1+ from _testbuffer import ndarray
2+
13import cv2
24from neuralNetwork import Net2 , Net
35import openCvTranforms .opencv_transforms .transforms as tf
46import torchvision .transforms as transforms
5- from timeit import default_timer as timer
67import torch
7- import numpy as np
8+ from PyQt5 .QtCore import *
9+ from time import time
810
911# functions to show an image
12+ class Signals (QObject ):
13+ '''
14+ Defines the signals available from a running worker thread.
15+
16+ Supported signals are:
17+
18+ finished
19+ No data
20+
21+ error
22+ `tuple` (exctype, value, traceback.format_exc() )
23+
24+ result
25+ `object` data returned from processing, anything
26+
27+ '''
28+ finished = pyqtSignal ()
29+ error = pyqtSignal (tuple )
30+ result = pyqtSignal (object , torch .Tensor )
31+
32+
33+ class GestureRecognition (QThread ):
34+ """main class with neural network"""
35+
36+ def __init__ (self , print ):
37+ super (GestureRecognition , self ).__init__ ()
38+ self .width = 80
39+ self .heigh = 60
40+ self .colors = [(255 , 0 , 0 ), (0 , 255 , 0 ), (0 , 0 , 255 )]
41+ self .labels = ['0' , '1' , '2' , '3' , '4' , '5' , '6' , '7' , '8' , '9' , '10' , '11' , '12' , '13' , '14' , '15' , '16' , '17' ,
42+ '18' , '19' ,
43+ '20' , '21' , '22' , '23' , '24' , '25' , '26' ]
44+ self .maxLen = 200
45+ self .step = 20
46+ self .offset = 5
47+ self .printFps = print
48+ self .thicc = 10
49+ self .device = cv2 .VideoCapture (0 , cv2 .CAP_DSHOW )
50+ self .net = Net (self .width , self .heigh )
51+ self .transform = transforms .Compose (
52+ [tf .Grayscale (),
53+ tf .Resize ((self .heigh , self .width )),
54+ tf .ToTensor (),
55+ tf .Normalize ((0.5 ,), (0.5 ,))
56+ ])
57+ self .signals = Signals ()
58+ self .running = True
59+ self .signals .finished .connect (self .stop )
60+
61+ def stop (self ):
62+ self .running = False
63+
64+ def drawStatistics (self , image , stats , chosen ):
65+ for i , data in enumerate (stats .view (- 1 )):
66+ imm = 10
67+ cv2 .rectangle (image , (self .offset , i * self .step ),
68+ (self .offset + (data * self .maxLen ), (i * self .step ) + self .thicc ), self .colors [i % 3 ], - 1 )
69+ cv2 .putText (image , self .labels [i ], (self .offset , i * self .step + 15 ), fontFace = cv2 .FONT_HERSHEY_SIMPLEX ,
70+ fontScale = 0.5 ,
71+ color = (255 , 255 , 255 ), thickness = 1 , lineType = - 1 )
72+ nnm = 20
73+
74+ def imshow (self , img ):
75+ img = img .view (self .heigh , self .width , 1 ).numpy () / 2 + 0.5 # unnormalize
76+ print (img .shape )
77+ # npimg = img.numpy()
78+ # cv2.imshow("preview", img)
79+
80+ def run (self ):
81+ scale = 2.5
82+ # width = vc.get(3) # 640
83+ # heigh = vc.get(4) # 480
84+ # fps = vc.get(5) # 60
85+
86+ # new settings
87+
88+ self .device .set (5 , 25 ) # set fps
89+ #print(self.device.get(16))
90+ #self.device.set(16, 1) #set to rgb
91+
92+ #print("width: " + str(self.device.get(3)) + " heigh: " + str(self.device.get(4)))
93+
94+ self .net = torch .load ("./savedMode2.pth" )
95+ self .net .eval ()
1096
11- width = 80
12- heigh = 60
13- colors = [(255 , 0 , 0 ), (0 , 255 , 0 ), (0 , 0 , 255 )]
14- labels = ['0' , '1' , '2' , '3' , '4' , '5' , '6' , '7' , '8' , '9' , '10' , '11' , '12' , '13' , '14' , '15' , '16' , '17' , '18' , '19' ,
