def exp_mei():
for i in range(20):
file = get_video_filename("walking", i + 1, np.random.randint(1, 5))
ac = acmhi.ActivityQuantifier("walking", filename=file)
mei, _ = ac.build_mei_mhi()
mei_image = np.int32(mei * 255.)
cv2.imwrite(
"output/mei_image_{}_person{}_d{}.png".format(
ac.action, ac.person_num, ac.d_num), mei_image)
def test_hu_moments(self):
for _ in range(100):
ac = acmhi.ActivityQuantifier(np.random.choice(ACTIONS))
meis, mhis = ac.build_mei_mhi()
for mhi in mhis:
hus = ac.hu_moments(mhi)
c_moms = cv2.moments(np.uint8(mhi))
compare = cv2.HuMoments(c_moms)
for i, hu in enumerate(hus):
self.assertTrue(np.isclose(hu, compare[i][0], rtol=0.01),
"{} != {} :: {}".format(hu, compare[i][0], i+1))
def test_binary_image(self):
ac = acmhi.ActivityQuantifier("walking", "person01_walking_d1_uncomp.avi")
video = ac.load_video("walking", "person01_walking_d1_uncomp.avi")
c = 0
curr_img = ac.prepare_frame(video.next())
while c < 25:
prev_img = curr_img
curr_img = ac.prepare_frame(video.next())
c += 1
result = ac.binary_mei(curr_img, prev_img)
cv2.imwrite("test_output/binary_img.png", result * 255)
cv2.imwrite("test_output/binary_img_curr.png", curr_img)
self.assertTrue(np.any(result), "Binary MEI Image")
def test_moments(self):
ac = acmhi.ActivityQuantifier(np.random.choice(ACTIONS))
meis, mhis = ac.build_mei_mhi()
for mhi in mhis:
M_00, M_10, M_01 = ac.img_moments(mhi)
moments = cv2.moments(np.uint8(mhi))
self.assertTrue(np.isclose(moments['m00'], M_00, atol=3.),
"{} != {}".format(moments['m00'], M_00)
)
self.assertTrue(np.isclose(moments['m10'], M_10, atol=3.),
"{} != {}".format(moments['m10'], M_10)
)
self.assertTrue(np.isclose(moments['m01'], M_01, atol=3.),
"{} != {}".format(moments['m01'], M_01)
)
def exp_mei_mhi(act="handclapping"):
for i in range(5):
print "Running mei and mhi images for " + act
file = get_video_filename(act, i + 1, 3) #np.random.randint(1,5))
ac = acmhi.ActivityQuantifier(act, filename=file)
meis, mhis = ac.build_mei_mhi()
for i, mhi in enumerate(mhis):
mhi_image = np.int32(np.around((np.float32(mhi) / ac.tau) * 255.))
mei_image = np.int32(meis[i] * 255.)
cv2.imwrite(
"output/mhi_image_{}_person{}_d{}_{}.png".format(
ac.action, ac.person_num, ac.d_num, i), mhi_image)
cv2.imwrite(
"output/mei_image_{}_person{}_d{}_{}.png".format(
ac.action, ac.person_num, ac.d_num, i), mei_image)
def test_central_moments(self):
ac = acmhi.ActivityQuantifier(np.random.choice(ACTIONS))
meis, mhis = ac.build_mei_mhi()
for mhi in mhis:
mus, vs = ac.central_moments(mhi)
c_moms = cv2.moments(np.uint8(mhi))
self.assertTrue(np.isclose(mus['20'], c_moms['mu20'], rtol=0.01),
"{} != {}".format(mus['20'], c_moms['mu20']))
self.assertTrue(np.isclose(mus['11'], c_moms['mu11'], rtol=0.01),
"{} != {}".format(mus['11'], c_moms['mu11']))
self.assertTrue(np.isclose(mus['12'], c_moms['mu12'], rtol=0.01),
"{} != {}".format(mus['12'], c_moms['mu12']))
self.assertTrue(np.isclose(mus['02'], c_moms['mu02'], rtol=0.1),
"{} != {}".format(mus['02'], c_moms['nu02']))
self.assertTrue(np.isclose(mus['03'], c_moms['mu03'], rtol=0.1),
"{} != {}".format(mus['03'], c_moms['nu03']))
self.assertTrue(np.isclose(mus['21'], c_moms['mu21'], rtol=0.1),
"{} != {}".format(mus['21'], c_moms['mu21']))
self.assertTrue(np.isclose(mus['30'], c_moms['mu30'], rtol=0.1),
"{} != {}".format(mus['30'], c_moms['mu30']))
def test_mhi(self):
ac = acmhi.ActivityQuantifier(np.random.choice(ACTIONS))
_, mhis = ac.build_mei_mhi()
c = 0
for mhi in mhis:
mhi_image = np.int32(np.around((np.float32(mhi) / ac.tau) * 255.))
cv2.imwrite(
"test_output/mhi_image_{}_person{}_d{}_{}.png".format(
ac.action,
ac.person_num,
ac.d_num,
c
),
mhi_image
)
c += 1
# MEI image should be neither all white or black
gtz = mhi_image[mhi_image > 0]
test = gtz[gtz < 255]
self.assertTrue(np.any(test), "MHI is incorrect, b&w")
def test_mei(self):
ac = acmhi.ActivityQuantifier(np.random.choice(ACTIONS))
meis, _ = ac.build_mei_mhi()
c = 0
for mei in meis:
mei_image = mei * 255
cv2.imwrite(
"test_output/mei_image_{}_person{}_d{}_{}.png".format(
ac.action,
ac.person_num,
ac.d_num,
c
),
mei_image
)
c += 1
# MEI image should be neither all white or black
self.assertFalse(np.all(mei_image == 255), "MEI is incorrect, all white")
self.assertFalse(np.all(mei_image == 0), "MEI is incorrect, all black")