对单个文件进行整合
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leaf
parent
55d9b207b1
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96cc01855c
94
TM.py
94
TM.py
@ -134,65 +134,45 @@ class HandDetector:
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class Main:
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def __init__(self):
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self.detector = None
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self.camera = cv2.VideoCapture(0, cv2.CAP_DSHOW)
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self.camera.set(3, 1280)
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self.camera.set(4, 720)
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self.camera = None
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# self.camera = cv2.VideoCapture(0, cv2.CAP_DSHOW)
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# self.camera.set(3, 1280)
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# self.camera.set(4, 720)
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def gesture_recognition(self):
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self.detector = HandDetector()
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while True:
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frame, img = self.camera.read()
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img = self.detector.find_hands(img)
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lm_list, bbox = self.detector.find_position(img)
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def gesture_recognition(self, img, detector):
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self.detector = detector
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lm_list, bbox = detector.find_position(img)
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if lm_list:
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x_1, y_1 = bbox["bbox"][0], bbox["bbox"][1]
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x1, x2, x3, x4, x5 = self.detector.fingers_up()
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if (np.linalg.norm(lm_list[4]-lm_list[8]) < 50) and (np.linalg.norm(lm_list[4]-lm_list[12]) < 50):
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cv2.putText(img, "7_SEVEN", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
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(0, 0, 255), 3)
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elif (np.linalg.norm(lm_list[4]-lm_list[8]) < 50) and (x4 == 1 and x5 == 1 and x3 == 1):
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cv2.putText(img, "OK", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
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(0, 0, 255), 3)
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elif (np.linalg.norm(lm_list[4]-lm_list[12]) < 50) and (x4 == 1 and x5 == 1 and x2 == 1):
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cv2.putText(img, "flip", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
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(0, 0, 255), 3)
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elif (x2 == 1 and x3 == 1) and (x4 == 0 and x5 == 0 and x1 == 0):
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cv2.putText(img, "2_TWO", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
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(0, 0, 255), 3)
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elif (x2 == 1 and x3 == 1 and x4 == 1) and (x1 == 0 and x5 == 0):
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cv2.putText(img, "3_THREE", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
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(0, 0, 255), 3)
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elif (x2 == 1 and x3 == 1 and x4 == 1 and x5 == 1) and (x1 == 0):
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cv2.putText(img, "4_FOUR", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
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(0, 0, 255), 3)
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elif x1 == 1 and x2 == 1 and x3 == 1 and x4 == 1 and x5 == 1:
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cv2.putText(img, "5_FIVE", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
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(0, 0, 255), 3)
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elif (x2 == 1 and x1 == 0) and (x3 == 0 and x4 == 0 and x5 == 0):
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cv2.putText(img, "1_ONE", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
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(0, 0, 255), 3)
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elif (x1 == 1 and x2 == 1) and (x3 == 0 and x4 == 0 and x5 == 0):
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cv2.putText(img, "8_EIGHT", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
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(0, 0, 255), 3)
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elif (x1 == 1 and x5 == 1) and (x3 == 0 and x4 == 0 and x2 == 0):
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cv2.putText(img, "6_SIX", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
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(0, 0, 255), 3)
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elif x1 and (x2 == 0 and x3 == 0 and x4 == 0 and x5 == 0):
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cv2.putText(img, "GOOD!", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
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(0, 0, 255), 3)
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elif (x1 == 1 and x2 == 1 and x5 == 1) and (x3 == 0 and x4 == 0):
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cv2.putText(img, "yo", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
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(0, 0, 255), 3)
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else:
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cv2.putText(img, "unknown", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
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(0, 0, 255), 3)
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cv2.imshow("camera", img)
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key = cv2.waitKey(1)
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if cv2.getWindowProperty('camera', cv2.WND_PROP_VISIBLE) < 1:
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break
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elif key == 27:
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break
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if lm_list:
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x_1, y_1 = bbox["bbox"][0], bbox["bbox"][1]
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x1, x2, x3, x4, x5 = detector.fingers_up()
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if (np.linalg.norm(lm_list[4]-lm_list[8]) < 50) and (np.linalg.norm(lm_list[4]-lm_list[12]) < 50):
