缩放轮廓向上/向外增长

问题描述

我有一个小脚本 (based on this answer) to detect objects on a white background. The script is working fine and detects the objects. For example, this image:

becomes this:

and I crop the boundingRect (red one).

I'll be doing further operations on this image. For example instead of a rectangle crop, I will be cropping just the contour. (Anyway, these are further problems to be faced.)

What I want to do, now, is scale up/grow the contour (green one). I'm not sure if scale and grow means the same thing in this context, because when I think of scale, there's usually a single point of origin/anchor point. With grow, it's relative to the edges. I want to have something like this (created in Photoshop):

So after I detect the object/find contours, I want to grow it by some value/ratio, so that I have some space/pixels to modify which won't affect the object. How can I do that?

Mentioned script:

# drop an image on this script file
img_path = Path(sys.argv[1])

# open image with Pillow and convert it to RGB if the image is CMYK
img = Image.open(str(img_path))
if img.mode == "CMYK":
    img = ImageCms.profileToProfile(img, "Color Profiles\\USWebCoatedSWOP.icc", "Color Profiles\\sRGB_Color_Space_Profile.icm", outputMode="RGB")

img      = cv2.cvtColor(numpy.array(img), cv2.COLOR_RGB2BGR)
gray     = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
threshed = cv2.threshold(gray, 240, 255, cv2.THRESH_BINARY_INV)[1]
kernel   = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (11,11))
morphed  = cv2.morphologyEx(threshed, cv2.MORPH_CLOSE, kernel)
contours = cv2.findContours(morphed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[-2]
contour  = sorted(contours, key=cv2.contourArea)[-1]

x, y, w, h = cv2.boundingRect(contour)

final = cv2.drawContours(img, contours, -1, (0,255,0), 2)
cv2.rectangle(final, (x,y), (x+w,y+h), (0,0,255), 2)

cv2.imshow("final", final)
cv2.waitKey(0)
cv2.destroyAllWindows()

---

解决方案

Thanks to HansHirse's suggestion (using morphological dilation), I've managed to make it work.

img_path = Path(sys.argv[1])

def cmyk_to_rgb(cmyk_img):
    img = Image.open(cmyk_img)
    if img.mode == "CMYK":
        img = ImageCms.profileToProfile(img, "Color Profiles\\USWebCoatedSWOP.icc", "Color Profiles\\sRGB_Color_Space_Profile.icm", outputMode="RGB")
    return cv2.cvtColor(numpy.array(img), cv2.COLOR_RGB2BGR)

def cv_threshold(img, thresh=128, maxval=255, type=cv2.THRESH_BINARY):
    if len(img.shape) == 3:
        img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    threshed = cv2.threshold(img, thresh, maxval, type)[1]
    return threshed

def find_contours(img, to_gray=None):
    kernel   = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (11,11))
    morphed  = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel)
    contours = cv2.findContours(morphed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    return contours[-2]

def mask_from_contours(ref_img, contours):
    mask = numpy.zeros(ref_img.shape, numpy.uint8)
    mask = cv2.drawContours(mask, contours, -1, (255,255,255), -1)
    return cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)

def dilate_mask(mask, kernel_size=10):
    kernel  = numpy.ones((kernel_size,kernel_size), numpy.uint8)
    dilated = cv2.dilate(mask, kernel, iterations=1)
    return dilated

def draw_contours(src_img, contours):
    canvas = cv2.drawContours(src_img.copy(), contours, -1, (0,255,0), 2)
    x, y, w, h = cv2.boundingRect(contours[-1])
    cv2.rectangle(canvas, (x,y), (x+w,y+h), (0,0,255), 2)
    return canvas

orig_img      = cmyk_to_rgb(str(img_path))
orig_threshed = cv_threshold(orig_img, 240, type=cv2.THRESH_BINARY_INV)
orig_contours = find_contours(orig_threshed)
orig_mask     = mask_from_contours(orig_img, orig_contours)
orig_output   = draw_contours(orig_img, orig_contours)

dilated_mask     = dilate_mask(orig_mask, 50)
dilated_contours = find_contours(dilated_mask)
dilated_output   = draw_contours(orig_img, dilated_contours)

cv2.imshow("orig_output", orig_output)
cv2.imshow("dilated_output", dilated_output)

cv2.waitKey(0)
cv2.destroyAllWindows()

I believe the code is self-explonatory enough. An example output:

Full script (again) can be found at show_dilated_contours.py

Update
As a bonus, later I wanted to smooth the contours. I've came across this blog post in which the author talks about how to smooth the edges of a shape (in Photoshop). The idea is really simple and can also be applied in OpenCV to smooth the contours. The steps are:

1. Create a mask from contours (or from the shape)
2. Blur the mask
3. Threshold the blurred mask (now, we have a smoother mask than the mask in step 1)
4. Find the contours again on the blurred + thresholded image. Since the mask/shape is smoother, we'll get smoother contours.

Example code and output:

# ... continuing previos code

# pass 1
smooth_mask_blurred   = cv2.GaussianBlur(dilated_mask, (21,21), 0)
smooth_mask_threshed1 = cv_threshold(smooth_mask_blurred)

# pass 2
smooth_mask_blurred   = cv2.GaussianBlur(smooth_mask_threshed1, (21,21), 0)
smooth_mask_threshed2 = cv_threshold(smooth_mask_blurred)

# find contours from smoothened mask
smooth_mask_contours = find_contours(smooth_mask_threshed2)
# draw the contours on the original image
smooth_mask_output   = draw_contours(orig_img, smooth_mask_contours)

cv2.imshow("dilated_output", dilated_output)
cv2.imshow("smooth_mask_output", smooth_mask_output)

Full code at show_smooth_contours.py.

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