv2.INTER_NEAREST,cv2.INTER_AREA]
+
+    output = cv2.warpAffine(imgn, M, (w, h),flags=cv2.INTER_LINEAR,borderMode=cv2.BORDER_CONSTANT)#
+
+    #---------------------------------------------------------------------------------------
+
+    # ptx1 = int(eye_left_gt_n[0]*M[0][0] + eye_left_gt_n[1]*M[0][1] + M[0][2])
+    # pty1 = int(eye_left_gt_n[0]*M[1][0] + eye_left_gt_n[1]*M[1][1] + M[1][2])
+    #
+    # ptx2 = int(eye_right_gt_n[0]*M[0][0] + eye_right_gt_n[1]*M[0][1] + M[0][2])
+    # pty2 = int(eye_right_gt_n[0]*M[1][0] + eye_right_gt_n[1]*M[1][1] + M[1][2])
+
+    return output
+
+def refine_face_bbox(bbox,img_shape):
+    height,width,_ = img_shape
+
+    x1,y1,x2,y2 = bbox
+
+    expand_w = (x2-x1)
+    expand_h = (y2-y1)
+
+    x1 -= expand_w*0.12
+    y1 -= expand_h*0.12
+    x2 += expand_w*0.12
+    y2 += expand_h*0.08
+
+    x1,y1,x2,y2 = int(x1),int(y1),int(x2),int(y2)
+
+    x1 = np.clip(x1,0,width-1)
+    y1 = np.clip(y1,0,height-1)
+    x2 = np.clip(x2,0,width-1)
+    y2 = np.clip(y2,0,height-1)
+
+    return (x1,y1,x2,y2)
+
+def get_faces_batch_attribute(face_multitask_model,face_euler_model,dets,img_raw,use_cuda,face_size = 256,vis = False):
+    if len(dets) == 0:
+        return None
+    img_align = img_raw.copy()
+    # 绘制图像
+    image_batch = None
+    r_bboxes = []
+    imgs_crop = []
+    for b in dets:
+        b = list(map(int, b))
+
+        r_bbox = refine_face_bbox((b[0],b[1],b[2],b[3]),img_raw.shape)
+        r_bboxes.append(r_bbox)
+        img_crop = img_raw[r_bbox[1]:r_bbox[3],r_bbox[0]:r_bbox[2]]
+        imgs_crop.append(img_crop)
+        img_ = cv2.resize(img_crop, (face_size,face_size), interpolation = cv2.INTER_LINEAR) # INTER_LINEAR INTER_CUBIC
+
+        img_ = img_.astype(np.float32)
+        img_ = (img_-128.)/256.
+
+        img_ = img_.transpose(2, 0, 1)
+        img_ = np.expand_dims(img_,0)
+
+        if image_batch is None:
+            image_batch = img_
+        else:
+            image_batch = np.concatenate((image_batch,img_),axis=0)
+
+    landmarks_pre,gender_pre,age_pre = face_multitask_model.predict(image_batch)
+    euler_angles = face_euler_model.predict(image_batch)
+
+    faces_message = None
+    for i in range(len(dets)):
+        x0,y0 = r_bboxes[i][0],r_bboxes[i][1]
+        face_w = r_bboxes[i][2]-r_bboxes[i][0]
+        face_h = r_bboxes[i][3]-r_bboxes[i][1]
+        dict_landmarks,eyes_center,face_area = draw_landmarks(img_raw,landmarks_pre[i],face_w,face_h,x0,y0,vis = False)
+
+        gray_ = cv2.cvtColor(img_align[r_bboxes[i][1]:r_bboxes[i][3],r_bboxes[i][0]:r_bboxes[i][2],:], cv2.COLOR_BGR2GRAY)
+
+        blur_ = cv2.Laplacian(gray_, cv2.CV_64F).var()
+
+        gender_max_index = np.argmax(gender_pre[i])#概率最大类别索引
+        score_gender = gender_pre[i][gender_max_index]# 最大概率
+
+        yaw,pitch,roll = euler_angles[i] # 欧拉角
+        if vis:
+            cv2.putText(img_raw, "yaw:{:.1f},pitch:{:.1f},roll:{:.1f}".format(yaw,pitch,roll),(int(r_bboxes[i][0]-20),int(r_bboxes[i][1]-30)),cv2.FONT_HERSHEY_DUPLEX, 0.65, (253,139,54), 5)
+            cv2.putText(img_raw, "yaw:{:.1f},pitch:{:.1f},roll:{:.1f}".format(yaw,pitch,roll),(int(r_bboxes[i][0]-20),int(r_bboxes[i][1]-30)),cv2.FONT_HERSHEY_DUPLEX, 0.65, (20,185,255), 1)
+
+            cv2.putText(img_raw, "{}".format(int(face_area)),(int(r_bboxes[i][0]-1),int(r_bboxes[i][3]-3)),cv2.FONT_HERSHEY_DUPLEX, 0.65, (253,39,54), 5) # face_area
+            cv2.putText(img_raw, "{}".format(int(face_area)),(int(r_bboxes[i][0]-1),int(r_bboxes[i][3]-3)),cv2.FONT_HERSHEY_DUPLEX, 0.65, (20,185,255), 1)
+        if gender_max_index == 1.:
+            gender_str = "male"
+        else:
+            gender_str = "female"
+
+        if abs(yaw)<45.:
+            face_align_output = face_alignment(img_align,eyes_center[0],eyes_center[1],
+                desiredLeftEye=(0.365, 0.38),desiredFaceWidth=112, desiredFaceHeight=None)
+        else:
+            face_align_output = face_alignment(img_align,eyes_center[0],eyes_center[1],
+                desiredLeftEye=(0.38, 0.40),desiredFaceWidth=112, desiredFaceHeight=None)
+
+        faces_message.append(
+            {
+                "xyxy":r_bboxes[i],
+                "age":age_pre[i][0],
+                "gender":gender_str,
+                "head_3d_angle":{"yaw":yaw,"pitch":pitch,"roll":roll}
+            }
+            )
+        # plot_box(r_bboxes[i][0:4], img_raw,label="{}, age: {:.1f}, unblur:{}".format(gender_str,age_pre[i][0],int(blur_)), color=(255,90,90), line_thickness=2)
+        if vis:
+            plot_box(r_bboxes[i][0:4], img_raw,label="{}, age: {:.1f}".format(gender_str,age_pre[i][0]), color=(255,90,90), line_thickness=2)
+
+
+    return faces_message

