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cungudafa / hand-keras-yolo3-recognize

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cungudafa authored 2020-08-08 09:50 . sign
import os
import time
import numpy as np
from pose.coco import general_coco_model
from pose.hand import general_hand_model
from pose.data_process import getBoneInformation, getHandsInformation
from pose.hand_fD import hand_fourierDesciptor
from yolo import YOLO
from cv2 import cv2
from PIL import Image
import matplotlib.pyplot as plt
plt.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 re
def 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
#---------------------------------
# coco
modelpath = "model/"
start = time.time()
pose_model = general_coco_model(modelpath) # 1.加载模型
print("[INFO]Pose Model loads time: ", time.time() - start)
# yolo
start = 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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