import cv2
import numpy as np
from model import EMR
# prevents opencl usage and unnecessary logging messages
cv2.ocl.setUseOpenCL(False)
EMOTIONS = [
'Angry', 'Disgusted', 'Fearful', 'Happy', 'Sad', 'Surprised', 'Neutral'
]
# Initialize object of EMR class
network = EMR()
network.build_network()
# In case you want to detect emotions on a video, provide the video file path instead of 0 for VideoCapture.
cap = cv2.VideoCapture('r.jpg')
font = cv2.FONT_HERSHEY_SIMPLEX
while True:
# Again find haar cascade to draw bounding box around face
ret, frame = cap.read()
if not ret:
break
facecasc = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = facecasc.detectMultiScale(gray, 1.3, 5)
if len(faces) > 0:
# draw box around faces
for face in faces:
(x, y, w, h) = face
(48, 48), interpolation=cv2.INTER_CUBIC) / 255.
except Exception:
print("----->Problem during resize")
return None
# return image
output = np.array(image)
output = np.reshape(output, (48, 48, 1))
return output
# # Create Training set and Testing Set
data, file_name = get_files()
network = EMR()
network.build_network()
# for item in data:
# try:
# result = network.predict([item])
# maxindex = np.argmax(result[0])
# # print(result[0])
# print(EMOTIONS[maxindex])
# except:
# result = None
# continue
ind = [0, 0, 0, 0, 0]
for i in range(len(data)):
face = max_area_face
# extract ROI of face
image = image[face[1]:(face[1] + face[2]), face[0]:(face[0] + face[3])]
try:
# resize the image so that it can be passed to the neural network
image = cv2.resize(image, (48,48), interpolation = cv2.INTER_CUBIC) / 255.
except Exception:
print("----->Problem during resize")
return None
return image
# Initialize object of EMR class
network = EMR()
network.build_network()
cap = cv2.VideoCapture(0)
font = cv2.FONT_HERSHEY_SIMPLEX
feelings_faces = []
# append the list with the emoji images
for index, emotion in enumerate(EMOTIONS):
feelings_faces.append(cv2.imread('./emojis/' + emotion + '.png', -1))
iC = 0
while True:
# Again find haar cascade to draw bounding box around face
ret, frame = cap.read()
facecasc = cv2.CascadeClassifier('haarcascade_files/haarcascade_frontalface_default.xml')
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# # Create Training set and Testing Set
training_data, training_labels, testing_data, testing_labels = make_sets()
try:
epoch = int(sys.argv[1])
except:
epoch = 50
# # Convert to numpy array
training_data = np.array(training_data)
training_labels = np.array(training_labels)
testing_data = np.array(testing_data)
testing_labels = np.array(testing_labels)
network = EMR()
network.build_network()
network.train(training_data, training_labels, testing_data, testing_labels,
epoch)
print("Finish")
# # Create NN Model
# network = tflearn.input_data(shape=[None, 48, 48, 1])
# print("Input data ", network.shape[1:])
# network = conv_2d(network, 64, 5, activation='relu')
# print("Conv1 ", network.shape[1:])
# network = max_pool_2d(network, 3, strides=2)
from model import EMR
from singleface import runDetection
import sys
import configparser
if __name__ == "__main__":
print("\n------------Emotion Detection Program------------\n")
network = EMR()
config = configparser.ConfigParser()
config.read('config.ini')
captures = int(config['process']['Captures'])
print(captures)
runDetection(captures)
# resize the image so that it can be passed to the neural network
image = cv2.resize(image,
(48, 48), interpolation=cv2.INTER_CUBIC) / 255.
except Exception:
print("----->Problem during resize")
return None
# return image
output = np.array(image)
output = np.reshape(output, (48, 48, 1))
return output
# Initialize object of EMR class
network = EMR()
network.build_network()
# name = input('Input videoname (abc.xyz) : ')
try:
name = sys.argv[1]
print(name)
except:
print('please fill the videoname in input_video')
exit()
cap = cv2.VideoCapture(name)
length = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
frame_number = 0
fps = cap.get(cv2.CAP_PROP_FPS)
def runDetection(captures):
# Initialize object of EMR class
network = EMR()
network.build_network()
cap = cv2.VideoCapture(0)
font = cv2.FONT_HERSHEY_SIMPLEX
feelings_faces = []
count = 0
miliseconds = 0
# append the list with the emoji images
for index, emotion in enumerate(EMOTIONS):
feelings_faces.append(cv2.imread('./emojis/' + emotion + '.png', -1))
while True:
# Again find haar cascade to draw bounding box around face
ret, frame = cap.read()
if not ret:
break
facecasc = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = facecasc.detectMultiScale(gray,
scaleFactor=1.3,
minNeighbors=5)
# compute softmax probabilities
result = network.predict(format_image(frame))
if result is not None:
# write the different emotions and have a bar to indicate probabilities for each class
for index, emotion in enumerate(EMOTIONS):
cv2.putText(frame, emotion, (10, index * 20 + 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)
cv2.rectangle(frame, (130, index * 20 + 10),
(130 + int(result[0][index] * 100),
(index + 1) * 20 + 4), (255, 0, 0), -1)
# find the emotion with maximum probability and display it
maxindex = np.argmax(result[0])
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(frame, EMOTIONS[maxindex], (10, 360), font, 2,
(255, 255, 255), 2, cv2.LINE_AA)
face_image = feelings_faces[maxindex]
count = count + 1
print("Emotion is: ", EMOTIONS[maxindex], " ID: ", maxindex + 1)
user = os.environ.get('USERNAME')
user = user.replace('.', ' ').title()
insert(user, maxindex + 1)
# for c in range(0, 3):
# # The shape of face_image is (x,y,4). The fourth channel is 0 or 1. In most cases it is 0, so, we assign the roi to the emoji.
# # You could also do: frame[200:320,10:130,c] = frame[200:320, 10:130, c] * (1.0 - face_image[:, :, 3] / 255.0)
# frame[200:320, 10:130, c] = face_image[:,:,c]*(face_image[:, :, 3] / 255.0) + frame[200:320, 10:130, c] * (1.0 - face_image[:, :, 3] / 255.0)
if len(faces) > 0:
# draw box around face with maximum area
max_area_face = faces[0]
for face in faces:
if face[2] * face[3] > max_area_face[2] * max_area_face[3]:
max_area_face = face
face = max_area_face
(x, y, w, h) = max_area_face
frame = cv2.rectangle(frame, (x, y - 50), (x + w, y + h + 10),
(255, 0, 0), 2)
cv2.imshow(
'Video',
cv2.resize(frame, None, fx=2, fy=2, interpolation=cv2.INTER_CUBIC))
if cv2.waitKey(1) & 0xFF == ord('q'):
break
if count == captures:
break
cap.release()
cv2.destroyAllWindows()
