- Code: Select all
import cv2
import numpy as np
import copy
import math
import os
def calculateFingers(res, drawing):
# convexity defect
hull = cv2.convexHull(res, returnPoints=False)
if len(hull) > 3:
defects = cv2.convexityDefects(res, hull)
if defects is not None:
cnt = 0
for i in range(defects.shape[0]): # calculate the angle
s, e, f, d = defects[i][0]
start = tuple(res[s][0])
end = tuple(res[e][0])
far = tuple(res[f][0])
a = math.sqrt((end[0] - start[0]) ** 2 + (end[1] - start[1]) ** 2)
b = math.sqrt((far[0] - start[0]) ** 2 + (far[1] - start[1]) ** 2)
c = math.sqrt((end[0] - far[0]) ** 2 + (end[1] - far[1]) ** 2)
angle = math.acos((b ** 2 + c ** 2 - a ** 2) / (2 * b * c)) # cosine theorem
if angle <= math.pi / 2: # angle less than 90 degree, treat as fingers
cnt += 1
cv2.circle(drawing, far, 8, [211, 84, 0], -1)
if cnt > 0:
return True, cnt+1
else:
return True, 0
return False, 0
# Open Camera
camera = cv2.VideoCapture(0)
camera.set(10, 200)
#while True:
while camera.isOpened():
#Main Camera
ret, frame = camera.read()
frame = cv2.bilateralFilter(frame, 5, 50, 100) # Smoothing
frame = cv2.flip(frame, 1) #Horizontal Flip
cv2.imshow('original', frame)
#Background Removal
bgModel = cv2.createBackgroundSubtractorMOG2(0, 50)
fgmask = bgModel.apply(frame)
kernel = np.ones((3, 3), np.uint8)
fgmask = cv2.erode(fgmask, kernel, iterations=1)
img = cv2.bitwise_and(frame, frame, mask=fgmask)
# Skin detect and thresholding
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
lower = np.array([0, 48, 80], dtype="uint8")
upper = np.array([20, 255, 255], dtype="uint8")
skinMask = cv2.inRange(hsv, lower, upper)
cv2.imshow('Threshold Hands', skinMask) # Getting the contours and convex hull
skinMask1 = copy.deepcopy(skinMask)
contours, hierarchy = cv2.findContours(skinMask1, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
length = len(contours)
maxArea = -1
if length > 0:
for i in range(length):
temp = contours[i]
area = cv2.contourArea(temp)
if area > maxArea:
maxArea = area
ci = i
res = contours[ci]
hull = cv2.convexHull(res)
drawing = np.zeros(img.shape, np.uint8)
cv2.drawContours(drawing, [res], 0, (0, 255, 0), 2)
cv2.drawContours(drawing, [hull], 0, (0, 0, 255), 3)
isFinishCal, cnt = calculateFingers(res, drawing)
print( "Fingers", cnt)
cv2.imshow('output', drawing)
k = cv2.waitKey(10)
if k == 27: # press ESC to exit
break
Tale codice dovrebbe riconoscere le dita "viste" e stamparne il numero.
Il problema è che rileva sempre il numero sbagliato di dita "viste" dalla telecamera.
Ho inserito tale codice in un Raspberry pi 3B.
Per favore aiutatemi a capire perchè non rileva il numero giusto di dita.