使用 Python 從圖像中刪除不需要的連接像素

Question

我是 Python 圖像處理的初學者，所以我需要幫助。 我正在嘗試使用下面發布的代碼從我的圖片中刪除連接像素的區域。 實際上，它有效但效果不佳。 我想要的是從我的圖像中去除像素區域，例如下面報告的圖片中標記為紅色的區域，以獲得干凈的圖片。 為檢測到的連接像素區域的尺寸設置最小和最大限制也很好。 帶有標記區域的圖片示例1 帶有標記區域的圖片示例 2

這是我目前的代碼：

### 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, 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 picture
gray=cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # Convert to grayscale
cv2.imshow("Original image", image) # Plot

edged=cv2.Canny(gray, 50, 200, 3) # Edges of areas detection
cnts=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 intensity
cnts=imutils.grab_contours(cnts)

mask=np.ones(image.shape[:2], dtype="uint8")*255 # Setup the mask with white background
# 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):
        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 image
cv2.imshow("Adopted mask", mask) # Plot
cv2.imshow("Cleaned image", image_cleaned) # Plot
cv2.imwrite("cleaned_025.jpg", image_cleaned) # Write in a file

Answer 1

您可以執行以下處理步驟：

• 使用cv2.threshold將圖像閾值cv2.threshold二值圖像。
  這不是必須的，但在您的情況下，灰色陰影看起來並不重要。
• 使用閉合形態學操作來閉合二值圖像中的小間隙。
• 使用cv2.findContours與cv2.RETR_EXTERNAL參數，讓周圍的白色集群輪廓（周邊）。
• 修改“壞輪廓”的邏輯，返回true，只有當區域很大時（假設你只想清理大的三個輪廓）。

這是更新后的代碼：

### 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()

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結果：

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為測試找到的標記輪廓：

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)

結果：

還有工作要做......

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更新

兩次迭代方法：

• 第一次迭代 - 刪除大輪廓。
• 第二次迭代 - 刪除小而明亮的輪廓。

這是代碼：

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)
###########################################################################

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標記輪廓：