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kcf_independent.py
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kcf_independent.py
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# coding=utf-8
import numpy as np
import cv2
import sys
import time
face_cascade = cv2.CascadeClassifier('C:\\code_env\\haarcascade_frontalface_default.xml')
eye_cascade = cv2.CascadeClassifier('C:\\code_env\\haarcascade_eye.xml')
cap = cv2.VideoCapture(0)
cap.release()
cap = cv2.VideoCapture(0)
cv2.destroyAllWindows()
fps = 0
fps_counter = 0
timer = time.time()
frames = 0
refind_timer = 0
okm = []
trackerm = []
# facem=[]
def refind():
global okm
global trackerm
ret, frame = cap.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = np.array(face_cascade.detectMultiScale(gray, 1.3, 5))
# cv2.imshow('old_frame1',frame)
k = cv2.waitKey(30) & 0xff
if k == 27:
cap.release()
cv2.destroyAllWindows()
sys.exit()
facem = []
if len(faces) != 0:
faces_x = faces[:, 0]
faces_y = faces[:, 1]
faces_w = faces[:, 2]
faces_h = faces[:, 3]
# face1 is the image of extracted face
for x, y, w, h in zip(faces_x, faces_y, faces_w, faces_h):
face1 = frame[y:y + h, x:x + w]
facem.append(face1)
cv2.imshow('face', face1)
print "facem len:", len(facem)
trackerm = []
okm = []
for x, y, w, h in zip(faces_x, faces_y, faces_w, faces_h):
tracker = cv2.TrackerKCF_create()
bbox = (x, y, w, h)
ok = tracker.init(frame, bbox)
trackerm.append(tracker)
okm.append(ok)
else:
print "no faces found in refind"
def init():
print 'inits'
global trackerm
# initial detection of faces
while 1:
ret, frame = cap.read()
frame = np.array(frame)
# print type(frame)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = np.array(face_cascade.detectMultiScale(gray, 1.3, 5))
cv2.imshow('output', frame)
k = cv2.waitKey(30) & 0xff
if k == 27:
cap.release()
cv2.destroyAllWindows()
sys.exit()
# x y w h for 1 face and face1 image
# facem stores the multiple face frames
facem = []
if len(faces) != 0:
faces_x = faces[:, 0]
faces_y = faces[:, 1]
faces_w = faces[:, 2]
faces_h = faces[:, 3]
print "faces_x", faces_x
print "faces_w", faces_w
print "faces_x+faces_w", faces_x + faces_w
# face1 is the image of extracted face
for x, y, w, h in zip(faces_x, faces_y, faces_w, faces_h):
face1 = frame[y:y + h, x:x + w]
facem.append(face1)
cv2.imshow('face', face1)
# print "faces", faces
# print "face1:", face1
print "facem len:", len(facem)
# if len(facem)>1:
cv2.imshow('face1', facem[0])
print "face1:dtype", type(facem[0])
print "face1", facem[0]
break
# kcf tracker initialisation
trackerm = []
okm = []
for x, y, w, h in zip(faces_x, faces_y, faces_w, faces_h):
tracker = cv2.TrackerKCF_create()
bbox = (x, y, w, h)
ok = tracker.init(frame, bbox)
trackerm.append(tracker)
okm.append(ok)
print 'init'
if __name__ == '__main__':
init()
while True:
global trackerm
# Read a new frame
ok, frame = cap.read()
if not ok:
break
okm = []
bboxm = []
for tracker in trackerm:
ok, bbox = tracker.update(frame)
okm.append(ok)
bboxm.append(bbox)
# Draw bounding box
okm = np.array(okm)
bboxm = np.array(bboxm)
for box_x, box_y, box_w, box_h in zip(bboxm[okm == True][:, 0], bboxm[okm == True][:, 1],
bboxm[okm == True][:, 2],
bboxm[okm == True][:, 3]):
p1 = (int(box_x), int(box_y))
p2 = (int(box_x + box_w), int(box_y + box_h))
cv2.rectangle(frame, p1, p2, (0, 255, 0), 2, 1)
# Exit if ESC pressed
k = cv2.waitKey(1) & 0xff
if k == 27:
cv2.destroyAllWindows()
cap.release()
break
# timer,fps, refind:finds the face and features again
fps_counter = fps_counter + 1
if time.time() - timer > 1:
print timer, ":", fps_counter
fps = fps_counter
fps_counter = 0
timer = time.time()
refind_timer = refind_timer + 1
# removing is ok cond gives good fit bounding box but reduces fps , significant if box is large
# if ok==0:
if refind_timer == 2:
print "before refine bbox:", bboxm
refind()
print "after refine bbox:", bboxm
refind_timer = 0
# tracker = cv2.TrackerKCF_create()
# ok = tracker.init(frame, bbox)
# print "refined ok", ok
# frames, refind:finds the face and features again
frames = frames + 1
if frames > fps:
frames = 0
# refind()
cv2.putText(frame, "Fps:" + str(fps), (100, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (255, 0, 0), 2)
# output
cv2.imshow('output', frame)
# print type(frame)
# dp.video_loop(data2, frame)
cap.release()