-
Notifications
You must be signed in to change notification settings - Fork 0
/
gui.py
118 lines (99 loc) · 4.03 KB
/
gui.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
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
import tkinter as tk
from tkinter import *
import cv2
from PIL import Image, ImageTk
import os
import numpy as np
import cv2
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D
from keras.optimizers import Adam
from keras.layers import MaxPooling2D
from keras.preprocessing.image import ImageDataGenerator
emotion_model = Sequential()
emotion_model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(48, 48, 1)))
emotion_model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
emotion_model.add(MaxPooling2D(pool_size=(2, 2)))
emotion_model.add(Dropout(0.25))
emotion_model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))
emotion_model.add(MaxPooling2D(pool_size=(2, 2)))
emotion_model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))
emotion_model.add(Dropout(0.25))
emotion_model.add(Flatten())
emotion_model.add(Dense(1024, activation='relu'))
emotion_model.add(Dropout(0.5))
emotion_model.add(Dense(7, activation='softmax'))
emotion_model.load_weights('emotion_model.h5')
cv2.ocl.setUseOpenCL(False)
emotion_dict = {0: "Angry", 1: "Disgusted", 2: "Fearful", 3: "Happy", 4: "Neutral",
5: "Sad", 6: "Surprised"}
emoji_dist = {0: "emojis/angry.png", 1: "emojis/disgusted.png", 2: "emojis/fearful.png",
3: "emojis/happy.png", 4: "emojis/neutral.png", 5: "emojis/sad.png", 6: "emojis/surpriced.png"}
global last_frame1
last_frame1 = np.zeros((480, 640, 3), dtype=np.uint8)
global cap1
show_text=[0]
def show_vid():
cap1 = cv2.VideoCapture(0)
if not cap1.isOpened():
print("Can't open camera1")
flag1, frame1 = cap1.read()
frame1 = cv2.resize(frame1, (600, 500))
bounding_box = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
gray_frame = cv2.cvtColor(frame1, cv2.COLOR_BGR2GRAY)
num_faces = bounding_box.detectMultiScale(gray_frame, scaleFactor=1.3, minNeighbors=5)
for (x,y,w,h) in num_faces:
cv2.rectangle(frame1, (x,y-50), (x+w, y+h+10), (255, 0, 0), 2)
roi_gray_frame = gray_frame[y: y+h, x: x+w]
cropped_img = np.expand_dims(np.expand_dims(cv2.resize(roi_gray_frame, (48, 48)), -1), 0)
prediction = emotion_model.predict(cropped_img)
maxindex = int(np.argmax(prediction))
show_text[0]=maxindex
if flag1 is None:
print("Major error!")
elif flag1:
global last_frame1
last_frame1 = frame1.copy()
pic = cv2.cvtColor(last_frame1, cv2.COLOR_BGR2RGB)
img = Image.fromarray(pic)
imgtk = ImageTk.PhotoImage(image=img)
lmain.imgtk = imgtk
lmain.configure(image=imgtk)
lmain.after(10, show_vid)
if cv2.waitKey(1) & 0xFF == ord('q'):
exit()
def show_vid2():
frame2=cv2.imread(emoji_dist[show_text[0]])
pic2 = cv2.cvtColor(frame2, cv2.COLOR_BGR2RGB)
img2 = Image.fromarray(frame2)
imgtk2 = ImageTk.PhotoImage(image=img2)
lmain2.imgtk2 = imgtk2
lmain3.configure(text=emotion_dict[show_text[0]],font=('arial',45,'bold'))
lmain2.configure(image=imgtk2)
lmain2.after(10, show_vid2)
if __name__ == '__main__':
root = tk.Tk()
# img = ImageTk.PhotoImage(Image.open("logo.png"))
heading = Label(root, bg='black')
heading.pack()
heading2 = Label(root, text="Photo to Emoji", pady=20, font=('arial', 45, 'bold'),
bg='black', fg='#CDCDCD')
heading2.pack()
lmain = tk.Label(master=root, padx=50, bd=10)
lmain2 = tk.Label(master=root, bd=10)
lmain3 = tk.Label(master=root, bd=10, fg='#CDCDCD', bg='black')
lmain.pack(side=LEFT)
lmain.place(x=50, y=250)
lmain3.pack()
lmain3.place(x=960, y=250)
lmain2.pack(side=RIGHT)
lmain2.place(x=900, y=350)
root.title("Photo to Emoji")
root.geometry("1400x900+100+10")
root['bg'] = 'black'
exitbutton = Button(root, text='Quit', fg="red", command= root.destroy,
font=('arial', 25, 'bold')).pack(side=BOTTOM)
show_vid()
show_vid2()
root.mainloop()