opencv模板匹配相同位置去除重復(fù)的框

import cv2
import numpy as np
img_rgb = cv2.imread('mario.jpg')
img_gray = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2GRAY)
template = cv2.imread('mario_coin.jpg', 0)
h, w = template.shape[:2]
 
res = cv2.matchTemplate(img_gray, template, cv2.TM_CCOEFF_NORMED)
threshold = 0.8
# 取匹配程度大于%80的坐標(biāo)
loc = np.where(res >= threshold)
#np.where返回的坐標(biāo)值(x,y)是(h,w)，注意h,w的順序
for pt in zip(*loc[::-1]):  
    bottom_right = (pt[0] + w, pt[1] + h)
    cv2.rectangle(img_rgb, pt, bottom_right, (0, 0, 255), 2)
cv2.imwrite("001.jpg",img_rgb)
cv2.imshow('img_rgb', img_rgb)
cv2.waitKey(0)

import cv2
import time
import numpy as np
 
def py_nms(dets, thresh):
    """Pure Python NMS baseline."""
    #x1、y1、x2、y2、以及score賦值
    # （x1、y1）（x2、y2）為box的左上和右下角標(biāo)
    x1 = dets[:, 0]
    y1 = dets[:, 1]
    x2 = dets[:, 2]
    y2 = dets[:, 3]
    scores = dets[:, 4]
    #每一個候選框的面積
    areas = (x2 - x1 + 1) * (y2 - y1 + 1)
    #order是按照score降序排序的
    order = scores.argsort()[::-1]
    # print("order:",order)
 
    keep = []
    while order.size > 0:
        i = order[0]
        keep.append(i)
        #計算當(dāng)前概率最大矩形框與其他矩形框的相交框的坐標(biāo)，會用到numpy的broadcast機制，得到的是向量
        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:]])
        #計算相交框的面積,注意矩形框不相交時w或h算出來會是負(fù)數(shù)，用0代替
        w = np.maximum(0.0, xx2 - xx1 + 1)
        h = np.maximum(0.0, yy2 - yy1 + 1)
        inter = w * h
        #計算重疊度IOU：重疊面積/（面積1+面積2-重疊面積）
        ovr = inter / (areas[i] + areas[order[1:]] - inter)
        #找到重疊度不高于閾值的矩形框索引
        inds = np.where(ovr = thresh)[0]
        # print("inds:",inds)
        #將order序列更新，由于前面得到的矩形框索引要比矩形框在原order序列中的索引小1，所以要把這個1加回來
        order = order[inds + 1]
    return keep
 
def template(img_gray,template_img,template_threshold):
    '''
    img_gray:待檢測的灰度圖片格式
    template_img:模板小圖，也是灰度化了
    template_threshold:模板匹配的置信度
    '''
 
    h, w = template_img.shape[:2]
    res = cv2.matchTemplate(img_gray, template_img, cv2.TM_CCOEFF_NORMED)
    start_time = time.time()
    loc = np.where(res >= template_threshold)#大于模板閾值的目標(biāo)坐標(biāo)
    score = res[res >= template_threshold]#大于模板閾值的目標(biāo)置信度
    #將模板數(shù)據(jù)坐標(biāo)進(jìn)行處理成左上角、右下角的格式
    xmin = np.array(loc[1])
    ymin = np.array(loc[0])
    xmax = xmin+w
    ymax = ymin+h
    xmin = xmin.reshape(-1,1)#變成n行1列維度
    xmax = xmax.reshape(-1,1)#變成n行1列維度
    ymax = ymax.reshape(-1,1)#變成n行1列維度
    ymin = ymin.reshape(-1,1)#變成n行1列維度
    score = score.reshape(-1,1)#變成n行1列維度
    data_hlist = []
    data_hlist.append(xmin)
    data_hlist.append(ymin)
    data_hlist.append(xmax)
    data_hlist.append(ymax)
    data_hlist.append(score)
    data_hstack = np.hstack(data_hlist)#將xmin、ymin、xmax、yamx、scores按照列進(jìn)行拼接
    thresh = 0.3#NMS里面的IOU交互比閾值
 
    keep_dets = py_nms(data_hstack, thresh)
    print("nms time:",time.time() - start_time)#打印數(shù)據(jù)處理到nms運行時間
    dets = data_hstack[keep_dets]#最終的nms獲得的矩形框
    return dets
if __name__ == "__main__":
    img_rgb = cv2.imread('mario.jpg')#需要檢測的圖片
    img_gray = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2GRAY)#轉(zhuǎn)化成灰色
    template_img = cv2.imread('mario_coin.jpg', 0)#模板小圖
    template_threshold = 0.8#模板置信度
    dets = template(img_gray,template_img,template_threshold)
    count = 0
    for coord in dets:
        cv2.rectangle(img_rgb, (int(coord[0]),int(coord[1])), (int(coord[2]),int(coord[3])), (0, 0, 255), 2)
    cv2.imwrite("result.jpg",img_rgb)