Python。如何从图像中切出具有特定颜色的区域 (OpenCV, Numpy)

Question 1

我一直在尝试编写一个Python脚本，它将一张图片作为输入，然后切出一个具有特定背景颜色的矩形。然而，对我的编码技巧造成的问题是，这个矩形在每张图片中都不是在一个固定的位置（位置是随机的）。

我不太了解如何管理numpy函数。我还读了一些关于OpenCV的资料，但我对它完全陌生。到目前为止，我只是通过".crop "函数来裁剪图片，但这样我就必须使用固定的值。

这就是输入图像的样子，现在我想检测黄色矩形的位置，然后将图像裁剪成其大小。

希望得到帮助，提前感谢。

编辑。@MarkSetchell的方法很好用，但对另一张测试图片发现了一个问题。另一张图片的问题是，在图片的顶部和底部有两个颜色相同的小像素，这导致了错误或不好的裁剪。

Question 2

我更新了我的答案，以应对与黄框相同颜色的嘈杂离群像素斑点的问题。这个方法是先在图像上运行一个3x3的中值过滤器，以去除这些斑点。

#!/usr/bin/env python3
import numpy as np
from PIL import Image, ImageFilter
# Open image and make into Numpy array
im = Image.open('image.png').convert('RGB')
na = np.array(im)
orig = na.copy()    # Save original
# Median filter to remove outliers
im = im.filter(ImageFilter.MedianFilter(3))
# Find X,Y coordinates of all yellow pixels
yellowY, yellowX = np.where(np.all(na==[247,213,83],axis=2))
top, bottom = yellowY[0], yellowY[-1]
left, right = yellowX[0], yellowX[-1]
print(top,bottom,left,right)
# Extract Region of Interest from unblurred original
ROI = orig[top:bottom, left:right]
Image.fromarray(ROI).save('result.png')
好的，你的黄色是rgb(247,213,83)，所以我们要找到所有黄色像素的X,Y坐标。
#!/usr/bin/env python3
from PIL import Image
import numpy as np
# Open image and make into Numpy array
im = Image.open('image.png').convert('RGB')
na = np.array(im)
# Find X,Y coordinates of all yellow pixels
yellowY, yellowX = np.where(np.all(na==[247,213,83],axis=2))
# Find first and last row containing yellow pixels
top, bottom = yellowY[0], yellowY[-1]
# Find first and last column containing yellow pixels
left, right = yellowX[0], yellowX[-1]
# Extract Region of Interest
ROI=na[top:bottom, left:right]
Image.fromarray(ROI).save('result.png')
# Get trim box of yellow pixels
trim=$(magick image.png -fill black +opaque "rgb(247,213,83)" -format %@ info:)
# Check how it looks
echo $trim
251x109+101+220
# Crop image to trim box and save as "ROI.png"
magick image.png -crop "$trim" ROI.png
If still using 形象化的Magickv6而不是v7，将magick改为convert。

Question 3


          
           
            
             我看到的是侧面和顶部的深灰色和浅灰色区域，一个白色区域，以及一个白色区域内有灰色三角形的黄色矩形。
            
            
             我建议的第一个阶段是将图像从RGB色彩空间转换为
             
              HSV
             
             色彩空间。
             

             The
             
              S
             
             HSV空间中的颜色通道，是 "颜色饱和度通道"。
             

             所有的无色（灰/黑/白）都是零，黄色像素在S通道的零以上。
            
            
             下一个阶段。
            
            
             Apply threshold on S channel (convert it to binary image).
             

             The yellow pixels goes to 255, and other goes to zero.
            
            
             Find contours in thresh (find only the outer contour - only the rectangle).
            
            
             Invert polarity of the pixels inside the rectangle.
             

             The gray triangles become 255, and other pixels are zeros.
            
            
             Find contours in thresh - find the gray triangles.
            
            hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
# Get the saturation plane - all black/white/gray pixels are zero, and colored pixels are above zero.
s = hsv[:, :, 1]
# Apply threshold on s - use automatic threshold algorithm (use THRESH_OTSU).
ret, thresh = cv2.threshold(s, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)
# Find contours in thresh (find only the outer contour - only the rectangle).
contours = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)[-2]  # [-2] indexing takes return value before last (due to OpenCV compatibility issues).
# Mark rectangle with green line
cv2.drawContours(img, contours, -1, (0, 255, 0), 2)
# Assume there is only one contour, get the bounding rectangle of the contour.
x, y, w, h = cv2.boundingRect(contours[0])
# Invert polarity of the pixels inside the rectangle (on thresh image).
thresh[y:y+h, x:x+w] = 255 - thresh[y:y+h, x:x+w]
# Find contours in thresh (find the triangles).
contours = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)[-2]  # [-2] indexing takes return value before last (due to OpenCV compatibility issues).
# Iterate triangle contours
for c in contours:
    if cv2.contourArea(c) > 4:  #  Ignore very small contours
        # Mark triangle with blue line
        cv2.drawContours(img, [c], -1, (255, 0, 0), 2)
# Show result (for testing).
cv2.imshow('img', img)
cv2.waitKey(0)
cv2.destroyAllWindows()
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
# Get the saturation plane - all black/white/gray pixels are zero, and colored pixels are above zero.
s = hsv[:, :, 1]
cv2.imwrite('s.png', s)
# Apply threshold on s - use automatic threshold algorithm (use THRESH_OTSU).
ret, thresh = cv2.threshold(s, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)
# Find contours
cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
cnts = imutils.grab_contours(cnts) 
# Find the contour with the maximum area.
c = max(cnts, key=cv2.contourArea)
# Get bounding rectangle
x, y, w, h = cv2.boundingRect(c)
# Crop the bounding rectangle out of img
out = img[y:y+h, x:x+w, :].copy()