模板匹配两个颜色不同的图像

时间:2017-07-21 13:22:11

标签: python opencv template-matching

我正在寻找

1:检测按钮

2:根据按钮的颜色确定获胜者

似乎模板匹配是我应该做的,但它在灰度中起作用。我检测的按钮是绿色和红色,但在灰度时看起来非常相同。我的想法是,如果我从图像和两个模板中减去两个颜色通道,当我将所有颜色转换为灰度时,两个模板图像看起来会有所不同并导致分数不同。

在实践中,它并非如此。我已经摆弄了很多这个并且两个模板得分都非常高,或者根本没有正确检测到按钮。我无法获得分歧。

我是OpenCV的新手,所以我的方法可能不太好。同样可能的是,我写的并不是我认为的那样。让我知道你的想法。我已经包含了我的代码和我正在使用的源图像。

import cv2
import numpy as np
from matplotlib import pyplot as plt

dire = cv2.imread('dire.jpg')
dire_template = cv2.imread('dire_template.jpg')
radiant = cv2.imread('radiant.jpg')
radiant_template = cv2.imread('radiant_template.jpg')

# color images are in the form BGR
# removing the B and G from the images makes the "continue" button more distinct between the two teams
# since dire is red while radiant is green
dire_red = dire.copy()
dire_red[:,:,0] = 0
dire_red[:,:,1] = 0

dire_template_red = dire_template.copy()
dire_template_red[:,:,0] = 0
dire_template_red[:,:,1] = 0

radiant_red = radiant.copy()
radiant_red[:,:,0] = 0
radiant_red[:,:,1] = 0

radiant_template_red = radiant_template.copy()
radiant_template_red[:,:,0] = 0
radiant_template_red[:,:,1] = 0


dire_gray = cv2.cvtColor(dire_red, cv2.COLOR_BGR2GRAY)
dire_template_gray = cv2.cvtColor(dire_template_red, cv2.COLOR_BGR2GRAY)
radiant_gray = cv2.cvtColor(radiant_red, cv2.COLOR_BGR2GRAY)
radiant_template_gray = cv2.cvtColor(radiant_template_red, cv2.COLOR_BGR2GRAY)

# plt.figure(0)
# plt.imshow(dire_red)
# plt.figure(1)
# plt.imshow(radiant_red)
# plt.figure(2)
# plt.imshow(dire_gray, cmap='gray')
# plt.figure(3)
# plt.imshow(radiant_gray, cmap='gray')
# plt.figure(4)
# plt.imshow(dire_template_red)
# plt.figure(5)
# plt.imshow(radiant_template_red)
# plt.figure(6)
# plt.imshow(dire_template_gray)
# plt.figure(7)
# plt.imshow(radiant_template_gray, cmap='gray')

# plt.show()

w, h = dire_template_gray.shape[::-1]

# All the 6 methods for comparison in a list
methods = ['cv2.TM_CCOEFF_NORMED', 
            'cv2.TM_CCORR_NORMED', 'cv2.TM_SQDIFF_NORMED']

for meth in methods:
    print(f'{meth}: ')
    # this would be the live image
    img = dire_gray.copy()
    method = eval(meth)

    # Apply template Matching
    dire_res = cv2.matchTemplate(img,dire_template_gray,method)
    radiant_res = cv2.matchTemplate(img,radiant_template_gray,method)


    dire_vals = [min_val, max_val, min_loc, max_loc] = cv2.minMaxLoc(dire_res)
    radiant_vals = [min_val, max_val, min_loc, max_loc] = cv2.minMaxLoc(radiant_res)

    print(dire_vals)
    print(radiant_vals)
    # print(f'min val: {min_val} max val: {max_val}')

    # If the method is TM_SQDIFF or TM_SQDIFF_NORMED, take minimum
    if method in [cv2.TM_SQDIFF, cv2.TM_SQDIFF_NORMED]:
        top_left = min_loc
    else:
        top_left = max_loc
    bottom_right = (top_left[0] + w, top_left[1] + h)

    cv2.rectangle(img,top_left, bottom_right, 255, 2)

    # plt.subplot(121),plt.imshow(res,cmap = 'gray')
    plt.subplot(121),plt.imshow(dire_res)
    plt.title('Matching Result'), plt.xticks([]), plt.yticks([])
    # plt.subplot(122),plt.imshow(img,cmap = 'gray')
    plt.subplot(122),plt.imshow(img)
    plt.title('Detected Point'), plt.xticks([]), plt.yticks([])
    plt.suptitle(meth)

    plt.show()

radiant.jpg dire.jpg radiant_template.jpg enter image description here

1 个答案:

答案 0 :(得分:2)

您的方法似乎正是您应该如何实施它。我做了同样的方法,这是我的结果:

步骤1:以彩色加载图像并获得灰度缩放

img_red = cv2.imread("red.jpg")
img_red_gray = cv2.cvtColor(img_red, cv2.COLOR_BGR2GRAY)

img_green = cv2.imread("green.jpg")
img_green_gray = cv2.cvtColor(img_green, cv2.COLOR_BGR2GRAY)

// template is required only in gray
template = cv2.imread("template.jpg", 0)

第2步:获取模板大小并执行模板匹配

w, h = template.shape[::-1]
method = cv2.TM_CCOEFF
res = cv2.matchTemplate(img_red_gray, template, method)

步骤3:获取图像中的模板位置并获得其平均颜色强度

min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
top_left = max_loc
color = cv2.mean(img_red[top_left[1]:top_left[1] + h, top_left[0]:top_left[0]+w])

附加:在主图像中绘制匹配

bottom_right = (top_left[0] + w, top_left[1] + h)
cv2.rectangle(img_red_gray, top_left, bottom_right, 255, 2)

结果:

Template in Green

Template in Red

红色图像颜色= (5.1372107567229515, 12.502939337085678, 72.62376485303315, 0.0) (B,G,R,A)

绿色图像颜色= (63.20187617260788, 85.38574108818011, 49.76873045653534, 0.0) (B,G,R,A)

正如@Dan建议的那样,您也可以在HSV中执行此操作以获得更高的差异。

您可以清楚地看到,如果图像中的模板为绿色或红色,现在您可以使用单个通道值说明。 希望它有所帮助!