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vision_test3.py
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vision_test3.py
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# encoding: utf-8
### 识别类型用原色,调节用手套(左手)
import cv2
import numpy as np
import math
import time
from naoqi import ALProxy
from naoobject import NAOCamera
from naoobject import NAOJoint
from naowalk import Move
# 找到颜色,输入一张只含待辨识颜色的照片
def findcolor(img):
img = cv2.cvtColor(img,cv2.COLOR_BGR2HSV)
shape = img.shape
for i in range(3):
new_min = []
new_max = []
for j in range(shape[0]):
new_min.append(min(img[j,:,i]))
new_max.append(max(img[j,:,i]))
print min(new_min), max(new_max)
# 平均背景法,读取40张图片中的背景,求取图片像素的平均值和绝对差
# 阈值设为[mean-constant*diff,mean+constant*diff],在这中间的为背景
def background(n):
mean = 0
thre = 25 #系数可调
for i in range(n):
img = cam.get_frame()
img = img[0]
# cv2.imwrite("photo%d.png"%(i+1),img)
mean_per = i/(i+1)
mean = cv2.addWeighted(mean,mean_per,img,1-mean_per,0)
if i > 0:
diff_per = (i-1)/i
diff = cv2.absdiff(img,pre_img)
mean_diff = cv2.addWeighted(diff,diff_per,diff,1-diff_per,0)
pre_img = img
time.sleep(0.1)
back_low = mean - thre*mean_diff
back_high = mean + thre*mean_diff
return back_low,back_high
# 针对肤色的thresh
def thresh(img):
upper_color = np.array([25, 145, 255])
lower_color = np.array([3, 35, 30])
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
mask = cv2.inRange(hsv, lower_color, upper_color)
return mask
# 针对彩色手套的thresh
def thresh2(img):
upper_color = np.array([25, 145, 255])
lower_color = np.array([5, 35, 30])
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
mask = cv2.inRange(hsv, lower_color, upper_color)
return mask
# 找到掩模图像中的形状轮廓
def handcontour(img):
mask = thresh2(img)
# cv2.imshow('hand', maska)
median = cv2.medianBlur(mask, 5)
median = cv2.erode(median, None, iterations=2)
mask = cv2.dilate(median, None, iterations=2)
mask = cv2.medianBlur(mask, 5)
#找到轮廓
image, contours, hierarchy = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
max = 0
maxarea = 0
#如果没有轮廓则识别失败返回None
if len(contours) == 0:
return []
#找到面积最大的轮廓为手
for i in range(len(contours)):
if cv2.contourArea(contours[i]) > maxarea:
maxarea = cv2.contourArea(contours[i])
max = i
img = cv2.drawContours(img, contours, max, (0, 0, 255), 2)
# cv2.imshow('hand',img)
cv2.waitKey(0)
return contours[max] #返回手的轮廓
# 通过模板匹配识别手的姿势
def gesture(shape,kinds):
best = 1
result = 0
for i in range(kinds):
tempshape = np.load('.\data\gg%d.npy' % i)
ret = cv2.matchShapes(shape, tempshape, 1, 0.0)
if ret < best:
best = ret
result = i + 1
return ret, result
# 找到手的轮廓的外接矩形,返回矩形中心,没找到就返回None
def handloc(contour):
M = cv2.moments(contour)
cx = int(M["m10"] / M["m00"])
cy = int(M["m01"] / M["m00"])
return cx,cy
# 根据机器人读取的图像识别动作
def trace(cam): # 移动方向
x = []
y = []
n = 7 # 连续获取n帧图像
for i in range(n):
# 此处应是获取一幅图,然后等待一段时间
time.sleep(0.2)
list_img,_ = cam.get_frame()
list_contour = handcontour(list_img) # 两个back一样 相当于没有背景
# cv2.imshow("window",list_img)
# cv2.waitKey(0)
while len(list_contour)<50:
img = cam.get_frame()
list_img = img[0]
list_contour = handcontour(list_img)
print len(list_contour)
x0,y0 = handloc(list_contour)
if x0 != 0 and y0 != 0:
x.append(x0)
y.append(y0)
x = np.array(x)
y = np.array(y)
print x,y
cv2.imshow('frame', list_img)
cv2.waitKey(500)
cv2.destroyWindow('frame')
grad_x = []
grad_y = []
for i in range(n-1):
grad_x0 = x[i+1] - x[i]
grad_y0 = y[i+1] - y[i]
mod = math.sqrt(grad_x0 ** 2 + grad_y0 ** 2)
if mod != 0:
grad_x0 = grad_x0/mod
grad_y0 = grad_y0/mod
grad_x.append(grad_x0)
grad_y.append(grad_y0)
grad_x = np.array(grad_x)
grad_y = np.array(grad_y)
