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()