def __call__(self, ori_img):
# img to tensor
assert isinstance(ori_img, np.ndarray), "input must be a numpy array!"
# 转换类型
img = ori_img.astype(np.float) / 255.
img = cv2.resize(img, self.size)
# https://blog.csdn.net/zzw000000/article/details/80320040
# 将numpy的格式转化为tensor格式
# permute(dims) 将tensor的维度换位
# unsqueeze()这个函数主要是对数据维度进行扩充。 ???
img = torch.from_numpy(img).float().permute(2, 0, 1).unsqueeze(0)
# forward
with torch.no_grad():
img = img.to(self.device)
out_boxes = self.net(img)
boxes = get_all_boxes(out_boxes,
self.conf_thresh,
self.net.num_classes,
use_cuda=self.use_cuda)[0]
boxes = nms(boxes, self.nms_thresh)
# print(boxes)
# plot boxes
if self.is_plot:
return self.plot_bbox(ori_img, boxes)
if len(boxes) == 0:
return None, None, None
height, width = ori_img.shape[:2]
boxes = np.vstack(boxes)
bbox = np.empty_like(boxes[:, :4])
if self.is_xywh:
# bbox x y w h
bbox[:, 0] = boxes[:, 0] * width
bbox[:, 1] = boxes[:, 1] * height
bbox[:, 2] = boxes[:, 2] * width
bbox[:, 3] = boxes[:, 3] * height
else:
# bbox xmin ymin xmax ymax
bbox[:, 0] = (boxes[:, 0] - boxes[:, 2] / 2.0) * width
bbox[:, 1] = (boxes[:, 1] - boxes[:, 3] / 2.0) * height
bbox[:, 2] = (boxes[:, 0] + boxes[:, 2] / 2.0) * width
bbox[:, 3] = (boxes[:, 1] + boxes[:, 3] / 2.0) * height
cls_conf = boxes[:, 5]
cls_ids = boxes[:, 6]
# print(cls_ids)
return bbox, cls_conf, cls_ids
def __call__(self, ori_img):
# img to tensor
assert isinstance(ori_img, np.ndarray), "input must be a numpy array!"
img = ori_img.astype(np.float) / 255.
img = cv2.resize(img, self.size)
img = torch.from_numpy(img).float().permute(2, 0, 1).unsqueeze(0)
# forward
with torch.no_grad():
img = img.to(self.device)
out_boxes = self.net(img)
boxes = get_all_boxes(out_boxes, self.conf_thresh,
self.net.num_classes, self.use_cuda)[0]
boxes = nms(boxes, self.nms_thresh)
print(type(boxes))
re_boxes = boxes
# print(boxes)
# plot boxes
if self.is_plot:
return self.plot_bbox(ori_img, boxes)
if len(boxes) == 0:
return None, None, None
height, width = ori_img.shape[:2]
boxes = np.vstack(boxes)
bbox = np.empty_like(boxes[:, :4])
if self.is_xywh:
# bbox x y w h
bbox[:, 0] = boxes[:, 0] * width
bbox[:, 1] = boxes[:, 1] * height
bbox[:, 2] = boxes[:, 2] * width
bbox[:, 3] = boxes[:, 3] * height
else:
# bbox xmin ymin xmax ymax
bbox[:, 0] = (boxes[:, 0] - boxes[:, 2] / 2.0) * width
bbox[:, 1] = (boxes[:, 1] - boxes[:, 3] / 2.0) * height
bbox[:, 2] = (boxes[:, 0] + boxes[:, 2] / 2.0) * width
bbox[:, 3] = (boxes[:, 1] + boxes[:, 3] / 2.0) * height
cls_conf = boxes[:, 5]
cls_ids = boxes[:, 6]
# print(bbox[:,0], bbox[:,1], bbox[:,2], bbox[:,3])
# return bbox, cls_conf, cls_ids
return re_boxes, cls_conf, cls_ids
def __call__(self, ori_img):
# img to tensor图像到张量
assert isinstance(ori_img, np.ndarray), "input must be a numpy array!"
img = ori_img.astype(np.float) / 255.
img = cv2.resize(img, self.size)
img = torch.from_numpy(img).float().permute(2, 0, 1).unsqueeze(0)
# forward(只进行前向传播且不计算梯度)
with torch.no_grad():
img = img.to(self.device)
out_boxes = self.net(img)
boxes = get_all_boxes(out_boxes,
self.conf_thresh,
self.num_classes,
use_cuda=self.use_cuda) # batch size is 1
# boxes = nms(boxes, self.nms_thresh)
boxes = post_process(boxes, self.net.num_classes, self.conf_thresh,
self.nms_thresh)[0].cpu()
# bbox xmin ymin xmax ymax
boxes = boxes[boxes[:, -2] > self.score_thresh, :]
if len(boxes) == 0:
bbox = torch.FloatTensor([]).reshape([0, 4])
cls_conf = torch.FloatTensor([])
cls_ids = torch.LongTensor([])
else:
height, width = ori_img.shape[:2]
bbox = boxes[:, :4]
if self.is_xywh:
# bbox x y w h
bbox = xyxy_to_xywh(bbox)
bbox = bbox * torch.FloatTensor([[width, height, width, height]])
cls_conf = boxes[:, 5]
cls_ids = boxes[:, 6].long()
return bbox.numpy(), cls_conf.numpy(), cls_ids.numpy()
