def callback(self, imgmsg):
face_cascade = cv2.CascadeClassifier(
'/opt/ros/kinetic/share/OpenCV-3.3.1-dev/haarcascades/haarcascade_frontalface_alt.xml'
) #./cascades/haarcascade_frontalface_alt.xml')
frame = self.bridge.imgmsg_to_cv2(imgmsg, "bgr8")
faces = face_cascade.detectMultiScale(frame,
scaleFactor=1.1,
minNeighbors=5)
bbox_list = [(x, y, w, h) for (x, y, w, h) in faces]
#print("\n","----Return Face Coordinator ......... : ", bbox_list,"\n")
fexpr_list = r.classify_image(self.emoC1.sess, self.emoC1.image,
self.emoC1.processed_image,
self.emoC1.processed_images,
self.emoC1.probs, self.emoC1.logger,
frame, bbox_list)
for idx, bbox in enumerate(bbox_list):
pos = (bbox[0], bbox[1])
xx = bbox[0]
yy = bbox[1]
wd = bbox[2]
ht = bbox[3]
answer = fexpr_list[idx][0]
answer1 = str(fexpr_list[idx][1])
answer2 = ":" + str(bbox_list[idx])
# print('----', bbox_list[idx], "===", idx, fexpr_list[idx][0],fexpr_list[idx][1])
## ============================================================
rospy.logout(
"---------------Detecting Face Loop Starting ----------------------------"
)
if (answer == 0):
rospy.loginfo("-----------Anger :" + answer1 + answer2)
self._pub1.publish("Anger :" + answer1 + answer2)
elif (answer == 1):
rospy.loginfo("---------Happy :" + answer1 + answer2)
self._pub1.publish("Happy :" + answer1 + answer2)
elif (answer == 2):
rospy.loginfo("-------- Neutral :" + answer1 + answer2)
self._pub1.publish("Neutral :" + answer1 + answer2)
elif (answer == 3):
rospy.loginfo("-------- Sad :" + answer1 + answer2)
self._pub1.publish("Sad :" + answer1 + answer2)
else:
rospy.loginfo("----------------unkwone emotion :" + answer1 +
answer2)
self._pub1.publish("unkwone emotion :" + answer1 + answer2)
r.draw_label(frame, answer, pos, wd, ht)
#cv2.rectangle(image, (x, y), (x + w, y + h), (255, 0, 0), 2)
cv2.rectangle(frame, pos, (xx + wd, yy + ht), (0, 0, 255), 2)
#----------------20181129-------------------------------------------------------------------------------
# adding report file in term of the statistic of face type at local
# crontab -e
self.newTime = int(time())
if (self.newTime - self.oldTime > 60):
ans1 = float(answer1)
ans1 = int(ans1 * 100)
write_json_file('client_faceType_emotion.json',
face_RepoFormat(answer, ans1))
self.oldTime = int(time())
#----------------20181129-------------------------------------------------------------------------------
msg = self.bridge.cv2_to_imgmsg(frame, "bgr8")
self._pub.publish(msg)
#cv2.imshow("listener", frame)
# cv2.waitKey(1)
print(
"---------------Detecting Loop Ending ............................."
)
import cv2
import cv2
import cv2
if __name__ == "__main__":
emoC1 = EmotionClassifier()
#emotionclassifier1.__init__()
#frame = cv2.imread('data/456.jpg')
#breakupTeacherStudent.mp4')#Marquess-Vayamos.avi')#news_kp_partial.mp4#456.jpg
ret, frame = cv2.VideoCapture('data/456.jpg').read()
bbox_list = [(1096, 618, 26, 32), (814, 550, 35, 33), (998, 145, 65, 87),
(65, 142, 67, 77), (313, 75, 73, 95)]
#fexpr_list = r.classify_image(frame, bbox_list)
fexpr_list = r.classify_image(emoC1.sess, emoC1.image,
emoC1.processed_image,
emoC1.processed_images, emoC1.probs,
emoC1.logger, frame, bbox_list)
print(fexpr_list)
for idx, bbox in enumerate(bbox_list): #range(len(bbox_list)):ro
#bbox = bbox_list[idx]
pos = (bbox[0], bbox[1])
wd = bbox[2]
ht = bbox[3]
fexpr = fexpr_list[idx][0]
r.draw_label(frame, fexpr, pos, wd, ht)
print("---Begin time :", Begin_localtime)
fd.write("\n" + "Begin time :" + Begin_localtime)
while (cap.isOpened()):
# if(count <1):
#count= count + 1
ret, frame = cap.read()
#frame = cv2.imread('data/789.jpg')
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# you should adjust detectMultiScale parameter to detect faces
faces = face_cascade.detectMultiScale(gray, 1.05, 5)
if (len(faces) > 0):
bbox_list = [(x, y, w, h) for (x, y, w, h) in faces]
#---------------------------------
# for f_idx in range(len(frames)):
fexpr_list = r.classify_image(
sess, image, processed_image, processed_images, probs, logger,
frame, bbox_list) #frames[f_idx], bbox_lists[f_idx])
#for b_idx in range(len(bbox_lists[f_idx])):
for b_idx in range(
len(bbox_list)): #b_idx #range( len(bbox_list) ): #faces
bbox = bbox_list[b_idx]
pos = (bbox[0], bbox[1])
wd = bbox[2]
ht = bbox[3]
#fexpr = fexpr_list[b_idx][0]
fexpr = fexpr_list[b_idx][0]
#r.draw_label(frames[f_idx], fexpr, pos, wd, ht)
r.draw_label(frame, fexpr, pos, wd, ht)
#cv2.imshow('image',frames[f_idx])
# red text