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cv2.putText(img, "7_SEVEN", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
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(0, 0, 255), 3)
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elif (x2 == 1 and x3 == 1) and (x4 == 0 and x5 == 0 and x1 == 0):
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cv2.putText(img, "2_TWO", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
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(0, 0, 255), 3)
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elif (x2 == 1 and x3 == 1 and x4 == 1) and (x1 == 0 and x5 == 0):
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cv2.putText(img, "3_THREE", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
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(0, 0, 255), 3)
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elif (x2 == 1 and x3 == 1 and x4 == 1 and x5 == 1) and (x1 == 0):
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cv2.putText(img, "4_FOUR", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
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(0, 0, 255), 3)
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elif x1 == 1 and x2 == 1 and x3 == 1 and x4 == 1 and x5 == 1:
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cv2.putText(img, "5_FIVE", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
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(0, 0, 255), 3)
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elif (x2 == 1 and x1 == 0) and (x3 == 0 and x4 == 0 and x5 == 0):
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cv2.putText(img, "1_ONE", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
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(0, 0, 255), 3)
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elif (x1 == 1 and x2 == 1) and (x3 == 0 and x4 == 0 and x5 == 0):
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cv2.putText(img, "8_EIGHT", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
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(0, 0, 255), 3)
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elif (x1 == 1 and x5 == 1) and (x3 == 0 and x4 == 0 and x2 == 0):
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cv2.putText(img, "6_SIX", (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
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(0, 0, 255), 3)
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else:
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return 1
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return 0
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if __name__ == '__main__':
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113
ai.py
113
ai.py
@ -6,13 +6,12 @@
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@ by: Leaf
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@ date: 2022-05-28
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"""
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import tkinter as tk
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import cv2
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import mediapipe as mp
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import torch
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import torch.nn as nn
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import numpy as np
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import tkinter as tk
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import shutil
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import math
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from scipy import stats
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@ -33,7 +32,7 @@ def rotate(angle, x, y, point_x, point_y):
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def normalize(x):
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max_x = np.max(x)
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min_x = np.min(x)
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return (x-min_x)/(max_x-min_x)
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return (x - min_x) / (max_x - min_x)
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class CNN(nn.Module):
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@ -57,7 +56,7 @@ class CNN(nn.Module):
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nn.MaxPool2d(2),
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)
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self.med = nn.Linear(32 * 11 * 2, 500)
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self.med2 = nn.Linear(1*21*3, 100)
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self.med2 = nn.Linear(1 * 21 * 3, 100)
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self.med3 = nn.Linear(100, 500)
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self.out = nn.Linear(500, m) # fully connected layer, output 10 classes
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@ -285,6 +284,23 @@ class Main:
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self.len_x = 22
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self.len_y = 4
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self.label = ''
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self.result = []
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self.disp = ""
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def change_state(self):
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self.label = self.entry.get() # 调用get()方法，将Entry中的内容获取出来
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self.top1.quit()
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if self.label == "":
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self.top1.destroy()
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def make_datasets(self, camera, datasets_dir="default", n=100):
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if datasets_dir == "default":
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return
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if exists(datasets_dir):
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shutil.rmtree(datasets_dir)
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mkdir(datasets_dir)
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self.camera = camera
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self.top1 = tk.Tk()
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self.top1.geometry('300x50')
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self.top1.title('请输入标签')
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@ -293,22 +309,6 @@ class Main:
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self.entry.place(x=80, y=10)
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tk.Button(self.top1, text='确定', command=self.change_state).place(x=235, y=5)
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def change_state(self):
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self.label = self.entry.get() # 调用get()方法，将Entry中的内容获取出来
