CODEWITHSUNDEEP
pythonopencv
Saievskiy Dmitriy
Saievskiy Dmitriy

Reputation: 37

Find hand contours only. Python. OpenCV

I have foreground mask, like this

If I use findContours method I receive this result

I want to find only main contours on hand without trash. How can I do it?

thresh = 100
    # get threshold image
    ret, thresh_img = cv.threshold(fgMask, thresh, 255, cv.THRESH_BINARY)
    # find contours
    contours, hierarchy = cv.findContours(thresh_img, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)

    # create an empty image for contours
    img_contours = np.zeros(img.shape)
    # draw the contours on the empty image
    cv.drawContours(img_contours, contours, -1, (0, 255, 0), 3)

    cv.imwrite("img.png", fgMask)
    cv.imwrite("img1.png", img_contours)

Upvotes: 0

Views: 1577

Answers (1)

Ceopee
Ceopee

Reputation: 326

Maybe you can try blurring image before threshold to reduce the noise. However, your foreground mask is quite bad actually for this purpose. Since I cannot see what you have done before, it is generally better to use HSV color space if you want to achieve human skin. Because

The color of human skin is created by a combination of blood (red) and melanin (yellow, brown). Skin colors lie between these two extreme hues and are somewhat saturated. HSV: (Hue-Saturation-Value) defined in a way that is similar to how humans perceive color.

img_path = "hand.jpg"
img = cv.imread(img_path)

# define the upper and lower boundaries of the HSV pixel intensities 
# to be considered 'skin'
hsvim = cv.cvtColor(img, cv.COLOR_BGR2HSV)
lower = np.array([0, 48, 80], dtype="uint8")
upper = np.array([20, 255, 255], dtype="uint8")
skinMask= cv.inRange(hsvim, lower, upper)

# blur the mask to help remove noise
skinMask= cv.blur(skinMask, (2, 2))

# get threshold image
ret, thresh = cv.threshold(skinMask, 100, 255, cv.THRESH_BINARY)
cv.imshow("thresh", thresh)

# draw the contours on the empty image
contours, hierarchy = cv.findContours(thresh, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
contours = max(contours, key=lambda x: cv.contourArea(x))
cv.drawContours(img, [contours], -1, (255, 255, 0), 2)
cv.imshow("contours", img)

cv.waitKey()

should resulted better likeenter image description here

Code Reference

Upvotes: 3

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