-
Notifications
You must be signed in to change notification settings - Fork 1
/
gest.py
102 lines (85 loc) · 2.9 KB
/
gest.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
from subprocess import call
import cv2, pickle
import numpy as np
import os
from keras.models import load_model
prediction = None
model = load_model('cnn_model_keras2.h5')
def get_image_size():
img = cv2.imread('gestures/0/100.jpg', 0)
return img.shape
image_x, image_y = get_image_size()
def keras_process_image(img):
img = cv2.resize(img, (image_x, image_y))
img = np.array(img, dtype=np.float32)
img = np.reshape(img, (1, image_x, image_y, 1))
return img
def keras_predict(model, image):
processed = keras_process_image(image)
pred_probab = model.predict(processed)[0]
pred_class = list(pred_probab).index(max(pred_probab))
return max(pred_probab), pred_class
def get_pred_text(pred_class):
if pred_class==0:
value="Function 1"
elif pred_class==2:
value="Function 2"
elif pred_class==5:
value="Function 3"
os.system("recognize_gesture.py")
exit()
elif pred_class==6:
value="Function 4"
call(["amixer", "-D", "pulse", "sset", "Master", "100%"])
elif pred_class==9:
value="Function 5"
call(["amixer", "-D", "pulse", "sset", "Master", "0%"])
else:
value=" "
return value
def get_hand_hist():
with open("hist", "rb") as f:
hist = pickle.load(f)
return hist
def recognize():
global prediction
cam = cv2.VideoCapture(1)
if cam.read()[0] == False:
cam = cv2.VideoCapture(0)
hist = get_hand_hist()
x, y, w, h = 300, 100, 300, 300
while True:
text = ""
img = cam.read()[1]
img = cv2.flip(img, 1)
imgHSV = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
dst = cv2.calcBackProject([imgHSV], [0, 1], hist, [0, 180, 0, 256], 1)
disc = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(10,10))
cv2.filter2D(dst,-1,disc,dst)
blur = cv2.GaussianBlur(dst, (11,11), 0)
blur = cv2.medianBlur(blur, 15)
thresh = cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)[1]
thresh = cv2.merge((thresh,thresh,thresh))
thresh = cv2.cvtColor(thresh, cv2.COLOR_BGR2GRAY)
thresh = thresh[y:y+h, x:x+w]
contours = cv2.findContours(thresh.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)[0]
if len(contours) > 0:
contour = max(contours, key = cv2.contourArea)
if cv2.contourArea(contour) > 10000:
x1, y1, w1, h1 = cv2.boundingRect(contour)
save_img = thresh[y1:y1+h1, x1:x1+w1]
if w1 > h1:
save_img = cv2.copyMakeBorder(save_img, int((w1-h1)/2) , int((w1-h1)/2) , 0, 0, cv2.BORDER_CONSTANT, (0, 0, 0))
elif h1 > w1:
save_img = cv2.copyMakeBorder(save_img, 0, 0, int((h1-w1)/2) , int((h1-w1)/2) , cv2.BORDER_CONSTANT, (0, 0, 0))
pred_probab, pred_class = keras_predict(model, save_img)
if pred_probab*100 > 80:
text = get_pred_text(pred_class)
cv2.putText(img, text, (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 2, 2)
cv2.rectangle(img, (x,y), (x+w, y+h), (0,255,0), 2)
cv2.imshow("Recognizing gesture", img)
cv2.imshow("thresh", thresh)
if cv2.waitKey(1) == ord('q'):
break
keras_predict(model, np.zeros((50, 50), dtype=np.uint8))
recognize()