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self.top1.quit()
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if self.label == "":
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self.top1.destroy()
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def make_datasets(self, datasets_dir="default", n=100):
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if datasets_dir == "default":
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return
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if exists(datasets_dir):
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shutil.rmtree(datasets_dir)
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mkdir(datasets_dir)
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if self.camera is None:
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self.camera = cv2.VideoCapture(0, cv2.CAP_DSHOW)
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self.camera.set(3, 1280)
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self.camera.set(4, 720)
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self.top1.mainloop()
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while not self.label == "":
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data = np.zeros([n, self.len_x, self.len_y])
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@ -349,7 +349,6 @@ class Main:
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open(datasets_dir + "/" + self.label + ".npz", "w")
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np.savez(datasets_dir + "/" + self.label + ".npz", label=self.label, data=data,
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handtype=hand_type, shape=shape_list)
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self.top1.mainloop()
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def train(self, datasets_dir="default"):
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@ -359,21 +358,75 @@ class Main:
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ai.load_datasets()
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ai.train_cnn()
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def gesture_recognition(self):
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if self.camera is None:
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self.camera = cv2.VideoCapture(0, cv2.CAP_DSHOW)
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self.camera.set(3, 1280)
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self.camera.set(4, 720)
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def gesture_recognition_camera(self, detector, img, cnn):
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self.detector = detector
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out_label = cnn.out_label
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img = self.detector.find_hands(img)
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lm_list, bbox = self.detector.find_position(img)
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if lm_list.any():
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x_1, y_1 = bbox["bbox"][0], bbox["bbox"][1]
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data = torch.Tensor(lm_list)
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data = data.unsqueeze(0)
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data = data.unsqueeze(0)
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test_output = cnn(data)
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self.result.append(torch.max(test_output, 1)[1].data.cpu().numpy()[0])
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if len(self.result) > 5:
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self.disp = str(out_label[stats.mode(self.result)[0][0]])
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self.result = []
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cv2.putText(img, self.disp, (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
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(0, 0, 255), 3)
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def gesture_recognition_video(self, filedir):
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self.detector = HandDetector()
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cnn = torch.load("CNN.pkl")
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out_label = cnn.out_label
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result = []
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disp = ""
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cap = cv2.VideoCapture(filedir)
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while True:
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frame, img = self.camera.read()
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ret, img = cap.read()
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img = self.detector.find_hands(img)
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lm_list, bbox = self.detector.find_position(img)
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if lm_list.any():
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x_1, y_1 = bbox["bbox"][0], bbox["bbox"][1]
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data = torch.Tensor(lm_list)
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data = data.unsqueeze(0)
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data = data.unsqueeze(0)
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test_output = cnn(data)
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result.append(torch.max(test_output, 1)[1].data.cpu().numpy()[0])
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if len(result) > 5:
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disp = str(out_label[stats.mode(result)[0][0]])
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result = []
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cv2.putText(img, disp, (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
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(0, 0, 255), 3)
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cv2.imshow("camera", img)
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key = cv2.waitKey(1)
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if cv2.getWindowProperty('camera', cv2.WND_PROP_VISIBLE) < 1:
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break
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elif key == 27:
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break
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cap.release()
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def gesture_recognition_img(self, filedir):
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self.detector = HandDetector()
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cnn = torch.load("CNN.pkl")
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out_label = cnn.out_label
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result = []
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disp = ""
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img = cv2.imread(filedir)
