I'm trying to detect and draw a rectangular contour on every painting on for example this image:

I followed some guides and did the following:

1. Grayscale conversion
2. Applied median blur
3. Sharpen image
4. Applied adaptive Threshold
5. Applied Morphological Gradient
6. Find contours
7. Draw contours

And got the following result:

I know it's messy but is there a way to somehow detect and draw a contour around the paintings better?

Here is the code I used:

path = '<PATH TO THE PICTURE>'

#reading in and showing original image
image = cv2.imread(path)
image = cv2.resize(image,(880,600)) # resize was nessecary because of the large images
cv2.imshow("original", image)
cv2.waitKey(0)
cv2.destroyAllWindows() 

# grayscale conversion
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
cv2.imshow("painting_gray", gray)
cv2.waitKey(0)
cv2.destroyAllWindows() 

# we need to find a way to detect the edges better so we implement a couple of things
# A little help was found on stackoverflow: https://stackoverflow.com/questions/55169645/square-detection-in-image 
median = cv2.medianBlur(gray,5)
cv2.imshow("painting_median_blur", median) #we use median blur to smooth the image
cv2.waitKey(0)
cv2.destroyAllWindows() 

# now we sharpen the image with help of following URL: https://www.analyticsvidhya.com/blog/2021/08/sharpening-an-image-using-opencv-library-in-python/ 
kernel = np.array([[0, -1, 0],
                [-1, 5,-1],
                [0, -1, 0]])
image_sharp = cv2.filter2D(src=median, ddepth=-1, kernel=kernel)
cv2.imshow('painting_sharpend', image_sharp)
cv2.waitKey(0)
cv2.destroyAllWindows()

# now we apply adapptive thresholding
# thresholding: https://opencv24-python-tutorials.readthedocs.io/en/latest/py_tutorials/py_imgproc/py_thresholding/py_thresholding.html#adaptive-thresholding 
thresh = cv2.adaptiveThreshold(src=image_sharp,maxValue=255,adaptiveMethod=cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
                            thresholdType=cv2.THRESH_BINARY,blockSize=61,C=20)   
cv2.imshow('thresholded image', thresh)
cv2.waitKey(0)
cv2.destroyAllWindows()

# lets apply a morphological transformation
kernel = np.ones((7,7),np.uint8)
gradient = cv2.morphologyEx(thresh, cv2.MORPH_GRADIENT, kernel)
cv2.imshow('dilated image', gradient)
cv2.waitKey(0)
cv2.destroyAllWindows()



# # lets now find the contours of the image
# # find contours: https://docs.opencv.org/4.x/dd/d49/tutorial_py_contour_features.html
contours, hierarchy = cv2.findContours(gradient, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
print("contours: ", len(contours))
print("hierachy: ", len(hierarchy))
print(hierarchy)


cv2.drawContours(image, contours, -1, (0,255,0), 3)
cv2.imshow("contour image", image)
cv2.waitKey(0)
cv2.destroyAllWindows() 

Tips, help or code is appreciated!

CodePudding user response：

Here's a simple approach:

1. Obtain binary image. We

   Code

   import cv2
   
   # Load image, grayscale, Gaussian blur, Otsu's threshold
   image = cv2.imread('1.jpeg')
   gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
   blur = cv2.GaussianBlur(gray, (13,13), 0)
   thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY   cv2.THRESH_OTSU)[1]
   
   # Two pass dilate with horizontal and vertical kernel
   horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (9,5))
   dilate = cv2.dilate(thresh, horizontal_kernel, iterations=2)
   vertical_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5,9))
   dilate = cv2.dilate(dilate, vertical_kernel, iterations=2)
   
   # Find contours, filter using contour threshold area, and draw rectangle
   cnts = cv2.findContours(dilate, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
   cnts = cnts[0] if len(cnts) == 2 else cnts[1]
   for c in cnts:
       area = cv2.contourArea(c)
       if area > 20000:
           x,y,w,h = cv2.boundingRect(c)
           cv2.rectangle(image, (x, y), (x   w, y   h), (36, 255, 12), 3)
   
   cv2.imshow('thresh', thresh)
   cv2.imshow('dilate', dilate)
   cv2.imshow('image', image)
   cv2.waitKey()