/
gui.py
121 lines (98 loc) · 4.33 KB
/
gui.py
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import tkinter as tk
from tkinter import *
import cv2
from PIL import Image, ImageTk
import os
import numpy as np
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(MaxPooling2D(pool_size=(2, 2)))
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('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",2:"./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("cant open the camera1")
flag1, frame1 = cap1.read()
frame1 = cv2.resize(frame1,(600,500))
bounding_box = cv2.CascadeClassifier('/home/shivam/.local/lib/python3.6/site-packages/cv2/data/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))
# cv2.putText(frame1, emotion_dict[maxindex], (x+20, y-60), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)
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,image=img,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()