def _detect(self, imgmsg, rects_msg):
bridge = cv_bridge.CvBridge()
im_orig = bridge.imgmsg_to_cv2(imgmsg, desired_encoding='bgr8')
im, im_scale = img_preprocessing(im_orig, self.pixel_means)
rects_orig = jsk_recognition_utils.rects_msg_to_ndarray(rects_msg)
if len(rects_orig) == 0:
return
rects = rects_orig * im_scale
scores, bbox_pred = self._im_detect(im, rects)
rects = RectArray(header=imgmsg.header)
labels = []
label_proba = []
for cls_id in range(1, len(self.target_names)):
_cls = scores[:, cls_id][:, np.newaxis]
_bbx = bbox_pred[:, cls_id * 4: (cls_id + 1) * 4]
dets = np.hstack((_bbx, _cls))
keep = nms(dets, self.nms_thresh)
dets = dets[keep, :]
orig_rects = cuda.cupy.asnumpy(rects_orig)[keep, :]
inds = np.where(dets[:, -1] >= self.conf_thresh)[0]
for i in inds:
_bbox = dets[i, :4]
x1, y1, x2, y2 = orig_rects[i]
width = x2 - x1
height = y2 - y1
center_x = x1 + 0.5 * width
center_y = y1 + 0.5 * height
dx, dy, dw, dh = _bbox
_center_x = dx * width + center_x
_center_y = dy * height + center_y
_width = np.exp(dw) * width
_height = np.exp(dh) * height
x1 = _center_x - 0.5 * _width
y1 = _center_y - 0.5 * _height
x2 = _center_x + 0.5 * _width
y2 = _center_y + 0.5 * _height
rect = Rect(x=x1, y=y1, width=x2-x1, height=y2-y1)
rects.rects.append(rect)
labels.append(cls_id)
label_proba.append(dets[:, -1][i])
# publish classification result
clss = ClassificationResult(
header=imgmsg.header,
classifier=self.classifier_name,
target_names=self.target_names,
labels=labels,
label_names=[self.target_names[l] for l in labels],
label_proba=label_proba,
)
self._pub_rects.publish(rects)
self._pub_class.publish(clss)
def _detect(self, imgmsg, rects_msg):
bridge = cv_bridge.CvBridge()
im_orig = bridge.imgmsg_to_cv2(imgmsg, desired_encoding='bgr8')
im, im_scale = img_preprocessing(im_orig, self.pixel_means)
rects_orig = jsk_recognition_utils.rects_msg_to_ndarray(rects_msg)
if len(rects_orig) == 0:
return
rects = rects_orig * im_scale
scores, bbox_pred = self._im_detect(im, rects)
rects = RectArray(header=imgmsg.header)
labels = []
label_proba = []
for cls_id in range(1, len(self.target_names)):
_cls = scores[:, cls_id][:, np.newaxis]
_bbx = bbox_pred[:, cls_id * 4:(cls_id + 1) * 4]
dets = np.hstack((_bbx, _cls))
keep = nms(dets, self.nms_thresh)
dets = dets[keep, :]
orig_rects = cuda.cupy.asnumpy(rects_orig)[keep, :]
inds = np.where(dets[:, -1] >= self.conf_thresh)[0]
for i in inds:
_bbox = dets[i, :4]
x1, y1, x2, y2 = orig_rects[i]
width = x2 - x1
height = y2 - y1
center_x = x1 + 0.5 * width
center_y = y1 + 0.5 * height
dx, dy, dw, dh = _bbox
_center_x = dx * width + center_x
_center_y = dy * height + center_y
_width = np.exp(dw) * width
_height = np.exp(dh) * height
x1 = _center_x - 0.5 * _width
y1 = _center_y - 0.5 * _height
x2 = _center_x + 0.5 * _width
y2 = _center_y + 0.5 * _height
rect = Rect(x=x1, y=y1, width=x2 - x1, height=y2 - y1)
