使用Python从图像中删除不需要的连接像素

Question

我是使用Python进行图像处理的初学者，因此我需要帮助。我正在尝试使用下面发布的代码从图片中删除连接像素的区域。实际上，它有效但并不好。我想要的是从我的图像中删除像素区域，例如下面报告的图片中标记为红色的区域，以获得干净的图片。为连接的像素的检测到的区域的尺寸设置最小和最大限制也很棒。带有标记区域的图片示例 1 具有标记区域的图片示例 2这是我目前的代码：### LOAD MODULES ###import numpy as npimport imutilsimport cv2def is_contour_bad(c): # Decide what I want to find and its features    peri=cv2.contourArea(c, True) # Find areas    approx=cv2.approxPolyDP(c, 0.3*peri, True) # Set areas approximation    return not len(approx)>2 # Threshold to decide if add an area to the mask for its removing (if>2 remove)### DATA PROCESSING ###image=cv2.imread("025.jpg") # Load a picturegray=cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # Convert to grayscalecv2.imshow("Original image", image) # Plotedged=cv2.Canny(gray, 50, 200, 3) # Edges of areas detectioncnts=cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) # Find contours: a curve joining all the continuous points (along the boundary), having same color or intensitycnts=imutils.grab_contours(cnts)mask=np.ones(image.shape[:2], dtype="uint8")*255 # Setup the mask with white background# Loop over the detected contoursfor c in cnts:    # If the contour satisfies "is_contour_bad", draw it on the mask    if is_contour_bad(c):        cv2.drawContours(mask, [c], -1, 0, -1) # (source image, list of contours, with -1 all contours in [c] pass, 0 is the intensity, -1 the thickness)image_cleaned=cv2.bitwise_and(image, image, mask=mask) # Remove the contours from the original imagecv2.imshow("Adopted mask", mask) # Plotcv2.imshow("Cleaned image", image_cleaned) # Plotcv2.imwrite("cleaned_025.jpg", image_cleaned) # Write in a file

Answer 1

您可以执行以下处理步骤：

• 使用 将图像阈值设置为二进制图像。
  这不是必须的，但在你的情况下，灰色阴影看起来并不重要。cv2.threshold

• 使用闭合形态学操作，用于闭合二进制图像中的小间隙。

• 与参数一起使用，用于获取白色聚类周围的等值线（周长）。cv2.findContourscv2.RETR_EXTERNAL

• 修改“不良轮廓”的逻辑，仅当面积较大时才返回 true（假设您只想清理大三个轮廓）。

以下是更新的代码：

### LOAD MODULES ###

import numpy as np

import imutils

import cv2

def is_contour_bad(c): # Decide what I want to find and its features

    peri = cv2.contourArea(c) # Find areas

    return peri > 50 # Large area is considered "bad"

### DATA PROCESSING ###

image = cv2.imread("025.jpg") # Load a picture

gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # Convert to grayscale

# Convert to binary image (all values above 20 are converted to 1 and below to 0)

ret, thresh_gray = cv2.threshold(gray, 20, 255, cv2.THRESH_BINARY)

# Use "close" morphological operation to close the gaps between contours

# https://stackoverflow.com/questions/18339988/implementing-imcloseim-se-in-opencv

thresh_gray = cv2.morphologyEx(thresh_gray, cv2.MORPH_CLOSE, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5,5)));

#Find contours on thresh_gray, use cv2.RETR_EXTERNAL to get external perimeter

_, cnts, _ = cv2.findContours(thresh_gray, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) # Find contours: a curve joining all the continuous points (along the boundary), having same color or intensity

image_cleaned = gray

# Loop over the detected contours

for c in cnts:

    # If the contour satisfies "is_contour_bad", draw it on the mask

    if is_contour_bad(c):

        # Draw black contour on gray image, instead of using a mask

        cv2.drawContours(image_cleaned, [c], -1, 0, -1)

#cv2.imshow("Adopted mask", mask) # Plot

cv2.imshow("Cleaned image", image_cleaned) # Plot

cv2.imwrite("cleaned_025.jpg", image_cleaned) # Write in a file

cv2.waitKey(0)

cv2.destroyAllWindows()

结果：

标记用于测试的轮廓：

for c in cnts:

    if is_contour_bad(c):

        # Draw green line for marking the contour

        cv2.drawContours(image, [c], 0, (0, 255, 0), 1)

结果：

仍有工作要做...

---

更新

两个迭代方法：

• 第一次迭代 - 删除大轮廓。

• 第二次迭代 - 删除小而明亮的轮廓。

代码如下：

import numpy as np

import imutils

import cv2

def is_contour_bad(c, thrs): # Decide what I want to find and its features

    peri = cv2.contourArea(c) # Find areas

    return peri > thrs # Large area is considered "bad"

image = cv2.imread("025.jpg") # Load a picture

gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # Convert to grayscale

# First iteration - remove the large contour

###########################################################################

# Convert to binary image (all values above 20 are converted to 1 and below to 0)

ret, thresh_gray = cv2.threshold(gray, 20, 255, cv2.THRESH_BINARY)

# Use "close" morphological operation to close the gaps between contours

# https://stackoverflow.com/questions/18339988/implementing-imcloseim-se-in-opencv

thresh_gray = cv2.morphologyEx(thresh_gray, cv2.MORPH_CLOSE, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5,5)));

#Find contours on thresh_gray, use cv2.RETR_EXTERNAL to get external perimeter

_, cnts, _ = cv2.findContours(thresh_gray, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) # Find contours: a curve joining all the continuous points (along the boundary), having same color or intensity

image_cleaned = gray

# Loop over the detected contours

for c in cnts:

    # If the contour satisfies "is_contour_bad", draw it on the mask

    if is_contour_bad(c, 1000):

        # Draw black contour on gray image, instead of using a mask

        cv2.drawContours(image_cleaned, [c], -1, 0, -1)

###########################################################################

# Second iteration - remove small but bright contours

###########################################################################

# In the second iteration, use high threshold

ret, thresh_gray = cv2.threshold(image_cleaned, 150, 255, cv2.THRESH_BINARY)

# Use "dilate" with small radius

thresh_gray = cv2.morphologyEx(thresh_gray, cv2.MORPH_DILATE, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2,2)));

#Find contours on thresh_gray, use cv2.RETR_EXTERNAL to get external perimeter

_, cnts, _ = cv2.findContours(thresh_gray, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) # Find contours: a curve joining all the continuous points (along the boundary), having same color or intensity

# Loop over the detected contours

for c in cnts:

    # If the contour satisfies "is_contour_bad", draw it on the mask

    # Remove contour if  area is above 20 pixels

    if is_contour_bad(c, 20):

        # Draw black contour on gray image, instead of using a mask

        cv2.drawContours(image_cleaned, [c], -1, 0, -1)

###########################################################################

标记轮廓：