abs_x = abs(max(x)-min(x))
abs_y = abs(max(y)-min(y))
return grad_x,grad_y,abs_x,abs_y
# hausdorff距离函数
def hausdist(grad_x,grad_y,temp_x,temp_y):
max_a = 0
max_b = 0
dist = 0
min_t = 500
for i in range(grad_x.shape[0]):
for j in range(temp_x.shape[0]):
dist = math.sqrt((grad_x[i]-temp_x[j])**2 + (grad_y[i]-temp_y[j])**2)
if dist < min_t:
min_t = dist
if min_t > max_a:
max_a = min_t
for i in range(temp_x.shape[0]):
for j in range(grad_x.shape[0]):
dist = math.sqrt((temp_x[i]-grad_x[j])**2 + (temp_y[i]-grad_y[j])**2)
if dist < min_t:
min_t = dist
if min_t > max_b:
max_b = min_t
return max(max_a,max_b)
# 识别手部动作,在识别出触发左手或右手动作后的姿势后调用
def handaction(cam):
mm = {1:"向下",2:"向上",3:"向左",4:"向右",5:"顺时针",6:"逆时针"}
mode = 0
haus = 500
dist = 500
grad_x,grad_y,abs_x,abs_y = trace(cam)
print abs_x,abs_y
if True: # 圆,但是要判断正逆时针
tx1 = grad_x[0] + grad_x[1]
ty1 = grad_y[0] + grad_y[1]
tx2 = tx1 / 2 - grad_x[0]
ty2 = ty1 / 2 - grad_y[0]
direction = tx1 * ty2 - ty1 * tx2
if direction > 0: # 拟时针
mode = 6 # 顺时针代号为5
else:
mode = 5
print "mode ",mode, mm[mode]
return mode
# 腿部动作识别
def legaction(cam):
mm = {0: "backward", 1: "forward", 2: "left", 3: "right"}
contourarea = []
# 这个10根据图片数量可以改
n = 10
x = []
y = []
for i in range(n):
time.sleep(0.2)
img = cam.get_frame()
img = img[0]
contour = handcontour(img)
while len(contour)<50:
print len(contour)
img = cam.get_frame()
img = img[0]
contour = handcontour(img)
x0, y0 = handloc(contour)
x.append(x0)
y.append(y0)
contourarea.append(cv2.contourArea(contour))
finalmove = contourarea[n - 1] - contourarea[0]
for i in range(n - 1):
contourarea[i] = contourarea[i + 1] - contourarea[i]
if contourarea[i] < 0:
contourarea[i] = -1
else:
contourarea[i] = 1
contourarea[n - 1] = 0
contourarea = np.array(contourarea, dtype='int')
print contourarea, finalmove
print abs(x[n - 1] - x[0])
if abs(x[n - 1] - x[0]) < 50:
if np.sum(contourarea) > round(0.3 * n) and finalmove > 0:
result = 1 # 向前是1
else:
# elif np.sum(contourarea) < -round(0.3*n) and finalmove < 0:
result = 0 # 向后是0
else:
x = np.array(x)
y = np.array(y)
grad_x = []
grad_y = []
for i in range(n - 1):
grad_x0 = x[i + 1] - x[i]
grad_y0 = y[i + 1] - y[i]
mod = math.sqrt(grad_x0 ** 2 + grad_y0 ** 2)
grad_x0 = grad_x0 / mod
grad_y0 = grad_y0 / mod
grad_x.append(grad_x0)
grad_y.append(grad_y0)
grad_x = np.array(grad_x)
grad_y = np.array(grad_y)
# grad_x, grad_y = trace()
haus = 500
for i in range(2, 4): # 只有左右移动
temp_x = np.load('.\data\motion%d.npy' % i)
temp_y = np.load('.\data\motion%d.npy' % i)
dist = hausdist(grad_x, grad_y, temp_x, temp_y)
if dist < haus:
haus = dist
result = i # 向左是2 向右是3这样
print result,mm[result]
return result
class HeadFollow(object):
def __init__(self, cam, head):
self.i = 0
self.errorx_pre = 0
self.errory_pre = 0
self.errorlist_x = []
self.errorlist_y = []
self.temp_con = np.zeros([2, 3])
self.cam = cam
self.head = head
# 这个函数是实时的,需要循环调用,在调用之前应当先记录一些图片以识别背景
def follow(self):
img, _ = cam.get_frame()
contour = handcontour(img)
while len(contour)<50:
img, _ = cam.get_frame()
contour = handcontour(img)
x, y = handloc(contour)
print(x, y)
yaw0, pitch0 = head.get_joint()
pitch = (math.atan((y - 120) / 271.8194)) + pitch0
yaw = math.atan((160 - x) / 271.7887) + yaw0
id = head.set_joint([[yaw, pitch]], [0.3])
motion_proxy.wait(id, 2000)
def get_gesture():
# ges = {"1":"拳头","2":"一根手指","3":"Yeah","4":"手掌","5":"六","6":"四根手指","7":"八"}
ges = {"1": "yeah", "2":"1", "3":"8"}
kinds = len(ges)
# 从摄像头获取一张图片
# val = input("Enter 1 to continue:") # 对应之后机器人拍脑袋或者怎么样接收动作
ret = 1
img, _ = cam.get_frame()
contour = handcontour(img)
while ret > 0.55 or len(contour) < 30 :
print "Matching ..."
