裁剪特定颜色区域并去除噪声区域(Python+OpenCV)
Crop the specific color region and remove the noisy regions (Python+OpenCV)
我在从彩色图像获取二值图像时遇到问题。 cv2.inRange()
函数用于获取图像的 mask
(与阈值处理类似),我想删除不需要的部分,最大限度地减少对 mask
图像的侵蚀。最大的问题是 mask
没有定期提取。
示例
破解:
典型一个
理想的:
我的第一个目标是把第二张图片做成第三张。我想得到面积最大的轮廓并删除其他轮廓(也适用于 mask
)是可行的。但是找不到怎么办。
第二个问题是我上面描述的想法不适用于第一张图片(破解)。这种图像可以丢弃。但无论如何它应该被标记为破解。到目前为止,我还没有这方面的想法。
我做了什么
这里是输入图片和代码42_1.jpg
class Real:
__ex_low=np.array([100,30,60])
__ex_high=np.array([140,80,214])
__ob_low=np.array([25,60,50]) #27,65,100])
__ob_high=np.array([50,255,255]) #[45,255,255])
def __opening(self, mask):
kernel = np.ones((3,3), np.uint8)
op = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)
return op
def __del_ext(self, img_got):
img = img_got[0:300,]
hsv = cv2.cvtColor(img,cv2.COLOR_BGR2HSV)
mask = cv2.inRange(hsv, self.__ex_low, self.__ex_high)
array1 = np.transpose(np.nonzero(mask))
array2 = np.nonzero(mask)
temp=array1.tolist()
xmin=min(array2[0]) #find the highest point covered blue
x,y,channel=img.shape
img=img[xmin:x,]
hsv=hsv[xmin:x,]
return img, hsv
def __init__(self, img_got):
img, hsv = self.__del_ext(img_got)
mask_temp = cv2.inRange(hsv, self.__ob_low, self.__ob_high)
mask = self.__opening(mask_temp)
array1 = np.transpose(np.nonzero(mask))
array2 = np.nonzero(mask)
ymin=min(array2[1])
ymax=max(array2[1])
xmin=min(array2[0])
xmax=max(array2[0])
self.x = xmax-xmin
self.y = ymax-ymin
self.ratio = self.x/self.y
# xmargin = int(self.x*0.05)
#ymargin = int(self.y*0.05)
self.img = img[(xmin):(xmax),(ymin):(ymax)]
self.mask = mask[(xmin):(xmax),(ymin):(ymax)]
#models = glob.glob("D:/Python36/images/motor/*.PNG")
img = cv2.imread("D:/Python36/images/0404/33_1.jpg")#<- input image
#last_size = get_last_size(models[-1])
#m2= Model(models[39],last_size)
r1 = Real(img)
cv2.imshow("2",r1.img)
cv2.imshow("3",r1.mask)
如果代码写成python3
就好了,但是什么都行。
总的来说,你的方法是可以的,除了错误的内核去横线。
我按以下步骤完成:
(1) Read and convert to HSV
(2) Find the target yellow color region in HSV
(3) morph-op to remove horizone lines
(4) crop the region
这是结果:
代码:
#!/usr/bin/python3
# 2018/04/16 13:20:07
# 2018/04/16 14:13:03
import cv2
import numpy as np
## (1) Read and convert to HSV
img = cv2.imread("euR2X.png")
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
## (2) Find the target yellow color region in HSV
hsv_lower = (25, 100, 50)
hsv_upper = (33, 255, 255)
mask = cv2.inRange(hsv, hsv_lower, hsv_upper)
## (3) morph-op to remove horizone lines
kernel = np.ones((5,1), np.uint8)
mask2 = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)
## (4) crop the region
ys, xs = np.nonzero(mask2)
ymin, ymax = ys.min(), ys.max()
xmin, xmax = xs.min(), xs.max()
croped = img[ymin:ymax, xmin:xmax]
pts = np.int32([[xmin, ymin],[xmin,ymax],[xmax,ymax],[xmax,ymin]])
cv2.drawContours(img, [pts], -1, (0,255,0), 1, cv2.LINE_AA)
cv2.imshow("croped", croped)
cv2.imshow("img", img)
cv2.waitKey()
References
:
what are recommended color spaces for detecting orange color in open cv?
