def test_camera():
""" Simple test srcipt; requires /dev/video0 """
app = ObjectDetectorTF(gpu=False, cmap={1: 'person'})
cam = cv2.VideoCapture(0)
fps = []
while True:
ret, img = cam.read()
if not ret:
print('camera capture failed')
break
t0 = time.time()
res = app(img)
t1 = time.time()
fps.append(1.0 / (t1 - t0 + 1e-9))
msk = (res['score'] > 0.5)
cls = res['class'][msk]
box = res['box'][msk]
score = res['score'][msk]
for box_, cls_ in zip(box, cls):
#ry0,rx0,ry1,rx1 = box_ # relative
draw_bbox(img, box_, str(cls_))
cv2.imshow('win', img)
k = cv2.waitKey(1)
if k in [ord('q'), 27]:
print('quitting...')
break
print('average fps: {}'.format(np.mean(fps[-100:])))
def data_cb(self, src, img, stamp):
# TODO : save data in cam_ and run data_cb in step()
# instead of inside the callback. May run into strange race conditions.
now = rospy.Time.now()
if (now - stamp).to_sec() > 0.5:
rospy.loginfo_throttle(
10.0, 'incoming data too old: [{}] @ {}'.format(src, stamp))
# too old
return
if not (src in self.cam_):
# should usually not reach here - error
return
cam = self.cam_[src]
if not cam.has_info_:
# should usually not reach here, maybe the first few frames
return
if self.dbg_:
# continuous detection
rsp = self.detect_cb(
DetectRequest(source=src,
track=False,
cid=DetectRequest.CID_NULL))
if rsp.success:
box = BoxUtils.convert(t.box_, BoxUtils.FMT_NCCWH,
BoxUtils.FMT_NYXYX)
draw_bbox(img, box, cls=None)
cv2.imshow('win', img)
cv2.waitKey(1)
def test_image(img='/tmp/image1.jpg'):
"""
Simple test script; requires /tmp/image1.jpg
"""
app = ObjectDetectorTF(model='model')
img = cv2.imread(img)
res = app(img)
msk = (res['score'] > 0.5)
cls = res['class'][msk]
box = res['box'][msk]
score = res['score'][msk]
print('score', score)
for box_, cls_ in zip(box, cls):
#ry0,rx0,ry1,rx1 = box_ # relative
draw_bbox(img, box_, str(cls_))
cv2.imshow('win', img)
cv2.waitKey(0)
def test_images(imgdir, recursive=True, is_root=True, shuffle=True, viz=True):
"""
Simple test script; operating on a directory
"""
#app = ObjectDetectorTF()
app = ObjectDetectorTF(
gpu=0.5,
#model='model2-drone-300x300',
#model='model4-drone-300x300',
model='model',
cmap={
1: 'drone',
2: 'person'
},
threshold=0.5,
threshold2=(0.375, 0.7))
if is_root and viz:
cv2.namedWindow('win', cv2.WINDOW_NORMAL)
fs = os.listdir(imgdir)
full = False
if shuffle:
np.random.shuffle(fs)
for f in fs:
f = os.path.join(imgdir, f)
if os.path.isdir(f):
if not recursive:
continue
if not test_images(
f, recursive, is_root=False, shuffle=shuffle, viz=viz):
break
img = cv2.imread(f)
if img is None:
continue
#res = app(img[..., ::-1])
res = app.detect(
img,
is_bgr=True,
)
#msk = (res['score'] > 0.5)
#if np.count_nonzero(msk) <= 0:
# continue
cls = res['class']
box = res['box']
score = res['score']
print('scores', score)
if viz:
for box_, cls_, val_ in zip(box, cls, score):
draw_bbox(img, box_, '{}:{}'.format(cls_, val_))
cv2.imshow('win', img)
k = cv2.waitKey(0)
if k in [27, ord('q')]:
break
else:
# went through all images without interruption
full = True
if is_root and viz:
cv2.destroyWindow('win')
cv2.destroyAllWindows()
return full
def process(self, src, img, stamp):
# source validation
cam = self.cam_[src]
if not cam.has_info_:
# should usually not reach here, maybe the first few frames
return
obs_new = []
# remove stale requests
self.reqs_ = self.filter_reqs(stamp, self.reqs_)
if len(self.reqs_) > 0:
print(len(self.reqs_))
req_msk = np.full(len(self.reqs_), True, dtype=np.bool)
if len(self.reqs_) > 0:
# TODO : looking out into the future with multiple parallel detectors ...
# d_res = []
# for d_cid, det in self.det_:
# if not np.any(d_cid in self.reqs_):
# continue
# d_res.append( det.detect(img) )
d_res = self.det_.detect(img, is_bgr=True)
for d in zip(d_res['class'], d_res['box'], d_res['score']):
d_add = False # flag to add detection to observations
for i_r, r in enumerate(self.reqs_):
if not req_msk[i_r]:
# already matched request
continue
if self.match_detection(r, d):
req_msk[i_r] = False
d_add = True
break
if not d_add:
continue
obs_new.append(
TrackData(
src=src,
cid=d[0],
img=img,
box=BoxUtils.convert(d[1], BoxUtils.FMT_NYXYX,
BoxUtils.FMT_NCCWH),
stamp=stamp.to_sec(),
meta=
None # << will be populated by TrackManager() if the observation is accepted
))
self.reqs_ = [r for (r, m) in zip(self.reqs_, req_msk) if m]
# append!
self.mgr_.append(obs_new)
self.mgr_.process(src, img, stamp.to_sec())
for t in self.mgr_.get_tracks():
if (t.src_ != src):
continue
draw_bbox(img, t.box_, fmt=BoxUtils.FMT_NCCWH, cls=None)
iw, ih = cam.model_.width, cam.model_.height
cx, cy = np.multiply(t.box_[:2], [iw, ih])
ray3d = cam.model_.projectPixelTo3dRay([cx, cy])
# self.pub_.publish(PointStamped(
# header=Header(frame_id=cam.model_.tfFrame(), stamp=stamp),
# point=Point(*ray3d)
# ))
if self.dbg_:
cv2.imshow('win', img)
cv2.waitKey(1)