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)):
try:
result = network.predict([data[i]])
name = file_name[i]
# print(name)
maxindex = np.argmax(result[0])
# print(result[0])
# print(EMOTIONS[maxindex])
ind[maxindex] += 1
os.rename(
name, "./output_picture/" +
str(EMOTIONS[maxindex] + str(ind[maxindex])) + '.PNG')
except:
result = None
continue
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
frame = cv2.rectangle(frame, (x, y - 30), (x + w, y + h + 10),
(255, 0, 0), 2)
newimg = cv2.cvtColor(frame[y:y + h, x:x + w], cv2.COLOR_BGR2GRAY)
cv2.imshow('naming', newimg)
newimg = cv2.resize(newimg, (48, 48),
interpolation=cv2.INTER_CUBIC) / 255.
result = network.predict(newimg)
if result is not None:
maxindex = np.argmax(result[0])
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(frame, EMOTIONS[maxindex], (x, y), font, 1,
(255, 255, 255), 2, cv2.LINE_AA)
asd = cv2.resize(frame, None, fx=2, fy=2, interpolation=cv2.INTER_CUBIC)
abc = cv2.resize(asd, (900, 600))
cv2.imshow('Video', abc)
cv2.waitKey(0)
cap.release()
cv2.destroyAllWindows()
frame_number += 1
# Quit when the input video file ends
if not ret:
break
facecasc = cv2.CascadeClassifier(
'./utility/emotionDetection/haarcascade_frontalface_default.xml')
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = facecasc.detectMultiScale(gray, 1.3, 5)
new_frame = format_image(frame)
# compute softmax probabilities
if (len(new_frame) == 48):
try:
result = network.predict([new_frame])
except:
continue
else:
result = None
# # compute softmax probabilities
# if(len(new_frame) == 48):
# try:
# result = network.predict([format_image(frame)])
# except:
# continue
# else:
# result = None
if result is not None:
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)
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]
if iC == 6:
if maxindex == 0: # if angry
keyboard.press(Key.left)
keyboard.release(Key.left)
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()