# !/usr/bin/env python
# -*- coding: utf-8 -*-
"""模板匹配.
对用户提供的调节参数:
1. threshod: 筛选阈值，默认为0.8
2. rgb: 彩色三通道,进行彩色权识别.
"""
import cv2
from .cal_confidence import cal_rgb_confidence
from .utils import generate_result, check_source_larger_than_search, img_mat_rgb_2_gray, print_run_time
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class TemplateMatching(object):
"""模板匹配."""
METHOD_NAME = "Template"
MAX_RESULT_COUNT = 10
def __init__(self, im_search, im_source, threshold=0.8, rgb=True):
super(TemplateMatching, self).__init__()
self.im_source = im_source
self.im_search = im_search
self.threshold = threshold
self.rgb = rgb
@print_run_time
def find_all_results(self):
"""基于模板匹配查找多个目标区域的方法."""
# 第一步：校验图像输入
check_source_larger_than_search(self.im_source, self.im_search)
# 第二步：计算模板匹配的结果矩阵res
res = self._get_template_result_matrix()
# 第三步：依次获取匹配结果
result = []
h, w = self.im_search.shape[:2]
while True:
# 本次循环中,取出当前结果矩阵中的最优值
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
# 求取可信度:
confidence = self._get_confidence_from_matrix(max_loc, max_val, w, h)
if confidence < self.threshold or len(result) > self.MAX_RESULT_COUNT:
break
# 求取识别位置: 目标中心 + 目标区域:
middle_point, rectangle = self._get_target_rectangle(max_loc, w, h)
one_good_match = generate_result(middle_point, rectangle, confidence)
result.append(one_good_match)
# 屏蔽已经取出的最优结果,进入下轮循环继续寻找:
# cv2.floodFill(res, None, max_loc, (-1000,), max(max_val, 0), flags=cv2.FLOODFILL_FIXED_RANGE)
cv2.rectangle(res, (int(max_loc[0] - w / 2), int(max_loc[1] - h / 2)), (int(max_loc[0] + w / 2), int(max_loc[1] + h / 2)), (0, 0, 0), -1)
return result if result else None
@print_run_time
def find_best_result(self):
"""基于kaze进行图像识别，只筛选出最优区域."""
"""函数功能：找到最优结果."""
# 第一步：校验图像输入
check_source_larger_than_search(self.im_source, self.im_search)
# 第二步：计算模板匹配的结果矩阵res
res = self._get_template_result_matrix()
# 第三步：依次获取匹配结果
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
h, w = self.im_search.shape[:2]
# 求取可信度:
confidence = self._get_confidence_from_matrix(max_loc, max_val, w, h)
# 求取识别位置: 目标中心 + 目标区域:
middle_point, rectangle = self._get_target_rectangle(max_loc, w, h)
best_match = generate_result(middle_point, rectangle, confidence)
# LOGGING.debug("[%s] threshold=%s, result=%s" % (self.METHOD_NAME, self.threshold, best_match))
return best_match if confidence >= self.threshold else None
def _get_confidence_from_matrix(self, max_loc, max_val, w, h):
"""根据结果矩阵求出confidence."""
# 求取可信度:
if self.rgb:
# 如果有颜色校验,对目标区域进行BGR三通道校验:
img_crop = self.im_source[max_loc[1]:max_loc[1] + h, max_loc[0]: max_loc[0] + w]
confidence = cal_rgb_confidence(img_crop, self.im_search)
else:
confidence = max_val
return confidence
def _get_template_result_matrix(self):
"""求取模板匹配的结果矩阵."""
# 灰度识别: cv2.matchTemplate( )只能处理灰度图片参数
s_gray, i_gray = img_mat_rgb_2_gray(self.im_search), img_mat_rgb_2_gray(self.im_source)
return cv2.matchTemplate(i_gray, s_gray, cv2.TM_CCOEFF_NORMED)
def _get_target_rectangle(self, left_top_pos, w, h):
"""根据左上角点和宽高求出目标区域."""
x_min, y_min = left_top_pos
# 中心位置的坐标:
x_middle, y_middle = int(x_min + w / 2), int(y_min + h / 2)
# 左下(min,max)->右下(max,max)->右上(max,min)
left_bottom_pos, right_bottom_pos = (x_min, y_min + h), (x_min + w, y_min + h)
right_top_pos = (x_min + w, y_min)
# 点击位置:
middle_point = (x_middle, y_middle)
# 识别目标区域: 点序:左上->左下->右下->右上, 左上(min,min)右下(max,max)
rectangle = (left_top_pos, left_bottom_pos, right_bottom_pos, right_top_pos)
return middle_point, rectangle