用 Python 中的 OpenCV 检测掌纹

Question

I'm studying OpenCV with python by working on a project which aims to detect the palm lines.

What I have done is basically use Canny edge detection and then apply Hough line detection on the edges but the outcome is not so good.

Here is the source code I am using:

original = cv2.imread(file)
img = cv2.cvtColor(original, cv2.COLOR_BGR2GRAY)
save_image_file(img, "gray")

img = cv2.equalizeHist(img)
save_image_file(img, "equalize")

img = cv2.GaussianBlur(img, (9, 9), 0)
save_image_file(img, "blur")

img = cv2.Canny(img, 40, 80)
save_image_file(img, "canny")

lined = np.copy(original) * 0
lines = cv2.HoughLinesP(img, 1, np.pi / 180, 15, np.array([]), 50, 20)
for line in lines:
    for x1, y1, x2, y2 in line:
        cv2.line(lined, (x1, y1), (x2, y2), (0, 0, 255))
save_image_file(lined, "lined")

output = cv2.addWeighted(original, 0.8, lined, 1, 0)
save_image_file(output, "output")

I tried different parameter sets of Gaussian kernel size and Canny low/high thresholds, but the outcome is either having too much noises, or missing (part of) major lines. Above picture is already the best I get, so far..

Is there anything I should do to get result improved, or any other approach would get better result?

Any help would be appreciated!

Answer 1

What you are looking for is really experimental. You have already done the most important function. I suggest that you tune your parameters to get a reasonable and a noisy number of lines, then you can make some filtering:

• using morphological filters,

• classification of lines (according to their lengths, fits on contrasted area...etc)

• improving your categories by dividing the area of palm (without fingers) into a grid (4x4.. where 4 vertical fingers corners can define the configs of the grid).

• calculate the gradient image, orientation of lines may help as well
• Make a search about the algorithm "cumulative level lines detection", it can help for the certainty of detected lines