方法1:cv2.matchtemplate之前的“mask”图像
只是为了好玩,我试着自己做了一个面具
cv2.matchTemplate
看看我能取得什么样的成绩。为了清楚起见,这不是一个正确的遮罩——我将所有像素设置为忽略一种颜色(黑色或白色)。这是为了避免只有tm sqdiff和tmcorru规范支持一个适当的掩码。
@alexander reynolds在评论中提出了一个很好的观点,即如果模板图像(我们试图找到的东西)有很多黑色或白色,就必须小心。对于许多问题,我们会知道
先验的
模板的外观,我们可以指定白色背景或黑色背景。
我用
cv2.multiply
numpy.multiply
.
cv2.multiply
它的另一个优点是,它会自动将结果剪辑到0到255的范围内。
import numpy as np
import cv2
import time
img = cv2.imread('clock1.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
template = cv2.imread('target.jpg')
t_h, t_w = template.shape[0:2] # template height and width
mask_background = 'WHITE'
start_time = time.time()
for i in range(100): # do 100 cycles for timing
# find circle in gray image using Hough transform
circles = cv2.HoughCircles(gray, method = cv2.HOUGH_GRADIENT, dp = 1,
minDist = 150, param1 = 50, param2 = 70,
minRadius = 131, maxRadius = 200)
i = circles[0,0]
x0 = i[0]
y0 = i[1]
r = i[2]
# display circle on color image
cv2.circle(img,(x0, y0), r,(0,255,0),2)
if mask_background == 'BLACK': # black = 0, white = 255 on grayscale
mask = np.zeros(img.shape, dtype = np.uint8)
elif mask_background == 'WHITE':
mask = 255*np.ones(img.shape, dtype = np.uint8)
cv2.circle(mask, (x0, y0), r, color = (1,1,1), thickness = -1)
img2 = cv2.multiply(img, mask) # element wise multiplication
# values > 255 are truncated at 255
# do the template match
result = cv2.matchTemplate(img2, template, cv2.TM_CCOEFF_NORMED)
# call minMaxLoc
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result)
# draw found rectangle on img
if max_val > 0.4:
cv2.rectangle(img, max_loc, (max_loc[0]+t_w, max_loc[1]+t_h), (0,255,0), 4)
fps = 100/(time.time()-start_time)
print('fps ', fps)
cv2.imwrite('output.jpg', img)
分析结果:
在原始问题中,使用这种方法相对于12.7 fps的性能影响很小。但是,它有一个缺点,就是它仍然会发现模板仍然停留在边缘上一点。根据问题的确切性质,这在许多应用中是可以接受的。
在这项技术中,我们从一个圆形掩码开始(如op中所示),然后用cv2.boxfilter修改它。我们改变了
anchor
从内核的默认中心到左上角(0,0)
import numpy as np
import cv2
import time
img = cv2.imread('clock1.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
template = cv2.imread('target.jpg')
t_h, t_w = template.shape[0:2] # template height and width
print('t_h, t_w ', t_h, ' ', t_w)
start_time = time.time()
for i in range(100):
# find circle in gray image using Hough transform
circles = cv2.HoughCircles(gray, method = cv2.HOUGH_GRADIENT, dp = 1,
minDist = 150, param1 = 50, param2 = 70,
minRadius = 131, maxRadius = 200)
i = circles[0,0]
x0 = i[0]
y0 = i[1]
r = i[2]
# display circle on color image
cv2.circle(img,(x0, y0), r,(0,255,0),2)
# do the template match
result = cv2.matchTemplate(img, template, cv2.TM_CCOEFF_NORMED)
# finally, here is the part that gets tricky. we want to find highest
# rated match inside circle and we'd like to use minMaxLoc
# start to make mask by drawing circle on zero array
mask = np.zeros(result.shape, dtype = np.float)
cv2.circle(mask, (x0, y0), r, color = 1, thickness = -1)
mask = cv2.boxFilter(mask,
ddepth = -1,
ksize = (t_w, t_h),
anchor = (0,0),
normalize = True,
borderType = cv2.BORDER_ISOLATED)
# mask now contains values from zero to 1. we want to make anything
# less than 1 equal to zero
_, mask = cv2.threshold(mask, thresh = 0.9999,
maxval = 1.0, type = cv2.THRESH_BINARY)
mask = mask.astype(np.uint8)
# call minMaxLoc
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result, mask = mask)
# draw found rectangle on img
if max_val > 0.4:
cv2.rectangle(img, max_loc, (max_loc[0]+t_w, max_loc[1]+t_h), (0,255,0), 4)
fps = 100/(time.time()-start_time)
print('fps ', fps)
cv2.imwrite('output.jpg', img)
此代码给出的掩码与op相同,但为11.89 fps。这项技术给我们带来了更高的精确度和略高于
方法1
.
