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模板匹配法.py
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模板匹配法.py
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import cv2
import numpy as np
from sklearn.cluster import KMeans
from sklearn.utils import shuffle
from math import cos, pi, sin
from 计算刻度值 import get_rad_val
methods = ['cv2.TM_CCOEFF', 'cv2.TM_CCOEFF_NORMED', 'cv2.TM_CCORR',
'cv2.TM_CCORR_NORMED', 'cv2.TM_SQDIFF', 'cv2.TM_SQDIFF_NORMED']
method = cv2.TM_CCOEFF
def get_match_rect(template,img,method):
'''获取模板匹配的矩形的左上角和右下角的坐标'''
w, h = template.shape[1], template.shape[0]
res = cv2.matchTemplate(img, template, method)
mn_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
# 使用不同的方法，对结果的解释不同
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)
return top_left,bottom_right
def get_center_point(top_left,bottom_right):
'''传入左上角和右下角坐标，获取中心点'''
c_x, c_y = ((np.array(top_left) + np.array(bottom_right)) / 2).astype(np.int)
return c_x,c_y
def get_circle_field_color(img,center,r,thickness):
'''获取中心圆形区域的色值集'''
temp=img.copy().astype(np.int)
cv2.circle(temp,center,r,-100,thickness=thickness)
return img[temp == -100]
def v2_by_center_circle(img,colors):
'''二值化通过中心圆的颜色集合'''
for i in range(img.shape[0]):
for j in range(img.shape[1]):
a = img[i, j]
if a in colors:
img[i, j] = 0
else:
img[i, j] = 255
def v2_by_k_means(img, circles):
'''使用k-means二值化'''
original_img = np.array(img, dtype=np.float64)
src = original_img.copy()
delta_y = int(original_img.shape[0] * (0.4))
delta_x = int(original_img.shape[1] * (0.4))
original_img = original_img[delta_y:-delta_y, delta_x:-delta_x]
h, w, d = src.shape
# print(w, h, d)
dts = min([w, h])
# print(dts)
r2 = (dts / 2) ** 2
c_x, c_y = w / 2, h / 2
c_x, c_y, r2 = circles[0, 0, 1], circles[0, 0, 1], (circles[0, 0, 2] / 2) ** 4
a: np.ndarray = original_img[:, :, 0:3].astype(np.uint8)
# 获取尺寸(宽度、长度、深度)
height, width = original_img.shape[0], original_img.shape[1]
depth = 3
# print(depth)
image_flattened = np.reshape(original_img, (width * height, depth))
'''
用K-Means算法在随机中选择1000个颜色样本中建立64个类。
每个类都可能是压缩调色板中的一种颜色。
'''
image_array_sample = shuffle(image_flattened, random_state=0)
estimator = KMeans(n_clusters=2, random_state=0)
estimator.fit(image_array_sample)
'''
我们为原始图片的每个像素进行类的分配。
'''
src_shape = src.shape
new_img_flattened = np.reshape(src, (src_shape[0] * src_shape[1], depth))
cluster_assignments = estimator.predict(new_img_flattened)
'''
我们建立通过压缩调色板和类分配结果创建压缩后的图片
'''
compressed_palette = estimator.cluster_centers_
# print(compressed_palette)
a = np.apply_along_axis(func1d=lambda x: np.uint8(compressed_palette[x]), arr=cluster_assignments, axis=0)
img = a.reshape(src_shape[0], src_shape[1], depth)
# print(compressed_palette[0, 0])
threshold = (compressed_palette[0, 0] + compressed_palette[1, 0]) / 2
img[img[:, :, 0] > threshold] = 255
img[img[:, :, 0] < threshold] = 0
# cv2.imshow('sd0', img)
for x in range(w):
for y in range(h):
distance = ((x - c_x) ** 2 + (y - c_y) ** 2)
if distance > r2:
pass
img[y, x] = (255, 255, 255)
# cv2.imshow('sd', img)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
return img
def get_pointer_rad(img, circles):
'''获取角度'''
shape = img.shape
# c_y, c_x, depth = int(shape[0] / 2), int(shape[1] / 2), shape[2]
c_x, c_y, r2 = circles[0, 0, 1], circles[0, 0, 1], circles[0, 0, 2]
x1=c_x+c_x*0.8
src = img.copy()
