diff --git a/applications/face_bioassay_local_app.py b/applications/face_bioassay_local_app.py new file mode 100644 index 0000000000000000000000000000000000000000..0d8eccfcf37d902ce44b7c2e99bfaceb26da50bc --- /dev/null +++ b/applications/face_bioassay_local_app.py @@ -0,0 +1,72 @@ +#-*-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() diff --git a/lib/face_bioassay_lib/cfg/face_bioassay.cfg b/lib/face_bioassay_lib/cfg/face_bioassay.cfg new file mode 100644 index 0000000000000000000000000000000000000000..619fde4b12e255cc6939bc064ea040ce06fbe18d --- /dev/null +++ b/lib/face_bioassay_lib/cfg/face_bioassay.cfg @@ -0,0 +1,14 @@ +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 diff --git a/lib/face_bioassay_lib/cores/face_fuction.py b/lib/face_bioassay_lib/cores/face_fuction.py new file mode 100644 index 0000000000000000000000000000000000000000..3f83ced0ab8cb64095b5f82f4f78b3f693b6e71c --- /dev/null +++ b/lib/face_bioassay_lib/cores/face_fuction.py @@ -0,0 +1,339 @@ +#-*-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 diff --git a/lib/face_bioassay_lib/utils/show_videos_thread.py b/lib/face_bioassay_lib/utils/show_videos_thread.py new file mode 100644 index 0000000000000000000000000000000000000000..8f4180d5198dc38b47470fba1003cedaab06a114 --- /dev/null +++ b/lib/face_bioassay_lib/utils/show_videos_thread.py @@ -0,0 +1,84 @@ +#-*-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) + #-------------------------------------- diff --git a/lib/face_bioassay_lib/utils/utils.py b/lib/face_bioassay_lib/utils/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..248c711a81dc66ef35dc9f220f82a7d31f294093 --- /dev/null +++ b/lib/face_bioassay_lib/utils/utils.py @@ -0,0 +1,15 @@ +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 diff --git a/main.py b/main.py index 9fafd5e321b33cbc56b0bcbf965747b176d38d5d..e504d3cbf248cdd2f02818210ea98d62e8565b44 100644 --- a/main.py +++ b/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 ~")