def process_video(frames, pnet, rnet, onet, sentiment_model):
crops = []
crop_idcs = []
new_frames = []
score = 100
emotion = 'NA'
for i, frame in enumerate(frames):
bbox, _ = detect_face.detect_face(frame, minsize, pnet, rnet, onet,
threshold, factor)
frame = frame.copy()
try:
for box in bbox:
w = box[2] - box[0]
h = box[3] - box[1]
# plot the box using cv2
crop_frame = frame[int(box[1]):int(box[1] + h),
int(box[0]):int(box[0] + w)]
if (i % 5 == 0):
emotion, score = sent.Sentiment_Analysis(
crop_frame, sentiment_model)
frame = cv2.putText(frame, '%s %.3f%%' % (emotion, score),
(int(box[0]), int(box[1] - 5)), font,
fontScale, fontColor, lineType)
crops.append(crop_frame)
crop_idcs.append(i)
frame = cv2.rectangle(frame, (int(box[0]), int(box[1])),
(int(box[0] + w), int(box[1] + h)),
(0, 0, 255), 2)
except Exception as e:
print(e)
new_frames.append(frame)
return crops, new_frames, crop_idcs
# Capture frame-by-frame
ret, frame = video_capture.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = faceCascade.detectMultiScale(gray,
scaleFactor=1.1,
minNeighbors=5,
minSize=(30, 30),
flags=cv2.CASCADE_SCALE_IMAGE)
# Draw a rectangle around the faces
for (x, y, w, h) in faces:
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
frame_crop = frame[y:y + h, x:x + w] #, y:y+h]
## Perform Deep Learning
model = sent.Transfer_learning()
Emotion, percentage = sent.Sentiment_Analysis(frame_crop, model)
cv2.imshow('Crop', frame_crop)
print(Emotion)
# Display the resulting frame
cv2.imshow('Video', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# When everything is done, release the capture
video_capture.release()
cv2.destroyAllWindows()
