77 lines
3.0 KiB
Python
77 lines
3.0 KiB
Python
import cv2
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import imutils
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import numpy as np
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from PIL.Image import Image
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import utils.matplotlib
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def match_resize(src: Image, tmpl: str, similarity=0.9, step=10, fast=False):
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src = cv2.cvtColor(np.asarray(src), cv2.COLOR_RGB2BGR)
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src_gray = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY)
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tmpl = cv2.imread(tmpl)
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tmpl_gray = cv2.cvtColor(tmpl, cv2.COLOR_BGR2GRAY)
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found = None
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tmp_r = 0
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for scale in np.linspace(0.2, 2, step)[::-1]:
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# 根据scale比例缩放图像，并保持其宽高比
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resized = imutils.resize(src_gray, width=int(src_gray.shape[1] * scale))
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r = src_gray.shape[1] / float(resized.shape[1])
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result = cv2.matchTemplate(resized, tmpl_gray, cv2.TM_CCOEFF_NORMED)
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found_location = convert_location(result, tmpl_gray, similarity, r)
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# 快速匹配, 如果缩放比例越大 相似度越低就直接退出循环 返回当前最佳结果
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if fast:
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if found is not None and found_location is not None:
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print(found[2], found_location[2])
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if tmp_r < r and found[2] > found_location[2]:
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break
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tmp_r = r
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# 如果我们找到了一个新的最大校正值，更新簿记变量值
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if found is None or (
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found_location is not None and found_location[2] >= similarity and found_location[2] > found[2]):
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found = found_location
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if found is None:
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return None
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x, y, _ = found
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print("x,y (%s, %s)" % (x, y))
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return found[0], found[1]
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def match(src: Image, tmpl: str, similarity=0.9):
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# cv::TM_CCOEFF_NORMED = 5 标准相关匹配
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method = cv2.TM_CCOEFF_NORMED
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src = cv2.cvtColor(np.asarray(src), cv2.COLOR_RGB2BGR)
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# 灰度图提高精度
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src_gray = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY)
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tmpl = cv2.imread(tmpl)
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tmpl_gray = cv2.cvtColor(tmpl, cv2.COLOR_BGR2GRAY)
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# print('imagetempl.shape:', tmpl_gray.shape)
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result = cv2.matchTemplate(src_gray, tmpl_gray, method)
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print(result)
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loc = convert_location(result, tmpl_gray, similarity)
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if loc is None:
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return
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x, y, _ = loc
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print("x,y (%s, %s)" % (x, y))
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return x, y
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def convert_location(result, tmpl, similarity, resize=1):
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method = 5
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min_max = cv2.minMaxLoc(result)
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if method == 0 or method == 1: # 根据不同的模式最佳匹配位置取值方法不同
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match_loc = min_max[2]
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else:
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match_loc = min_max[3]
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right_bottom = (match_loc[0] + tmpl.shape[1], match_loc[1] + tmpl.shape[0])
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# print('result.min_max:', min_max, 'method=', method)
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# print('match_loc:', match_loc, type(match_loc))
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# print('right_bottom', right_bottom, type(right_bottom))
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x = int(match_loc[0] * resize) + int((right_bottom[0] - match_loc[0]) / 2 * resize)
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y = int(match_loc[1] * resize) + int((right_bottom[1] - match_loc[1]) / 2 * resize)
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print("(%s, %s) 缩放比例: %s, 相似度: %s" % (x, y, resize, min_max[1]))
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if min_max[1] < similarity:
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return None
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return x, y, min_max[1]
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