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img = self.detector.find_hands(img)
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while True:
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lm_list, bbox = self.detector.find_position(img)
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if lm_list.any():
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x_1, y_1 = bbox["bbox"][0], bbox["bbox"][1]
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data = torch.Tensor(lm_list)
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@ -402,4 +455,8 @@ if __name__ == '__main__':
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my_datasets_dir = "test"
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solution.make_datasets(my_datasets_dir, 100)
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solution.train(my_datasets_dir)
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solution.gesture_recognition()
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dir_video = "C:/Users/Liar/Pictures/Camera Roll/WIN_20220630_20_11_47_Pro.mp4"
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dir_img = "C:/Users/Liar/Pictures/Camera Roll/WIN_20220630_20_01_22_Pro.jpg"
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# solution.gesture_recognition_camera()
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# solution.gesture_recognition_video(dir_video)
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# solution.gesture_recognition_img(dir_img)
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476
ai_UI.py
476
ai_UI.py
@ -1,476 +0,0 @@
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# -*- coding:utf-8 -*-
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"""
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信号设计课程小组设计
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@ by: Leaf
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@ date: 2022-05-28
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"""
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import tkinter as tk
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import cv2
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import mediapipe as mp
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import torch
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import torch.nn as nn
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import numpy as np
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import shutil
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import math
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from scipy import stats
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from os.path import exists
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from os import mkdir
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from pathlib import Path
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from torch.utils.data import DataLoader, TensorDataset
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# 旋转函数
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def rotate(angle, x, y, point_x, point_y):
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px = (x - point_x) * math.cos(angle) - (y - point_y) * math.sin(angle) + point_x
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py = (x - point_x) * math.sin(angle) + (y - point_y) * math.cos(angle) + point_y
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return px, py
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# 归一化
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def normalize(x):
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max_x = np.max(x)
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min_x = np.min(x)
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return (x - min_x) / (max_x - min_x)
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class CNN(nn.Module):
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def __init__(self, m):
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super(CNN, self).__init__()
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self.out_label = []
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self.conv1 = nn.Sequential(
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nn.Conv2d(
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in_channels=1,
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out_channels=16,
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kernel_size=5,
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stride=1,
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padding=2,
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),
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nn.ReLU(),
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nn.MaxPool2d(kernel_size=1),
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)
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self.conv2 = nn.Sequential(
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nn.Conv2d(16, 32, 5, 1, 2),
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nn.ReLU(),
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nn.MaxPool2d(2),
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)
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self.med = nn.Linear(32 * 11 * 2, 500)
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self.med2 = nn.Linear(1 * 21 * 3, 100)
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self.med3 = nn.Linear(100, 500)
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self.out = nn.Linear(500, m) # fully connected layer, output 10 classes
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def forward(self, x):
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x = self.conv1(x)
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x = self.conv2(x)
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x = x.view(x.size(0), -1) # 展平多维的卷积图成 (batch_size, 32 * 7 * 7)
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x = self.med(x)
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# x = self.med2(x)
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# x = self.med3(x)
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output = self.out(x)
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return output
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class HandDetector:
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"""
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使用mediapipe库查找手。导出地标像素格式。添加了额外的功能。
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如查找方式，许多手指向上或两个手指之间的距离。而且提供找到的手的边界框信息。
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"""
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def __init__(self, mode=False, max_hands=2, detection_con=0.5, min_track_con=0.5):
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"""
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:param mode: 在静态模式下，对每个图像进行检测
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:param max_hands: 要检测的最大手数
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:param detection_con: 最小检测置信度
