OpenCV+Python3.5 简易手势识别的实现

def video_capture():
 cap = cv2.VideoCapture(0)
 while True:
 # capture frame-by-frame
 ret, frame = cap.read()

 # our operation on the frame come here
 # gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) 可选择灰度化

 # display the resulting frame
 cv2.imshow('frame', frame)
 if cv2.waitKey(1) & 0xFF == ord('q'): # 按q键退出
 break
 # when everything done , release the capture
 cap.release()
 cv2.destroyAllWindows()

def ellipse_detect(img):
 # 椭圆肤色检测模型
 skinCrCbHist = np.zeros((256, 256), dtype=np.uint8)
 cv2.ellipse(skinCrCbHist, (113, 155), (23, 15), 43, 0, 360, (255, 255, 255), -1)

 YCRCB = cv2.cvtColor(img, cv2.COLOR_BGR2YCR_CB)
 (y, cr, cb) = cv2.split(YCRCB)
 skin = np.zeros(cr.shape, dtype=np.uint8)
 (x, y) = cr.shape
 for i in range(0, x):
 for j in range(0, y):
 CR = YCRCB[i, j, 1]
 CB = YCRCB[i, j, 2]
 if skinCrCbHist[CR, CB] > 0:
 skin[i, j] = 255
 dst = cv2.bitwise_and(img, img, mask=skin)
 return dst

# 膨胀
def dilate_demo(image):
 # 灰度化
 gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
 # 二值化
 ret, binary = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
 # 定义结构元素的形状和大小
 kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
 # 膨胀操作
 dst = cv2.dilate(binary, kernel)
 return dst


# 腐蚀
def erode_demo(image):
 # 灰度化
 gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
 # 二值化
 ret, binary = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
 # 定义结构元素的形状和大小
 kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (15, 15))
 # 腐蚀操作
 dst = cv2.erode(binary, kernel)
 return dst


# 滤波
def img_blur(image):
 # 腐蚀操作
 # img_erode = erode_demo(image)
 # 膨胀操作
 img_dilate = dilate_demo(image)

 # 均值滤波
 # blur = cv2.blur(image, (5, 5))
 # 高斯滤波
 blur = cv2.GaussianBlur(img_dilate, (3, 3), 0)
 return blur

# Canny边缘检测v
def canny_detect(image):
 edges = cv2.Canny(image, 50, 200)
 return edges

 # 轮廓匹配
 value = [0, 0, 0]
 value[0] = cv2.matchShapes(img_contour, img1, 1, 0.0)
 value[1] = cv2.matchShapes(img_contour, img2, 1, 0.0)
 value[2] = cv2.matchShapes(img_contour, img3, 1, 0.0)
 min_index = np.argmin(value)
 if min_index == 0: # 剪刀
  print(text[int(min_index)], value)
 elif min_index == 1 and w*h < 25000: # 石头
  print(text[int(min_index)], value)
 elif min_index == 1 and w*h >= 25000: # 剪刀
  print(text[0], value)
 elif min_index == 2 and w * h > 30000: # 布
  print(text[int(min_index)], value)
 elif min_index == 2 and w * h <= 30000: # 剪刀
  print(text[0], value)