已经从图像中提取形状

Question

我正在尝试提取这部分

来自此

我试图检测形状，没有办法，训练一束虹彩...（没有负数），....位置可以变化（并非全部插入）并且角度也不相同。我不能一一裁剪:-(

任何建议？在此先感谢

PS原始图像在这里https://pasteboard.co/JaTSoJF.png（对不起，> 2Mb）

在完成@ganeshtata之后，我们得到了

import cv2
import numpy as np
img = cv2.imread('cropsmall.png')
height, width = img.shape[:2]
green_channel = img[:,0:] # Blue channel extraction
res = cv2.fastNlMeansDenoising(green_channel, None, 3, 7, 21) # Non-local means denoising
cv2.imshow('denoised',res)
edges = cv2.Canny(res, 11, 11, 3) # Edge detection
kernel = np.ones((30, 30),np.uint8)
closing = cv2.morphologyEx(edges, cv2.MORPH_CLOSE, kernel) # Morphological closing
im2, contours, hierarchy = cv2.findContours(closing, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) # Find all contours in the image

for cnt in contours: # Iterate through all contours
    x, y, w, h = cv2.boundingRect(cnt) # Reject contours whose height is less than half the image height
    if h < height / 2:
        continue
    y = 0 # Assuming that all shapes start from the top of the image
    cv2.rectangle(img, (x, y), \
          (x + w, y + h), (0, 255, 0), 2)
    cv2.imshow('IMG',img)
    cv2.imwrite("test.jpg",img)
    cv2.waitKey(0)

给我们

不错...

Answer 1

我使用以下方法来提取问题中指定的模式。

1. 读取图像并从图像中提取蓝色通道。

   import cv2
   import numpy as np
   img = cv2.imread('image.png') 
   height, width = img.shape[:2]
   blue_channel = img[:,:,0]
   

   蓝色频道-

2. 在蓝色通道图像上应用OpenCV的Non-local Means Denoising algorithm。这样可以确保图像中的大多数随机噪声都得到平滑处理。

   res = cv2.fastNlMeansDenoising(blue_channel, None, 3, 7, 21)
   

   去噪图像-

3. 应用Canny边缘检测。

   edges = cv2.Canny(res, 1, 10, 3)
   

   边缘输出-

4. 应用Morpological Closing尝试关闭图像中的小间隙/孔。

   kernel = np.ones((30, 30),np.uint8)
   closing = cv2.morphologyEx(edges, cv2.MORPH_CLOSE, kernel)
   

   应用形态学封闭后的图像-

5. 使用cv2.findContours查找图像中的所有轮廓。找到所有轮廓后，我们可以使用cv2.boundingRect确定每个轮廓的边界框。

   im2, contours, hierarchy = cv2.findContours(closing, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) # Find all contours
   
   for cnt in contours: # Iterate through all contours
       x, y, w, h = cv2.boundingRect(cnt) $ Get contour bounding box
       if h < height / 2: # Reject contours whose height is less than half the image height
           continue
       y = 0  # Assuming that all shapes start from the top of the image
       cv2.rectangle(img, (x, y), \
             (x + w, y + h), (0, 255, 0), 2)
   

6. 最终结果-

完整代码-

import cv2
import numpy as np
img = cv2.imread('image.png')
height, width = img.shape[:2]
blue_channel = img[:,:,0] # Blue channel extraction
res = cv2.fastNlMeansDenoising(blue_channel, None, 3, 7, 21) # Non-local means denoising
edges = cv2.Canny(res, 1, 10, 3) # Edge detection
kernel = np.ones((30, 30),np.uint8)
closing = cv2.morphologyEx(edges, cv2.MORPH_CLOSE, kernel) # Morphological closing
im2, contours, hierarchy = cv2.findContours(closing, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) # Find all contours in the image

for cnt in contours: # Iterate through all contours
    x, y, w, h = cv2.boundingRect(cnt) # Reject contours whose height is less than half the image height
    if h < height / 2:
        continue
    y = 0 # Assuming that all shapes start from the top of the image
    cv2.rectangle(img, (x, y), \
          (x + w, y + h), (0, 255, 0), 2)

注意-此方法适用于您发布的示例图像。它可能/可能不会针对所有图像进行概括。