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:param min_track_con: 最小跟踪置信度
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"""
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self.results = None
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self.mode = mode
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self.max_hands = max_hands
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self.modelComplex = 1
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self.detection_con = detection_con
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self.min_track_con = min_track_con
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# 初始化手部的识别模型
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self.mpHands = mp.solutions.hands
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self.hands = self.mpHands.Hands(static_image_mode=self.mode,
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max_num_hands=self.max_hands,
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min_detection_confidence=self.detection_con,
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||||
min_tracking_confidence=self.min_track_con)
|
||||
self.mpDraw = mp.solutions.drawing_utils # 初始化绘图器
|
||||
self.tipIds = [4, 8, 12, 16, 20] # 指尖列表
|
||||
self.fingers = []
|
||||
self.lmList = []
|
||||
self.re_lmList = []
|
||||
|
||||
def find_hands(self, img, draw=True):
|
||||
"""
|
||||
从图像(BRG)中找到手部。
|
||||
:param img: 用于查找手的图像。
|
||||
:param draw: 在图像上绘制输出的标志。
|
||||
:return: 带或不带图形的图像
|
||||
"""
|
||||
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # 将传入的图像由BGR模式转标准的Opencv模式——RGB模式，
|
||||
self.results = self.hands.process(img_rgb)
|
||||
|
||||
if self.results.multi_hand_landmarks:
|
||||
for handLms in self.results.multi_hand_landmarks:
|
||||
if draw:
|
||||
self.mpDraw.draw_landmarks(img, handLms,
|
||||
self.mpHands.HAND_CONNECTIONS)
|
||||
return img
|
||||
|
||||
def find_position(self, img, hand_no=0, draw=True):
|
||||
"""
|
||||
查找单手的地标并将其放入列表中像素格式。还可以返回手部的周围的边界框。
|
||||
:param img: 要查找的主图像
|
||||
:param hand_no: 如果检测到多只手，则为手部id
|
||||
:param draw: 在图像上绘制输出的标志。(默认绘制矩形框)
|
||||
:return: 像素格式的手部关节位置列表；手部边界框
|
||||
"""
|
||||
x_list = []
|
||||
y_list = []
|
||||
bbox_info = []
|
||||
self.lmList = []
|
||||
h, w, c = img.shape
|
||||
if self.results.multi_hand_landmarks:
|
||||
my_hand = self.results.multi_hand_landmarks[hand_no]
|
||||
for i, lm in enumerate(my_hand.landmark):
|
||||
px, py = int(lm.x * w), int(lm.y * h)
|
||||
x_list.append(px)
|
||||
y_list.append(py)
|
||||
self.lmList.append([lm.x, lm.y, 0])
|
||||
if draw:
|
||||
cv2.circle(img, (px, py), 5, (255, 0, 255), cv2.FILLED)
|
||||
x_min, x_max = min(x_list), max(x_list)
|
||||
y_min, y_max = min(y_list), max(y_list)
|
||||
box_w, box_h = x_max - x_min, y_max - y_min
|
||||
bbox = x_min, y_min, box_w, box_h
|
||||
cx, cy = bbox[0] + (bbox[2] // 2), bbox[1] + (bbox[3] // 2)
|
||||
bbox_info = {"id": hand_no, "bbox": bbox, "center": (cx, cy), "shape": (h, w)}
|
||||
|
||||
if draw:
|
||||
cv2.rectangle(img, (bbox[0] - 20, bbox[1] - 20),
|
||||
(bbox[0] + bbox[2] + 20, bbox[1] + bbox[3] + 20),
|
||||
(0, 255, 0), 2)
|
||||
|
||||
self.revolve(img)
|
||||
self.re_lmList = np.array(self.re_lmList)
|
||||
if self.re_lmList.any():
|
||||
self.re_lmList = np.concatenate((np.zeros((21, 1)), self.re_lmList), axis=1)
|
||||
self.re_lmList = np.concatenate((self.re_lmList, np.zeros((1, 4))), axis=0)
|
||||
|
||||
return self.re_lmList, bbox_info
|
||||
|
||||
def revolve(self, img, draw=True):
|
||||
"""
|
||||
旋转手势识别点
|
||||
:param img: 要查找的主图像
|
||||
:param draw: 在图像上绘制输出的标志。(默认绘制矩形框)
|
||||
:return: 像素格式的手部关节位置列表
|
||||
"""
|
||||
h, w, c = img.shape
|
||||
if len(self.lmList) >= 21:
|
||||
# print(self.lmList)
|
||||
self.re_lmList = []
|
||||
point_x = self.lmList[0][0]
|
||||
point_y = self.lmList[0][1]
|
||||
delta_x = self.lmList[13][0] - point_x
|
||||
delta_y = self.lmList[13][1] - point_y
|
||||
if delta_y == 0:
|
||||
if delta_x < 0:
|
||||
theta = math.pi / 2
|
||||
else:
|
||||
theta = -math.pi / 2
|
||||
else:
|
||||
theta = math.atan(delta_x / delta_y)
|
||||
if delta_y > 0:
|
||||
theta = theta + math.pi
|
||||
# print(theta*180/math.pi)
|
||||
for i in self.lmList:
|
||||
px, py = rotate(theta, i[0] * w, i[1] * h, point_x * w, point_y * h)
|
||||
self.re_lmList.append([px, py, 0])
|
||||
if draw:
|
||||
cv2.circle(img, (int(px), int(py)), 5, (0, 0, 255), cv2.FILLED)
|
||||
# 归一化
|
||||
x_array = normalize(np.array(self.re_lmList)[:, 0])
|
||||
# print(x_array)
|
||||
for i in range(len(x_array)):
|
||||
self.re_lmList[i][0] = x_array[i]
|
||||
y_array = normalize(np.array(self.re_lmList)[:, 1])
|
||||
for i in range(len(y_array)):
|
||||
self.re_lmList[i][1] = x_array[i]
|
||||
else:
|
||||
self.re_lmList = self.lmList
|
||||
return self.re_lmList
|
||||
|
||||
def hand_type(self):
|
||||
"""
|
||||
检查传入的手部 是左还是右
|
||||
:return: 1 或 0
|
||||
"""
|
||||
if self.results.multi_hand_landmarks:
|
||||
if self.lmList[17][0] < self.lmList[5][0]:
|
||||
return 1
|
||||
else:
|
||||
return 0
|
||||
|
||||
|
||||
class AI:
|
||||
def __init__(self, datasets_dir):
|
||||
self.EPOCH = 20
|
||||
self.BATCH_SIZE = 2
|
||||
self.LR = 10e-5
|
||||
self.DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
self.datasets_dir = datasets_dir
|
||||
self.train_loader = None
|
||||
self.m = 0
|
||||
self.out_label = [] # CNN网络输出后数字标签转和字符串标签的映射关系
|
||||
|
||||
def load_datasets(self):
|
||||
train_data = []
|
||||
train_label = []
|
||||
self.m = 0
|
||||
for file in Path(self.datasets_dir).rglob("*.npz"):
|
||||
data = np.load(str(file))
|
||||
train_data.append(data["data"])
|
||||
label_number = np.ones(len(data["data"])) * len(self.out_label)
|
||||
train_label.append(label_number)
|
||||
self.out_label.append(data["label"])
|
||||
self.m += 1
|
||||
train_data = torch.Tensor(np.concatenate(train_data, axis=0))
|
||||
train_data = train_data.unsqueeze(1)
|
||||
train_label = torch.tensor(np.concatenate(train_label, axis=0)).long()
|
||||
|
||||
dataset = TensorDataset(train_data, train_label)
|
||||
self.train_loader = DataLoader(dataset, batch_size=self.BATCH_SIZE, shuffle=True)
|
||||
return self.m
|
||||
|
||||
def train_cnn(self):
|
||||
cnn = CNN(self.m).to(self.DEVICE)
|
||||
optimizer = torch.optim.Adam(cnn.parameters(), self.LR) # optimize all cnn parameters
|
||||
loss_func = nn.CrossEntropyLoss() # the target label is not one-hotted
|
||||
|
||||
for epoch in range(self.EPOCH):
|
||||
for step, (data, target) in enumerate(self.train_loader):
|
||||
# 分配 batch data, normalize x when iterate train_loader
|
||||
data, target = data.to(self.DEVICE), target.to(self.DEVICE)
|
||||
output = cnn(data) # cnn output
|
||||
loss = loss_func(output, target) # cross entropy loss
|
||||
optimizer.zero_grad() # clear gradients for this training step
|
||||
loss.backward() # backpropagation, compute gradients
|
||||
optimizer.step() # apply gradients
|
||||
if (step + 1) % 50 == 0: # 输出结果
|
||||
print(
|
||||
"\r[Epoch: %d] [%d/%d (%0.f %%)][Loss: %f]"
|
||||
% (
|
||||
epoch + 1,
|
||||
(step + 1) * len(data),
|
||||
