NMS

＃　ＳＮＭＳ
#coding=utf-8
import numpy as np

def py_cpu_nms(dets, thresh):
    """Pure Python NMS baseline."""
    # tl_x,tl_y,br_x,br_y及score
    x1 = dets[:, 0]
    y1 = dets[:, 1]
    x2 = dets[:, 2]
    y2 = dets[:, 3]
    scores = dets[:, 4]

    #计算每个检测框的面积，并对目标检测得分进行降序排序
    areas = (x2 - x1 + 1) * (y2 - y1 + 1)
    order = scores.argsort()[::-1]

    keep = []   #保留框的结果集合
    while order.size > 0:
        i = order[0]
        keep.append(i)　　#保留该类剩余box中得分最高的一个
        # 计算最高得分矩形框与剩余矩形框的相交区域
        xx1 = np.maximum(x1[i], x1[order[1:]])
        yy1 = np.maximum(y1[i], y1[order[1:]])
        xx2 = np.minimum(x2[i], x2[order[1:]])
        yy2 = np.minimum(y2[i], y2[order[1:]])

       #计算相交的面积,不重叠时面积为0
        w = np.maximum(0.0, xx2 - xx1 + 1)
        h = np.maximum(0.0, yy2 - yy1 + 1)
        inter = w * h
        
        #计算IoU：重叠面积 /（面积1+面积2-重叠面积）
        ovr = inter / (areas[i] + areas[order[1:]] - inter)

        #保留IoU小于阈值的box
        inds = np.where(ovr <= thresh)[0]
        order = order[inds + 1]   #注意这里索引加了1,因为ovr数组的长度比order数组的长度少一个

    return keep
    
 if __name__ == '__main__':
    dets = np.array([[100,120,170,200,0.98],
                     [20,40,80,90,0.99],
                     [20,38,82,88,0.96],
                     [200,380,282,488,0.9],
                     [19,38,75,91, 0.8]])

    py_cpu_nms(dets, 0.5)
    
# LNMS

import numpy as np
from shapely.geometry import Polygon

def intersection(g, p):
    #取g,p中的几何体信息组成多边形
    g = Polygon(g[:8].reshape((4, 2)))
    p = Polygon(p[:8].reshape((4, 2)))

    # 判断g,p是否为有效的多边形几何体
    if not g.is_valid or not p.is_valid:
        return 0

    # 取两个几何体的交集和并集
    inter = Polygon(g).intersection(Polygon(p)).area
    union = g.area + p.area - inter
    if union == 0:
        return 0
    else:
        return inter/union

def weighted_merge(g, p):
    # 取g,p两个几何体的加权（权重根据对应的检测得分计算得到）
    g[:8] = (g[8] * g[:8] + p[8] * p[:8])/(g[8] + p[8])
    
    #合并后的几何体的得分为两个几何体得分的总和
    g[8] = (g[8] + p[8])
    return g

def standard_nms(S, thres):
    #标准NMS
    order = np.argsort(S[:, 8])[::-1]
    keep = []
    while order.size > 0:
        i = order[0]
        keep.append(i)
        ovr = np.array([intersection(S[i], S[t]) for t in order[1:]])
        inds = np.where(ovr <= thres)[0]
        order = order[inds+1]
        
    return S[keep]

def nms_locality(polys, thres=0.3):
    '''
    locality aware nms of EAST
    :param polys: a N*9 numpy array. first 8 coordinates, then prob
    :return: boxes after nms
    '''
    S = []    #合并后的几何体集合
    p = None   #合并后的几何体
    for g in polys:
        if p is not None and intersection(g, p) > thres:    #若两个几何体的相交面积大于指定的阈值，则进行合并
            p = weighted_merge(g, p)
        else:    #反之，则保留当前的几何体
            if p is not None:
                S.append(p)
            p = g
    if p is not None:
        S.append(p)
    if len(S) == 0:
        return np.array([])
    return standard_nms(np.array(S), thres)

if __name__ == '__main__':
    # 343,350,448,135,474,143,369,359
    print(Polygon(np.array([[343, 350], [448, 135],
                            [474, 143], [369, 359]])).area)
                            
