我是使用Python進行圖像處理的初學者，因此我需要幫助。我正在嘗試使用下面發布的代碼從圖片中刪除連接像素的區域。實際上，它有效但并不好。我想要的是從我的圖像中刪除像素區域，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。為連接的像素的檢測到的區域的尺寸設置最小和最大限制也很棒。帶有標記區域的圖片示例 1 具有標記區域的圖片示例 2這是我目前的代碼：### 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
1 回答
慕少森
TA貢獻2019條經驗 獲得超9個贊
您可以執行以下處理步驟：
使用 將圖像閾值設置為二進制圖像。
這不是必須的，但在你的情況下，灰色陰影看起來并不重要。cv2.threshold使用閉合形態學操作，用于閉合二進制圖像中的小間隙。
與參數一起使用，用于獲取白色聚類周圍的等值線（周長）。
cv2.findContourscv2.RETR_EXTERNAL修改“不良輪廓”的邏輯，僅當面積較大時才返回 true（假設您只想清理大三個輪廓）。
以下是更新的代碼：
### 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()
結果：
標記用于測試的輪廓：
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：
仍有工作要做...
更新
兩個迭代方法：
第一次迭代 - 刪除大輪廓。
第二次迭代 - 刪除小而明亮的輪廓。
代碼如下：
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)
###########################################################################
標記輪廓：
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