len(self.train_loader.dataset),
|
||||
100. * (step + 1) / len(self.train_loader),
|
||||
loss.item()
|
||||
), end="")
|
||||
|
||||
cnn.out_label = self.out_label
|
||||
torch.save(cnn, 'CNN.pkl')
|
||||
print("训练结束")
|
||||
|
||||
|
||||
class Main:
|
||||
def __init__(self):
|
||||
self.camera = None
|
||||
self.detector = HandDetector()
|
||||
self.default_datasets = "Datasets"
|
||||
self.len_x = 22
|
||||
self.len_y = 4
|
||||
self.label = ''
|
||||
self.top1 = tk.Tk()
|
||||
self.top1.geometry('300x50')
|
||||
self.top1.title('请输入标签')
|
||||
tk.Label(self.top1, text='Label:').place(x=27, y=10)
|
||||
self.entry = tk.Entry(self.top1, width=15)
|
||||
self.entry.place(x=80, y=10)
|
||||
tk.Button(self.top1, text='确定', command=self.change_state).place(x=235, y=5)
|
||||
|
||||
def change_state(self):
|
||||
self.label = self.entry.get() # 调用get()方法，将Entry中的内容获取出来
|
||||
self.top1.quit()
|
||||
if self.label == "":
|
||||
self.top1.destroy()
|
||||
|
||||
def make_datasets(self, datasets_dir="default", n=100):
|
||||
if datasets_dir == "default":
|
||||
return
|
||||
if exists(datasets_dir):
|
||||
shutil.rmtree(datasets_dir)
|
||||
mkdir(datasets_dir)
|
||||
if self.camera is None:
|
||||
self.camera = cv2.VideoCapture(0, cv2.CAP_DSHOW)
|
||||
self.camera.set(3, 1280)
|
||||
self.camera.set(4, 720)
|
||||
self.top1.mainloop()
|
||||
while not self.label == "":
|
||||
data = np.zeros([n, self.len_x, self.len_y])
|
||||
shape_list = np.zeros([n, 2], dtype=np.int16)
|
||||
hand_type = np.zeros(n, dtype=np.int8)
|
||||
|
||||
count = 0
|
||||
cv2.startWindowThread()
|
||||
while True:
|
||||
frame, img = self.camera.read()
|
||||
img = self.detector.find_hands(img)
|
||||
result = np.zeros((self.len_x, self.len_y))
|
||||
|
||||
lm_list, bbox = self.detector.find_position(img)
|
||||
for i in range(len(lm_list)):
|
||||
result[i] = np.array(lm_list[i])
|
||||
if result.sum() > 0: # 假设矩阵不为0，即捕捉到手部时
|
||||
|
||||
shape = bbox["shape"]
|
||||
x_1, y_1 = bbox["bbox"][0], bbox["bbox"][1]
|
||||
data[count] = result
|
||||
hand_type[count] = self.detector.hand_type()
|
||||
shape_list[count] = np.array(shape)
|
||||
count += 1
|
||||
cv2.putText(img, str("{}/{}".format(count, n)), (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
|
||||
(0, 255, 0), 3)
|
||||
|
||||
cv2.imshow("camera", img)
|
||||
key = cv2.waitKey(100)
|
||||
if cv2.getWindowProperty('camera', cv2.WND_PROP_VISIBLE) < 1:
|
||||
break
|
||||
elif key == 27:
|
||||
break
|
||||
elif count == n - 1:
|
||||
break
|
||||
cv2.destroyAllWindows()
|
||||
|
||||
open(datasets_dir + "/" + self.label + ".npz", "w")
|
||||
np.savez(datasets_dir + "/" + self.label + ".npz", label=self.label, data=data,
|
||||
handtype=hand_type, shape=shape_list)
|
||||
self.top1.mainloop()
|
||||
|
||||
def train(self, datasets_dir="default"):
|
||||
if datasets_dir == "default":
|
||||
datasets_dir = self.default_datasets
|
||||
ai = AI(datasets_dir)
|
||||
ai.load_datasets()
|
||||
ai.train_cnn()
|
||||
|
||||
def gesture_recognition_camera(self):
|
||||
if self.camera is None:
|
||||
self.camera = cv2.VideoCapture(0, cv2.CAP_DSHOW)
|
||||
self.camera.set(3, 1280)
|
||||
self.camera.set(4, 720)
|
||||
self.detector = HandDetector()
|
||||
cnn = torch.load("CNN.pkl")
|
||||
out_label = cnn.out_label
|
||||
result = []
|
||||
disp = ""
|
||||
while True:
|
||||
frame, img = self.camera.read()
|
||||
img = self.detector.find_hands(img)
|
||||
lm_list, bbox = self.detector.find_position(img)
|
||||
|
||||
if lm_list.any():
|
||||
x_1, y_1 = bbox["bbox"][0], bbox["bbox"][1]
|
||||
data = torch.Tensor(lm_list)
|
||||
data = data.unsqueeze(0)
|
||||
data = data.unsqueeze(0)
|
||||
|
||||
test_output = cnn(data)
|
||||
result.append(torch.max(test_output, 1)[1].data.cpu().numpy()[0])
|
||||
if len(result) > 5:
|
||||
disp = str(out_label[stats.mode(result)[0][0]])
|
||||
result = []
|
||||
|
||||
cv2.putText(img, disp, (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
|
||||
(0, 0, 255), 3)
|
||||
|
||||
cv2.imshow("camera", img)
|
||||
key = cv2.waitKey(1)
|
||||
if cv2.getWindowProperty('camera', cv2.WND_PROP_VISIBLE) < 1:
|
||||
break
|
||||
elif key == 27:
|
||||
break
|
||||
|
||||
def gesture_recognition_video(self, filedir):
|
||||
self.detector = HandDetector()
|
||||
cnn = torch.load("CNN.pkl")
|
||||
out_label = cnn.out_label
|
||||
result = []
|
||||
disp = ""
|
||||
cap = cv2.VideoCapture(filedir)
|
||||
while True:
|
||||
ret, img = cap.read()
|
||||
img = self.detector.find_hands(img)
|
||||
lm_list, bbox = self.detector.find_position(img)
|
||||
|
||||
if lm_list.any():
|
||||
x_1, y_1 = bbox["bbox"][0], bbox["bbox"][1]
|
||||
data = torch.Tensor(lm_list)
|
||||
data = data.unsqueeze(0)
|
||||
data = data.unsqueeze(0)
|
||||
|
||||
test_output = cnn(data)
|
||||
result.append(torch.max(test_output, 1)[1].data.cpu().numpy()[0])
|
||||
if len(result) > 5:
|
||||
disp = str(out_label[stats.mode(result)[0][0]])
|
||||
result = []
|
||||
|
||||
cv2.putText(img, disp, (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
|
||||
(0, 0, 255), 3)
|
||||
|
||||
cv2.imshow("camera", img)
|
||||
key = cv2.waitKey(1)
|
||||
if cv2.getWindowProperty('camera', cv2.WND_PROP_VISIBLE) < 1:
|
||||
break
|
||||
elif key == 27:
|
||||
break
|
||||
cap.release()
|
||||
|
||||
|
||||
def gesture_recognition_img(self, filedir):
|
||||
self.detector = HandDetector()
|
||||
cnn = torch.load("CNN.pkl")
|
||||
out_label = cnn.out_label
|
||||
result = []
|
||||
disp = ""
|
||||
img = cv2.imread(filedir)
|
||||
img = self.detector.find_hands(img)
|
||||
while True:
|
||||
|
||||
lm_list, bbox = self.detector.find_position(img)
|
||||
|
||||
if lm_list.any():
|
||||
x_1, y_1 = bbox["bbox"][0], bbox["bbox"][1]
|
||||
data = torch.Tensor(lm_list)
|
||||
data = data.unsqueeze(0)
|
||||
data = data.unsqueeze(0)
|
||||
|
||||
test_output = cnn(data)
|
||||
result.append(torch.max(test_output, 1)[1].data.cpu().numpy()[0])
|
||||
if len(result) > 5:
|
||||
disp = str(out_label[stats.mode(result)[0][0]])
|
||||
result = []
|
||||
|
||||
cv2.putText(img, disp, (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
|
||||
(0, 0, 255), 3)
|
||||
|
||||
cv2.imshow("camera", img)
|
||||
key = cv2.waitKey(1)
|
||||
if cv2.getWindowProperty('camera', cv2.WND_PROP_VISIBLE) < 1:
|
||||
break
|
||||
elif key == 27:
|
||||
break
|
||||
|
||||
if __name__ == '__main__':
|
||||
solution = Main()
|
||||
my_datasets_dir = "test"
|
||||
# solution.make_datasets(my_datasets_dir, 100)
|
||||
# solution.train(my_datasets_dir)
|
||||
dir_video = "C:/Users/Liar/Pictures/Camera Roll/WIN_20220630_20_11_47_Pro.mp4"
|
||||
dir_img = "C:/Users/Liar/Pictures/Camera Roll/WIN_20220630_20_01_22_Pro.jpg"
|
||||
solution.gesture_recognition_camera()
|
||||
# solution.gesture_recognition_video(dir_video)
|
||||
# solution.gesture_recognition_img(dir_img)
|
||||
@ -36,9 +36,9 @@ def normalize(x):
|
||||
return (x-min_x)/(max_x-min_x)
|
||||
|
||||
|
||||
class CNN(nn.Module):
|
||||
class CNNTwo(nn.Module):
|
||||
def __init__(self, m):
|
||||
super(CNN, self).__init__()
|
||||
super(CNNTwo, self).__init__()
