thickness = 2
# fgs[view] = draw_polylines (fgs[view], b, color, thickness )
# b = np.matrix (b)
bottom_polylines = b
# get blobs that contain this
is_found = False
for bl in blobs :
if is_contained (bl, middle_point) :
print ("Found middle")
is_found = True
# get top polylines
top_polylines = TSIUtil.get_top_polylines (b, bl)
# print (top_polylines)
"""
max_cp_y = np.max (b[:, 1]) - 3 # small tradeoff
box_floor = []
(x,y,w,h) = cv2.boundingRect (bl)
for cp in b.tolist () :
cp_h = list (cp)
# cp_h[1] -= abs (y - max_cp_y)
cp_h[1] -= HEIGHT
cv2.line (fgs[view], tuple (cp), tuple (cp_h), color, thickness=thickness)
box_floor.append (cp_h)
fgs = {}
dsts = {}
prev_dsts = {}
fdiff_view = {} # frame difference camera image
fdiff_tsi = {} # frame difference tsi image
fdiff_epi = {} # frame difference epi image
bm_epi = {}
tsi_object = {}
epi_object = {}
masks = {}
for view in VIEW:
img = cv2.imread(img_path.format(ses_id, view), 1)
points = GT['session{}'.format(ses_id)][view]
corner = TSIUtil.get_corner_ground(vp[view]['vp1'], vp[view]['vp2'],
points)
# get rectangular homography mapping
corner_gt = np.float32(corner)
corner_wrap = np.float32([[0, 300], [0, 0], [1000, 0], [1000, 300]])
M[view] = cv2.getPerspectiveTransform(corner_gt, corner_wrap)
# initialize tsi object
tsi_object[view] = TSIUtil.TSI(M[view], VDL_IDX=0)
# epi_object[view] = TSIUtil.EPI (M[view], VDL_IDX=75)
# for 3 frame difference
prev_img = [None, None]
prev_tsi = [None, None]
prev_epi = [None, None]
for i in range(2):
tsi_object = {}
fdiff_tsi = {}
# miscelanous
empty = np.zeros((1000, 700)).astype('uint8')
random.seed(200)
unique_color = []
for i in range(100):
unique_color.append((random.randrange(0, 255), random.randrange(0, 255),
random.randrange(0, 255)))
for view in VIEW:
img = cv2.imread(img_path.format(ses_id, view), 1)
points = GT['session{}'.format(ses_id)][view]
corner = TSIUtil.get_corner_ground(vp[view]['vp1'], vp[view]['vp2'],
points)
# get rectangular homography mapping
corner_gt = np.float32(corner)
corner_wrap = np.float32([[0, 300], [0, 0], [1000, 0], [1000, 300]])
M[view] = cv2.getPerspectiveTransform(corner_gt, corner_wrap)
M_inv[view] = cv2.getPerspectiveTransform(corner_wrap, corner_gt)
# for epi object
epi_object[view] = TSIUtil.EPI(
M[view],
size=(1000, 300),
VDL_IDX=70,
VDL_SIZE=3,
)
M = {} # matrix homography
M_inv = {} # matrix homography
imgs_color = {} # for saving image color each view
bm_dst = {}
fdiff_dst = {}
fdiff_view = {}
masks = {}
fgs = {}
prev_imgs_color = {}
for view in VIEW:
img = cv2.imread(img_path.format(ses_id, view), 1)
points = GT['session{}'.format(ses_id)][view]
corner = TSIUtil.get_corner_ground(vp[view]['vp1'], vp[view]['vp2'],
points)
# get rectangular homography mapping
corner_gt = np.float32(corner)
corner_wrap = np.float32([[0, 300], [0, 0], [1000, 0], [1000, 300]])
M[view] = cv2.getPerspectiveTransform(corner_gt, corner_wrap)
M_inv[view] = cv2.getPerspectiveTransform(corner_wrap, corner_gt)
# for 3 frame difference
prev_dst = [None, None]
prev_view = [None, None]
for i in range(2):
img = next(fi[view])
prev_imgs_color[view] = img
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
masks = {}
for view in VIEW:
img = cv2.imread(img_path.format(ses_id, view), 1)
points = GT['session{}'.format(ses_id)][view]
corner = get_corner_ground(vp[view]['vp1'], vp[view]['vp2'], points)