                            
# rnms
#coding=utf-8
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import numpy as np
import cv2
import tensorflow as tf

def nms_rotate(decode_boxes, scores, iou_threshold, max_output_size,
               use_angle_condition=False, angle_threshold=0, use_gpu=False, gpu_id=0):
    """
    :param boxes: format [x_c, y_c, w, h, theta]
    :param scores: scores of boxes
    :param threshold: iou threshold (0.7 or 0.5)
    :param max_output_size: max number of output
    :return: the remaining index of boxes
    """
    if use_gpu:
        #采用gpu方式
        keep = nms_rotate_gpu(boxes_list=decode_boxes,
                              scores=scores,
                              iou_threshold=iou_threshold,
                              angle_gap_threshold=angle_threshold,
                              use_angle_condition=use_angle_condition,
                              device_id=gpu_id)

        keep = tf.cond(
            tf.greater(tf.shape(keep)[0], max_output_size),
            true_fn=lambda: tf.slice(keep, [0], [max_output_size]),
            false_fn=lambda: keep)
    else:　#采用cpu方式
        keep = tf.py_func(nms_rotate_cpu,
                          inp=[decode_boxes, scores, iou_threshold, max_output_size],
                          Tout=tf.int64)
    return keep

def nms_rotate_cpu(boxes, scores, iou_threshold, max_output_size):
    keep = []　#保留框的结果集合
    order = scores.argsort()[::-1]　#对检测结果得分进行降序排序
    num = boxes.shape[0]　#获取检测框的个数

    suppressed = np.zeros((num), dtype=np.int)
    for _i in range(num):
        if len(keep) >= max_output_size:　　＃若当前保留框集合中的个数大于max_output_size时，直接返回
            break

        i = order[_i]
        if suppressed[i] == 1:　＃对于抑制的检测框直接跳过
            continue
        keep.append(i)　　#保留当前框的索引
        r1 = ((boxes[i, 1], boxes[i, 0]), (boxes[i, 3], boxes[i, 2]), boxes[i, 4])  ＃根据box信息组合成opencv中的旋转bbox
        print("r1:{}".format(r1))
        area_r1 = boxes[i, 2] * boxes[i, 3]　　＃计算当前检测框的面积
        for _j in range(_i + 1, num):　　＃对剩余的而进行遍历
            j = order[_j]
            if suppressed[i] == 1:
                continue
            r2 = ((boxes[j, 1], boxes[j, 0]), (boxes[j, 3], boxes[j, 2]), boxes[j, 4])
            area_r2 = boxes[j, 2] * boxes[j, 3]
            inter = 0.0

            int_pts = cv2.rotatedRectangleIntersection(r1, r2)[1]　＃求两个旋转矩形的交集，并返回相交的点集合
            if int_pts is not None:
                order_pts = cv2.convexHull(int_pts, returnPoints=True)　#求点集的凸边形
                int_area = cv2.contourArea(order_pts)　　#计算当前点集合组成的凸边形的面积
                inter = int_area * 1.0 / (area_r1 + area_r2 - int_area + 0.0000001)

            if inter >= iou_threshold:　　#对大于设定阈值的检测框进行滤除
                suppressed[j] = 1

    return np.array(keep, np.int64)

# gpu的实现方式
def nms_rotate_gpu(boxes_list, scores, iou_threshold, use_angle_condition=False, angle_gap_threshold=0, device_id=0):
    if use_angle_condition:
        y_c, x_c, h, w, theta = tf.unstack(boxes_list, axis=1)
        boxes_list = tf.transpose(tf.stack([x_c, y_c, w, h, theta]))
        det_tensor = tf.concat([boxes_list, tf.expand_dims(scores, axis=1)], axis=1)
        keep = tf.py_func(rotate_gpu_nms,
                          inp=[det_tensor, iou_threshold, device_id],
                          Tout=tf.int64)
        return keep
    else:
        y_c, x_c, h, w, theta = tf.unstack(boxes_list, axis=1)
        boxes_list = tf.transpose(tf.stack([x_c, y_c, w, h, theta]))
        det_tensor = tf.concat([boxes_list, tf.expand_dims(scores, axis=1)], axis=1)
        keep = tf.py_func(rotate_gpu_nms,
                          inp=[det_tensor, iou_threshold, device_id],
                          Tout=tf.int64)
        keep = tf.reshape(keep, [-1])
        return keep

if __name__ == '__main__':
    boxes = np.array([[50, 40, 100, 100, 0],
                      [60, 50, 100, 100, 0],
                      [50, 30, 100, 100, -45.],
                      [200, 190, 100, 100, 0.]])

