def detect(self, im, mean):
im_data=prepare_img(im, mean)
_=self.caffe_model.forward2({
"data": im_data[np.newaxis, :],
"im_info": np.array([[im_data.shape[1], im_data.shape[2]]], np.float32)
})
rois=self.caffe_model.blob("rois")
scores=self.caffe_model.blob("scores")
return rois, scores
def detect(self, im, mean):
im_data=prepare_img(im, mean)
_=self.caffe_model.forward2({
"data": im_data[np.newaxis, :],
"im_info": np.array([[im_data.shape[1], im_data.shape[2]]], np.float32)
})
rois=self.caffe_model.blob("rois")
scores=self.caffe_model.blob("scores")
return rois, scores
def detect(self, im, mean):
im_data = prepare_img(im, mean)
print "TextProposalDetector.detect: im_data shape: {}".format(
im_data.shape)
print "TextProposalDetector.detect: data shape: {}".format(
im_data[np.newaxis, :].shape)
_ = self.caffe_model.forward2({
"data":
im_data[np.newaxis, :],
"im_info":
np.array([[im_data.shape[1], im_data.shape[2]]], np.float32)
})
rois = self.caffe_model.blob("rois")
scores = self.caffe_model.blob("scores")
xside = self.caffe_model.blob("anchor_bbox_xside_pred")
return rois, scores, xside
def detect(self, im, mean):
"""
通过ctpn网络初步形成proposals
"""
im_data = prepare_img(im, mean)
# caffe的前向传播
# 参数为前向传播必须的数据
# data,im_info
_ = self.caffe_model.forward2({
"data":
im_data[np.newaxis, :],
"im_info":
np.array([[im_data.shape[1], im_data.shape[2]]], np.float32)
})
# 得到caffe模型的proposal层的输出结果
rois = self.caffe_model.blob("rois")
scores = self.caffe_model.blob("scores")
return rois, scores
def forward(self, bottom, top):
while True:
name = self._imnames[self._image_index].split('.')[0]
self._image_index += 1
if self._image_index == len(self._imnames):
self._image_index = 0
image = cv2.imread(name + '.jpg')
image, scale = resize_im(image, cfg.SCALE, cfg.MAX_SCALE)
data = prepare_img(image, cfg.MEAN)
im_info = np.array([[data.shape[1], data.shape[2]]], np.float32)
data = data[np.newaxis, :]
gt_boxes_list = []
with open(name + '.txt', 'r') as f:
for line in f:
line_data = line.split(',')
gt_boxes_list.append([
int(line_data[0]),
int(line_data[1]),
int(line_data[2]),
int(line_data[3])
])
gt_boxes_list = [[int(x * scale) for x in box]
for box in gt_boxes_list]
divide_gt_boxes_list = []
side_pos = []
for box in gt_boxes_list:
x1 = box[0]
y1 = box[1]
x2 = box[2] + box[0] - 1
y2 = box[3] + box[1] - 1
if y2 - y1 + 1 <= cfg.TEXT_PROPOSALS_WIDTH / 2:
continue
if x2 - x1 + 1 < cfg.TEXT_PROPOSALS_WIDTH:
continue
start = x1
while start % cfg.TEXT_PROPOSALS_WIDTH != 0:
start += 1
end = x2 + 1
while end % cfg.TEXT_PROPOSALS_WIDTH != 0:
end -= 1
begin_flag = 1
tmp_side_pos = []
while start < end:
if inside_image(start, y1, im_info[0, :]) and inside_image(
start + cfg.TEXT_PROPOSALS_WIDTH - 1, y2,
im_info[0, :]):
divide_gt_boxes_list.append([
start, y1, start + cfg.TEXT_PROPOSALS_WIDTH - 1, y2
])
if begin_flag:
begin_flag = 0
tmp_side_pos.append(x1)
else:
if start + cfg.TEXT_PROPOSALS_WIDTH == end:
tmp_side_pos.append(x2)
else:
tmp_side_pos.append(-1)
start += cfg.TEXT_PROPOSALS_WIDTH
for p in tmp_side_pos:
side_pos.append(p)
gt_boxes = np.array(divide_gt_boxes_list)
side_pos = np.array(side_pos)
if len(divide_gt_boxes_list):
break
top[0].reshape(*(data.shape))
top[0].data[...] = data.astype(np.float32, copy=False)
top[1].reshape(*(im_info.shape))
top[1].data[...] = im_info.astype(np.float32, copy=False)
top[2].reshape(*(gt_boxes.shape))
top[2].data[...] = gt_boxes.astype(np.float32, copy=False)
top[3].reshape(*(side_pos.shape))
top[3].data[...] = side_pos.astype(np.float32, copy=False)
