By seeing above image now you are very excited for implement it (like me). So not wasting too much time let’s jump to the code.

通过看到上面的图像，您现在很高兴实现它(像我一样)。 因此，不要浪费太多时间，让我们跳转到代码。

OpenCV (OpenCV)

OpenCV (Open Source Computer Vision Library) is an open source computer vision and machine learning software library. OpenCV was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in the commercial products.

OpenCV (开源计算机视觉库)是一个开源计算机视觉和机器学习软件库。 OpenCV构建旨在为计算机视觉应用程序提供通用的基础结构，并加速在商业产品中使用机器感知。

导入库 (Importing Libraries)

• cv2: opencv [pip install opencv]

  cv2： opencv [pip install opencv]

• numpy: for handling arrays as well as for math [pip install numpy]

  numpy：用于处理数组和数学[pip install numpy]

import cv2 as cvimport numpy as np

阅读影像 (Reading Image)

img_path = "data/palm.jpg"img = cv.imread(img_path)cv.imshow('palm image',img)

皮肤面具 (SkinMask)

It is used for highlighting specific color on image.

它用于突出显示图像上的特定颜色。

• hsvim : Change BGR (blue, green, red) image to HSV (hue, saturation, value).hsvim：将BGR(蓝色，绿色，红色)图像更改为HSV(色相，饱和度，值)。
• lower : lower range of skin color in HSV.较低：HSV中皮肤的颜色范围较低。
• upper : upper range of skin color in HSV.upper：HSV中皮肤颜色的上限。
• skinRegionHSV : Detect skin on the range of lower and upper pixel values in the HSV colorspace.skinRegionHSV：在HSV色彩空间的上下像素值范围内检测皮肤。
• blurred: bluring image to improve masking.模糊：使图像模糊以改善遮罩。
• thresh : applying threshing.脱粒：脱粒。

hsvim = cv.cvtColor(img, cv.COLOR_BGR2HSV)lower = np.array([0, 48, 80], dtype = "uint8")upper = np.array([20, 255, 255], dtype = "uint8")skinRegionHSV = cv.inRange(hsvim, lower, upper)blurred = cv.blur(skinRegionHSV, (2,2))ret,thresh = cv.threshold(blurred,0,255,cv.THRESH_BINARY)cv.imshow("thresh", thresh)

等高线 (Contours)

Now let’s finding contours on the image.

现在让我们在图像上找到轮廓。

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)

凸包 (Convex Hull)

hull = cv.convexHull(contours)cv.drawContours(img, [hull], -1, (0, 255, 255), 2)cv.imshow("hull", img)

凸缺陷 (Convexity Defects)

Any deviation of the object from this hull can be considered as convexity defect.

物体与该船体的任何偏离都可以视为凸度缺陷。

hull = cv.convexHull(contours, returnPoints=False)defects = cv.convexityDefects(contours, hull)

余弦定理 (Cosine Theorem)

Now, this is Math time! Let’s understand cosine theorem.

现在，这是数学时间！ 让我们了解余弦定理。

In trigonometry, the law of cosines relates the lengths of the sides of a triangle to the cosine of one of its angles. Using notation as in Fig. 1, the law of cosines states where γ denotes the angle contained between sides of lengths a and b and opposite the side of length c.

在三角学中，余弦定律将三角形边的长度与其角度之一的余弦相关。 使用如图1所示的符号表示，余弦定律表明，其中γ表示长度a和b的边之间的长度以及与长度c的边相对的角度。

式 (Formula)

By seeing this formula now we understand that if we have; a,b and gama then we also find c as well as if we have; a,b,c then we also find gamma (vice-versa)

通过现在看这个公式，我们知道如果有的话； a，b和gama然后我们也找到c以及是否有c ; a，b，c然后我们也找到伽玛(反之亦然)

For finding gamma this formula is used:

为了找到伽玛，使用以下公式：

使用余弦定理识别手指 (Using Cosine theorem to recognize fingers)

Sorry! for this dirty MS-Paint I am not a artist.

抱歉! 对于这个肮脏的MS-Paint，我不是艺术家。

In Fig. 2, I am draw a Side: a,b,c and angle: gamma. Now this gamma is always less than 90 degree, So we can say: If gamma is less than 90 degree or pi/2 we consider it as a finger.

在图2中，我画了一个Side： a，b，c和angle： gamma。 现在，该伽马始终小于90度，因此可以说：如果伽马小于90度或pi / 2 ，则将其视为手指。

数手指 (Counting Finger)

Note: if you not familiar with Convexity Defects please go and read this article by opencv docs. click here

注意：如果您不熟悉凸出缺陷，请转到opencv docs阅读本文。 点击这里

Convexity Defects returns an array where each row contains these values :

凸性缺陷返回一个数组，其中每一行都包含以下值：

• start point

  起点

• end point

  终点

• farthest point

  最远点

• approximate distance to farthest point

  到最远点的大概距离

By, this point we can easily derive Sides: a,b,c (see CODE) and from cosine theorem we can also derive gamma or angle between two finger. As you read earlier, if gamma is less than 90 degree we treated it as a finger. After knowing gamma we just draw circle with radius 4 in approximate distance to farthest point. And after we just simple put text in images we represent finger counts (cnt).

通过这一点，我们可以轻松得出Sides： a，b，c (请参见CODE)，并且根据余弦定理，我们还可以得出两根手指之间的 伽马或角度。 如您先前所读，如果伽马小于90度，我们会将其视为手指。 知道伽玛后，我们只需画一个半径为4的圆， 到最远点的近似距离即可。 在将文本简单地放入图像中之后，我们就表示手指数(cnt)。

if defects is not None:  cnt = 0for i in range(defects.shape[0]):  # calculate the angle  s, e, f, d = defects[i][0]  start = tuple(contours[s][0])  end = tuple(contours[e][0])  far = tuple(contours[f][0])  a = np.sqrt((end[0] - start[0]) ** 2 + (end[1] - start[1]) ** 2)  b = np.sqrt((far[0] - start[0]) ** 2 + (far[1] - start[1]) ** 2)  c = np.sqrt((end[0] - far[0]) ** 2 + (end[1] - far[1]) ** 2)  angle = np.arccos((b ** 2 + c ** 2 - a ** 2) / (2 * b * c))  #      cosine theorem  if angle <= np.pi / 2:  # angle less than 90 degree, treat as fingers    cnt += 1    cv.circle(img, far, 4, [0, 0, 255], -1)if cnt > 0:  cnt = cnt+1cv.putText(img, str(cnt), (0, 50), cv.FONT_HERSHEY_SIMPLEX,1, (255, 0, 0) , 2, cv.LINE_AA)

让我们看看最终结果 (Let’s see our final result)

cv.imshow('final_result',img)

You can also do it for Videos, just by calling “cv.VideoCapture()”. If You want the code you can get in my GitHub: finger_counting_video.py

您也可以通过调用“ cv.VideoCapture()”来对视频执行此操作。 如果需要代码，可以在我的GitHub中获取： finger_counting_video.py

您也可以在YouTube中观看此教程视频。 (You can also watch this tutorial video in YouTube.)