|
||||
self.out_label = []
|
||||
self.conv1 = nn.Sequential(
|
||||
nn.Conv2d(
|
||||
@ -250,7 +250,7 @@ class AI:
|
||||
return self.m
|
||||
|
||||
def train_cnn(self):
|
||||
cnn = CNN(self.m).to(self.DEVICE)
|
||||
cnn = CNNTwo(self.m).to(self.DEVICE)
|
||||
optimizer = torch.optim.Adam(cnn.parameters(), self.LR) # optimize all cnn parameters
|
||||
loss_func = nn.CrossEntropyLoss() # the target label is not one-hotted
|
||||
|
||||
@ -275,7 +275,7 @@ class AI:
|
||||
), end="")
|
||||
|
||||
cnn.out_label = self.out_label
|
||||
torch.save(cnn, 'CNN.pkl')
|
||||
torch.save(cnn, 'CNN_two.pkl')
|
||||
print("训练结束")
|
||||
|
||||
|
||||
@ -287,13 +287,9 @@ class Main:
|
||||
self.len_x = 44
|
||||
self.len_y = 4
|
||||
self.label = ''
|
||||
self.top1 = tk.Tk()
|
||||
self.top1.geometry('300x50')
|
||||
self.top1.title('请输入标签')
|
||||
tk.Label(self.top1, text='Label:').place(x=27, y=10)
|
||||
self.entry = tk.Entry(self.top1, width=15)
|
||||
self.entry.place(x=80, y=10)
|
||||
tk.Button(self.top1, text='确定', command=self.change_state).place(x=235, y=5)
|
||||
|
||||
self.result = []
|
||||
self.disp = ""
|
||||
|
||||
def change_state(self):
|
||||
self.label = self.entry.get() # 调用get()方法，将Entry中的内容获取出来
|
||||
@ -301,16 +297,27 @@ class Main:
|
||||
if self.label == "":
|
||||
self.top1.destroy()
|
||||
|
||||
def make_datasets(self, datasets_dir="default", n=100):
|
||||
def on_closing(self):
|
||||
self.label = ""
|
||||
self.top1.destroy()
|
||||
|
||||
def make_datasets(self, camera, datasets_dir="default", n=100):
|
||||
if datasets_dir == "default":
|
||||
return
|
||||
if exists(datasets_dir):
|
||||
shutil.rmtree(datasets_dir)
|
||||
mkdir(datasets_dir)
|
||||
if self.camera is None:
|
||||
self.camera = cv2.VideoCapture(0, cv2.CAP_DSHOW)
|
||||
self.camera.set(3, 1280)
|
||||
self.camera.set(4, 720)
|
||||
self.camera = camera
|
||||
|
||||
self.top1 = tk.Tk()
|
||||
self.top1.geometry('300x50')
|
||||
self.top1.title('请输入标签')
|
||||
self.top1.protocol("WM_DELETE_WINDOW", self.on_closing)
|
||||
tk.Label(self.top1, text='Label:').place(x=27, y=10)
|
||||
self.entry = tk.Entry(self.top1, width=15)
|
||||
self.entry.place(x=80, y=10)
|
||||
tk.Button(self.top1, text='确定', command=self.change_state).place(x=235, y=5)
|
||||
|
||||
self.top1.mainloop()
|
||||
while not self.label == "":
|
||||
data = np.zeros([n, self.len_x, self.len_y])
|
||||
@ -369,47 +376,34 @@ class Main:
|
||||
ai.load_datasets()
|
||||
ai.train_cnn()
|
||||
|
||||
def gesture_recognition(self):
|
||||
if self.camera is None:
|
||||
self.camera = cv2.VideoCapture(0, cv2.CAP_DSHOW)
|
||||
self.camera.set(3, 1280)
|
||||
self.camera.set(4, 720)
|
||||
self.detector = HandDetector()
|
||||
cnn = torch.load("CNN.pkl")
|
||||
def gesture_recognition(self, detector, img, cnn):
|
||||
self.detector = detector
|
||||
out_label = cnn.out_label
|
||||
result = []
|
||||
disp = ""
|
||||
while True:
|
||||
frame, img = self.camera.read()
|
||||
img, is_two_hand = self.detector.find_hands(img)
|
||||
if is_two_hand:
|
||||
lm_list1, bbox1 = self.detector.find_position(img, 0)
|
||||
lm_list2, bbox2 = self.detector.find_position(img, 1)
|
||||
if lm_list1.any() and lm_list2.any():
|
||||
x_1, y_1 = bbox1["bbox"][0], bbox1["bbox"][1]
|
||||
x_2, y_2 = bbox2["bbox"][0], bbox2["bbox"][1]
|
||||
lm_list = np.concatenate((lm_list1, lm_list2), axis=0)
|
||||
data = torch.Tensor(lm_list)
|
||||
data = data.unsqueeze(0)
|
||||
data = data.unsqueeze(0)
|
||||
img, is_two_hand = self.detector.find_hands(img)
|
||||
if is_two_hand:
|
||||
lm_list1, bbox1 = self.detector.find_position(img, 0)
|
||||
lm_list2, bbox2 = self.detector.find_position(img, 1)
|
||||
if lm_list1.any() and lm_list2.any():
|
||||
x_1, y_1 = bbox1["bbox"][0], bbox1["bbox"][1]
|
||||
x_2, y_2 = bbox2["bbox"][0], bbox2["bbox"][1]
|
||||
lm_list = np.concatenate((lm_list1, lm_list2), axis=0)
|
||||
data = torch.Tensor(lm_list)
|
||||
data = data.unsqueeze(0)
|
||||
data = data.unsqueeze(0)
|
||||
|
||||
test_output = cnn(data)
|
||||
result.append(torch.max(test_output, 1)[1].data.cpu().numpy()[0])
|
||||
if len(result) > 4:
|
||||
disp = str(out_label[stats.mode(result)[0][0]])
|
||||
result = []
|
||||
test_output = cnn(data)
|
||||
self.result.append(torch.max(test_output, 1)[1].data.cpu().numpy()[0])
|
||||
if len(self.result) > 4:
|
||||
self.disp = str(out_label[stats.mode(self.result)[0][0]])
|
||||
self.result = []
|
||||
|
||||
cv2.putText(img, disp, (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
|
||||
(0, 0, 255), 3)
|
||||
cv2.putText(img, disp, (x_2, y_2), cv2.FONT_HERSHEY_PLAIN, 3,
|
||||
(0, 0, 255), 3)
|
||||
|
||||
cv2.imshow("camera", img)
|
||||
key = cv2.waitKey(1)
|
||||
if cv2.getWindowProperty('camera', cv2.WND_PROP_VISIBLE) < 1:
|
||||
break
|
||||
elif key == 27:
|
||||
break
|
||||
cv2.putText(img, self.disp, (x_1, y_1), cv2.FONT_HERSHEY_PLAIN, 3,
|
||||
(0, 0, 255), 3)
|
||||
cv2.putText(img, self.disp, (x_2, y_2), cv2.FONT_HERSHEY_PLAIN, 3,
|
||||
(0, 0, 255), 3)
|
||||
else:
|
||||
return 1
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
165
gr.py
Normal file
165
gr.py
Normal file
@ -0,0 +1,165 @@
|
||||
import TM
|
||||
import ai
|
||||
import ai_two
|
||||
import cv2
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
|
||||
|
||||
class CNN(nn.Module):
|
||||
def __init__(self, m):
|
||||
super(CNN, self).__init__()
|
||||
self.out_label = []
|
||||
self.conv1 = nn.Sequential(
|
||||
nn.Conv2d(
|
||||
in_channels=1,
|
||||
out_channels=16,
|
||||
kernel_size=5,
|
||||
stride=1,
|
||||
padding=2,
|
||||
),
|
||||
nn.ReLU(),
|
||||
nn.MaxPool2d(kernel_size=1),
|
||||
)
|
||||
self.conv2 = nn.Sequential(
|
||||
nn.Conv2d(16, 32, 5, 1, 2),
|
||||
nn.ReLU(),
|
||||
nn.MaxPool2d(2),
|
||||
)
|
||||
self.med = nn.Linear(32 * 11 * 2, 500)
|
||||
self.med2 = nn.Linear(1 * 21 * 3, 100)
|
||||
self.med3 = nn.Linear(100, 500)
|
||||
self.out = nn.Linear(500, m) # fully connected layer, output 10 classes
|
||||
|
||||
def forward(self, x):
|
||||
x = self.conv1(x)
|
||||
x = self.conv2(x)
|
||||
x = x.view(x.size(0), -1) # 展平多维的卷积图成 (batch_size, 32 * 7 * 7)
|
||||
x = self.med(x)
|
||||
# x = self.med2(x)
|
||||
# x = self.med3(x)
|
||||
output = self.out(x)
|
||||
return output
|
||||
|
||||
|
||||
class CNNTwo(nn.Module):
|
||||
def __init__(self, m):
|
||||
super(CNNTwo, self).__init__()
|
||||
self.out_label = []
|
||||
self.conv1 = nn.Sequential(
|
||||
nn.Conv2d(
|
||||
in_channels=1,
|
||||
out_channels=16,
|
||||
kernel_size=5,
|
||||
stride=1,
|
||||
padding=2,
|
||||
),
|
||||
nn.ReLU(),
|
||||
nn.MaxPool2d(kernel_size=2),
|
||||
)
|
||||
self.conv2 = nn.Sequential(
|
||||
nn.Conv2d(16, 32, 5, 1, 2),
|
||||
nn.ReLU(),
|
||||
nn.MaxPool2d(2),
|
||||
)
|
||||
self.med = nn.Linear(32 * 11 * 1, 500)
|
||||
self.med2 = nn.Linear(1*21*3, 100)
|
||||
self.med3 = nn.Linear(100, 500)
|
||||
self.out = nn.Linear(500, m) # fully connected layer, output 10 classes
|
||||
|
||||
def forward(self, x):
|
||||
x = self.conv1(x)
|
||||
x = self.conv2(x)
|
||||
x = x.view(x.size(0), -1) # 展平多维的卷积图成 (batch_size, 32 * 7 * 7)
