def infer():
#Load model
network = EmotionRecognition()
network.build_network()
#Predict
result = network.predict(format_image_norm(image))
label = np.argmax(result[0])
print("Emotion: " + str(EMOTIONS[np.argmax(result[0])]))
return label
image = image[face[1]:(face[1] + face[2]), face[0]:(face[0] + face[3])]
# Resize image to network size
try:
image = cv2.resize(image, (SIZE_FACE, SIZE_FACE),
interpolation=cv2.INTER_CUBIC) / 255.
except Exception:
print("[+] Problem during resize")
return None
# cv2.imshow("Lol", image)
# cv2.waitKey(0)
return image
# Load Model
network = EmotionRecognition()
network.build_network()
video_capture = cv2.VideoCapture(0)
font = cv2.FONT_HERSHEY_SIMPLEX
feelings_faces = []
for index, emotion in enumerate(EMOTIONS):
feelings_faces.append(cv2.imread('./emojis/' + emotion + '.png', -1))
while True:
# Capture frame-by-frame
ret, frame = video_capture.read()
# Predict result with network
result = network.predict(format_image(frame))
# Chop image to face
face = max_area_face
image = image[face[1]:(face[1] + face[2]), face[0]:(face[0] + face[3])]
# Resize image to network size
try:
image = cv2.resize(image, (SIZE_FACE, SIZE_FACE), interpolation = cv2.INTER_CUBIC) / 255.
except Exception:
print("[+] Problem during resize")
return None
# cv2.imshow("Lol", image)
# cv2.waitKey(0)
return image
# Load Model
network = EmotionRecognition()
network.build_network()
video_capture = cv2.VideoCapture(0)
font = cv2.FONT_HERSHEY_SIMPLEX
feelings_faces = []
for index, emotion in enumerate(EMOTIONS):
feelings_faces.append(cv2.imread('./emojis/' + emotion + '.png', -1))
while True:
# Capture frame-by-frame
ret, frame = video_capture.read()
# Predict result with network
result = network.predict(format_image(frame))
camera_view = Image.fromarray(camera_view)
camera_view = ImageTk.PhotoImage(image=camera_view)
self.camera_view_canvas.create_image(camera_view.width() / 2 + 100,
camera_view.height() / 2,
image=camera_view)
self.update_idletasks()
self.update()
# When everything is done, release the capture
video_capture.release()
cv2.destroyAllWindows()
if __name__ == '__main__':
# Load Model
network = EmotionRecognition()
network.build_network() # build AlexNet, a kind of CNN
musicplayer = musicPlayer.musicPlayer()
camera_lock = threading.Lock()
font = cv2.FONT_HERSHEY_SIMPLEX
#load the emotion icon
feelings_faces = []
for index, emotion in enumerate(EMOTIONS):
feelings_faces.append(cv2.imread('./emojis/' + emotion + '.png', -1))
obj = SmartBudy()
obj.mainloop()
#GUI part
# -*- coding: utf-8 -*-
import matplotlib.pyplot as plt
import numpy as np
from constants import *
from emotion_recognition import EmotionRecognition
from dataset_loader import DatasetLoader
# Load Model
network = EmotionRecognition()
network.build_network(loadModel=True)
data = DatasetLoader()
data.load_from_save(data_source='fer2013')
images = data.images_test
labels = data.labels_test
print('[+] Loading Data')
data = np.zeros((len(EMOTIONS), len(EMOTIONS)))
for i in range(images.shape[0]):
if i % 1000 == 0:
print("Progress: {}/{} {:.2f}%".format(i, images.shape[0],
i * 100.0 / images.shape[0]))
result = network.predict(images[i])
data[np.argmax(labels[i]), np.argmax(result[0])] += 1
print("Accuracy: %f" % (np.sum(data.diagonal()) / np.sum(data)))
# Take % by column