text_proposal_connector.py 文件源码

def get_text_lines(self, text_proposals, scores, im_size):
        # tp=text proposal
        tp_groups=self.group_text_proposals(text_proposals, scores, im_size)
        text_lines=np.zeros((len(tp_groups), 8), np.float32)

        for index, tp_indices in enumerate(tp_groups):
            text_line_boxes=text_proposals[list(tp_indices)]
            num = np.size(text_line_boxes)
            X = (text_line_boxes[:,0] + text_line_boxes[:,2]) / 2
            Y = (text_line_boxes[:,1] + text_line_boxes[:,3]) / 2
            z1 = np.polyfit(X,Y,1)
            p1 = np.poly1d(z1)


            x0=np.min(text_line_boxes[:, 0])
            x1=np.max(text_line_boxes[:, 2])

            offset=(text_line_boxes[0, 2]-text_line_boxes[0, 0])*0.5

            lt_y, rt_y=self.fit_y(text_line_boxes[:, 0], text_line_boxes[:, 1], x0+offset, x1-offset)
            lb_y, rb_y=self.fit_y(text_line_boxes[:, 0], text_line_boxes[:, 3], x0+offset, x1-offset)

            # the score of a text line is the average score of the scores
            # of all text proposals contained in the text line
            score=scores[list(tp_indices)].sum()/float(len(tp_indices))

            text_lines[index, 0]=x0
            text_lines[index, 1]=min(lt_y, rt_y)
            text_lines[index, 2]=x1
            text_lines[index, 3]=max(lb_y, rb_y)
            text_lines[index, 4]=score
            text_lines[index, 5]=z1[0]
            text_lines[index, 6]=z1[1]
            height = np.mean( (text_line_boxes[:,3]-text_line_boxes[:,1]) )
            text_lines[index, 7]= height + 2.5
        #text_lines=clip_boxes(text_lines, im_size)


        return text_lines