我在从彩色图像获取二值图像时遇到问题。 cv2.inRange()
函数用于获取图像的 mask
(与阈值处理类似),我想删除不需要的部分,最大限度地减少对 mask
图像的侵蚀。最大的问题是 mask
没有定期提取。
示例
破解:
典型一个
理想的:
我的第一个目标是把第二张图片做成第三张。我想得到面积最大的轮廓并删除其他轮廓(也适用于 mask
)是可行的。但是找不到怎么办。
第二个问题是我上面描述的想法不适用于第一张图片(破解)。这种图像可以丢弃。但无论如何它应该被标记为破解。到目前为止,我还没有这方面的想法。
我做了什么
这里是输入图片和代码42_1.jpg
class Real:
__ex_low=np.array([100,30,60])
__ex_high=np.array([140,80,214])
__ob_low=np.array([25,60,50]) #27,65,100])
__ob_high=np.array([50,255,255]) #[45,255,255])
def __opening(self, mask):
kernel = np.ones((3,3), np.uint8)
op = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)
return op
def __del_ext(self, img_got):
img = img_got[0:300,]
hsv = cv2.cvtColor(img,cv2.COLOR_BGR2HSV)
mask = cv2.inRange(hsv, self.__ex_low, self.__ex_high)
array1 = np.transpose(np.nonzero(mask))
array2 = np.nonzero(mask)
temp=array1.tolist()
xmin=min(array2[0]) #find the highest point covered blue
x,y,channel=img.shape
img=img[xmin:x,]
hsv=hsv[xmin:x,]
return img, hsv
def __init__(self, img_got):
img, hsv = self.__del_ext(img_got)
mask_temp = cv2.inRange(hsv, self.__ob_low, self.__ob_high)
mask = self.__opening(mask_temp)
array1 = np.transpose(np.nonzero(mask))
array2 = np.nonzero(mask)
ymin=min(array2[1])
ymax=max(array2[1])
xmin=min(array2[0])
xmax=max(array2[0])
self.x = xmax-xmin
self.y = ymax-ymin
self.ratio = self.x/self.y
# xmargin = int(self.x*0.05)
#ymargin = int(self.y*0.05)
self.img = img[(xmin):(xmax),(ymin):(ymax)]
self.mask = mask[(xmin):(xmax),(ymin):(ymax)]
#models = glob.glob("D:/Python36/images/motor/*.PNG")
img = cv2.imread("D:/Python36/images/0404/33_1.jpg")#<- input image
#last_size = get_last_size(models[-1])
#m2= Model(models[39],last_size)
r1 = Real(img)
cv2.imshow("2",r1.img)
cv2.imshow("3",r1.mask)
如果代码写成python3
就好了,但是什么都行。
总的来说,你的方法是可以的,除了错误的内核去横线。
我按以下步骤完成:
(1) Read and convert to HSV
(2) Find the target yellow color region in HSV
(3) morph-op to remove horizone lines
(4) crop the region
这是结果:
代码:
#!/usr/bin/python3
# 2018/04/16 13:20:07
# 2018/04/16 14:13:03
import cv2
import numpy as np
## (1) Read and convert to HSV
img = cv2.imread("euR2X.png")
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
## (2) Find the target yellow color region in HSV
hsv_lower = (25, 100, 50)
hsv_upper = (33, 255, 255)
mask = cv2.inRange(hsv, hsv_lower, hsv_upper)
## (3) morph-op to remove horizone lines
kernel = np.ones((5,1), np.uint8)
mask2 = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)
## (4) crop the region
ys, xs = np.nonzero(mask2)
ymin, ymax = ys.min(), ys.max()
xmin, xmax = xs.min(), xs.max()
croped = img[ymin:ymax, xmin:xmax]
pts = np.int32([[xmin, ymin],[xmin,ymax],[xmax,ymax],[xmax,ymin]])
cv2.drawContours(img, [pts], -1, (0,255,0), 1, cv2.LINE_AA)
cv2.imshow("croped", croped)
cv2.imshow("img", img)
cv2.waitKey()
References
:
what are recommended color spaces for detecting orange color in open cv?