print len(contour)
img, _ = cam.get_frame()
contour = handcontour(img)
if len(contour) > 0:
ret, g = gesture(contour,kinds)
time.sleep(0.2)
cx, cy = handloc(contour)
print "Match degree %f !(0 is perfect)" % ret
print "Gesture %d ("%g, ges['%d'%g],") matched!" # 对应机器人语音提示
print "Center = ", cx,cy
cv2.imshow("gesture",img)
cv2.waitKey(2000)
cv2.destroyWindow("gesture")
return g
if __name__ == "__main__":
ip = "169.254.81.2"
cam_proxy = None
tts = None
try:
cam_proxy = ALProxy("ALVideoDevice", ip, 9559)
tts = ALProxy("ALTextToSpeech", ip, 9559)
motion_proxy = ALProxy("ALMotion", ip, 9559)
except Exception:
raise RuntimeError("Failed to create proxy.")
cam = NAOCamera(cam_proxy)
head = NAOJoint(motion_proxy, ["Head"])
LLeg = NAOJoint(motion_proxy, ["LLeg"])
RLeg = NAOJoint(motion_proxy, ["RLeg"])
head.set_stiffness(1.0)
head.set_joint([[0.0, 0.0]], [1.0])
ges = {"1": "yeah", "2":"6", "3":"8"}
for i in range(2):
time.sleep(1)
img =cam.get_frame()
img = img[0]
tts.say("开始识别。")
time.sleep(2)
head_follow = HeadFollow(cam, head)
g = get_gesture()
# tts.say("%d"%g)
# tts.say("Done. Done.")
time.sleep(3)
if g == 1:
exit()
tts.say("手势夜。开始跟踪手部。")
head.set_stiffness(1.0)
for i in range(40):
head_follow.follow()
head.set_joint([[0.0, 0.0]], [1.0])
elif g == 2: # 手掌 -- 控制手臂
tts.say("手势1。")
mode = handaction(cam) # back_low 不重要,只是为了凑个参数
tts.say("模式%d。"%mode)
RArm = NAOJoint(motion_proxy, ["RArm"])
RArm.set_stiffness(1.0)
RArm.set_joint([[math.pi/2, -math.pi/6, math.pi/2, math.pi/3, 0]], [1.0])
if mode >= 5: # 六种mode,对应向上、下、左、右、顺、逆时针转动
l = 0.2 # 检测间隙0.2s
a = 0 # 现在的角度(通过函数获取)
# 设定电机转动
time.sleep(1)
img2, _ = cam.get_frame()
contour2 = handcontour(img2)
x_now, y_now = handloc(contour2)
while True: # 现在的角度没有到达极限(留一点余地
time.sleep(l)
x_pre = x_now
y_pre = y_now
img2, _ = cam.get_frame()
contour2 = handcontour(img2)
if len(contour2) > 0:
x_now, y_now = handloc(contour2)
dis = math.sqrt(abs(x_pre - x_now) ** 2 + abs(y_pre - y_now) ** 2)
if dis <= 10:
break
if mode == 5: a = a + 3
if mode == 6: a = a - 3
if a > 115:
a = 115
if a < -115:
a = -115
RArm.set_joint([[math.pi / 2, -math.pi / 6, math.pi / 2, math.pi / 3, a*math.pi/180]], [1.0])
RArm.rest()
elif g == 3: # yeah -- 控制腿
tts.say("手势8。")
mode = legaction(cam) # back_low 不重要,只是为了凑个参数
tts.say("模式%d"%mode) # 四种mode,对应左转、右转、前进、后退
move = Move(LLeg, RLeg, motion_proxy)
q = 0
if mode is 1:
q = math.pi
if mode is 0:
q = 0
if mode is 3:
q = -math.pi/2
if mode is 2:
q = math.pi/2
def nop(): pass
move.forward(0.16, q, nop)
Body = NAOJoint(motion_proxy, ["Body"])
Body.set_stiffness(1.0)
time.sleep(1)
Body.rest()
time.sleep(5)
cv2.destroyAllWindows()