def apply_12_net(image, iname, nn, p_level=16, k=1.18, debug=1):
py = fr.get_frame_pyramid(image, k=k, t=p_level)
suff = nn.nn_name[:2]
patch_size = int(suff)
rows, cols, d = image.shape
s = time()
rbb = []
## check last k levels
k = 1
t_level = p_level - k
for m, frame in enumerate(py):
if m < t_level:
continue
H, W, dim = frame.shape
X_test = []
pnt = []
for u in range(0, H - patch_size + 1, 4):
for v in range(0, W - patch_size + 1, 4):
subframe = fr.get_patch(frame, u, v, (patch_size, patch_size))
#subframe = frame[y:y+h,x:x+w]
X_test.append(fr.frame_to_vect(subframe))
pnt.append((v, u))
pnt = sp.array(pnt)
X_test = sp.array(X_test)
pred = nn.predict(X_test)
pnt = pnt[pred == 1]
if len(pred[pred == 1]) == 0:
print('no detection on', str(m))
continue
else:
print('detection on', str(m))
bb = []
for p in pnt:
i = p[0]
j = p[1]
if debug:
cv2.rectangle(frame, (i, j), (i + patch_size, j + patch_size),
(255, 0, 255), 1)
bb.append([i, j, i + patch_size, j + patch_size])
for e in bb:
x1, y1, x2, y2 = e
ox1 = math.floor(image.shape[1] * (x1 / frame.shape[1]))
oy1 = math.floor(image.shape[0] * (y1 / frame.shape[0]))
ox2 = math.floor(image.shape[1] * (x2 / frame.shape[1]))
oy2 = math.floor(image.shape[0] * (y2 / frame.shape[0]))
if m >= p_level - k:
rbb.append([ox1, oy1, ox2, oy2])
if debug:
cv2.imwrite(
iname[:-4] + '_pipeline_' + 'lev' + str(m) + '_' + nn.nn_name +
'_.jpg', frame)
f = time()
print('time is:', f - s)
return sp.array(rbb)
def apply_12_net(image,iname,nn,p_level = 16,k = 1.18,debug = 1):
py = fr.get_frame_pyramid(image,k=k,t=p_level)
suff = nn.nn_name[:2]
patch_size = int(suff)
rows,cols,d = image.shape
s = time()
rbb = []
## check last k levels
k = 1
t_level = p_level-k
for m,frame in enumerate(py):
if m < t_level:
continue
H,W,dim = frame.shape
X_test = []
pnt = []
for u in range(0,H - patch_size + 1,4):
for v in range(0,W - patch_size + 1,4):
subframe = fr.get_patch(frame,u,v,(patch_size,patch_size))
#subframe = frame[y:y+h,x:x+w]
X_test.append(fr.frame_to_vect(subframe))
pnt.append((v,u))
pnt = sp.array(pnt)
X_test = sp.array(X_test)
pred = nn.predict(X_test)
pnt = pnt[pred==1]
if len(pred[pred==1]) == 0:
print('no detection on',str(m))
continue
else:
print('detection on',str(m))
bb = []
for p in pnt:
i = p[0]
j = p[1]
if debug:
cv2.rectangle(frame, (i, j), (i+patch_size, j+patch_size), (255,0,255), 1)
bb.append([i,j,i+patch_size,j+patch_size])
for e in bb:
x1,y1,x2,y2 = e
ox1 = math.floor(image.shape[1] * (x1/frame.shape[1]))
oy1 = math.floor(image.shape[0] * (y1/frame.shape[0]))
ox2 = math.floor(image.shape[1] * (x2/frame.shape[1]))
oy2 = math.floor(image.shape[0] * (y2/frame.shape[0]))
if m >= p_level - k:
rbb.append([ox1,oy1,ox2,oy2])
if debug:
cv2.imwrite(iname[:-4]+'_pipeline_' + 'lev'+str(m) + '_' + nn.nn_name + '_.jpg',frame)
f = time()
print('time is:',f-s)
return sp.array(rbb)
def main():
iname = 'img/test/p1.jpg'
nn12 = Cnnl('12-net').__load_model_old__('12-net_lasagne_.pickle')
nn24 = Cnnl(nn_name = '24-net',subnet=nn12).__load_model_old__('24-net_lasagne_.pickle')
nn48 = Cnnl(nn_name = '48-net',subnet=nn24).__load_model_old__('48-net_lasagne_.pickle')
nn_calib12 = Cnnl('12-calib_net').__load_model_old__('12-calib_net_lasagne_.pickle')
nn_calib24 = Cnnl('24-calib_net').__load_model_old__('24-calib_net_lasagne_.pickle')
#nn_calib48 = Cnnl('48-calib_net').__load_model_old__('48-calib_net_lasagne_.pickle')
image = fr.get_frame(iname)
p = 11
k = 1.41
t_level = 0
gbb = []
gp = []
print('Start....')
s = time()
py = fr.get_frame_pyramid(image,scale=k,steps=p)
for m,frame in enumerate(py):
if m < t_level:
continue
bb,p = apply_12_net(image = image,frame=frame,iname = iname,nn = nn12,m=m,debug=0)
keep = util.nms(bb,p,T=0.5)
bb = bb[keep] if keep != [] else bb
p = p[keep] if keep != [] else p
cnt_detect = len(bb)
if cnt_detect == 0:
continue
#draw_bboxes(iname,bb,c='#-r_12--' + str(m))
for b in bb:
gbb.append(b)
for pp in p:
gp.append(pp)
gbb = sp.array(gbb)
gp = sp.array(gp)
#draw_bboxes(iname,gbb,c='0-r_12')
bb = gbb
p = gp
bb = apply_calib_net(image = image, iname = iname,bb=bb,nn=nn_calib12)
cnt_detect = len(bb)
#draw_bboxes(iname,bb,c='1-r_calib_12')
keep = util.nms(bb,p,T=0.3)
bb = bb[keep] if keep != [] else bb
p = p[keep] if keep != [] else p
#draw_bboxes(iname,bb,c='2-r_nms_12')
bb,p = apply_24_48_net(image = image, iname = iname, bb = bb,nn = nn24,debug=0 )
cnt_detect = len(bb)
#draw_bboxes(iname,bb,c='3-r_24')
bb = apply_calib_net(image = image, iname = iname,bb = bb,nn=nn_calib24)
cnt_detect = len(bb)
#draw_bboxes(iname,bb,c='4-r_calib_24')
keep = util.nms(bb,p,T=0.3)
bb = bb[keep] if keep != [] else bb
p = p[keep] if keep != [] else p
#draw_bboxes(iname,bb,c='5-r_nms_24')
cnt_detect = len(bb)
bb,p = apply_24_48_net(image = image, iname = iname, bb = bb,nn = nn48,debug=1)
cnt_detect = len(bb)
draw_bboxes(iname,bb,c='6-r_48',debug=1)
# bb = apply_calib_net(image = image,iname = iname,bb=bb,nn=nn_calib48)
# draw_bboxes(iname,bb,c='6-r_calib_48')
#
# bb = util.nms(bb,T=0.2)
# draw_bboxes(iname,bb,c='7-r_nms_48_final')
print('Finish...',time()-s)