# get rectangular homography mapping
corner_gt = np.float32(corner)
corner_wrap = np.float32([[0, 300], [0, 0], [1000, 0], [1000, 300]])
M[view] = cv2.getPerspectiveTransform(corner_gt, corner_wrap)
# for initialization
ts_img[view] = None # np.zeros ((300, 1000, 3))
tsi_object[view] = TSIUtil.TSI(M[view], VDL_IDX=0)
# for 3 frame difference
prev_img = [None, None]
prev_tsi = [None, None]
for i in range(2):
img = next(fi[view])
img_color = img.copy()
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# save background
prev_img[i] = img
prev_tsi[i] = tsi_object[view].apply(img)
fdiff_tsi[view] = FrameDifference(*prev_tsi)
# tsi
tsi_object = {}
fdiff_tsi = {}
# miscelanous
empty = np.zeros((300, 1000)).astype('uint8')
random.seed(200)
for view in VIEW:
logger.info("-- Start {} view".format(view))
# frame iterator
logger.info("-- Crate FrameIterator object")
fi[view] = FrameIterator(os.path.join(occlusion_clip_path, case, view))
points = GT['session{}'.format(ses_id)][view]
corner = TSIUtil.get_corner_ground(vp[view]['vp1'], vp[view]['vp2'],
points)
# get rectangular homography mapping
corner_gt = np.float32(corner)
corner_wrap = np.float32([[0, 300], [0, 0], [1000, 0], [1000, 300]])
M[view] = cv2.getPerspectiveTransform(corner_gt, corner_wrap)
M_inv[view] = cv2.getPerspectiveTransform(corner_wrap, corner_gt)
# for epi object
logger.info("-- Create {} EPI object".format(tot_epi))
epi_object[view] = []
for idx in epi_line_idx:
epi_object[view].append(
TSIUtil.EPI(
M[view],
size=(1000, 300),
VP = VPLoader()
ses_id = 0
vp = VP.get_session(ses_id)
fi = FrameIteratorLoader.get_session(ses_id)
M = {} # matrix homography
imgs_color = {} # for saving image color each view
fgs = {}
fdiff_view = {} # frame difference camera image
masks = {}
for view in VIEW:
img = cv2.imread(img_path.format(ses_id, view), 1)
points = GT['session{}'.format(ses_id)][view]
corner = TSIUtil.get_corner_ground(vp[view]['vp1'], vp[view]['vp2'],
points)
# get rectangular homography mapping
corner_gt = np.float32(corner)
corner_wrap = np.float32([[0, 300], [0, 0], [1000, 0], [1000, 300]])
M[view] = cv2.getPerspectiveTransform(corner_gt, corner_wrap)
# for 3 frame difference
prev_img = [None, None]
for i in range(2):
img = next(fi[view])
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# save background
prev_img[i] = img
dsts = {}
fgs = {}
fdiff_tsi = {} # frame difference tsi image
fdiff_view = {} # frame difference camera image
tsi_object = {}
posision_x_gt = [980, 758, 711]
posision_x_gt = posision_x_gt[::-1]
tsis = {}
for view in VIEW:
img = cv2.imread(img_path.format(ses_id, view), 1)
# fi[view] = FrameIterator ('/home/adib/My Git/traffic-monitoring/test/fusion/result/sync_25fps_paper/{}'.format (view))
points = GT['session{}'.format(ses_id)][view]
corner = TSIUtil.get_corner_ground(vp[view]['vp1'], vp[view]['vp2'],
points)
# get rectangular homography mapping
corner_gt = np.float32(corner)
corner_wrap = np.float32([[0, 300], [0, 0], [1000, 0], [1000, 300]])
M[view] = cv2.getPerspectiveTransform(corner_gt, corner_wrap)
M_inv[view] = cv2.getPerspectiveTransform(corner_wrap, corner_gt)
# initialize tsi object
tsi_object[view] = TSIUtil.TSI(M[view], VDL_IDX=0)
# for 3 frame difference
prev_img = [None, None]
prev_tsi = [None, None]
for i in range(2):
img = next(fi[view])