Hi, I'm a beginner in OpenCV and python, and I'm trying to use Kalman with Haar cascade face detection, in addition to showing the location and the time, I managed to show the location and time with the face detection, but when I tried to use Kalman I had this problem (AttributeError) and some time different problem, now I comment the code (I couldnt mange to run Kalman therefore its commented now # for Kalman 2), could anyone help in this???
Thank you ...
import cv2 import itertools import time # time import numpy as np
for Kalman 1
class Pedestrian(): """Pedestrian class each pedestrian is composed of a ROI, an ID and a Kalman filter so we create a Pedestrian class to hold the object state """ def __init__(self, id, frame, track_window): """init the pedestrian object with track window coordinates""" # set up the roi self.id = int(id) x,y,w,h = track_window self.track_window = track_window self.roi = cv2.cvtColor(frame[y:y+h, x:x+w], cv2.COLOR_BGR2HSV) roi_hist = cv2.calcHist([self.roi], [0], None, [16], [0, 180]) self.roi_hist = cv2.normalize(roi_hist, roi_hist, 0, 255,cv2.NORM_MINMAX) # set up the kalman self.kalman = cv2.KalmanFilter(4,2) self.kalman.measurementMatrix = np.array([[1,0,0,0],[0,1,0,0]],np.float32) self.kalman.transitionMatrix = np.array([[1,0,1,0],[0,1,0,1],[0,0,1,0],[0,0,0,1]],np.float32) self.kalman.processNoiseCov = np.array([[1,0,0,0],[0,1,0,0],[0,0,1,0],[0,0,0,1]],np.float32) * 0.03 self.measurement = np.array((2,1), np.float32) self.prediction = np.zeros((2,1), np.float32) self.term_crit = ( cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10,1 ) self.center = None self.update(frame)
end of for kalman 1
import cv2
import time # time
import numpy as np
time_count = 0 # time face_cascade =cv2.CascadeClassifier('/home/wattar/opencv/opencv-3.0.0/data/haarcascades/haarcascade_frontalface_alt.xml') cap = cv2.VideoCapture(0) scaling_factor = 1 start = time.time() # time
while True: ret, frame = cap.read() frame = cv2.resize(frame, None, fx=scaling_factor, fy=scaling_factor, interpolation=cv2.INTER_AREA) gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) face_rects = face_cascade.detectMultiScale(gray, 1.3, 5) for (x,y,w,h) in face_rects: cv2.rectangle(frame, (x,y), (x+w,y+h), (0,255,0), 3) print ('X,Y is : %s, %s' % (x,y)) font = cv2.FONT_HERSHEY_SIMPLEX cv2.putText(frame,'X,Y is : %s, %s' % (x,y),(x,y), font, 1,(255,255,255),2)
for Kalman 2
# for i, p in frame.iteritems(): # p.update(frame)
# for i, p in Pedestrian.iteritems(): # print ("%s", Pedestrian.iteritmes) # p.update(frame) # print (p.update(frame))
cv2.imshow('Face Detector', frame)
c = cv2.waitKey(1)
end = time.time()
print(end - start)
'''
end_time = time.time()
if(face_rects):
time_count+= end_time - start_time
if(time_count > 2):
print('time')
else:
time_count = 0
''' if c == 27: break cap.release() cv2.destroyAllWindows()