    scores = np.array([0.99, 0.88, 0.66, 0.77])
    keep = nms_rotate(tf.convert_to_tensor(boxes, dtype=tf.float32), tf.convert_to_tensor(scores, dtype=tf.float32),
                      0.7, 5)
    import os
    os.environ["CUDA_VISIBLE_DEVICES"] = '0'
    with tf.Session() as sess:
        print(sess.run(keep))
        

# PNMS

#coding=utf-8
import numpy as np
from shapely.geometry import *

def py_cpu_pnms(dets, thresh):
    # 获取检测坐标点及对应的得分
    bbox = dets[:, :4]
    scores = dets[:, 4]　

    #这里文本的标注采用14个点，这里获取的是这14个点的偏移
    info_bbox = dets[:, 5:33]   

    #保存最终点坐标
    pts = []
    for i in xrange(dets.shape[0]):
        pts.append([[int(bbox[i, 0]) + info_bbox[i, j], int(bbox[i, 1]) + info_bbox[i, j+1]] for j in xrange(0,28,2)])

    areas = np.zeros(scores.shape)
    #得分降序
    order = scores.argsort()[::-1]
    inter_areas = np.zeros((scores.shape[0], scores.shape[0]))

    for il in xrange(len(pts)):
        ＃当前点集组成多边形，并计算该多边形的面积
        poly = Polygon(pts[il])
        areas[il] = poly.area
        
        #多剩余的进行遍历
        for jl in xrange(il, len(pts)):
            polyj = Polygon(pts[jl])
            
            #计算两个多边形的交集，并计算对应的面积
            inS = poly.intersection(polyj)
            inter_areas[il][jl] = inS.area
            inter_areas[jl][il] = inS.area

    #下面做法和nms一样
    keep = []
    while order.size > 0:
        i = order[0]
        keep.append(i)
        ovr = inter_areas[i][order[1:]] / (areas[i] + areas[order[1:]] - inter_areas[i][order[1:]])
        inds = np.where(ovr <= thresh)[0]
        order = order[inds + 1]
        
    return keep
# MNMS

#coding=utf-8
#############################################
# mask nms　实现
# 2018.11.23 add
#############################################
import cv2
import numpy as np
import imutils
import copy

EPS=0.00001

def get_mask(box,mask):
    """根据box获取对应的掩膜"""
    tmp_mask=np.zeros(mask.shape,dtype="uint8")
    tmp=np.array(box.tolist(),dtype=np.int32).reshape(-1,2)
    cv2.fillPoly(tmp_mask, [tmp], (255))
    tmp_mask=cv2.bitwise_and(tmp_mask,mask)
    return tmp_mask,cv2.countNonZero(tmp_mask)


def comput_mmi(area_a,area_b,intersect):
    """
    计算MMI,2018.11.23 add
    :param mask_a: 实例文本a的mask的面积
    :param mask_b: 实例文本b的mask的面积
    :param intersect: 实例文本a和实例文本b的相交面积
    :return:
    """
    if area_a==0 or area_b==0:
        area_a+=EPS
        area_b+=EPS
        print("the area of text is 0")
    return max(float(intersect)/area_a,float(intersect)/area_b)


def mask_nms(dets, mask, thres=0.3):
    """
    mask nms 实现函数
    :param dets: 检测结果，是一个N*9的numpy,
    :param mask: 当前检测的mask
    :param thres: 检测的阈值
    """
    # 获取bbox及对应的score
    bbox_infos=dets[:,:8]
    scores=dets[:,8]

    keep=[]
    order=scores.argsort()[::-1]
    print("order:{}".format(order))
    nums=len(bbox_infos)
    suppressed=np.zeros((nums), dtype=np.int)
    print("lens:{}".format(nums))

    # 循环遍历
    for i in range(nums):
        idx=order[i]
        if suppressed[idx]==1:
            continue
        keep.append(idx)
        mask_a,area_a=get_mask(bbox_infos[idx],mask)
        for j in range(i,nums):
            idx_j=order[j]
            if suppressed[idx_j]==1:
                continue
            mask_b, area_b =get_mask(bbox_infos[idx_j],mask)

            # 获取两个文本的相交面积
            merge_mask=cv2.bitwise_and(mask_a,mask_b)
            area_intersect=cv2.countNonZero(merge_mask)

            #计算MMI
            mmi=comput_mmi(area_a,area_b,area_intersect)
            # print("area_a:{},area_b:{},inte:{},mmi:{}".format(area_a,area_b,area_intersect,mmi))

            if mmi >= thres:
                suppressed[idx_j] = 1

    return dets[keep]