lib/face_bioassay_lib/utils/show_videos_thread.py

+#-*-coding:utf-8-*-
+'''
+DpCas-Light
+||||      ||||       ||||         ||        ||||
+||  ||    ||  ||   ||    ||      ||||     ||    ||
+||    ||  ||   || ||      ||    ||  ||     ||
+||    ||  ||  ||  ||           ||====||      ||||
+||    ||  ||||    ||      ||  ||======||         ||
+||  ||    ||       ||    ||  ||        ||  ||    ||
+||||      ||         ||||   ||          ||   ||||
+
+/------------------ Who You Want 2 See ------------------/
+'''
+# date:2020-12-12
+# Author: Eric.Lee
+# function: show clip video
+import os
+import cv2
+import copy
+import time
+
+import threading
+from threading import current_thread, Lock
+
+import  psutil
+import numpy as np
+import random
+
+lock = Lock()
+def run_one_process(path,process_id,vis):
+    
+    lock.acquire()
+    video_ = cv2.VideoCapture(path)
+    lock.release()
+    while True:
+        ret, img_ = video_.read()
+
+        if ret:
+           #------------------------------------------------
+            if vis:
+                cv2.namedWindow('video_seg_{}'.format(process_id),0)
+                cv2.resizeWindow('video_seg_{}'.format(process_id), 300, 210);
+                cv2.moveWindow('video_seg_{}'.format(process_id), (process_id%6)*300+60,int(process_id/6)*230)
+                cv2.imshow('video_seg_{}'.format(process_id),img_)
+                if cv2.waitKey(300) == 27:
+                    flag_break =True
+                    break
+        else:
+            break
+    if vis:
+        cv2.waitKey(30000)
+        cv2.destroyWindow('video_seg_{}'.format(process_id))
+
+def run_show(path,vis):
+    seg_num = len(os.listdir(path))
+
+    videos_path = os.listdir(path)
+    # #--------------------------------------
+    st_ = time.time()
+    process_list = []
+    for i in range(0,seg_num):
+        # print(video_list[i])
+        t = threading.Thread(target=run_one_process, args=(path + videos_path[i],i,vis))
+        process_list.append(t)
+
+
+    for i in range(0,seg_num):
+        process_list[i].start()
+
+    print(' start run ~ ')
+
+    for i in range(0,seg_num):
+        process_list[i].join()# 设置主线程等待子线程结束
+
+    del process_list
+    et_ = time.time()
+
+
+if __name__ == "__main__":
+    path = './video/'
+    vis = True
+
+    run_show_hights(path,vis)
+    #--------------------------------------