rects.rects.append(rect)
labels.append(cls_id)
label_proba.append(dets[:, -1][i])
# publish classification result
clss = ClassificationResult(
header=imgmsg.header,
classifier=self.classifier_name,
target_names=self.target_names,
labels=labels,
label_names=[self.target_names[l] for l in labels],
label_proba=label_proba,
)
self._pub_rects.publish(rects)
self._pub_class.publish(clss)
def _draw_result(self, im, clss, bbox, rects, nms_thresh, conf):
for cls_id in range(1, len(self.target_names)):
_cls = clss[:, cls_id][:, np.newaxis]
_bbx = bbox[:, cls_id * 4 : (cls_id + 1) * 4]
dets = np.hstack((_bbx, _cls))
keep = nms(dets, nms_thresh)
dets = dets[keep, :]
orig_rects = cuda.cupy.asnumpy(rects)[keep, :]
inds = np.where(dets[:, -1] >= conf)[0]
for i in inds:
_bbox = dets[i, :4]
x1, y1, x2, y2 = orig_rects[i]
width = x2 - x1
height = y2 - y1
center_x = x1 + 0.5 * width
center_y = y1 + 0.5 * height
dx, dy, dw, dh = _bbox
_center_x = dx * width + center_x
_center_y = dy * height + center_y
_width = np.exp(dw) * width
_height = np.exp(dh) * height
x1 = _center_x - 0.5 * _width
y1 = _center_y - 0.5 * _height
x2 = _center_x + 0.5 * _width
y2 = _center_y + 0.5 * _height
cv2.rectangle(im, (int(x1), int(y1)), (int(x2), int(y2)), (0, 0, 255), 2, cv2.CV_AA)
ret, baseline = cv2.getTextSize(self.target_names[cls_id], cv2.FONT_HERSHEY_SIMPLEX, 1.0, 1)
cv2.rectangle(im, (int(x1), int(y2) - ret[1] - baseline), (int(x1) + ret[0], int(y2)), (0, 0, 255), -1)
cv2.putText(
im,
self.target_names[cls_id],
(int(x1), int(y2) - baseline),
cv2.FONT_HERSHEY_SIMPLEX,
1.0,
(255, 255, 255),
1,
cv2.CV_AA,
)
return im
def _draw_result(self, im, clss, bbox, rects, nms_thresh, conf):
for cls_id in range(1, len(self.target_names)):
_cls = clss[:, cls_id][:, np.newaxis]
_bbx = bbox[:, cls_id * 4: (cls_id + 1) * 4]
dets = np.hstack((_bbx, _cls))
keep = nms(dets, nms_thresh)
dets = dets[keep, :]
orig_rects = cuda.cupy.asnumpy(rects)[keep, :]
inds = np.where(dets[:, -1] >= conf)[0]
for i in inds:
_bbox = dets[i, :4]
x1, y1, x2, y2 = orig_rects[i]
width = x2 - x1
height = y2 - y1
center_x = x1 + 0.5 * width
center_y = y1 + 0.5 * height
dx, dy, dw, dh = _bbox
_center_x = dx * width + center_x
_center_y = dy * height + center_y
_width = np.exp(dw) * width
_height = np.exp(dh) * height
x1 = _center_x - 0.5 * _width
y1 = _center_y - 0.5 * _height
x2 = _center_x + 0.5 * _width
y2 = _center_y + 0.5 * _height
cv2.rectangle(im, (int(x1), int(y1)), (int(x2), int(y2)),
(0, 0, 255), 2, cv2.CV_AA)
ret, baseline = cv2.getTextSize(
self.target_names[cls_id], cv2.FONT_HERSHEY_SIMPLEX,
1.0, 1)
cv2.rectangle(im, (int(x1), int(y2) - ret[1] - baseline),
(int(x1) + ret[0], int(y2)), (0, 0, 255), -1)
cv2.putText(im, self.target_names[cls_id],
(int(x1), int(y2) - baseline),
cv2.FONT_HERSHEY_SIMPLEX, 1.0,
(255, 255, 255), 1, cv2.CV_AA)
return im