在此基础上加上NMS算法，

NMS是经常伴随图像区域检测的算法，作用是去除重复的区域

"""
 -*- coding: utf-8 -*-
 author： Hao Hu
 @date   2021/12/2 10:52 PM
"""
import cv2
img_path = 'sample1.jpg'
import numpy as np

def non_max_suppression_fast(boxes, overlapThresh):
    """将矩形框中的矩形框去掉"""
    # 空数组检测
    if len(boxes) == 0:
        return []
    # 将类型转为float
    if boxes.dtype.kind == "i":
        boxes = boxes.astype("float")
    pick = []
    # 四个坐标数组
    x1 = boxes[:, 0]
    y1 = boxes[:, 1]
    x2 = boxes[:, 2]
    y2 = boxes[:, 3]
    area = (x2 - x1 + 1) * (y2 - y1 + 1)  # 计算面积数组
    idxs = np.argsort(y2)  # 返回的是右下角坐标从小到大的索引值

    # 开始遍历删除重复的框
    while len(idxs) > 0:
        # 将最右下方的框放入pick数组
        last = len(idxs) - 1
        i = idxs[last]
        pick.append(i)
        # 找到剩下的其余框中最大的坐标x1y1，和最小的坐标x2y2,
        xx1 = np.maximum(x1[i], x1[idxs[:last]])
        yy1 = np.maximum(y1[i], y1[idxs[:last]])
        xx2 = np.minimum(x2[i], x2[idxs[:last]])
        yy2 = np.minimum(y2[i], y2[idxs[:last]])

        # 计算重叠面积占对应框的比例
        w = np.maximum(0, xx2 - xx1 + 1)
        h = np.maximum(0, yy2 - yy1 + 1)
        overlap = (w * h) / area[idxs[:last]]
        # 如果占比大于阈值，则删除
        idxs = np.delete(idxs, np.concatenate(([last], np.where(overlap > overlapThresh)[0])))

    return boxes[pick].astype("int")

def get_word_area(img_path):
    """得到检测图像中的文本区域，画出轮廓"""
    mser = cv2.MSER_create()
    img = cv2.imread(img_path)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    vis = img.copy()
    regions, _ = mser.detectRegions(gray)
    hulls = [cv2.convexHull(p.reshape(-1, 1, 2)) for p in regions]

    # 绘制目前的矩形文本框
    # mser = cv2.MSER_create()
    # cv2.polylines(vis, hulls, 1, (0, 255, 0))

    keep = []
    for c in hulls:
        x, y, w, h = cv2.boundingRect(c)
        keep.append([x, y, x + w, y + h])

    '''NMS是经常伴随图像区域检测的算法，作用是去除重复的区域，
        在人脸识别、物体检测等领域都经常使用，全称是非极大值抑制（non maximum suppression），
        就是抑制不是极大值的元素，所以用在这里就是抑制不是最大框的框，也就是去除大框中包含的小框'''
    # 使用NMS算法
    # keep2 = np.array(keep)
    # pick = non_max_suppression_fast(keep2, 0.5)
    # for (startX, startY, endX, endY) in pick:
    #     cv2.rectangle(vis, (startX, startY), (endX, endY), (255, 185, 120), 2)
    # 直接使用holyholes算法
    cv2.polylines(vis, hulls, 1, (0, 255, 0))
    cv2.imshow("before use NMS", vis)
    if cv2.waitKey(0) == 9:
        cv2.destroyAllWindows()

if __name__ == '__main__':
    img_path = 'sample1.jpg'
    get_word_area(img_path)