lib/face_bioassay_lib/utils/utils.py

+import os
+
+"""Parses the data configuration file"""
+def parse_data_cfg(path):
+    print('data_cfg ： ',path)
+    options = dict()
+    with open(path, 'r',encoding='UTF-8') as fp:
+        lines = fp.readlines()
+    for line in lines:
+        line = line.strip()
+        if line == '' or line.startswith('#'):
+            continue
+        key, value = line.split('=')
+        options[key.strip()] = value.strip()
+    return options

main.py

@@ -16,37 +16,67 @@
 # function: main
 
 import os
+import argparse
 import warnings
 warnings.filterwarnings("ignore")
 import sys
 sys.path.append("./components/") # 添加模型组件路径
 
-from applications.handpose_local_app import main_handpose_x #加载 handpose 应用
-from applications.wyw2s_local_app import main_wyw2s #加载 who you want 2 see 应用
-# from applications.video_analysis_app import main_video_analysis #加载 video_analysis 应用
 def demo_logo():
     print("\n/*********************************/")
     print("/---------------------------------/\n")
     print("       WELCOME : DpCas-Light      ")
-    print("        << APP_X >>         ")
-    print("   Copyright 2021 Eric.Lee2021   ")
+    print("           << APP_X >>         ")
+    print("    Copyright 2021 Eric.Lee2021   ")
     print("        Apache License 2.0       ")
     print("\n/---------------------------------/")
     print("/*********************************/\n")
 
 if __name__ == '__main__':
     demo_logo()
+    parser = argparse.ArgumentParser(description= " DpCas : << Deep Learning Componentized Application System >> ")
+    parser.add_argument('-app', type=int, default = 0,
+        help = "handpose_x:0, wyw2s:1, face_bioassay:2, video_ana:3, face_pay:4, drive:5") # 设置 App Example
 
-    APP_P = "wyw2s"
+    app_dict = {
+        0:"handpose_x",
+        1:"wyw2s",
+        2:"face_bioassay",
+        3:"video_ana",
+        4:"face_pay",
+        5:"drive"}
+
+    args = parser.parse_args()# 解析添加参数
+
+    APP_P = app_dict[args.app]
 
     if APP_P == "handpose_x": # 手势识别
+        from applications.handpose_local_app import main_handpose_x #加载 handpose 应用
         cfg_file = "./lib/hand_lib/cfg/handpose.cfg"
         main_handpose_x(cfg_file)#加载 handpose 应用
-        
+
     elif APP_P == "wyw2s": # 基于人脸识别的视频剪辑
+        from applications.wyw2s_local_app import main_wyw2s #加载 who you want 2 see 应用
         cfg_file = "./lib/wyw2s_lib/cfg/wyw2s.cfg"
         main_wyw2s(video_path = "./video/f1.mp4",cfg_file = cfg_file)#加载 who you want 2 see  应用
 
-    # elif APP_P == "video_ana": # 基于人脸识别的视频剪辑
-    #     main_video_analysis(video_path = "./video/f3.mp4")#加载 who you want 2 see  应用
+    elif APP_P == "face_bioassay":
+        from applications.face_bioassay_local_app import main_face_bioassay #face_bioassay 应用
+        cfg_file = "./lib/face_bioassay_lib/cfg/face_bioassay.cfg"
+        main_face_bioassay(video_path = "./video/f1.mp4",cfg_file = cfg_file)#加载 face_bioassay  应用
+
+    # elif APP_P == "video_ana":
+    #     from applications.VideoAnalysis_app import main_VideoAnalysis #加载 video_analysis 应用
+    #     main_VideoAnalysis(video_path = "./video/f3.mp4")#加载 video_analysis  应用
+    #
+    # elif APP_P == "face_pay":
+    #     cfg_file = "./lib/facepay_lib/cfg/facepay.cfg"
+    #     from applications.FacePay_local_app import main_facePay #加载 face pay 应用
+    #     main_facePay(video_path = 0,cfg_file = cfg_file) # 加载 face pay  应用
+    #
+    # elif APP_P == "drive":
+    #     from applications.DangerousDriveWarning_local_app import main_DangerousDriveWarning #加载 危险驾驶预警 应用
+    #     cfg_file = "./lib/dfmonitor_lib/cfg/dfm.cfg"
+    #     main_DangerousDriveWarning(video_path = "./video/drive1.mp4",cfg_file = cfg_file)
+
     print(" well done ~")