def plot(s):
x = s[7::3]
z = s[9::3]
plot_list_new(x, z)
def plot(s, predict, label, idx):
x = s[7::3]
z = s[9::3]
list1 = np.concatenate(([predict], [label]))
plot_list_new(x, z, list1, idx)
last_valid_x=[]
last_valid_y=[]
last_contour_centers=None
num_images = 0
time0=time.time()
img = cv2.imread(path,0)
cimg, edge_detected_image, contour_centers = ImageProcessing(img)
cimg, contour_centers = ContourCenterCheck(contour_centers, cimg, NUM_PINS=NUM_PINS)
contour_centers=CenterRegister(contour_centers, cimg)
print(len(contour_centers))
central_pos=np.average(contour_centers, axis=0)
print(central_pos)
p=(np.array(contour_centers)-central_pos).tolist()
select_pos= p[8:14]+p[16:23]+p[25:33]+p[35:44]+p[46:56]+p[58:69]+p[71:81]+p[83:92]+p[94:102]+p[104:111]+p[113:119] # select 91
average_mag=np.average(np.abs(select_pos))
print(average_mag)
norm_pos=select_pos/average_mag
np.save('real_pos', norm_pos)
plot_list_new(norm_pos)
cimg = PlotCenters(contour_centers, cimg)
cv2.imwrite(save_path,cimg)
time3 = time.time()
print('time: {:4f}, {:4f} ,{:4f}'.format(time1-time0, time2-time1, time3-time2))
def plot(s):
x=s[1::3]
z=s[3::3]
plot_list_new(x,z)
contour_centers = PointCheck(contour_centers, last_contour_centers, max_dis=15) # larger than 15 is mis-registered
if cnt>=10:
last_contour_centers = contour_centers
norm_pos=Norm(contour_centers)
norm2sim=0.5674
norm_pos_sim=norm_pos*norm2sim # transform norm to sim
norm_pos_=np.transpose(norm_pos_sim).reshape(-1) # ((x,y), (x,y),,) -> ((x,x,,),(y,y,,))
predict = classifier.predict_one_value(norm_pos_)[0]
print('x: ', norm_pos_[:91])
print('y: ', norm_pos_[91:])
colli_pos=predict[6:]/norm2sim # x,z axis of collision; to norm scale
# print(colli_pos)
plot_list_new(norm_pos, cnt, colli_pos)
# print('rotate: ', predict[3:6])
cnt+=1
print('Num: ', cnt)
print('Prediction: ', predict)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# When everything done, release the capture
cap.release()
cv2.destroyAllWindows()