15- '20' , '21' , '22' , '23' , '24' , '25' , '26' ];
16- maxLen = 200
17- step = 20
18- offset = 5
19- thicc = 10
20-
21-
22- def drawStatistics (image , stats , chosen ):
23- for i , data in enumerate (stats .view (- 1 )):
24- imm = 10
25- cv2 .rectangle (image , (offset , i * step ), (offset + (data * maxLen ), (i * step ) + thicc ), colors [i % 3 ], - 1 )
26- cv2 .putText (image , labels [i ], (offset , i * step + 15 ), fontFace = cv2 .FONT_HERSHEY_SIMPLEX , fontScale = 0.5 ,
27- color = (255 , 255 , 255 ), thickness = 1 , lineType = - 1 )
28- nnm = 20
29-
30-
31- def imshow (img ):
32- img = img .view (heigh , width , 1 ).numpy () / 2 + 0.5 # unnormalize
33- print (img .shape )
34- # npimg = img.numpy()
35- cv2 .imshow ("preview" , img )
36-
37-
38- cv2 .namedWindow ("preview" )
39- vc = cv2 .VideoCapture (0 , cv2 .CAP_DSHOW )
40- scale = 2.5
41- # width = vc.get(3) # 640
42- # heigh = vc.get(4) # 480
43- # fps = vc.get(5) # 60
44-
45- # new settings
46-
47- vc .set (5 , 24 ) # set fps
48-
49- print ("width: " + str (vc .get (3 )) + " heigh: " + str (vc .get (4 )))
50- net = Net (width , heigh )
51- net = torch .load ("./savedMode2.pth" )
52- net .eval ()
53-
54- transform = transforms .Compose (
55- [tf .Grayscale (),
56- tf .Scale ((heigh , width )),
57- tf .ToTensor (),
58- tf .Normalize ((0.5 ,), (0.5 ,))
59- ])
60-
61- if vc .isOpened (): # try to get the first frame
62- rval , frame = vc .read ()
63- print (frame .shape )
64- else :
65- rval = False
66- out = torch .zeros (27 )
67- while rval :
68- start = timer ()
69- rval , frame = vc .read ()
70- # frame = cv2.imread(r'./kinect_leap_dataset/acquisitions/P1/G1/0_rgb.png')
71- # print(frame.shape)
72- frameWithStats = frame .copy ()
73- drawStatistics (frameWithStats , out , 1 )
74- cv2 .imshow ("preview" , frameWithStats )
75- frame = transform (frame ).view (- 1 , 1 , heigh , width )
76- print (frame .shape )
77- # imshow(frame)
78- out = net (frame )
79- print (out .shape )
80- end = timer ()
81- print (end - start )
82- key = cv2 .waitKey (1 )
83- if key == 27 : # exit on ESC
84- break # todo change to kayboard real time check
85- cv2 .destroyWindow ("preview" )
97+ if self .device .isOpened (): # try to get the first frame
98+ rval , frame = self .device .read ()
99+ # print(frame.shape)
100+ else :
101+ rval = False
102+ out = torch .zeros (27 )
103+ start = time ()
104+ fps = 0
105+ while rval and self .running :
106+ if time () - start < 1 :
107+ fps += 1
108+ else :
109+ start = time ()
110+ self .printFps (fps )
111+ fps = 0
112+ rval , frame = self .device .read ()
113+ # frame = cv2.imread(r'./kinect_leap_dataset/acquisitions/P1/G1/0_rgb.png')
114+ frame = cv2 .cvtColor (frame , cv2 .COLOR_BGR2RGB )
115+ frameWithStats = frame .copy ()
116+ #print(frameWithStats.shape)
117+ self .drawStatistics (frameWithStats , out , 1 )
118+ # cv2.imshow("preview", frameWithStats)
119+ frame = self .transform (frame ).view (- 1 , 1 , self .heigh , self .width )
120+ #print(frame.shape)
121+ #imshow(frame)
122+ out = self .net (frame )
123+ self .signals .result .emit (frameWithStats , out )
124+ #print(out.shape)
0 commit comments