Open=cv2.morphologyEx(blur,cv2.MORPH_OPEN,kernel)
时间: 2024-06-14 15:07:12 浏览: 11
`cv2.morphologyEx`函数是OpenCV中的形态学操作函数,用于对图像进行形态学处理。其中,`cv2.MORPH_OPEN`表示开运算,它是先进行腐蚀操作,再进行膨胀操作。开运算可以用于去除图像中的噪点和小的对象。
下面是一个使用`cv2.morphologyEx`函数进行开运算的示例代码:
```python
import cv2
import numpy as np
# 读取图像
img = cv2.imread('image.jpg', 0)
# 定义结构元素
kernel = np.ones((5,5), np.uint8)
# 进行开运算
opening = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel)
# 显示结果
cv2.imshow('Original Image', img)
cv2.imshow('Opening Image', opening)
cv2.waitKey(0)
cv2.destroyAllWindows()
```
在上述代码中,`img`是输入的图像,`kernel`是定义的结构元素,`opening`是进行开运算后得到的图像。通过`cv2.imshow`函数可以显示原始图像和开运算后的图像。
相关问题
讲解这段代码:def read_img_and_convert_to_binary(filename): #读取待处理的图片 original_img = cv2.imread(filename) # print(original_img) #将原图分辨率缩小SCALSIZE倍,减少计算复杂度 original_img = cv2.resize(original_img,(np.int(original_img.shape[1]/SCALSIZE),np.int(original_img.shape[0]/SCALSIZE)), interpolation=cv2.INTER_AREA) #降噪 blur = cv2.GaussianBlur(original_img, (5, 5), 0) #将彩色图转化成灰度图 img_gray = cv2.cvtColor(blur,cv2.COLOR_BGR2GRAY) #图片开(opening)处理,用来降噪,使图片中的字符边界更圆滑,没有皱褶 kernel = np.ones((3, 3), np.uint8) opening = cv2.morphologyEx(img_gray, cv2.MORPH_OPEN, kernel) kernel2 = np.ones((3,3), np.uint8) opening = cv2.dilate(opening, kernel2, iterations=1) # Otsu's thresholding after Gaussian filtering # 采用otsu阈值法将灰度图转化成只有0和1的二值图 blur = cv2.GaussianBlur(opening,(13,13),0) #ret, binary_img = cv2.threshold(img_gray, 120, 1, cv2.THRESH_BINARY_INV) ret,binary_img = cv2.threshold(blur,0,1,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU) return original_img,binary_img
这段代码是一个函数,名为`read_img_and_convert_to_binary`,它的功能是读取一张待处理的图片并将其转换为二值图像。下面是对代码的逐行解释:
1. `original_img = cv2.imread(filename)`:使用OpenCV库的`imread`函数读取指定路径的图片,并将其存储在`original_img`变量中。
2. `original_img = cv2.resize(original_img,(np.int(original_img.shape[1]/SCALSIZE),np.int(original_img.shape[0]/SCALSIZE)), interpolation=cv2.INTER_AREA)`:将原图的分辨率缩小`SCALSIZE`倍,通过调整图像大小减少计算复杂度,结果存储在`original_img`变量中。
3. `blur = cv2.GaussianBlur(original_img, (5, 5), 0)`:使用高斯模糊对`original_img`进行降噪处理,减少图像中的噪点,结果存储在`blur`变量中。
4. `img_gray = cv2.cvtColor(blur,cv2.COLOR_BGR2GRAY)`:将降噪后的彩色图像转换为灰度图像,便于后续处理,结果存储在`img_gray`变量中。
5. `kernel = np.ones((3, 3), np.uint8)`:创建一个3x3的矩阵,用于后续图像形态学操作。
6. `opening = cv2.morphologyEx(img_gray, cv2.MORPH_OPEN, kernel)`:对灰度图像进行形态学开运算(opening),通过腐蚀和膨胀操作使字符边界更加平滑,结果存储在`opening`变量中。
7. `kernel2 = np.ones((3,3), np.uint8)`:创建另一个3x3的矩阵,用于后续膨胀操作。
8. `opening = cv2.dilate(opening, kernel2, iterations=1)`:对开运算后的图像进行膨胀操作,进一步平滑字符边界,结果仍存储在`opening`变量中。
9. `blur = cv2.GaussianBlur(opening,(13,13),0)`:再次使用高斯模糊对图像进行降噪处理,参数(13,13)表示高斯核的大小。
10. `ret, binary_img = cv2.threshold(blur,0,1,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)`:使用Otsu阈值法将灰度图像转换为二值图像。该阈值法会自动选择一个合适的阈值,将图像分为黑白两部分,结果存储在`binary_img`变量中。
11. 最后,函数返回原始图像`original_img`和二值图像`binary_img`。
这段代码主要进行了图像预处理的步骤,包括降噪、灰度化、形态学操作和阈值处理,最终得到二值图像以供后续处理使用。
import cv2 import numpy as np img = cv2. imread(' blood. BMP',-1) cv2. imshow("source".img) dst = cv2.blur(img,(3,3)) ret, thresh = cv2.threshold(dst,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU) cv2. imshow("thresh".thresh) kernel = np.ones((4,4),np.uint8) opening = cv2.morphologyEx (thresh,cv2. MORPH_OPEN,kernel,iterations=2) kernel1 = np.ones((3, 3).np.uint8) close = cv2.morphologyEx(openning,cv2.MORPH_CLOSE,kernel1) cv2. imshow("opening",close) temp = close.copy () h, w = close. shape[:2] mask= np.zeros((h+2,w+2).np.uint8) cv2.floodFill(temp,mask,(230,145),255) temp_inv = cv2.bitwise_not(temp) result = close|temp_inv cv2. imshow("result",result) contours, hirearchy = cv2. findContours(result,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE) count = 0 area = 0 for i in contours: if cv2.contourArea(i)>73: area+=cv2.contourArea(i) count+=1 count1 = 0 for i in contours: if cv2. contourArea(i)>73: count1+=1 if cv2. contourArea(i)>(1.25*area/count): count1+=1 if cv2. contourArea(i)>(2.1*area/count): count1+=1 print("细胞有%d个"%count1) cv2. waitKey(0)
这段代码中有一些语法错误和拼写错误。以下是修正后的代码:
```
import cv2
import numpy as np
img = cv2.imread('blood.BMP', -1)
cv2.imshow("source", img)
dst = cv2.blur(img, (3, 3))
ret, thresh = cv2.threshold(dst, 0, 255, cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
cv2.imshow("thresh", thresh)
kernel = np.ones((4, 4), np.uint8)
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=2)
kernel1 = np.ones((3, 3), np.uint8)
close = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, kernel1)
cv2.imshow("opening", close)
temp = close.copy()
h, w = close.shape[:2]
mask = np.zeros((h+2, w+2), np.uint8)
cv2.floodFill(temp, mask, (230, 145), 255)
temp_inv = cv2.bitwise_not(temp)
result = close | temp_inv
cv2.imshow("result", result)
contours, hierarchy = cv2.findContours(result, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
count = 0
area = 0
for i in contours:
if cv2.contourArea(i) > 73:
area += cv2.contourArea(i)
count += 1
count1 = 0
for i in contours:
if cv2.contourArea(i) > 73:
count1 += 1
if cv2.contourArea(i) > (1.25 * area / count):
count1 += 1
if cv2.contourArea(i) > (2.1 * area / count):
count1 += 1
print("细胞有%d个" % count1)
cv2.waitKey(0)
```
注意:这段代码仅供参考,可能还需要根据实际情况进行调整和修改。