|
||||
x = self.med(x)
|
||||
# x = self.med2(x)
|
||||
# x = self.med3(x)
|
||||
output = self.out(x)
|
||||
return output
|
||||
|
||||
|
||||
class Main:
|
||||
def __init__(self):
|
||||
self.camera = cv2.VideoCapture(0, cv2.CAP_DSHOW)
|
||||
self.camera.set(3, 1280)
|
||||
self.camera.set(4, 720)
|
||||
|
||||
self.tm_detector = TM.HandDetector()
|
||||
self.ai_detector = ai.HandDetector()
|
||||
self.at_detector = ai_two.HandDetector()
|
||||
|
||||
self.tm_main = TM.Main()
|
||||
self.ai_main = ai.Main()
|
||||
self.at_main = ai_two.Main()
|
||||
|
||||
def gr_img(self, filedir, diy):
|
||||
print(filedir)
|
||||
img = cv2.imread(filedir)
|
||||
if diy:
|
||||
cnn = torch.load("CNN.pkl")
|
||||
cnn_two = torch.load("CNN_two.pkl")
|
||||
tm_img = self.tm_detector.find_hands(img)
|
||||
while True:
|
||||
is_one_hand = self.at_main.gesture_recognition(self.at_detector, img, cnn_two)
|
||||
if is_one_hand:
|
||||
not_match = self.ai_main.gesture_recognition_camera(self.ai_detector, img, cnn)
|
||||
if not_match:
|
||||
self.tm_main.gesture_recognition(tm_img, self.tm_detector)
|
||||
|
||||
cv2.imshow("camera", img)
|
||||
key = cv2.waitKey(1)
|
||||
if cv2.getWindowProperty('camera', cv2.WND_PROP_VISIBLE) < 1:
|
||||
break
|
||||
elif key == 27:
|
||||
break
|
||||
|
||||
def gr_video(self, filedir, diy):
|
||||
cap = cv2.VideoCapture(filedir)
|
||||
if diy:
|
||||
cnn = torch.load("CNN.pkl")
|
||||
cnn_two = torch.load("CNN_two.pkl")
|
||||
while True:
|
||||
ret, img = cap.read()
|
||||
tm_status = self.tm_main.gesture_recognition(self.tm_detector.find_hands(img), self.tm_detector)
|
||||
if tm_status and diy:
|
||||
is_one_hand = self.at_main.gesture_recognition(self.at_detector, img, cnn_two)
|
||||
if is_one_hand:
|
||||
self.ai_main.gesture_recognition_camera(self.ai_detector, img, cnn)
|
||||
|
||||
cv2.imshow("camera", img)
|
||||
key = cv2.waitKey(1)
|
||||
if cv2.getWindowProperty('camera', cv2.WND_PROP_VISIBLE) < 1:
|
||||
break
|
||||
elif key == 27:
|
||||
break
|
||||
cap.release()
|
||||
|
||||
def gr_realtime(self, diy):
|
||||
if diy:
|
||||
cnn = torch.load("CNN.pkl")
|
||||
cnn_two = torch.load("CNN_two.pkl")
|
||||
while True:
|
||||
frame, img = self.camera.read()
|
||||
tm_status = self.tm_main.gesture_recognition(self.tm_detector.find_hands(img), self.tm_detector)
|
||||
if tm_status and diy:
|
||||
is_one_hand = self.at_main.gesture_recognition(self.at_detector, img, cnn_two)
|
||||
if is_one_hand:
|
||||
self.ai_main.gesture_recognition_camera(self.ai_detector, img, cnn)
|
||||
|
||||
cv2.imshow("camera", img)
|
||||
key = cv2.waitKey(1)
|
||||
if cv2.getWindowProperty('camera', cv2.WND_PROP_VISIBLE) < 1:
|
||||
break
|
||||
elif key == 27:
|
||||
break
|
||||
|
||||
def ai_input(self):
|
||||
self.ai_main.make_datasets(self.camera, "ai_datasets", 100)
|
||||
self.ai_main.train("ai_datasets")
|
||||
self.at_main.make_datasets(self.camera, "ai_two_datasets", 100)
|
||||
self.at_main.train("ai_two_datasets")
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main = Main()
|
||||
main.gr_img("C:/Users/leafl/Pictures/图片1.png", 0)
|
||||
112
main.py
Normal file
112
main.py
Normal file
@ -0,0 +1,112 @@
|
||||
# -*- coding:utf-8 -*-
|
||||
|
||||
"""
|
||||
信号设计课程小组设计
|
||||
|
||||
@ by: Leaf
|
||||
@ date: 2022-05-28
|
||||
"""
|
||||
import gr
|
||||
|
||||
import tkinter as tk
|
||||
from tkinter import filedialog, Button, Label, Frame, ACTIVE, LEFT
|
||||
from PIL import Image, ImageTk
|
||||
|
||||
|
||||
class DisplayImage:
|
||||
"""用于展示选择的图片"""
|
||||
def __init__(self, master):
|
||||
self.master = master
|
||||
master.title("GUI")
|
||||
self.Text_lab0 = Label(master, text='已加载图像/视频')
|
||||
self.Text_lab0.pack(pady=10)
|
||||
self.image_frame = Frame(master, bd=0, height=300, width=300, bg='white', highlightthickness=2,
|
||||
highlightbackground='gray', highlightcolor='black')
|
||||
self.image_frame.pack()
|
||||
|
||||
self.Text_label = Label(master, text='加载待识别影像/视频')
|
||||
self.Text_label.place(x=60, y=410)
|
||||
self.Choose_image = Button(master, command=self.choose_img, text="图像",
|
||||
width=7, default=ACTIVE, borderwidth=0)
|
||||
self.Choose_image.place(x=50, y=450)
|
||||
self.Choose_image = Button(master, command=self.choose_video, text="视频",
|
||||
width=7, default=ACTIVE, borderwidth=0)
|
||||
self.Choose_image.place(x=120, y=450)
|
||||
self.Text_label2 = Label(master, text='运行手势识别程序')
|
||||
self.Text_label2.place(x=60, y=500)
|
||||
self.image_mosaic = Button(master, command=self.gesture_recognition, text="Gesture recognition",
|
||||
width=17, default=ACTIVE, borderwidth=0)
|
||||
self.image_mosaic.place(x=50, y=540)
|
||||
self.Text_label3 = Label(master, text='运行实时手势识别程序')
|
||||
self.Text_label3.place(x=300, y=410)
|
||||
self.realtime = Button(master, command=self.realtime_gr, text="Realtime\n gesture recognition",
|
||||
width=17, height=6, default=ACTIVE, borderwidth=0)
|
||||
self.realtime.place(x=300, y=450)
|
||||
self.Text_label4 = Label(master, text='录入自定义手势')
|
||||
self.Text_label4.place(x=180, y=610)
|
||||
self.input = Button(master, command=self.input_image, text="Input gesture",
|
||||
width=42, default=ACTIVE, borderwidth=0)
|
||||
self.input.place(x=60, y=650)
|
||||
|
||||
self.gr = gr.Main()
|
||||
self.temp_dir = "temp"
|
||||
self.mode = 0
|
||||
self.directory = ""
|
||||
self.diy = 1
|
||||
|
||||
def choose_img(self):
|
||||
self.mode = 1
|
||||
# 清空框架中的内容
|
||||
for widget in self.image_frame.winfo_children():
|
||||
widget.destroy()
|
||||
|
||||
self.directory = filedialog.askopenfilename()
|
||||
|
||||
# 布局所选图片
|
||||
img = Image.open(self.directory).resize((300, 300))
|
||||
img.save(self.temp_dir + "/photo.png")
|
||||
image = ImageTk.PhotoImage(image=img)
|
||||
label = Label(self.image_frame, highlightthickness=0, borderwidth=0)
|
||||
label.configure(image=image)
|
||||
label.pack(side=LEFT, expand=True)
|
||||
|
||||
def choose_video(self):
|
||||
# 清空框架中的内容
|
||||
self.mode = 2
|
||||
for widget in self.image_frame.winfo_children():
|
||||
widget.destroy()
|
||||
|
||||
self.directory = filedialog.askopenfilename()
|
||||
|
||||
# 布局所选图片
|
||||
img = Image.open(self.temp_dir+"/video.jpg").resize((300, 300))
|
||||
img.save(self.temp_dir + "/photo.png")
|
||||
image = ImageTk.PhotoImage(image=img)
|
||||
label = Label(self.image_frame, highlightthickness=0, borderwidth=0)
|
||||
label.configure(image=image)
|
||||
label.pack(side=LEFT, expand=True)
|
||||
|
||||
def gesture_recognition(self):
|
||||
if self.mode == 1:
|
||||
self.gr.gr_img(self.directory, self.diy)
|
||||
elif self.mode == 2:
|
||||
self.gr.gr_video(self.directory, self.diy)
|
||||
|
||||
def realtime_gr(self):
|
||||
self.gr.gr_realtime(self.diy)
|
||||
|
||||
def input_image(self):
|
||||
self.diy = 1
|
||||
self.gr.ai_input()
|
||||
|
||||
|
||||
def main():
|
||||
window = tk.Tk()
|
||||
DisplayImage(window)
|
||||
window.title('手势识别')
|
||||
window.geometry('500x720')
|
||||
window.mainloop()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
Loading…
x
Reference in New Issue
Block a user