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cungudafa / hand-keras-yolo3-recognize

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cungudafa   sign 2020-08-08
import osimport timeimport numpy as npfrom pose.coco import general_coco_modelfrom pose.hand import general_hand_modelfrom pose.data_process import getBoneInformation, getHandsInformationfrom pose.hand_fD import hand_fourierDesciptorfrom yolo import YOLOfrom cv2 import cv2from PIL import Imageimport matplotlib.pyplot as pltplt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号import redef bgImg_save(img_path,save_path):    # 读取图像    print("[INFO]",img_path)    image = cv2.imread(img_path)    # 图像像素大小一致    img = cv2.resize(image, (256, 256), interpolation=cv2.INTER_CUBIC)    # pose骨骼    start = time.time()    bone_points = pose_model.getBoneKeypoints(img)  # 2.骨骼关键点    lineimage,dotimage,black_np = pose_model.vis_bone_pose(img, bone_points)  # 骨骼连线图、标记图显示cv2格式    list1 = getBoneInformation(bone_points)  # 3.骨骼特征    # yolo手    image = Image.open(img_path)    lineimage = Image.fromarray(cv2.cvtColor(lineimage,cv2.COLOR_BGR2RGB))# cv2图片转PIL    black_np = Image.fromarray(cv2.cvtColor(black_np,cv2.COLOR_BGR2RGB))    line_image,labelinfo,hand_ROI_PIL = _yolo.detect_image(image,black_np) # (原图,lineimage线图,黑幕图)    print("[INFO]Model predicts time: ", time.time() - start)    # info = []    # for i in range(len(list1)):    #     info.append(list1[i])    # for j in range(len(labelinfo)):    #     info.append(labelinfo[j])    # print(labelinfo)    line_image.save(save_path)#---------------------------------#  1.加载模型#---------------------------------# cocomodelpath = "model/"start = time.time()pose_model = general_coco_model(modelpath)  # 1.加载模型print("[INFO]Pose Model loads time: ", time.time() - start)# yolostart = time.time()_yolo = YOLO() # 1.加载模型print("[INFO]yolo Model loads time: ", time.time() - start)imgpath='D:/myworkspace/dataset/My_test/bagofwords/you/you_1_32.png'bgImg_save(imgpath,'')'''X = []  # 定义图像名称Y = []  # 定义图像分类类标# Z = [] #定义图像像素path = 'D:/myworkspace/dataset/My_test/dataset/hand_classification/'savepath = 'D:/myworkspace/dataset/My_test/dataset/hand_background_classification/'for idx, labelname in enumerate(os.listdir(path)):    if ".txt" not in labelname:        f = os.path.join(path,labelname)        s = os.path.join(savepath,labelname)        if not os.path.exists(s):            os.makedirs(s)        for i, imgname in enumerate(os.listdir(f)):            imgpath = os.path.join(f,imgname)            #X.append(imgpath)            #Y.append(labelname)            save_path = os.path.join(s,imgname)            bgImg_save(imgpath,save_path)            #num = re.findall(".*_(.*)_.*", imgname)            # if num[0]=="7":            #     bgImg_save(imgpath,save_path)'''
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