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目录

前言

1.引入库

2.__init__.py文件中代码部分代码

3. 示例代码

前言

看到别人的代码中使用aircv, 查看了相关信息，记录一下

1.引入库

import aircv

2.__init__.py文件中代码部分代码

def find_template(im_source, im_search, threshold=0.5, rgb=False, bgremove=False):
    '''
    @return find location
    if not found; return None
    '''
    result = find_all_template(im_source, im_search, threshold, 1, rgb, bgremove)
    return result[0] if result else None

def find_all_template(im_source, im_search, threshold=0.5, maxcnt=0, rgb=False, bgremove=False):
    '''
    Locate image position with cv2.templateFind

    Use pixel match to find pictures.

    Args:
        im_source(string): 图像、素材
        im_search(string): 需要查找的图片
        threshold: 阈值，当相识度小于该阈值的时候，就忽略掉

    Returns:
        A tuple of found [(point, score), ...]

    Raises:
        IOError: when file read error
    '''
    # method = cv2.TM_CCORR_NORMED
    # method = cv2.TM_SQDIFF_NORMED
    method = cv2.TM_CCOEFF_NORMED

    if rgb:
        s_bgr = cv2.split(im_search) # Blue Green Red
        i_bgr = cv2.split(im_source)
        weight = (0.3, 0.3, 0.4)
        resbgr = [0, 0, 0]
        for i in range(3): # bgr
            resbgr[i] = cv2.matchTemplate(i_bgr[i], s_bgr[i], method)
        res = resbgr[0]*weight[0] + resbgr[1]*weight[1] + resbgr[2]*weight[2]
    else:
        s_gray = cv2.cvtColor(im_search, cv2.COLOR_BGR2GRAY)
        i_gray = cv2.cvtColor(im_source, cv2.COLOR_BGR2GRAY)
        # 边界提取(来实现背景去除的功能)
        if bgremove:
            s_gray = cv2.Canny(s_gray, 100, 200)
            i_gray = cv2.Canny(i_gray, 100, 200)

        res = cv2.matchTemplate(i_gray, s_gray, method)
    w, h = im_search.shape[1], im_search.shape[0]

    result = []
    while True:
        min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
        if method in [cv2.TM_SQDIFF, cv2.TM_SQDIFF_NORMED]:
            top_left = min_loc
        else:
            top_left = max_loc
        if DEBUG: 
            print('templmatch_value(thresh:%.1f) = %.3f' %(threshold, max_val)) # not show debug
        if max_val < threshold:
            break
        # calculator middle point
        middle_point = (top_left[0]+w/2, top_left[1]+h/2)
        result.append(dict(
            result=middle_point,
            rectangle=(top_left, (top_left[0], top_left[1] + h), (top_left[0] + w, top_left[1]), (top_left[0] + w, top_left[1] + h)),
            confidence=max_val
        ))
        if maxcnt and len(result) >= maxcnt:
            break
        # floodfill the already found area
        cv2.floodFill(res, None, max_loc, (-1000,), max_val-threshold+0.1, 1, flags=cv2.FLOODFILL_FIXED_RANGE)
    return result

ac.find_template(im_source, im_search, threshold=0.5, rgb=False, bgremove=False)
im_source：被查找的源图
im_search：要查找的模板图
threshold：值在0到1之间。因为图像匹配并不需要每个像素精确一致，可以模糊匹配，所以这个值设定得越高，找到的区域就越接近模板图，但设得太高就有可能找不到。
rgb: 默认为False. rgb为True，则进行rgb三通道的拆分。
rgb为False, 则进行灰度处理。bgremove：是否去除背景。如果这个值设置为True，那么函数内部会用Canny算子先提取图像轮廓，再做查找

match_result:{
    'result': (x,y),        #tuple，表示识别结果的中心点
    'rectangle':[            #二位数组，表示识别结果的矩形四个角
        [left, top],
        [left, bottom],
        [right, top],
        [right, bottom]
    ],
    'confidence': percentage   #识别结果的置信度,在0-1之间，越大越精准
}

3. 示例代码

def pic_similar_judge(original_image, new_image):
    original_image = ac.imread(original_image)
    new_image = ac.imread(new_image)
    similar = ac.find_template(original_image, new_image, threshold=1, rgb=False, bgremove=True)  # 老图片跟新图片对比
    return similar


if __name__ == '__main__':
    original = r"D:\original.png"
    new = r"D:\new.png"
    print(pic_similar_judge(original, new))

根据__init__.py中的代码，similar值如下：

return result[0] if result else None

图片匹配失败，则返回None.

匹配成功，则返回值如：{‘result’: (108.5, 29.0), ‘rectangle’: ((0, 0), (0, 58), (217, 0), (217, 58)), ‘confidence’: 1.0}