# l_line = c_x
# r_line = shape[1] - c_x
# l_rate = 0.8
# r_rate = 0.5
# thickness = 1
# x_start = int(c_x * (1 - l_rate))
# x_end = int(c_x + r_line * r_rate)
# y_start = c_y - thickness
# y_end = c_y + thickness
# # img[y_start:y_end+1,x_start:x_end+1]=(0,0,255)
# src = img.copy()
# img = img[:, :, 0]
# # print(x_end - x_start)
# temp = img[y_start:y_end + 1, x_start:x_end + 1]
# print(temp.shape,'temp_shape',np.argwhere(temp).shape)
# index = np.argwhere(temp).reshape(temp.shape[0], -1, 2)
# index[:, :, 1] += x_start
# x1 = index[:, :, 1].copy()[1][0]
# print(index[:, :, 1].shape, index.shape)
freq_list = []
for i in range(361):
x = (x1 - c_x) * cos(i * pi / 180) + c_x
y = (x1 - c_x) * sin(i * pi / 180) + c_y
temp = src.copy()
cv2.line(temp, (c_x, c_y), (int(x), int(y)), (0, 0, 255), thickness=3)
t1 = img.copy()
t1[temp[:, :, 2] == 255] = 255
c = img[temp[:, :, 2] == 255]
points = c[c == 0]
# freq_list.append((len(points), i))
freq_list.append((len(points), (x, y)))
cv2.imshow('d', temp)
cv2.imshow('d1', t1)
cv2.waitKey(1)
# print('当前角度：',max(freq_list, key=lambda x: x[0]),'度')
cv2.waitKey(0)
cv2.destroyAllWindows()
return max(freq_list, key=lambda x: x[0])
if __name__ == '__main__':
for x in range(1, 32):
#获取测试图像
img_s = cv2.imread('test/(%s).jpg'%x)
img=cv2.cvtColor(img_s, cv2.COLOR_BGR2GRAY)
template = cv2.imread('template1.png')
template=cv2.cvtColor(template,cv2.COLOR_BGR2GRAY)
#匹配并返回矩形坐标
top_left,bottom_right = get_match_rect(template,img,method)
c_x,c_y=get_center_point(top_left,bottom_right)
print('圆心', c_x, c_y)
#绘制矩形
cv2.rectangle(img_s, top_left, bottom_right, 255, 2)
cv2.imshow('img',cv2.resize(img_s,(int(img.shape[1]*0.5),int(img.shape[0]*0.5))))
cv2.waitKey(0)
cv2.destroyAllWindows()
#################################################################
new = img_s[top_left[1]:bottom_right[1] + 1, top_left[0]:bottom_right[0] + 1]
template = cv2.imread('template.png')
top_left, bottom_right = get_match_rect(template, new, method=method)
new_ = new[top_left[1]:bottom_right[1] + 1, top_left[0]:bottom_right[0] + 1]
# 二值化图像
cv2.imshow('ererss',new_)
img=v2_by_k_means(new_)
rad=get_pointer_rad(img)
#################################################################
print('对应刻度', get_rad_val(rad[1]))
#绘制矩形
# 第一次处理之后的区域截取
# new = img[top_left[1]:bottom_right[1] + 1, top_left[0]:bottom_right[0] + 1]
# #第二次的模板
# template1 = cv2.imread('template.png')
# show_img=new.copy()
# print(show_img.shape)
# template1=cv2.cvtColor(template1,cv2.COLOR_BGR2GRAY)
# #第二次模板的宽、高
# w, h = template1.shape[::-1]
# #获取第二次的匹配后的矩形
# top_left1, bottom_right1=get_match_rect(template1,new,method=method)
#
# cv2.rectangle(new, top_left1, bottom_right1, 255, 2)
# #绘制中心区域
# c_x,c_y=get_center_point(top_left1, bottom_right1)
# center=(c_x,c_y)
# cv2.circle(show_img,center,3,255,3)
# #获取中心区域的颜色值
# colors=get_circle_field_color(new,center,3,3)
# print(colors,'colors')
# #二值化图像
# v2_by_center_circle(new,colors)
#
# cv2.imshow('img1',new)
# cv2.waitKey(0)
cv2.destroyAllWindows()
# for meth in methods:
# img = img2.copy()
# '''
# exec可以用来执行储存在字符串货文件中的python语句
# 例如可以在运行时生成一个包含python代码的字符串
# 然后使用exec语句执行这些语句
# eval语句用来计算存储在字符串中的有效python表达式
# '''
# method = eval(meth)
# # Apply template matching
# res = cv2.matchTemplate(img, template, method)
# mn_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
# # 使用不同的方法，对结果的解释不同
# 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)
# cv2.imshow('img',img)
# cv2.waitKey(0)
# cv2.destroyAllWindows()