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hand_Video_processing.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
import csv
import copy
import argparse
from sklearn.model_selection import train_test_split
import os
import cv2 as cv
import numpy as np
import mediapipe as mp
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument("--device", type=int, default=0)
parser.add_argument("--width", help='cap width', type=int, default=960)
parser.add_argument("--height", help='cap height', type=int, default=640)
parser.add_argument('--use_static_image_mode', action='store_true')
parser.add_argument("--min_detection_confidence",
help='min_detection_confidence',
type=float,
default=0.6)
parser.add_argument("--min_tracking_confidence",
help='min_tracking_confidence',
type=int,
default=0.5)
args = parser.parse_args()
return args
def main():
# 引数解析 #################################################################
# 引数解析 #################################################################
args = get_args()
cap_width = args.width
cap_height = args.height
use_static_image_mode = args.use_static_image_mode
min_detection_confidence = args.min_detection_confidence
min_tracking_confidence = args.min_tracking_confidence
print(use_static_image_mode,min_tracking_confidence,min_detection_confidence)
# mediapipe 调用解决手的包
mp_hands = mp.solutions.hands
hands = mp_hands.Hands(
static_image_mode=use_static_image_mode,
max_num_hands=1,
min_detection_confidence=min_detection_confidence,
min_tracking_confidence=min_tracking_confidence,)
path = 'D:\\my_Ai_project\\hand-gesture-recognition-using-mediapipe-main\\data\\Pajinsen'
sava_path = './data_process/Pajinsen'
classify = {}
# all_landmarks = [] # 所有视频的关键点
for index,name in enumerate(os.listdir(path)):
classify[index] = name
classifies_dir = os.path.join(path,name)
train,val = train_test_split(os.listdir(classifies_dir),train_size=0.9,shuffle=True)
print('训练集视频数据开始预处理')
for train_i in train:
video_path = os.path.join(classifies_dir,train_i)
savepath = os.path.join(sava_path,f"train/{train_i.split('.')[0]}.txt")
video_preprocessing(path=video_path, cap_width=cap_width, cap_height=cap_height, hands=hands, index=index, savepath=savepath)
print('验证集视频数据开始预处理')
for val_i in val:
video_path = os.path.join(classifies_dir, val_i)
savepath = os.path.join(sava_path, f"val/{val_i.split('.')[0]}.txt")
video_preprocessing(path=video_path, cap_width=cap_width, cap_height=cap_height, hands=hands, index=index,
savepath=savepath)
with open('./dataloader/pajinsen_labels.txt','a') as f:
length = len(classify)
for i in range(length):
text = str(i) +' '+ classify[i] +'\n'
f.write(text)
f.close()
# shuff = []
# for i in range(len(all_landmarks)):
# shuff.append(i)
# np.random.shuffle(shuff)
# print(shuff)
# save_path_train = 'D:\\my_Ai_project\\hand-gesture-recognition-using-mediapipe-main\\data_process\\Pajinsen\\train'
# for i in range(int(len(all_landmarks)*0.8)):
# filename_path = os.path.join(save_path_train, 'video' +str(i)+ '.txt')
# for j in range(len(all_landmarks[shuff[i]])):
# logging_csv(all_landmarks[shuff[i]][j], filename_path)
#
# save_path_train = 'D:\\my_Ai_project\\hand-gesture-recognition-using-mediapipe-main\\data_process\\Pajinsen\\val'
# for i in range(int(len(all_landmarks) * 0.8+1),len(all_landmarks)):
# filename_path = os.path.join(save_path_train, 'video' +str(i)+ '.txt')
# for j in range(len(all_landmarks[shuff[i]])):
# logging_csv(all_landmarks[shuff[i]][j], filename_path)
def calc_bounding_rect(image, landmarks):
image_width, image_height = image.shape[1], image.shape[0]
landmark_array = np.empty((0, 2), int)
for _, landmark in enumerate(landmarks.landmark):
landmark_x = min(int(landmark.x * image_width), image_width - 1)
landmark_y = min(int(landmark.y * image_height), image_height - 1)
landmark_point = [np.array((landmark_x, landmark_y))]
landmark_array = np.append(landmark_array, landmark_point, axis=0)
x, y, w, h = cv.boundingRect(landmark_array)
return w,h
def calc_landmark_list(image, landmarks):
image_width, image_height = image.shape[1], image.shape[0]
landmark_point = []
# キーポイント
for _, landmark in enumerate(landmarks.landmark):
landmark_x = min(int(landmark.x * image_width), image_width - 1)
landmark_y = min(int(landmark.y * image_height), image_height - 1)
# landmark_z = landmark.z
landmark_point.append([landmark_x, landmark_y])
return landmark_point
def logging_csv(landmark_list, filename):
with open(filename, 'a') as f:
f.write(str(landmark_list).replace('[','').replace(']','')+'\n')
f.close()
def video_preprocessing(path, cap_width, cap_height,hands,index,savepath):
every_n_frames = 1
cap = cv.VideoCapture(path)
num = 0
print('一个视频的手关键点坐标开始记录')
while True:
ret, image = cap.read()
if not ret:
break
if (num % every_n_frames == 0):
image = cv.resize(image, (cap_width, cap_height))
image = cv.cvtColor(image, cv.COLOR_BGR2RGB)
image.flags.writeable = False
results = hands.process(image) # 检测图片中是否有手
image.flags.writeable = True
# results.multi_hand_landmarks 每只手的关键点的信息，坐标，关键点的id
if results.multi_hand_landmarks is not None:
# 针对一只手进行操作
for hand_landmarks in results.multi_hand_landmarks:
# 学习数据存储
base_x, base_y = 0, 0
landmark_point = [] # 记录每个图片的关键点坐标
landmark_point.append(index)
w,h = calc_bounding_rect(image,hand_landmarks)
for point_index, landmark in enumerate(hand_landmarks.landmark):
# landmark_z = landmark.z
print(point_index)
if point_index == 0:
base_x, base_y = (landmark.x*cap_width)/w, (landmark.y*cap_height)/h
landmark_point.append((landmark.x*cap_width)/w - base_x)
landmark_point.append((landmark.y*cap_height)/h - base_y)
print('一张图片的手坐标记录结束')
logging_csv(landmark_point, filename=savepath)
num += 1
print('一个视频的手关键点坐标记录结束')
cap.release()
if __name__ == '__main__':
main()